{"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/SkyCryptWebsite/SkyCrypt/blob/2b6371edc87c614419aad15fc8fe21f40f1df330/src/stats/items/mining.js", "id": "javascript-0-bfdabd935df46ace546ad26da733d330", "text": "import { getTexture } from \"../../custom-resources.js\";\nimport { getLevelByXp } from \"../skills/leveling.js\";\nimport * as constants from \"../../constants.js\";\nimport * as helper from \"../../helper.js\";\nimport * as stats from \"../../stats.js\";\nimport { fileURLToPath } from \"url\";\nimport path from \"path\";\n\nconst __dirname = path.dirname(fileURLToPath(import.meta.url));\n\nexport function getHotmItems(userProfile, packs) {\n const data = userProfile.mining_core;\n const output = [];\n\n // Filling the space with empty items\n for (let index = 0; index < 10 * 9; index++) {\n output.push(helper.generateItem());\n }\n\n if (!data) {\n return output;\n }\n\n const hotmLevelData = data.experience ? getLevelByXp(data.experience, { type: \"hotm\" }) : 0;\n const nodes = data.nodes\n ? Object.fromEntries(Object.entries(data.nodes).filter(([key, value]) => !key.startsWith(\"toggle_\")))\n : {};\n const toggles = data.nodes\n ? Object.fromEntries(Object.entries(data.nodes).filter(([key, value]) => key.startsWith(\"toggle_\")))\n : {};\n const mcdata = stats.getMiningCoreData(userProfile);\n\n // Check for missing node classes\n for (const nodeId in nodes) {\n if (constants.HOTM.nodes[nodeId] == undefined) {\n // throw new Error(`Missing Heart of the Mountain node: ${nodeId}`);\n }\n }\n\n // Processing nodes\n for (const nodeId in constants.HOTM.nodes) {\n const enabled = toggles[`toggle_${nodeId}`] ?? true;\n const level = nodes[nodeId] ?? 0;\n const node = new constants.HOTM.nodes[nodeId]({\n level,\n enabled,\n nodes,\n hotmLevelData,\n selectedPickaxeAbility: data.selected_pickaxe_ability,\n });\n\n output[node.position10x9 - 1] = helper.generateItem({\n display_name: node.name,\n id: node.itemData.id,\n Damage: node.itemData.Damage,\n glowing: node.itemData.glowing,\n tag: {\n display: {\n Name: node.displayName,\n Lore: node.lore,\n },\n ExtraAttributes: {\n id: getHotMPerkId(node),\n },\n },\n position: node.position10x9,\n });\n }\n\n // Processing HotM tiers\n for (let tier = 1; tier <= constants.HOTM.tiers; tier++) {\n const hotm = new constants.HOTM.hotm(tier, hotmLevelData);\n\n output[hotm.position10x9 - 1] = helper.generateItem({\n display_name: `Tier ${tier}`,\n id: hotm.itemData.id,\n Damage: hotm.itemData.Damage,\n glowing: hotm.itemData.glowing,\n tag: {\n display: {\n Name: hotm.displayName,\n Lore: hotm.lore,\n },\n ExtraAttributes: {\n id: getHOTMLevelId(hotm),\n },\n },\n position: hotm.position10x9,\n });\n }\n\n // Processing HotM items (stats, hc crystals, reset)\n for (const itemClass of constants.HOTM.items) {\n const item = new itemClass({\n resources: {\n token_of_the_mountain: mcdata.tokens,\n mithril_powder: mcdata.powder.mithril,\n gemstone_powder: mcdata.powder.gemstone,\n glacite_powder: mcdata.powder.glacite,\n },\n crystals: mcdata.crystal_nucleus.crystals,\n last_reset: mcdata.hotm_last_reset,\n });\n\n output[item.position10x9 - 1] = helper.generateItem({\n display_name: helper.removeFormatting(item.displayName),\n id: item.itemData.id,\n Damage: item.itemData.Damage,\n glowing: item.itemData.glowing,\n texture_path: item.itemData?.texture_path,\n tag: {\n display: {\n Name: item.displayName,\n Lore: item.lore,\n },\n ExtraAttributes: {\n id: item.itemData.skyblock_id,\n },\n },\n position: item.position10x9,\n });\n }\n\n // Processing textures\n output.forEach((item) => {\n const customTexture = getTexture(item, {\n ignore_id: false,\n pack_ids: packs,\n hotm: true,\n });\n\n if (customTexture) {\n item.animated = customTexture.animated;\n item.texture_path = \"/\" + customTexture.path;\n item.texture_pack = customTexture.pack.config;\n item.texture_pack.base_path =\n \"/\" + path.relative(path.resolve(__dirname, \"..\", \"public\"), customTexture.pack.base_path);\n }\n });\n\n return output;\n}\n\n/**\n * Returns the level ID of a HOTM based on the progress. Created to improve performance by avoiding regex and string operations.\n * @param {Object} hotm - The HOTM object containing tier, level, xpCurrent, and xpForNext properties.\n * @returns {string} - The level ID of the HOTM.\n */\nfunction getHOTMLevelId(hotm) {\n const progress = hotm.tier <= hotm.level ? 1 : hotm.level + 1 === hotm.tier ? hotm.xpCurrent / hotm.xpForNext : 0;\n if (hotm.tier === 1 || hotm.tier === 10) {\n return `hotm_level_${hotm.tier}_${getHOTMLvLTier(progress)}`;\n }\n\n return `hotm_level_${getHOTMLvLTier(progress)}`;\n}\n\n/**\n * Returns the HotM perk ID based on the given perk object.\n * @param {Object} perk - The perk object containing level and max_level properties.\n * @returns {string} - The HotM perk ID.\n */\nfunction getHotMPerkId(perk) {\n const progress = perk.level / perk.max_level;\n\n return `hotm_perk_${perk.positionType}_${perk.positionType === \"cross\" ? Math.ceil(getHotMPerkTier(progress) / 2) : getHotMPerkTier(progress)}`;\n}\n\nfunction getHOTMLvLTier(progress) {\n const tiers = [0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.875, 1];\n if (progress === 0) {\n return 0;\n }\n\n for (let i = 0; i < tiers.length; i++) {\n if (progress < tiers[i]) {\n return i;\n }\n }\n return tiers.length;\n}\n\nfunction getHotMPerkTier(progress) {\n const tiers = [0.01, 0.125, 0.25, 0.375, 0.5, 0.625, 0.875, 1];\n if (progress === 0) {\n return 0;\n }\n\n for (let i = 0; i < tiers.length; i++) {\n if (progress < tiers[i]) {\n return i + 1;\n }\n }\n\n return 9;\n}\n", "text_preview": "import { getTexture } from \"../../custom-resources.js\"; import { getLevelByXp } from \"../skills/leveling.js\"; import * as constants from \"../../constants.js\"; import * as helper from \"../../helper.js\"; import * as stats from \"../../stats.js", "input_sha256": "34cd95e295656035461d641767fedaafb28243bd74f34a0dd2696cc20d2770af", "text_sha256": "bfdabd935df46ace546ad26da733d3301831c2cdafff0d6f1edbf081e59b5435", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8001, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.06781966389525, "noise_severity": 0.02159460839334665, "missing_content_severity": 0.020822117079746204, "factual_density": 3.3641154436759213, "pretraining_utility": 0.3701884244762826, "math_density": 1.3259140051401617, "code_density": 3.7737845915628623, "reasoning_density": 0.8080622115532811, "education_level": 2.037353527098552, "creative_writing_quality": -0.26741365064876166}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.02688677804586792, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967109237461136, "secondary": 0.9609099857664404, "tertiary": 0.9889657541621286, "structured_records": 0.033252044926654484, "procedural_steps": 0.002687511189047962, "worked_solution": 0.005594650251299173, "contextual_dialogue": 4.396246980014329e-06, "content_quality": 0.5809369788444128, "noise_severity": 0.9820044930055445, "missing_content_severity": 0.9826482357668782, "factual_density": 0.4700962030632677, "pretraining_utility": 0.46798343307036716, "math_density": 0.2580270738660553, "code_density": 0.8114871596357186, "reasoning_density": 0.32661482370559913, "education_level": 0.7371006287526499, "creative_writing_quality": 0.9996299283395536, "primary_topic": 0.8958656391257246, "secondary_topic": 0.5075126086873956}, "probability_margin": {"primary": 0.9953218413050309, "secondary": 0.9434388209318207, "tertiary": 0.9847278298228538, "education_level": 0.6691077521040889, "creative_writing_quality": 0.9995590004986656, "primary_topic": 0.8144841646542, "secondary_topic": 0.16631206950151556}, "raw_probabilities": {"primary": {"Academic Writing": 2.4714668198131166e-05, "Content Listing": 2.1038011359107977e-05, "Creative Writing": 1.2788102637845592e-05, "Customer Support Page": 7.085667134954425e-06, "Q&A Forum": 8.823613860508503e-06, "Discussion Thread": 5.717224617118454e-06, "Comment Section": 1.0516312491406802e-05, "Missing Content": 1.873623663625812e-05, "Knowledge Article": 6.709601947981503e-05, "Legal Notices": 2.3042931886516755e-05, "News Article": 3.6628745248616786e-06, "Organizational Announcement": 8.182278115386622e-06, "Nonfiction Writing": 8.573845832370003e-06, "Organizational About Page": 8.910654455585875e-06, "Personal About Page": 9.359741829896342e-06, "Personal Blog": 6.011836021919787e-06, "Product Page": 8.832908751466795e-06, "Spam / Ads": 6.30703503262717e-06, "Structured Data": 0.0015261272849946897, "Technical Writing": 0.9968479685900256, "Transcript / Interview": 9.711132701872512e-06, "Tutorial / How-To Guide": 0.00011715756068792002, "User Reviews": 5.777298744419396e-06, "Other / 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0.0005034189959499093, "User Reviews": 3.8474877105988254e-05, "NONE": 0.9625387363595054}, "tertiary": {"Academic Writing": 5.490528512808793e-05, "Content Listing": 9.768663934477153e-05, "Creative Writing": 7.581580459311352e-05, "Customer Support Page": 3.361542988605063e-05, "Q&A Forum": 2.0465045688674528e-05, "Discussion Thread": 1.5425185433974578e-05, "Comment Section": 8.854324207795959e-05, "Missing Content": 0.00016015207206868521, "Knowledge Article": 0.00036014737872025606, "Legal Notices": 0.00011084621729186967, "News Article": 2.8292078357991932e-05, "Organizational Announcement": 2.704489670632152e-05, "Nonfiction Writing": 3.81460924001252e-05, "Organizational About Page": 4.810150794342712e-05, "Personal About Page": 2.8049589138891814e-05, "Personal Blog": 2.5045380429850103e-05, "Product Page": 3.49065798916957e-05, "Spam / Ads": 2.7411137951714345e-05, "Structured Data": 0.0046976845825194655, "Technical Writing": 0.00432153578497158, "Transcript / Interview": 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"2020-08-03T00:20:06.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 292, "forks": 129, "issues": 42, "pull_requests": 14}, "file_meta": {"content_id": "b7e2f83132f79545f551ae4c685605ba20793f46", "size_bytes": 5708, "file_timestamp": 1734452611, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2b6371edc87c614419aad15fc8fe21f40f1df330", "url": "https://github.com/SkyCryptWebsite/SkyCrypt/blob/2b6371edc87c614419aad15fc8fe21f40f1df330/src/stats/items/mining.js"}, "gpt4o_tokens": 1565, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.0678196638952502, "noise_severity": 0.02159460839334665, "missing_content_severity": 0.020822117079746204, "factual_density": 3.3641154436759213, "pretraining_utility": 0.3701884244762826, "math_density": 1.3259140051401617, "code_density": 3.7737845915628623, "reasoning_density": 0.8080622115532811, "education_level": 2.037353527098552, "creative_writing_quality": -0.26741365064876166}, "score_applicability": {"education_level": 0.9981300587684567, "creative_writing_quality": 0.0003083930503718665}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/rotemakiki/MusicForAll/blob/cd7243771b6475aebf8ad2c6e335e84db51f9364/static/js/login.js", "id": "javascript-0-939d0b9fe6e5d1a78061b279d2b1d515", "text": "// Login Page - Enhanced User Experience and Functionality\n\ndocument.addEventListener('DOMContentLoaded', function() {\n initFormValidation();\n initAnimations();\n initKeyboardShortcuts();\n initAutoFocus();\n initPasswordToggle();\n initFormSubmission();\n\n console.log('🔐 Login page loaded with enhanced features');\n});\n\n// Real-time form validation\nfunction initFormValidation() {\n const emailInput = document.getElementById('email');\n const passwordInput = document.getElementById('password');\n const submitBtn = document.querySelector('.submit-btn');\n\n if (!emailInput || !passwordInput || !submitBtn) return;\n\n // Email validation\n emailInput.addEventListener('input', function() {\n validateEmail(this);\n updateSubmitButton();\n });\n\n emailInput.addEventListener('blur', function() {\n validateEmail(this);\n });\n\n // Password validation\n passwordInput.addEventListener('input', function() {\n validatePassword(this);\n updateSubmitButton();\n });\n\n passwordInput.addEventListener('blur', function() {\n validatePassword(this);\n });\n\n function validateEmail(input) {\n const email = input.value.trim();\n const emailRegex = /^[^\\s@]+@[^\\s@]+\\.[^\\s@]+$/;\n\n removeValidationClasses(input);\n\n if (email === '') {\n return false;\n } else if (!emailRegex.test(email)) {\n addErrorClass(input, 'כתובת אימייל לא תקינה');\n return false;\n } else {\n addSuccessClass(input);\n return true;\n }\n }\n\n function validatePassword(input) {\n const password = input.value;\n\n removeValidationClasses(input);\n\n if (password === '') {\n return false;\n } else if (password.length < 6) {\n addErrorClass(input, 'סיסמה חייבת להכיל לפחות 6 תווים');\n return false;\n } else {\n addSuccessClass(input);\n return true;\n }\n }\n\n function updateSubmitButton() {\n const emailValid = validateEmail(emailInput);\n const passwordValid = validatePassword(passwordInput);\n\n if (emailValid && passwordValid) {\n submitBtn.disabled = false;\n submitBtn.style.opacity = '1';\n } else {\n submitBtn.disabled = true;\n submitBtn.style.opacity = '0.6';\n }\n }\n\n function addErrorClass(input, message) {\n input.classList.add('error');\n showValidationMessage(input, message, 'error');\n }\n\n function addSuccessClass(input) {\n input.classList.add('success');\n removeValidationMessage(input);\n }\n\n function removeValidationClasses(input) {\n input.classList.remove('error', 'success');\n removeValidationMessage(input);\n }\n\n function showValidationMessage(input, message, type) {\n removeValidationMessage(input);\n\n const messageDiv = document.createElement('div');\n messageDiv.className = `validation-message ${type}`;\n messageDiv.textContent = message;\n messageDiv.style.cssText = `\n font-size: 0.85rem;\n margin-top: 5px;\n padding: 5px 10px;\n border-radius: 6px;\n animation: fadeIn 0.3s ease-out;\n ${type === 'error' ?\n 'color: #dc3545; background: rgba(220, 53, 69, 0.1);' :\n 'color: #28a745; background: rgba(40, 167, 69, 0.1);'\n }\n `;\n\n input.parentNode.appendChild(messageDiv);\n }\n\n function removeValidationMessage(input) {\n const existingMessage = input.parentNode.querySelector('.validation-message');\n if (existingMessage) {\n existingMessage.remove();\n }\n }\n\n // Add validation styles to CSS if not already present\n if (!document.getElementById('validation-styles')) {\n const validationStyles = document.createElement('style');\n validationStyles.id = 'validation-styles';\n validationStyles.textContent = `\n .form-input.error {\n border-color: #dc3545 !important;\n box-shadow: 0 0 0 4px rgba(220, 53, 69, 0.1) !important;\n }\n\n .form-input.success {\n border-color: #28a745 !important;\n box-shadow: 0 0 0 4px rgba(40, 167, 69, 0.1) !important;\n }\n\n @keyframes fadeIn {\n from { opacity: 0; transform: translateY(-10px); }\n to { opacity: 1; transform: translateY(0); }\n }\n `;\n document.head.appendChild(validationStyles);\n }\n}\n\n// Enhanced animations\nfunction initAnimations() {\n // Stagger animation for form elements\n const formElements = document.querySelectorAll('.form-group, .submit-btn, .register-link');\n formElements.forEach((element, index) => {\n element.style.opacity = '0';\n element.style.transform = 'translateY(20px)';\n element.style.animation = `slideInUp 0.6s ease-out ${index * 0.1}s forwards`;\n });\n\n // Add slideInUp animation if not present\n if (!document.getElementById('animation-styles')) {\n const animationStyles = document.createElement('style');\n animationStyles.id = 'animation-styles';\n animationStyles.textContent = `\n @keyframes slideInUp {\n to {\n opacity: 1;\n transform: translateY(0);\n }\n }\n `;\n document.head.appendChild(animationStyles);\n }\n\n // Flash message animations\n const flashMessages = document.querySelectorAll('.flash-message');\n flashMessages.forEach((message, index) => {\n message.style.animationDelay = `${index * 0.2}s`;\n });\n\n // Auto-hide flash messages after 5 seconds\n setTimeout(() => {\n flashMessages.forEach(message => {\n message.style.animation = 'slideOutRight 0.5s ease-out forwards';\n setTimeout(() => message.remove(), 500);\n });\n }, 5000);\n\n if (!document.getElementById('flash-animation-styles')) {\n const flashAnimationStyles = document.createElement('style');\n flashAnimationStyles.id = 'flash-animation-styles';\n flashAnimationStyles.textContent = `\n @keyframes slideOutRight {\n to {\n opacity: 0;\n transform: translateX(100%);\n }\n }\n `;\n document.head.appendChild(flashAnimationStyles);\n }\n}\n\n// Keyboard shortcuts and accessibility\nfunction initKeyboardShortcuts() {\n document.addEventListener('keydown', function(e) {\n // Alt + L to focus email\n if (e.altKey && e.key === 'l') {\n e.preventDefault();\n const emailInput = document.getElementById('email');\n if (emailInput) emailInput.focus();\n }\n\n // Alt + P to focus password\n if (e.altKey && e.key === 'p') {\n e.preventDefault();\n const passwordInput = document.getElementById('password');\n if (passwordInput) passwordInput.focus();\n }\n\n // Enter to submit form (if valid)\n if (e.key === 'Enter') {\n const submitBtn = document.querySelector('.submit-btn');\n if (submitBtn && !submitBtn.disabled) {\n submitBtn.click();\n }\n }\n\n // Escape to clear form\n if (e.key === 'Escape') {\n clearForm();\n }\n });\n\n console.log('Keyboard shortcuts: Alt+L (email), Alt+P (password), Enter (submit), Esc (clear)');\n}\n\n// Auto-focus first empty field\nfunction initAutoFocus() {\n const emailInput = document.getElementById('email');\n const passwordInput = document.getElementById('password');\n\n setTimeout(() => {\n if (emailInput && !emailInput.value) {\n emailInput.focus();\n } else if (passwordInput && !passwordInput.value) {\n passwordInput.focus();\n }\n }, 500); // Delay to allow animations to complete\n}\n\n// Password visibility toggle\nfunction initPasswordToggle() {\n const passwordInput = document.getElementById('password');\n if (!passwordInput) return;\n\n const toggleButton = document.createElement('button');\n toggleButton.type = 'button';\n toggleButton.className = 'password-toggle';\n toggleButton.innerHTML = '👁️';\n toggleButton.title = 'הצג/הסתר סיסמה';\n\n // Style the toggle button\n toggleButton.style.cssText = `\n position: absolute;\n left: 16px;\n top: 50%;\n transform: translateY(-50%);\n background: none;\n border: none;\n cursor: pointer;\n font-size: 1.2rem;\n color: #64748b;\n transition: color 0.3s ease;\n z-index: 10;\n `;\n\n // Add hover effect\n toggleButton.addEventListener('mouseenter', () => {\n toggleButton.style.color = '#667eea';\n });\n\n toggleButton.addEventListener('mouseleave', () => {\n toggleButton.style.color = '#64748b';\n });\n\n // Toggle functionality\n toggleButton.addEventListener('click', () => {\n const type = passwordInput.getAttribute('type') === 'password' ? 'text' : 'password';\n passwordInput.setAttribute('type', type);\n toggleButton.innerHTML = type === 'password' ? '👁️' : '🙈';\n });\n\n // Add to password input container\n passwordInput.parentNode.style.position = 'relative';\n passwordInput.style.paddingLeft = '50px';\n passwordInput.parentNode.appendChild(toggleButton);\n}\n\n// Enhanced form submission\nfunction initFormSubmission() {\n const form = document.querySelector('.login-form, #login-form, #force-login-form');\n const submitBtn = document.querySelector('.submit-btn');\n\n if (!form || !submitBtn) return;\n\n form.addEventListener('submit', function(e) {\n e.preventDefault();\n\n // Add loading state\n submitBtn.classList.add('loading');\n submitBtn.textContent = 'מתחבר...';\n\n // Validate form\n const emailInput = document.getElementById('email') || document.getElementById('force-password');\n const passwordInput = document.getElementById('password') || document.getElementById('force-password');\n\n if (!emailInput || !passwordInput) {\n removeLoadingState();\n return;\n }\n\n // Simulate network delay for better UX\n setTimeout(() => {\n // Submit the form\n form.submit();\n }, 800);\n });\n\n function removeLoadingState() {\n submitBtn.classList.remove('loading');\n submitBtn.textContent = 'התחבר';\n }\n}\n\n// Form utilities\nfunction clearForm() {\n const inputs = document.querySelectorAll('.form-input');\n inputs.forEach(input => {\n input.value = '';\n input.classList.remove('error', 'success');\n });\n\n // Remove validation messages\n const validationMessages = document.querySelectorAll('.validation-message');\n validationMessages.forEach(message => message.remove());\n\n // Focus first input\n if (inputs.length > 0) {\n inputs[0].focus();\n }\n}\n\n// Force login modal enhancements\nfunction initForceLoginModal() {\n const modal = document.querySelector('.force-login-modal');\n if (!modal) return;\n\n // Close modal on ESC key\n document.addEventListener('keydown', function(e) {\n if (e.key === 'Escape') {\n closeModal();\n }\n });\n\n // Close modal on outside click\n modal.addEventListener('click', function(e) {\n if (e.target === modal) {\n closeModal();\n }\n });\n\n function closeModal() {\n modal.style.animation = 'fadeOut 0.3s ease-out';\n setTimeout(() => {\n window.location.href = '/login';\n }, 300);\n }\n\n // Add fadeOut animation\n if (!document.getElementById('modal-animation-styles')) {\n const modalAnimationStyles = document.createElement('style');\n modalAnimationStyles.id = 'modal-animation-styles';\n modalAnimationStyles.textContent = `\n @keyframes fadeOut {\n from { opacity: 1; }\n to { opacity: 0; }\n }\n `;\n document.head.appendChild(modalAnimationStyles);\n }\n}\n\n// Remember user preferences\nfunction initUserPreferences() {\n const emailInput = document.getElementById('email');\n if (!emailInput) return;\n\n // Load remembered email\n const rememberedEmail = localStorage.getItem('rememberedEmail');\n if (rememberedEmail) {\n emailInput.value = rememberedEmail;\n // Focus password field if email is pre-filled\n const passwordInput = document.getElementById('password');\n if (passwordInput) {\n setTimeout(() => passwordInput.focus(), 500);\n }\n }\n\n // Save email on successful login attempt\n const form = document.querySelector('.login-form');\n if (form) {\n form.addEventListener('submit', function() {\n const email = emailInput.value.trim();\n if (email && email.includes('@')) {\n localStorage.setItem('rememberedEmail', email);\n }\n });\n }\n}\n\n// Enhanced error handling\nfunction showError(message, duration = 5000) {\n // Remove existing error messages\n const existingErrors = document.querySelectorAll('.dynamic-error');\n existingErrors.forEach(error => error.remove());\n\n const errorDiv = document.createElement('div');\n errorDiv.className = 'flash-message error dynamic-error';\n errorDiv.innerHTML = `\n ⚠️\n ${message}\n `;\n\n const formPanel = document.querySelector('.login-form-panel');\n if (formPanel) {\n formPanel.insertBefore(errorDiv, formPanel.firstChild);\n\n // Auto-remove after duration\n setTimeout(() => {\n errorDiv.style.animation = 'slideOutRight 0.5s ease-out forwards';\n setTimeout(() => errorDiv.remove(), 500);\n }, duration);\n }\n}\n\nfunction showSuccess(message, duration = 3000) {\n const successDiv = document.createElement('div');\n successDiv.className = 'flash-message success dynamic-success';\n successDiv.innerHTML = `\n ✅\n ${message}\n `;\n\n const formPanel = document.querySelector('.login-form-panel');\n if (formPanel) {\n formPanel.insertBefore(successDiv, formPanel.firstChild);\n\n setTimeout(() => {\n successDiv.style.animation = 'slideOutRight 0.5s ease-out forwards';\n setTimeout(() => successDiv.remove(), 500);\n }, duration);\n }\n}\n\n// Network status indicator\nfunction initNetworkStatus() {\n window.addEventListener('online', () => {\n showSuccess('חיבור לאינטרנט חודש');\n });\n\n window.addEventListener('offline', () => {\n showError('אין חיבור לאינטרנט', 0); // Don't auto-hide\n });\n}\n\n// Performance monitoring for login\nfunction initPerformanceMonitoring() {\n const startTime = performance.now();\n\n window.addEventListener('load', () => {\n const loadTime = performance.now() - startTime;\n console.log(`🚀 Login page loaded in ${Math.round(loadTime)}ms`);\n\n // Show performance warning if page loads slowly\n if (loadTime > 3000) {\n console.warn('⚠️ Slow page load detected');\n }\n });\n}\n\n// Enhanced accessibility features\nfunction initAccessibility() {\n // Add ARIA labels\n const emailInput = document.getElementById('email');\n const passwordInput = document.getElementById('password');\n\n if (emailInput) {\n emailInput.setAttribute('aria-label', 'כתובת אימייל');\n emailInput.setAttribute('aria-describedby', 'email-help');\n }\n\n if (passwordInput) {\n passwordInput.setAttribute('aria-label', 'סיסמה');\n passwordInput.setAttribute('aria-describedby', '');\n }\n\n // Add focus indicators for keyboard navigation\n const focusableElements = document.querySelectorAll('input, button, a');\n focusableElements.forEach(element => {\n element.addEventListener('focus', function() {\n this.style.outline = '2px solid #667eea';\n this.style.outlineOffset = '2px';\n });\n\n element.addEventListener('blur', function() {\n this.style.outline = 'none';\n });\n });\n}\n\n// Initialize all features when force login modal is present\nif (document.querySelector('.force-login-modal')) {\n initForceLoginModal();\n}\n\n// Initialize user preferences\nwindow.addEventListener('load', () => {\n initUserPreferences();\n initNetworkStatus();\n initPerformanceMonitoring();\n initAccessibility();\n});\n\n// Global utilities\nwindow.loginUtils = {\n showError,\n showSuccess,\n clearForm\n};\n\n// Console helper messages\nconsole.log(`\n🔐 Login Page Features:\n- Real-time validation\n- Keyboard shortcuts (Alt+L, Alt+P, Enter, Esc)\n- Password visibility toggle\n- Auto-focus and remember email\n- Enhanced error handling\n- Accessibility improvements\n`);\n\n// Add ripple effect to buttons\nfunction 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scale(4);\n opacity: 0;\n }\n }\n `;\n document.head.appendChild(rippleStyles);\n }\n\n this.style.position = 'relative';\n this.style.overflow = 'hidden';\n this.appendChild(ripple);\n\n setTimeout(() => {\n ripple.remove();\n }, 600);\n });\n });\n}\n\n// Initialize ripple effect\nsetTimeout(addRippleEffect, 100);\n", "text_preview": "// Login Page - Enhanced User Experience and Functionality document.addEventListener('DOMContentLoaded', function() { initFormValidation(); initAnimations(); initKeyboardShortcuts(); initAutoFocus(); initPasswordToggle(); initFormSubmission", "input_sha256": "c5dc18f8b26e75c7f0b1bb613dfd485fbc186f672a6610af551d0a44aa1b8940", "text_sha256": "939d0b9fe6e5d1a78061b279d2b1d515d3ef1ca338d98aee702ca18462dfb24f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8003, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": 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0.7195133186743915, "1": 0.13588615689751743, "2": 0.1197817260646404, "3": 0.013690301706971763, "4": 0.01112849665647901}, "education_level": {"none": 0.0025432757023017673, "0": 0.007254396708879891, "1": 0.16239953605518023, "2": 0.8073077091273461, "3": 0.017843593557945743, "4": 0.002651488848346384}, "creative_writing_quality": {"not_applicable": 0.9983426757301886, "-2": 0.00022778722876434383, "-1": 0.0003768495994069683, "0": 0.0008409453365353549, "1": 0.00014712491851129973, "2": 6.461718659330839e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00019199641532880392, "Art & Design": 0.010642351361903685, "Law & Crime": 0.00013541067742608732, "Education & Careers": 0.0001413828841644652, "Entertainment & Media": 0.0017974834893929835, "Fashion & Beauty": 9.519520337841997e-05, "Business, Economics & Finance": 0.00016500023740538574, "Food & Dining": 8.275610113540923e-05, "Games & Gaming": 0.0003651447031875449, "Electronics & Hardware": 0.00020121963063228214, "Health, Healthcare & Wellness": 8.458060077252646e-05, "History & Geography": 7.904350128542559e-05, "Home, Pets & Hobbies": 8.917493516139596e-05, "Industry & Production": 6.683067360334764e-05, "Language, Literature & Philosophy": 0.0001112597663207444, "Politics & Government": 4.5484983145045315e-05, "Religion & Spirituality": 8.159905603175803e-05, "Science, Mathematics & Engineering": 0.00014526059675108795, "Relationships & Community": 9.839881195399102e-05, "Software Usage & Applications": 0.002849982139696965, "Programming & Software Engineering": 0.9810413852921883, "Sports & Fitness": 0.00011604622903489071, "Vehicles & Transportation": 5.768630232109029e-05, "Travel & Tourism": 0.00010813557284935716, "Other / Uncovered Subject": 0.0009364916162622461, "Unknown / Insufficient Content": 0.00027069921866680515}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.001306411780652788, "Art & Design": 0.20582361794158138, "Law & Crime": 0.0014163901917464273, "Education & 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Tourism": 0.0005274958830770426, "Other / Uncovered Subject": 0.0018149116665767304, "NONE": 0.7192107648614435}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": 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"https://github.com/rotemakiki/MusicForAll/blob/cd7243771b6475aebf8ad2c6e335e84db51f9364/static/js/login.js"}, "gpt4o_tokens": 4052, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.44125941699380844, "noise_severity": 0.29609643258630086, "missing_content_severity": 0.10492485695976468, "factual_density": 2.7931371869775403, "pretraining_utility": 0.4149180600493242, "math_density": 0.14331260193936046, "code_density": 3.743776299651959, "reasoning_density": 0.46103450077362956, "education_level": 1.845846186132472, "creative_writing_quality": -0.335519593459559}, "score_applicability": {"education_level": 0.9974567242976983, "creative_writing_quality": 0.001657324269811275}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/apper-apps/edutrack-dart-intel/blob/43ad9be8abea83bd648ad0291d19594703108a7e/src/services/api/studentService.js", "id": "javascript-0-07dfd48222b261e04b5ff03dbfd591aa", "text": "import { toast } from 'react-toastify';\n\nclass StudentService {\n constructor() {\n // Initialize ApperClient with Project ID and Public Key\n const { ApperClient } = window.ApperSDK;\n this.apperClient = new ApperClient({\n apperProjectId: import.meta.env.VITE_APPER_PROJECT_ID,\n apperPublicKey: import.meta.env.VITE_APPER_PUBLIC_KEY\n });\n this.tableName = 'student';\n }\n\n async getAll() {\n try {\n const params = {\n fields: [\n { field: { Name: \"Name\" } },\n { field: { Name: \"email\" } },\n { field: { Name: \"phone\" } },\n { field: { Name: \"grade_level\" } },\n { field: { Name: \"section\" } },\n { field: { Name: \"enrollment_date\" } },\n { field: { Name: \"photo_url\" } },\n { field: { Name: \"status\" } },\n { field: { Name: \"Tags\" } },\n { field: { Name: \"Owner\" } }\n ],\n orderBy: [\n {\n fieldName: \"Name\",\n sorttype: \"ASC\"\n }\n ],\n pagingInfo: {\n limit: 100,\n offset: 0\n }\n };\n\n const response = await this.apperClient.fetchRecords(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return [];\n }\n\n return response.data || [];\n } catch (error) {\n console.error(\"Error fetching students:\", error);\n toast.error(\"Failed to fetch students\");\n return [];\n }\n }\n\n async getById(id) {\n try {\n const params = {\n fields: [\n { field: { Name: \"Name\" } },\n { field: { Name: \"email\" } },\n { field: { Name: \"phone\" } },\n { field: { Name: \"grade_level\" } },\n { field: { Name: \"section\" } },\n { field: { Name: \"enrollment_date\" } },\n { field: { Name: \"photo_url\" } },\n { field: { Name: \"status\" } },\n { field: { Name: \"Tags\" } },\n { field: { Name: \"Owner\" } }\n ]\n };\n\n const response = await this.apperClient.getRecordById(this.tableName, id, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return null;\n }\n\n return response.data;\n } catch (error) {\n console.error(`Error fetching student with ID ${id}:`, error);\n toast.error(\"Failed to fetch student\");\n return null;\n }\n }\n\n async create(studentData) {\n try {\n // Only include Updateable fields\n const params = {\n records: [{\n Name: studentData.Name,\n email: studentData.email,\n phone: studentData.phone,\n grade_level: parseInt(studentData.grade_level),\n section: studentData.section,\n enrollment_date: studentData.enrollment_date,\n photo_url: studentData.photo_url || \"\",\n status: studentData.status,\n Tags: studentData.Tags || \"\",\n Owner: studentData.Owner\n }]\n };\n\n const response = await this.apperClient.createRecord(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return null;\n }\n\n if (response.results) {\n const successfulRecords = response.results.filter(result => result.success);\n const failedRecords = response.results.filter(result => !result.success);\n \n if (failedRecords.length > 0) {\n console.error(`Failed to create ${failedRecords.length} records:${JSON.stringify(failedRecords)}`);\n \n failedRecords.forEach(record => {\n record.errors?.forEach(error => {\n toast.error(`${error.fieldLabel}: ${error.message}`);\n });\n if (record.message) toast.error(record.message);\n });\n }\n \n if (successfulRecords.length > 0) {\n toast.success(\"Student created successfully\");\n return successfulRecords[0].data;\n }\n }\n\n return null;\n } catch (error) {\n console.error(\"Error creating student:\", error);\n toast.error(\"Failed to create student\");\n return null;\n }\n }\n\n async update(id, studentData) {\n try {\n // Only include Updateable fields\n const params = {\n records: [{\n Id: parseInt(id),\n Name: studentData.Name,\n email: studentData.email,\n phone: studentData.phone,\n grade_level: parseInt(studentData.grade_level),\n section: studentData.section,\n enrollment_date: studentData.enrollment_date,\n photo_url: studentData.photo_url || \"\",\n status: studentData.status,\n Tags: studentData.Tags || \"\",\n Owner: studentData.Owner\n }]\n };\n\n const response = await this.apperClient.updateRecord(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return null;\n }\n\n if (response.results) {\n const successfulUpdates = response.results.filter(result => result.success);\n const failedUpdates = response.results.filter(result => !result.success);\n \n if (failedUpdates.length > 0) {\n console.error(`Failed to update ${failedUpdates.length} records:${JSON.stringify(failedUpdates)}`);\n \n failedUpdates.forEach(record => {\n record.errors?.forEach(error => {\n toast.error(`${error.fieldLabel}: ${error.message}`);\n });\n if (record.message) toast.error(record.message);\n });\n }\n \n if (successfulUpdates.length > 0) {\n toast.success(\"Student updated successfully\");\n return successfulUpdates[0].data;\n }\n }\n\n return null;\n } catch (error) {\n console.error(\"Error updating student:\", error);\n toast.error(\"Failed to update student\");\n return null;\n }\n }\n\n async delete(id) {\n try {\n const params = {\n RecordIds: [parseInt(id)]\n };\n\n const response = await this.apperClient.deleteRecord(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return false;\n }\n\n if (response.results) {\n const successfulDeletions = response.results.filter(result => result.success);\n const failedDeletions = response.results.filter(result => !result.success);\n \n if (failedDeletions.length > 0) {\n console.error(`Failed to delete ${failedDeletions.length} records:${JSON.stringify(failedDeletions)}`);\n \n failedDeletions.forEach(record => {\n if (record.message) toast.error(record.message);\n });\n }\n \n if (successfulDeletions.length > 0) {\n toast.success(\"Student deleted successfully\");\n return true;\n }\n }\n\n return false;\n } catch (error) {\n console.error(\"Error deleting student:\", error);\n toast.error(\"Failed to delete student\");\n return false;\n }\n }\n\n async getByGradeLevel(gradeLevel) {\n try {\n const params = {\n fields: [\n { field: { Name: \"Name\" } },\n { field: { Name: \"email\" } },\n { field: { Name: \"phone\" } },\n { field: { Name: \"grade_level\" } },\n { field: { Name: \"section\" } },\n { field: { Name: \"enrollment_date\" } },\n { field: { Name: \"photo_url\" } },\n { field: { Name: \"status\" } },\n { field: { Name: \"Tags\" } },\n { field: { Name: \"Owner\" } }\n ],\n where: [\n {\n FieldName: \"grade_level\",\n Operator: \"EqualTo\",\n Values: [parseInt(gradeLevel)]\n }\n ],\n orderBy: [\n {\n fieldName: \"Name\",\n sorttype: \"ASC\"\n }\n ]\n };\n\n const response = await this.apperClient.fetchRecords(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return [];\n }\n\n return response.data || [];\n } catch (error) {\n console.error(\"Error fetching students by grade level:\", error);\n toast.error(\"Failed to fetch students by grade level\");\n return [];\n }\n }\n\n async search(query) {\n try {\n const params = {\n fields: [\n { field: { Name: \"Name\" } },\n { field: { Name: \"email\" } },\n { field: { Name: \"phone\" } },\n { field: { Name: \"grade_level\" } },\n { field: { Name: \"section\" } },\n { field: { Name: \"enrollment_date\" } },\n { field: { Name: \"photo_url\" } },\n { field: { Name: \"status\" } },\n { field: { Name: \"Tags\" } },\n { field: { Name: \"Owner\" } }\n ],\n whereGroups: [\n {\n operator: \"OR\",\n subGroups: [\n {\n operator: \"OR\",\n conditions: [\n {\n fieldName: \"Name\",\n operator: \"Contains\",\n values: [query]\n },\n {\n fieldName: \"email\",\n operator: \"Contains\",\n values: [query]\n }\n ]\n }\n ]\n }\n ],\n orderBy: [\n {\n fieldName: \"Name\",\n sorttype: \"ASC\"\n }\n ]\n };\n\n const response = await this.apperClient.fetchRecords(this.tableName, params);\n \n if (!response.success) {\n console.error(response.message);\n toast.error(response.message);\n return [];\n }\n\n return response.data || [];\n } catch (error) {\n console.error(\"Error searching students:\", error);\n toast.error(\"Failed to search students\");\n return [];\n }\n }\n}\n\nexport const studentService = new StudentService();", "text_preview": "import { toast } from 'react-toastify'; class StudentService { constructor() { // Initialize ApperClient with Project ID and Public Key const { ApperClient } = window.ApperSDK; this.apperClient = new ApperClient({ apperProjectId: import.met", "input_sha256": "c3fca893ff99dbd5b834cbcdd2f4bba84cb9000b0902fb34aba864b0c8c989e8", "text_sha256": "07dfd48222b261e04b5ff03dbfd591aa27f734bc84ee7c784649360d00fa8cdb", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8002, "input_policy": "text-and-optional-url-v1", 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2.220446049250313e-16, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10968, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10968, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:06.614145+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b664f519-38ef-4972-966e-d78747d14e77", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "e650304c2b7de0e3151403e2a5ef8f64ba77d3fb68605c04ba13a9c2742fd1a0", "source_metadata": {"uuid": "b664f519-38ef-4972-966e-d78747d14e77", "repo_name": "apper-apps/edutrack-dart-intel", "relative_path": "src/services/api/studentService.js", "category": "WEB", "algo_rel_score": 0.9674464464187622, "quality_score": 6.702017784118652, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 5, "repo_created_at": "2025-07-08T12:03:49.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e43a41da02ad547d1e1bf8df4881448568b66924", "size_bytes": 10105, "file_timestamp": 1751978318, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "43ad9be8abea83bd648ad0291d19594703108a7e"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 5, "repo_created_at": "2025-07-08T12:03:49.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e43a41da02ad547d1e1bf8df4881448568b66924", "size_bytes": 10105, "file_timestamp": 1751978318, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "43ad9be8abea83bd648ad0291d19594703108a7e", "url": "https://github.com/apper-apps/edutrack-dart-intel/blob/43ad9be8abea83bd648ad0291d19594703108a7e/src/services/api/studentService.js"}, "gpt4o_tokens": 2183, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.100828684065464, "noise_severity": 0.07063521332863372, "missing_content_severity": 0.007952521365793086, "factual_density": 2.8958122408759617, "pretraining_utility": 0.49527104726776167, "math_density": 0.0829187426420138, "code_density": 3.784196241390628, "reasoning_density": 0.26611491067248705, "education_level": 2.1608804358456117, "creative_writing_quality": -0.2510106774023023}, "score_applicability": {"education_level": 0.998904686604085, "creative_writing_quality": 0.000385597395003681}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/hgwood/falcorception/blob/23bf2eb120ce69f176502df2313114aa1c6e13a0/server/index.js", "id": "javascript-0-8b9201e51b40b5fe4b406784a98fca28", "text": "\"use strict\"\n\nconst _ = require(\"lodash\")\nconst shortid = require(\"shortid\")\nconst express = require(\"express\")\nconst serveStatic = require(\"serve-static\")\nconst bodyParser = require(\"body-parser\")\nconst falcorRouter = require(\"falcor-router\")\nconst falcorExpress = require(\"falcor-express\")\nconst fs = require(\"fs\")\nconst path = require(\"path\")\nconst Firebase = require(\"firebase\")\nconst requestPromise = require(\"request-promise\")\n\nconst app = express()\n\napp.use(serveStatic(\"../client\"))\n\napp.use(bodyParser.urlencoded({extended: false}))\n\napp.use(\"/falcorception.json\", falcorExpress.dataSourceRoute(function () {\n return new falcorRouter([\n {\n route: \"meta.napis\",\n get: () => ({path: [\"meta\", \"napis\"], value: 2001}),\n },\n {\n route: \"apis[{integers:indices}]\",\n get(pathSet) {\n const data = rw()\n const apis = _(data.apisById)\n .omit(\"length\")\n .values()\n .orderBy(\"created\", \"asc\")\n .value()\n return _(pathSet.indices)\n .zipObject(pathSet.indices)\n .mapValues(_.propertyOf(apis))\n .pickBy(_.identity)\n .map((api, index) => ({\n path: [\"apis\", index],\n value: {$type: \"ref\", value: [\"apisById\", api.id]}}))\n .value()\n },\n },\n {\n route: \"apisById[{keys:ids}][{keys:props}]\",\n get(pathSet) {\n const data = rw()\n return _(pathSet.ids)\n .map(_.propertyOf(data.apisById))\n .flatMap(api => _.map(pathSet.props, prop => ({\n path: [\"apisById\", api.id, prop],\n value: api[prop]})))\n .value()\n },\n set(updates) {\n rw(data => {\n return _.merge(data, updates)\n })\n return {jsonGraph: updates}\n }\n },\n {\n route: \"['apis', 'apisById'].length\",\n get(pathSet) {\n return [{path: pathSet, value: rw().apisById.length}]\n },\n },\n {\n route: \"apisById[{keys:ids}].routes.create\",\n call(pathSet, args) {\n const apiId = pathSet.ids[0] // let's ignore the rest for now\n const route = {\n id: shortid.generate(),\n name: args[0],\n method: args[1],\n matcher: args[2],\n created: new Date().toISOString(),\n source: {id: args[3]},\n query: args[4]\n }\n const newLength = rw(function (model) {\n model.apisById[apiId].routes[route.id] = route\n return model.apisById[apiId].routes.length += 1\n })\n const data = rw()\n const source = data.sources[route.source.id]\n runApi[apiId].push(createFalcorRoute(route, source))\n return _(route)\n .pickBy(_.negate(_.isObjectLike))\n .map((value, key) => {\n return {path: [\"apisById\", apiId, \"routes\", \"byIds\", route.id, key], value}\n })\n .push({path: [\"apisById\", apiId, \"routes\", \"length\"], value: newLength})\n .push({path: [\"apisById\", apiId, \"routes\", \"lastAdded\"], value: {$type: \"ref\", value: [\"apisById\", apiId, \"routes\", \"byIds\", route.id]}})\n .push({path: [\"apisById\", apiId, \"routes\", \"mostRecentFirst\", newLength - 1], value: {$type: \"ref\", value: [\"apisById\", apiId, \"routes\", \"byIds\", route.id]}})\n .value()\n },\n },\n {\n route: \"apis.create\",\n call(callPath, args) {\n const name = args[0]\n const id = shortid.generate()\n const created = new Date().toISOString()\n const url = `/${id}`\n const api = {id, name, created, url, routes: {length: 0}}\n const newLength = rw(function (model) {\n model.apisById[id] = api\n return model.apisById.length += 1\n })\n runApi(api)\n return {\n paths: [[\"apis\", [\"lastAdded\", newLength - 1, \"length\"]]],\n jsonGraph: {\n apis: {\n lastAdded: {$type: \"ref\", value: [\"apisById\", id]},\n [newLength - 1]: {$type: \"ref\", value: [\"apisById\", id]},\n length: newLength,\n },\n },\n }\n },\n },\n {\n route: \"apisById[{keys:ids}].routes.mostRecentFirst[{integers:indices}]\",\n get(pathSet) {\n const data = rw()\n return _(pathSet.ids)\n .zipObject(pathSet.ids)\n .mapValues(_.propertyOf(data.apisById))\n .mapValues(\"routes\")\n .mapValues(routes => _.orderBy(_.values(_.omit(routes, \"length\")), [\"created\"], [\"asc\"]))\n .mapValues(routes => _.pick(routes, pathSet.indices))\n .flatMap((routes, apiId) => {\n return _.map(routes, (route, index) => {\n return {path: [\"apisById\", apiId, \"routes\", \"mostRecentFirst\", index], value: {$type: \"ref\", value: [\"apisById\", apiId, \"routes\", \"byIds\", route.id]}}\n })\n })\n .value()\n },\n },\n {\n route: \"apisById[{keys:ids}].routes.length\",\n get(pathSet) {\n const data = rw()\n return _(pathSet.ids)\n .zipObject(pathSet.ids)\n .mapValues(_.propertyOf(data.apisById))\n .mapValues(\"routes.length\")\n .map((length, apiId) => ({path: [\"apisById\", apiId, \"routes\", \"length\"], value: length}))\n .value()\n },\n },\n {\n route: \"apisById[{keys:apiIds}].routes.byIds[{keys:routeIds}][{keys:props}]\",\n get(pathSet) {\n const data = rw()\n return _(pathSet.apiIds)\n .zipObject(pathSet.apiIds)\n .mapValues(_.propertyOf(data.apisById))\n .mapValues(\"routes\")\n .mapValues(routes => _.pick(routes, pathSet.routeIds))\n .flatMap((routes, apiId) => {\n return _.flatMap(routes, (route, routeId) => {\n return _.map(pathSet.props, prop => {\n return {path: [\"apisById\", apiId, \"routes\", \"byIds\", routeId, prop], value: route[prop]}\n })\n })\n })\n .value()\n },\n },\n {\n route: \"apisById[{keys:apiIds}].routes.byIds[{keys:routeIds}].source\",\n get(pathSet) {\n const data = rw()\n return _(pathSet.apiIds)\n .zipObject(pathSet.apiIds)\n .mapValues(_.propertyOf(data.apisById))\n .mapValues(\"routes\")\n .mapValues(routes => _.pick(routes, pathSet.routeIds))\n .flatMap((routes, apiId) => {\n return _.flatMap(routes, (route, routeId) => {\n return {\n path: [\"apisById\", apiId, \"routes\", \"byIds\", routeId, \"source\"],\n value: {$type: \"ref\", value: [\"sources\", \"by\", \"id\", route.source.id]},\n }\n })\n })\n .value()\n },\n },\n {\n route: \"sources.create\",\n call(pathSet, args) {\n const source = {\n id: shortid.generate(),\n name: args[0],\n created: new Date().toISOString(),\n kind: args[1],\n config: args[2]\n }\n try {\n if (source.kind === \"firebase\") {\n new Firebase(source.config.url)\n }\n const newLength = rw(model => {\n model.sources[source.id] = source\n return model.sources.length += 1\n })\n const refToNewSource = {\n $type: \"ref\",\n value: [\"sources\", \"by\", \"id\", source.id]\n }\n return {\n paths: [[\"sources\", [\"lastAdded\", \"length\"]], [\"sources\", \"by\", \"creation\", newLength]],\n jsonGraph: {\n sources: {\n by: {creation: {[newLength]: refToNewSource}},\n lastAdded: refToNewSource,\n length: newLength,\n },\n },\n }\n } catch (e) {\n return {\n paths: [[\"sources\", [\"lastAdded\"]]],\n jsonGraph: {\n sources: {\n lastAdded: {\n $type: \"error\",\n value: e.message,\n },\n },\n },\n }\n }\n },\n },\n {\n route: \"sources.by.creation[{keys:indices}]\",\n get(pathSet) {\n const data = rw()\n return _(data.sources)\n .omit(\"length\", \"availableKinds\")\n .values()\n .sortBy(\"created\")\n .pick(pathSet.indices)\n .map((source, index) => ({path: [\"sources\", \"by\", \"creation\", index], value: {$type: \"ref\", value: [\"sources\", \"by\", \"id\", source.id]}}))\n .value()\n },\n },\n {\n route: \"sources.by.id[{keys:ids}][{keys:props}]\",\n get(pathSet) {\n const data = rw()\n return _(data.sources)\n .pick(pathSet.ids)\n .flatMap(source => {\n return _.map(pathSet.props, prop => ({path: [\"sources\", \"by\", \"id\", source.id, prop], value: source[prop]}))\n })\n .value()\n },\n },\n ])\n}))\n\napp.listen(9009)\n\n_(rw().apisById)\n .omit(\"length\")\n .values()\n .each(runApi)\n\nfunction rw(mutator) {\n const dataPath = path.join(__dirname, \"data.json\")\n const data = JSON.parse(fs.readFileSync(dataPath))\n const result = mutator ? mutator(data) : data\n fs.writeFileSync(dataPath, JSON.stringify(data, null, 2))\n return result\n}\n\nfunction runApi(api) {\n const data = rw()\n const healthRoute = {\n route: \"health\",\n get() {\n return {path: [\"health\"], value: \"OK\"}\n },\n }\n const routes = _(api.routes || {})\n .omit(\"length\")\n .values()\n .map(route => {\n const source = data.sources[route.source.id]\n return createFalcorRoute(route, source)\n })\n .push(healthRoute)\n .value()\n runApi[api.id] = routes\n app.use(api.url, falcorExpress.dataSourceRoute(function () {\n return new falcorRouter(routes)\n }))\n}\n\nfunction createFalcorRoute(route, source) {\n return ({firebase: firebaseRoute, rest: restRoute}[source.kind] || fakeRoute)(route, source.config)\n}\n\nfunction fakeRoute(routeDefinition) {\n return {\n route: routeDefinition.matcher,\n [routeDefinition.method](pathSet) {\n const firstPath = _.map(pathSet, subpath => _.isArray(subpath) ? subpath[0] : subpath)\n return [{path: firstPath, value: \"The API is running this route but the route is not implemented\"}]\n },\n }\n}\n\nfunction firebaseRoute(routeDefinition, sourceConfig) {\n const source = new Firebase(sourceConfig.url)\n const queryTemplate = _.template(routeDefinition.query)\n return {\n route: routeDefinition.matcher,\n [routeDefinition.method](pathSet) {\n const firstPath = _.map(pathSet, subpath => _.isArray(subpath) ? subpath[0] : subpath)\n const renderedQuery = queryTemplate(pathSet)\n return source.child(renderedQuery).once(\"value\").then(snapshot => {\n return [{path: firstPath, value: {$type: \"atom\", value: snapshot.val()}}]\n })\n },\n }\n}\n\nfunction restRoute(routeDefinition, sourceConfig) {\n const defaultOptions = {baseUrl: sourceConfig.url, json: true}\n const queryTemplate = _.template(routeDefinition.query)\n const locationTemplate = _.template(routeDefinition.location)\n return {\n route: routeDefinition.matcher,\n [routeDefinition.method](pathSet, args) {\n if (routeDefinition.method === \"call\") {\n const requestOptions = renderRequestOptions(pathSet, args)\n return requestPromise(requestOptions).then(response => {\n const path = _.toPath(locationTemplate(response))\n const value = {$type: \"atom\", value: response}\n return [{path, value}]\n })\n } else if (routeDefinition.queryRendering === \"once\") {\n return Promise.all([runGet(pathSet)])\n } else {\n return Promise.all(_.map(expandPreservingShortcuts(pathSet), path => {\n return runGet(path)\n }))\n }\n },\n }\n function runGet(pathOrPathSet) {\n runGet.cache = runGet.cache || {}\n const requestOptions = renderRequestOptions(pathOrPathSet)\n const responsePromise = _.has(runGet.cache, requestOptions.url) ?\n Promise.resolve(runGet.cache[requestOptions.url]) :\n requestPromise(requestOptions)\n return responsePromise.then(response => {\n runGet.cache[requestOptions.url] = response\n const path = _.toArray(pathOrPathSet)\n const falcorResponse = buildFalcorResponse(path, response)\n const value = {$type: \"atom\", value: falcorResponse}\n return {path, value}\n })\n }\n function renderRequestOptions(path, args) {\n const renderedQuery = queryTemplate(_.assign({}, path, {args: _.map(args, arg => JSON.stringify(arg))}))\n const maybeJsonQuery = tryParseJson(renderedQuery)\n const requestOptions = _.defaults(maybeJsonQuery.json ? maybeJsonQuery.value : {url: maybeJsonQuery.value}, defaultOptions)\n return requestOptions\n }\n function buildFalcorResponse(path, restResponse) {\n const pathToRestResponse = _.takeWhile(path, pathSegment => !_.has(restResponse, pathSegment))\n const falcorResponse = _.set({}, pathToRestResponse, restResponse)\n return _.get(falcorResponse, path)\n }\n}\n\nfunction tryParseJson(maybeJsonString) {\n try {\n return {json: true, value: JSON.parse(maybeJsonString)}\n } catch (e) {\n return {json: false, value: maybeJsonString}\n }\n}\n\n/**\n * Same as `expand` but preserves shortcut properties.\n *\n * {0: \"a\", 1: [\"b\", \"c\"], 2: \"d\", ids: [\"b\", \"c\"]} -> [\n * {0: \"a\", 1: \"b\", 2: \"d\", ids: \"b\"},\n * {0: \"a\", 1: \"c\", 2: \"d\", ids: \"c\"}\n * ]\n */\nfunction expandPreservingShortcuts(pathSet) {\n const pathSetWithoutShortcuts = _.toArray(pathSet)\n const indicesOfShortcuts = _(pathSet)\n .omit(\"length\")\n .pickBy((value, key) => isNaN(Number(key)))\n .mapValues(pathSegments => _.indexOf(pathSetWithoutShortcuts, pathSegments))\n .value()\n return _.map(expand(pathSet), path => {\n const shortcuts = _.mapValues(indicesOfShortcuts, _.propertyOf(path))\n return _.assign(shortcuts, path, {length: path.length})\n })\n}\n\n/**\n * Expands a compressed path set into the corresponding set of full paths.\n *\n * [\"a\", [\"b\", \"c\"], \"d\"] -> [[\"a\", \"b\", \"d\"], [\"a\", \"c\", \"d\"]]\n */\nfunction expand(pathSet) {\n if (_.isEmpty(pathSet)) return [[]]\n const head = _.head(pathSet)\n const headPathSegments = _.isArray(head) ? head : [head]\n return _.flatMap(headPathSegments, headPathSegment => {\n return _.map(expand(_.tail(pathSet)), pathSetTail => {\n return [headPathSegment, ...pathSetTail]\n })\n })\n}\n", "text_preview": "\"use strict\" const _ = require(\"lodash\") const shortid = require(\"shortid\") const express = require(\"express\") const serveStatic = require(\"serve-static\") const bodyParser = require(\"body-parser\") const falcorRouter = require(\"falcor-router", "input_sha256": "27885106f7d5c979b84325c126ef25e85fae006a1056968d51fc8ff19f5a10cd", "text_sha256": "8b9201e51b40b5fe4b406784a98fca282918d8240f65c1acdba8e8e40e0535bc", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8004, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, 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"commit_id": "23bf2eb120ce69f176502df2313114aa1c6e13a0", "url": "https://github.com/hgwood/falcorception/blob/23bf2eb120ce69f176502df2313114aa1c6e13a0/server/index.js"}, "gpt4o_tokens": 3647, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9384516312970861, "noise_severity": 0.02195252500668915, "missing_content_severity": 0.02784048155717372, "factual_density": 3.293785611109304, "pretraining_utility": 0.5912156890411453, "math_density": 0.17061484080020603, "code_density": 3.906794321971093, "reasoning_density": 0.5701642985953573, "education_level": 2.601597237335718, "creative_writing_quality": -0.30545368591236954}, "score_applicability": {"education_level": 0.9989536324110202, "creative_writing_quality": 0.0003420136769188652}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": 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baseURL =\"https://backend.codecrack.tech\";\nconst uploadURL = `${baseURL}/api/files`;\nconst emailURL = `${baseURL}/api/files/send`;\n\nconst maxAllowedSize = 100 * 1024 * 1024; //100mb\n\n// This is useful when the file input element is hidden or styled to be invisible\nbrowseBtn.addEventListener(\"click\", () => {\n // Simulate a click on the file input element to open the file browser and allow the user to select a file\n fileInput.click();\n});\n\n// Add an event listener to the drop zone to handle file drops\ndropZone.addEventListener(\"drop\", (e) => {\n // Prevent the default browser behavior for dropping files\n e.preventDefault();\n // Get the files dropped on the drop zone,The dataTransfer object is part of the Drag and Drop API, allowing access to data being transferred during drag-and-drop operations.\n const files = e.dataTransfer.files;\n if (files.length === 1) {\n if (files[0].size < maxAllowedSize) {\n // Set the file input element's files property to the dropped file\n fileInput.files = files;\n uploadFile();\n } else {\n showToast(\"Max file size is 100MB\");\n }\n } else if (files.length > 1) {\n showToast(\"You can't upload multiple files\");\n }\n // Remove the \"dragged\" class from the drop zone to reset its appearance\n dropZone.classList.remove(\"dragged\");\n});\n\ndropZone.addEventListener(\"dragover\", (e) => {\n e.preventDefault();\n dropZone.classList.add(\"dragged\");\n});\n\ndropZone.addEventListener(\"dragleave\", (e) => {\n e.preventDefault();\n dropZone.classList.remove(\"dragged\");\n});\n\n// file input change and uploader\nfileInput.addEventListener(\"change\", (e) => {\n e.preventDefault();\n if (fileInput.files[0].size > maxAllowedSize) {\n showToast(\"Max file size is 100MB\");\n fileInput.value = \"\"; // reset the input\n return;\n }\n\n uploadFile();\n\n});\n\n// Add an event listener to the copy URL button\ncopyURLBtn.addEventListener(\"click\", async () => {\n // Write the value of the fileURL input field to the clipboard\n await navigator.clipboard.writeText(fileURL.value);\n showToast(\"Copied to clipboard\");\n});\n// Add an event listener to the select fileURL input field\nfileURL.addEventListener(\"click\", () => {\n // Select all text in the input field when it's clicked\n fileURL.select();\n});\n\nconst uploadFile = () => {\n // fileInput.files returns a FileList object, which is an array-like object containing the selected files\n files = fileInput.files;\n // FormData is a built-in JavaScript object that allows us to create a set of key-value pairs\n const formData = new FormData();\n \n //selecting the first file from the FileList object (files[0]) and appending it to the FormData object with the key \"myfile\"\n formData.append(\"myfile\", files[0]);\n \n // Show the uploader progress container\n progressContainer.style.display = \"block\";\n \n // XMLHttpRequest is a built-in JavaScript object that allows us to make HTTP requests\n const xhr = new XMLHttpRequest();\n \n // The onprogress event is triggered periodically during the upload process, allowing us to update the progress indicator\n\n xhr.upload.onprogress = function (event) {\n let percent = Math.round((100 * event.loaded) / event.total);\n progressPercent.innerText = percent;\n // bgProgress and progressBar are HTML elements that display the upload progress as a visual bar\n const scaleX = `scaleX(${percent / 100})`;\n bgProgress.style.transform = scaleX;\n progressBar.style.transform = scaleX;\n };\n // The onerror event is triggered if there is an error during the upload process\n xhr.upload.onerror = function () {\n showToast(`Error in upload: ${xhr.standing}`);\n fileInput.value = \"\";\n };\n // Listen for the response from the server\n // The onreadystatechange event is triggered whenever the readyState property of the XMLHttpRequest object changes\n xhr.onreadystatechange = function () {\n // Check if the upload is complete\n // readyState 4 indicates that the request has completed\n if (xhr.readyState == XMLHttpRequest.DONE) {\n // Call the success function with the response text\n \n onFileUploadSuccess(xhr.responseText);\n }\n };\n // Prepare the request to the server\n xhr.open(\"POST\", uploadURL); // Set up a POST request to the specified upload URL\n // Send the request along with the file data\n xhr.send(formData); // This initiates the file upload to the server\n};\n\nconst onFileUploadSuccess = (res) => {\n fileInput.value = \"\";\n\n standing.innerText = \"Uploaded\";\n\n // - The button was disabled during the upload process to prevent multiple submissions.\n // - The button text is changed back to \"Send\" to indicate it is now active and ready for the user to send the link.\n emailForm[2].removeAttribute(\"disabled\");\n emailForm[2].innerText = \"Send\";\n\n progressContainer.style.display = \"none\";\n\n // - The server's response is expected to include a URL for the uploaded file.\n // - The destructuring syntax `{ file: url }` extracts the `file` property from the response and assigns it to the `url` variable.\n const { file: url } = JSON.parse(res);\n\n // Make the sharing container visible, which allows the user to share the file URL\n sharingContainer.style.display = \"block\";\n\n // Set the value of the file URL input field to the received URL\n\n fileURL.value = url;\n\n};\n\n// Add an event listener to the form that triggers when the form is submitted\nemailForm.addEventListener(\"submit\", (e) => {\n // Prevent the default form submission, which would cause a page reload\n e.preventDefault();\n\n // Disable the submit button to prevent multiple submissions during the email sending process\n emailForm[2].setAttribute(\"disabled\", \"true\");\n\n emailForm[2].innerText = \"Sending\";\n\n const url = fileURL.value;\n\n const formData = {\n // Extract the unique file identifier (UUID) from the URL\n uuid: url.split(\"/\").splice(-1, 1)[0],\n\n emailTo: emailForm.elements[\"to-email\"].value,\n emailFrom: emailForm.elements[\"from-email\"].value,\n };\n // Use the Fetch API to send the form data to the server\n fetch(emailURL, {\n method: \"POST\",\n // Set the request headers to indicate that the request body is in JSON format\n headers: {\n \"Content-Type\": \"application/json\",\n },\n // Convert formData to a JSON string because the Fetch API requires\n // the body to be a string when using 'application/json'.\n body: JSON.stringify(formData),\n })\n .then((res) => res.json())\n .then((data) => {\n if (data.success) {\n showToast(\"Email Sent\");\n sharingContainer.style.display = \"none\";\n }\n });\n});\n\nlet toastTimer;\n// the toast function\nconst showToast = (msg) => {\n clearTimeout(toastTimer);\n toast.innerText = msg;\n toast.classList.add(\"show\");\n toastTimer = setTimeout(() => {\n toast.classList.remove(\"show\");\n }, 2000);\n};\n", "text_preview": "const dropZone = document.querySelector(\".drop-zone\"); const fileInput = document.querySelector(\"#fileInput\"); 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false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44a0e6cae94809bcdc46ba3f08663d5c32bd9c3c"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 15, "repo_created_at": "2024-07-30T06:13:24.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b5a853e22e2b85dd2aff96d665f061f706b8dfda", "size_bytes": 7579, "file_timestamp": 1723393512, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "44a0e6cae94809bcdc46ba3f08663d5c32bd9c3c", "url": "https://github.com/err-abhishek/SweetShare/blob/44a0e6cae94809bcdc46ba3f08663d5c32bd9c3c/frontend/index.js"}, "gpt4o_tokens": 1760, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6773627053347989, "noise_severity": 0.08783155933250056, "missing_content_severity": 0.03947759102829373, "factual_density": 3.18694222309219, "pretraining_utility": 0.4587556986373857, "math_density": 0.616453454546987, "code_density": 3.772869850751673, "reasoning_density": 0.4830939441755253, "education_level": 2.011893960889801, "creative_writing_quality": -0.5247015841246694}, "score_applicability": {"education_level": 0.9985438018295133, "creative_writing_quality": 0.000917783221655228}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/devNmin/HomeSketcher/blob/62c29f042eae5143dcf39a77270cf90a581dcb45/3D/src/geometry.js", "id": "javascript-0-70aa2a1b7d20e6ebec73fad7606e8ee4", "text": "export const LARGE_DISTANCE = 1e9;\nexport const MARGIN_OF_ERROR = 1e-6;\nexport const REFERENCE_DIRECTION = 0;\n\nexport const degreesToRadians = (degrees) => (degrees * Math.PI) / 180.0;\n\nexport const radiansToDegrees = (radians) => radians * (180.0 / Math.PI);\n\nexport const vector = (from, to) => ({ x: from.x - to.x, y: from.y - to.y });\n\nexport const magnitude = (vector) =>\n Math.sqrt(Math.pow(vector.x, 2) + Math.pow(vector.y, 2));\n\nexport const distance = (from, to) => magnitude(vector(from, to));\n\nexport const polarToCartesian = ({ angle, range }, orig = { x: 0, y: 0 }) => {\n let angleInRadians = degreesToRadians(REFERENCE_DIRECTION + angle);\n let cartesian = {\n x: range * Math.cos(angleInRadians),\n y: range * Math.sin(angleInRadians)\n };\n return { x: orig.x + cartesian.x, y: orig.y + cartesian.y };\n};\n\nexport const cartesianToPolar = (point, orig = { x: 0, y: 0 }) => {\n let v = vector(point, orig);\n let range = magnitude(v);\n let angle = radiansToDegrees(Math.atan2(v.y, v.x));\n return { range, angle: angle - REFERENCE_DIRECTION };\n};\n\nexport const pointIsOnSegment = ({ x, y }, [a, b]) =>\n x - MARGIN_OF_ERROR <= Math.max(a.x, b.x) &&\n x + MARGIN_OF_ERROR >= Math.min(a.x, b.x) &&\n y - MARGIN_OF_ERROR <= Math.max(a.y, b.y) &&\n y + MARGIN_OF_ERROR >= Math.min(a.y, b.y);\n\nexport const lineIntersectsLine = ([a, b], [c, d]) => {\n let a1 = b.y - a.y;\n let b1 = a.x - b.x;\n let c1 = a1 * a.x + b1 * a.y;\n\n let a2 = d.y - c.y;\n let b2 = c.x - d.x;\n let c2 = a2 * c.x + b2 * c.y;\n\n let determinant = a1 * b2 - a2 * b1;\n\n if (determinant === 0) {\n // parallel lines, never intersect\n return false;\n } else {\n let x = (b2 * c1 - b1 * c2) / determinant;\n let y = (a1 * c2 - a2 * c1) / determinant;\n\n return { x, y };\n }\n};\n\nconst segmentIntersectsSegment = (l1, l2) => {\n let pointOfIntersection = lineIntersectsLine(l1, l2);\n if (\n pointOfIntersection &&\n pointIsOnSegment(pointOfIntersection, l1) &&\n pointIsOnSegment(pointOfIntersection, l2)\n ) {\n return pointOfIntersection;\n } else {\n return false;\n }\n};\n\nexport const isClockwise = (a, b) => {\n return b > a || a - b > 180;\n};\n\nexport const nearestIntersection = (from, angle, walls, coords) => {\n let to = polarToCartesian({ range: LARGE_DISTANCE, angle }, from);\n let intersections = walls\n .map(([a, b]) =>\n segmentIntersectsSegment([from, to], [coords[a], coords[b]])\n )\n .filter((intersection) => !!intersection)\n .map((intersection) => ({\n intersection,\n distance: distance(intersection, from)\n }))\n .sort((a, b) => a.distance - b.distance);\n\n return intersections?.[0]?.intersection;\n};\n\nexport const calcCentroid = (points) => {\n let sum = points.reduce(\n ({ x, y }, p) => ({\n x: x + p.x,\n y: y + p.y\n }),\n { x: 0, y: 0 }\n );\n return { x: sum.x / points.length, y: sum.y / points.length };\n};\n\nexport const arrangeClockwise = (arrayOfPoints, origin) => {\n let o = origin || calcCentroid(arrayOfPoints);\n let polar = arrayOfPoints.map((p) => cartesianToPolar(p, o));\n let clockwise = polar.sort((a, b) => a.angle - b.angle);\n return clockwise.map((p) => polarToCartesian(p, o));\n};\n\nexport const getVisibilityPolygon = (from, cartesianCoords) => {\n // wall corners as indexes into the cartesian coords array\n let walls = cartesianCoords.map((_, i) => [\n i,\n (i + 1) % cartesianCoords.length\n ]);\n\n // get all points in polar coords, giving us angles from camera\n let polar = cartesianCoords.map((c) => cartesianToPolar(c, from));\n\n // reduce to the set of unique angles that we need to test\n const unique = Array.from(\n polar.reduce((r, { angle }) => r.add(angle), new Set())\n );\n\n // sort into clockwise order\n const clockwise = unique.sort((a, b) => a - b);\n\n // produce angles slightly CCW and CW of each angle in the list\n const checkPoints = clockwise\n .map((angle) => [angle - 0.01, angle, angle + 0.01])\n .flat();\n\n // for each angle, find the nearest intersection with a wall\n return checkPoints.map((angle) => {\n return nearestIntersection(from, angle, walls, cartesianCoords);\n });\n};\n", "text_preview": "export const LARGE_DISTANCE = 1e9; export const MARGIN_OF_ERROR = 1e-6; export const REFERENCE_DIRECTION = 0; export const degreesToRadians = 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VideoThumbnails from 'expo-video-thumbnails';\nimport { Video } from 'react-native-compressor';\n\n// Utility Functions for persistent storage\nconst initializeVideoFilesDirectory = async () => {\n try {\n const videoFilesDirectory = `${FileSystem.documentDirectory}videofiles/`;\n await FileSystem.makeDirectoryAsync(videoFilesDirectory, { intermediates: true });\n return videoFilesDirectory;\n } catch (error) {\n console.error('ERROR: Failed to initialize videofiles directory in compression service:', error);\n throw error;\n }\n};\n\nclass VideoCompressionService {\n constructor() {\n this.maxFileSize = 15 * 1024 * 1024; // 15MB default limit\n this.compressionQuality = {\n high: { videoBitrate: 2000000, audioBitrate: 192000 },\n medium: { videoBitrate: 1000000, audioBitrate: 128000 },\n low: { videoBitrate: 500000, audioBitrate: 96000 }\n };\n }\n\n /**\n * Get video file information including duration, size, and dimensions\n */\n async getVideoInfo(uri) {\n try {\n const fileInfo = await FileSystem.getInfoAsync(uri);\n \n if (!fileInfo.exists) {\n throw new Error('Video file does not exist');\n }\n\n // Try to get video metadata using thumbnail generation\n let metadata = null;\n try {\n const thumbnail = await VideoThumbnails.getThumbnailAsync(uri, {\n time: 0,\n quality: 0.1,\n });\n \n if (thumbnail.uri) {\n // Video is valid, we can work with it\n metadata = {\n isValid: true,\n thumbnail: thumbnail.uri\n };\n }\n } catch (thumbnailError) {\n console.warn('Could not generate thumbnail:', thumbnailError);\n metadata = { isValid: false };\n }\n\n return {\n ...fileInfo,\n sizeMB: Math.round(fileInfo.size / 1024 / 1024 * 100) / 100,\n metadata\n };\n } catch (error) {\n console.error('Error getting video info:', error);\n throw error;\n }\n }\n\n /**\n * Determine if video needs compression based on file size\n */\n needsCompression(videoInfo, maxSizeMB = null) {\n const limit = maxSizeMB || (this.maxFileSize / 1024 / 1024);\n return videoInfo.sizeMB > limit;\n }\n\n /**\n * Calculate optimal recording settings to stay under size limit\n */\n getOptimalRecordingSettings(durationSeconds, targetSizeMB = 15) {\n // Calculate required bitrate to stay under target size\n // Formula: File Size (MB) = (Video Bitrate + Audio Bitrate) * Duration (s) / 8 / 1024 / 1024\n \n const targetBytes = targetSizeMB * 1024 * 1024;\n const audioBitrate = 128000; // 128 kbps audio\n const overhead = 0.1; // 10% overhead for container and metadata\n \n const availableBitsPerSecond = (targetBytes * 8 / durationSeconds) * (1 - overhead);\n const videoBitrate = Math.max(300000, availableBitsPerSecond - audioBitrate); // Min 300kbps\n \n let quality = 'low';\n if (videoBitrate >= 1500000) {\n quality = 'high';\n } else if (videoBitrate >= 800000) {\n quality = 'medium';\n }\n\n return {\n quality,\n videoBitrate: Math.round(videoBitrate),\n audioBitrate,\n estimatedSizeMB: Math.round((videoBitrate + audioBitrate) * durationSeconds / 8 / 1024 / 1024 * 100) / 100\n };\n }\n\n /**\n * Basic video validation\n */\n async validateVideo(uri) {\n try {\n const info = await this.getVideoInfo(uri);\n \n const validations = {\n fileExists: info.exists,\n hasValidSize: info.size > 0,\n isNotTooLarge: info.sizeMB <= 50, // Hard limit of 50MB\n isVideoValid: info.metadata?.isValid !== false\n };\n\n const isValid = Object.values(validations).every(v => v === true);\n\n return {\n isValid,\n info,\n validations,\n errors: Object.entries(validations)\n .filter(([key, value]) => !value)\n .map(([key]) => this.getValidationErrorMessage(key))\n };\n } catch (error) {\n return {\n isValid: false,\n errors: [`Validation failed: ${error.message}`]\n };\n }\n }\n\n getValidationErrorMessage(validationType) {\n const messages = {\n fileExists: 'Video file not found',\n hasValidSize: 'Video file is empty or corrupted',\n isNotTooLarge: 'Video file is too large (over 50MB)',\n isVideoValid: 'Video format is not supported or file is corrupted'\n };\n return messages[validationType] || 'Unknown validation error';\n }\n\n /**\n * Compresses a video using react-native-compressor.\n */\n async createCompressedCopy(sourceUri, options = {}) {\n try {\n const {\n compressionMethod = 'auto', // 'auto', 'manual', or 'off'\n maxSizeMB = 15,\n minFileSizeForCompression = 1, // Don't compress files smaller than 1MB\n progressCallback,\n } = options;\n\n if (compressionMethod === 'off') {\n console.log('Compression is turned off.');\n return sourceUri;\n }\n\n const sourceInfo = await this.getVideoInfo(sourceUri);\n if (!sourceInfo.exists) {\n throw new Error('Source video file not found for compression.');\n }\n \n if (sourceInfo.sizeMB < minFileSizeForCompression) {\n console.log(`Video size (${sourceInfo.sizeMB}MB) is below threshold (${minFileSizeForCompression}MB), skipping compression.`);\n return sourceUri;\n }\n\n console.log(`Starting compression for: ${sourceUri} (${sourceInfo.sizeMB}MB)`);\n\n const result = await Video.compress(\n sourceUri,\n {\n compressionMethod: 'auto',\n },\n (progress) => {\n if (progressCallback) {\n progressCallback(progress);\n }\n console.log('Compression Progress: ', progress);\n }\n );\n\n\n if (!result) {\n throw new Error('Video compression failed to return a valid path.');\n }\n\n const compressedTempUri = result;\n\n // Add retry logic for getVideoInfo on the temporary compressed file\n let compressedInfo = null;\n const MAX_INFO_RETRIES = 5;\n const INFO_RETRY_DELAY_MS = 500; // 0.5 seconds\n\n for (let i = 0; i < MAX_INFO_RETRIES; i++) {\n try {\n compressedInfo = await this.getVideoInfo(compressedTempUri);\n if (compressedInfo.exists && compressedInfo.size > 0) {\n console.log(`LOG: Compressed file info retrieved successfully after ${i + 1} attempts.`);\n break;\n }\n } catch (infoError) {\n console.warn(`WARN: Attempt ${i + 1} to get compressed file info failed:`, infoError);\n }\n await new Promise(resolve => setTimeout(resolve, INFO_RETRY_DELAY_MS));\n }\n\n if (!compressedInfo || !compressedInfo.exists || compressedInfo.size === 0) {\n throw new Error('Compressed video file is invalid or empty after retries.');\n }\n\n console.log(`Compression successful: ${compressedTempUri} (${compressedInfo.sizeMB}MB)`);\n\n // Define the persistent directory and ensure it exists\n const videoFilesDirectory = await initializeVideoFilesDirectory();\n\n // Create a unique file name for the persistent compressed video\n const fileName = `compressed_video_${Date.now()}.mp4`;\n const persistentCompressedUri = `${videoFilesDirectory}${fileName}`;\n\n // Copy the compressed video from the temporary cache to the persistent directory\n await FileSystem.copyAsync({ from: compressedTempUri, to: persistentCompressedUri });\n console.log(`LOG: Moved compressed video to persistent storage: ${persistentCompressedUri}`);\n\n // Clean up the temporary compressed file from cache\n await FileSystem.deleteAsync(compressedTempUri, { idempotent: true });\n console.log(`LOG: Deleted temporary compressed file from cache: ${compressedTempUri}`);\n\n // Optional: Delete the original source file if it's in a temp directory\n if (sourceUri.includes(FileSystem.cacheDirectory)) {\n await FileSystem.deleteAsync(sourceUri, { idempotent: true });\n console.log(`LOG: Deleted temporary source file: ${sourceUri}`);\n }\n\n return persistentCompressedUri;\n } catch (error) {\n console.error('Video compression failed:', error);\n // Fallback to returning the original URI if compression fails\n return sourceUri;\n }\n }\n\n /**\n * Clean up temporary video files\n */\n async cleanupTempFiles() {\n try {\n const documentDir = FileSystem.documentDirectory;\n const files = await FileSystem.readDirectoryAsync(documentDir);\n \n const videoFiles = files.filter(file => \n file.includes('compressed_') || \n file.includes('recording_') ||\n file.includes('temp_video_')\n );\n\n for (const file of videoFiles) {\n try {\n const filePath = `${documentDir}${file}`;\n const fileInfo = await FileSystem.getInfoAsync(filePath);\n \n // Delete files older than 1 hour\n const oneHourAgo = Date.now() - (60 * 60 * 1000);\n if (fileInfo.modificationTime < oneHourAgo) {\n await FileSystem.deleteAsync(filePath, { idempotent: true });\n console.log(`Cleaned up old video file: ${file}`);\n }\n } catch (error) {\n console.warn(`Could not clean up file ${file}:`, error);\n }\n }\n } catch (error) {\n console.warn('Error during cleanup:', error);\n }\n }\n\n /**\n * Get recommended camera settings for recording\n */\n getRecommendedCameraSettings(maxDurationSeconds = 30, targetSizeMB = 15) {\n const settings = this.getOptimalRecordingSettings(maxDurationSeconds, targetSizeMB);\n \n return {\n maxDuration: maxDurationSeconds,\n quality: settings.quality === 'high' ? 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'480p' : '360p',\n videoBitrate: settings.videoBitrate,\n audioBitrate: settings.audioBitrate,\n videoCodec: 'h264',\n audioCodec: 'aac',\n estimatedSizeMB: settings.estimatedSizeMB\n };\n }\n}\n\nexport default new VideoCompressionService();", "text_preview": "// services/videoCompressionService.js import * as FileSystem from 'expo-file-system'; import * as VideoThumbnails from 'expo-video-thumbnails'; import { Video } from 'react-native-compressor'; // Utility Functions for persistent storage co", "input_sha256": "5c191746c23a3983b61f2d2de8d65f56a256d2017151e0fd6f863a3f97ac6fe8", "text_sha256": "7b30949c5b682df5188ebb11e94a079b9b2ada21d3992657d4dbfa32ac5cc5e4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8008, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 2, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9091566034769516, "noise_severity": 0.03484694846261728, "missing_content_severity": 0.0035665956814552925, "factual_density": 3.1878496174020805, "pretraining_utility": 0.6477258565645923, "math_density": 1.3780539426027911, "code_density": 3.815662389084568, "reasoning_density": 1.5657282351810653, "education_level": 2.196409846836456, "creative_writing_quality": -0.19899643553030263}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01002905959031819, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9976662442265868, "secondary": 0.979457585328927, "tertiary": 0.9899226909883897, "structured_records": 0.03595701510230743, "procedural_steps": 0.08334057736718162, "worked_solution": 0.006854471529678703, "contextual_dialogue": 5.18488431547578e-06, "content_quality": 0.5682003578142044, "noise_severity": 0.9709608762811522, "missing_content_severity": 0.9970278369321206, "factual_density": 0.4878444771234506, "pretraining_utility": 0.5385203186751036, "math_density": 0.48707992343335604, "code_density": 0.8463853242371396, "reasoning_density": 0.10296839372337974, "education_level": 0.7319442153227351, "creative_writing_quality": 0.9994126023230099, "primary_topic": 0.9906795607724779, "secondary_topic": 0.90764657594774}, "probability_margin": {"primary": 0.9966531640928185, "secondary": 0.9707413636996843, "tertiary": 0.9867801838102502, "education_level": 0.589054286734032, "creative_writing_quality": 0.9992587983806429, "primary_topic": 0.988556173346763, "secondary_topic": 0.876229656174137}, "raw_probabilities": {"primary": {"Academic Writing": 2.709532608163005e-05, "Content Listing": 1.3567936820925333e-05, "Creative Writing": 1.1611459826302505e-05, "Customer Support Page": 7.750683285549931e-06, "Q&A Forum": 8.797311124093601e-06, "Discussion Thread": 4.7893916717965365e-06, "Comment Section": 1.1742177009942328e-05, "Missing Content": 1.747050759089836e-05, "Knowledge Article": 0.00010110730130120994, "Legal Notices": 2.2085075272023582e-05, "News Article": 3.2258584410028145e-06, "Organizational Announcement": 7.944411872458606e-06, "Nonfiction Writing": 7.653214435088085e-06, "Organizational About Page": 8.868085138220076e-06, "Personal About Page": 8.861203595868922e-06, "Personal Blog": 5.63987145856767e-06, "Product Page": 7.606275225475775e-06, "Spam / Ads": 7.897851818098936e-06, "Structured Data": 0.0003619448690223681, "Technical Writing": 0.997763484050479, "Transcript / Interview": 1.295208039424307e-05, "Tutorial / How-To Guide": 0.0004627910750977215, "User Reviews": 4.794025376997783e-06, "Other / Unknown": 0.0011103199576604937}, "secondary": {"Academic Writing": 0.0001361479536027125, "Content Listing": 7.316443730167276e-05, "Creative Writing": 0.00010099595633108152, "Customer Support Page": 6.52983731287217e-05, "Q&A Forum": 4.233284038859883e-05, "Discussion Thread": 3.404721477048133e-05, "Comment Section": 0.00015956186054583618, "Missing Content": 0.00028769163633484763, "Knowledge Article": 0.0010070732471524286, "Legal Notices": 0.00025548127455927067, "News Article": 5.3671730956220073e-05, "Organizational Announcement": 5.103866359334264e-05, "Nonfiction Writing": 5.4224648551210994e-05, "Organizational About Page": 6.822309963312196e-05, "Personal About Page": 5.2184049707276015e-05, "Personal Blog": 4.6363154481708956e-05, "Product Page": 3.9822161135246035e-05, "Spam / Ads": 6.581619480438844e-05, "Structured Data": 0.005613592329482145, "Technical Writing": 0.009572155573870684, "Transcript / Interview": 7.154166501439812e-05, "Tutorial / How-To Guide": 0.0018065879270332728, "User Reviews": 2.946473406639375e-05, "NONE": 0.980313519273555}, "tertiary": {"Academic Writing": 5.643468531023983e-05, "Content Listing": 4.635368958083894e-05, "Creative Writing": 5.613275662674238e-05, "Customer Support Page": 3.3419691809741336e-05, "Q&A Forum": 1.7778592335626842e-05, "Discussion Thread": 1.4660081466621621e-05, "Comment Section": 8.184331735465485e-05, "Missing Content": 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0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": 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"cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:10.524210+00:00", "scoring_key_slot": "primary", "dataset_uid": "2d9ac281-b82b-4b61-a188-e9142398971e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b8063e45dc2ccc973fa10d7aa1dc6f639e28c83a69ce485a7b1dc90ddc0a1252", "source_metadata": {"uuid": "2d9ac281-b82b-4b61-a188-e9142398971e", "repo_name": "himfirstmedia/share-jesus-today", "relative_path": "services/videoCompressionService.js", "category": "TOOL", "algo_rel_score": 0.9332797527313232, "quality_score": 7.480288982391357, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-07-11T11:27:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c6c3f849ff0f663697a14f218edab47585428973", "size_bytes": 10107, "file_timestamp": 1753277934, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "937f0f122e12f6702d3e824e2e35f163b1f9002c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-07-11T11:27:51.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c6c3f849ff0f663697a14f218edab47585428973", "size_bytes": 10107, "file_timestamp": 1753277934, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "937f0f122e12f6702d3e824e2e35f163b1f9002c", "url": "https://github.com/himfirstmedia/share-jesus-today/blob/937f0f122e12f6702d3e824e2e35f163b1f9002c/services/videoCompressionService.js"}, "gpt4o_tokens": 2343, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9091566034769516, "noise_severity": 0.03484694846261728, "missing_content_severity": 0.0035665956814552925, "factual_density": 3.1878496174020805, "pretraining_utility": 0.6477258565645923, "math_density": 1.3780539426027911, "code_density": 3.815662389084568, "reasoning_density": 1.5657282351810653, "education_level": 2.196409846836456, "creative_writing_quality": -0.19899643553030263}, "score_applicability": {"education_level": 0.9991017211212039, "creative_writing_quality": 0.0004894980641583868}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/haduc25/Projects-2024/blob/cb08d64855cc894de29c0ea5201d2e1dde5651fe/MERNStack-2024/QLCH6688/frontend/src/context/StoreContext.jsx", "id": "javascript-0-5340f52af54cd2daaecf272f751e8565", "text": "import axios from 'axios';\nimport { createContext, useEffect, useState } from 'react';\n// import { product_list } from '../assets/assets_vn';\n// import { products as product_list } from '../assets/products.js';\n\n// Tạo Context: Context này sẽ giúp chia sẻ dữ liệu trong toàn bộ ứng dụng mà không cần phải truyền qua props từng cấp\nexport const StoreContext = createContext(null);\n\n/** StoreContextProvider Component\n * StoreContextProvider là một component bao bọc (wrapper) được sử dụng để cung cấp giá trị của product_list cho các component con thông qua Context.\n * contextValue: Được khởi tạo với product_list, đây chính là giá trị sẽ được truyền tới các component con.\n * StoreContext.Provider: Sử dụng Provider để cung cấp contextValue cho tất cả các component con bên trong ({props.children}).\n */\nconst StoreContextProvider = (props) => {\n const [cartItems, setCartItems] = useState({});\n // backend url\n const url = 'http://localhost:6868/';\n const urlImage = 'http://localhost:6868/images/';\n // const [token, setToken] = useState('');\n const [token, setToken] = useState('');\n const [product_list, setProductList] = useState([]);\n\n // ADD TO CART\n const addToCart = async (itemId) => {\n if (!cartItems[itemId]) {\n // Thêm món ăn vào giỏ hàng: tạo ra một đối tượng mới, sao chép tất cả các mục từ prev, và thêm mục mới cho itemId với giá trị là 1.\n setCartItems((prev) => ({ ...prev, [itemId]: 1 }));\n } else {\n // Cập nhật số lượng món ăn nếu đã tồn tại: lấy số lượng hiện tại (prev[itemId]) và cộng thêm 1\n setCartItems((prev) => ({ ...prev, [itemId]: prev[itemId] + 1 }));\n }\n\n if (token) {\n await axios.post(url + 'api/cart/add', { itemId }, { headers: { token } });\n }\n };\n\n // REMOVE FROM CART\n const removeFromCart = async (itemId) => {\n // CASE 1\n setCartItems((prev) => ({ ...prev, [itemId]: prev[itemId] - 1 }));\n\n // CASE 2\n // if (cartItems[itemId] > 1) {\n // // Cập nhật số lượng món ăn nếu đã tồn tại: lấy số lượng hiện tại (prev[itemId]) và trừ đi 1\n // setCartItems((prev) => ({ ...prev, [itemId]: prev[itemId] - 1 }));\n // } else {\n // // Xóa món ăn khỏi giỏ hàng: tạo ra đối tượng mới, sao chép tất cả các mục từ prev, và loại bỏ mục mới cho itemId\n // setCartItems((prev) => {\n // const newCartItems = { ...prev };\n // delete newCartItems[itemId];\n // return newCartItems;\n // });\n // }\n\n if (token) {\n await axios.post(url + 'api/cart/remove', { itemId }, { headers: { token } });\n }\n };\n\n // UPDATE CART (Theo số lượng user nhập vào)\n const updateCartItemQuantity = async (itemId, quantity) => {\n // Đảm bảo số lượng hợp lệ (>= 0)\n if (quantity < 0) return;\n\n setCartItems((prev) => ({\n ...prev,\n [itemId]: quantity,\n }));\n\n // Cập nhật số lượng trong API nếu có token\n if (token) {\n await axios.post(url + 'api/cart/update', { itemId, quantity }, { headers: { token } });\n }\n };\n\n const getTotalCartAmount = () => {\n let totalAmount = 0;\n\n for (const item in cartItems) {\n if (cartItems[item] > 0) {\n // Tìm sản phẩm tương ứng trong danh sách product_list dựa trên _id | Để tăng hiệu suất tìm kiếm có thể thay `find()` bằng cách sử dụng `map()`\n let itemInfo = product_list.find((product) => product._id === item);\n // Tính tổng tiền\n totalAmount += itemInfo.sellingPrice * cartItems[item];\n }\n }\n return totalAmount;\n };\n\n const getTotalCartQuantity = () => {\n let totalQuantity = 0;\n\n for (const item in cartItems) {\n if (cartItems[item] > 0) {\n totalQuantity += cartItems[item];\n }\n }\n\n return totalQuantity;\n };\n\n const fetchProductList = async () => {\n const response = await axios.get(url + 'api/sanpham/danhsachsanpham');\n setProductList(response.data.data);\n };\n\n const loadCartData = async (token) => {\n const response = await axios.post(url + 'api/cart/get', {}, { headers: { token } });\n setCartItems(response.data.cartData);\n };\n\n // Test\n // useEffect(() => {\n // console.log('cartItems: ', cartItems);\n // }, [cartItems]);\n\n // get data from local storage\n useEffect(() => {\n async function loadData() {\n await fetchProductList();\n // if (localStorage.getItem('token')) {\n // setToken(localStorage.getItem('token'));\n // await loadCartData(localStorage.getItem('token'));\n // }\n }\n loadData();\n }, []);\n\n // Global Functions\n const utilityFunctions = {\n getTotalItems: () => Object.values(cartItems).reduce((sum, qty) => sum + qty, 0), // Tổng số lượng sản phẩm trong giỏ hàng\n calculateDiscount: (totalAmount, discountPercentage) => totalAmount * (1 - discountPercentage / 100), // Hàm tính giảm giá\n getCartItems: () => cartItems, // Trả về giỏ hàng hiện tại\n formatCurrency: (amount) =>\n new Intl.NumberFormat('vi-VN', { style: 'currency', currency: 'VND' }).format(amount),\n formatDateTimeFromISO8601ToVietNamDateTime: (dateString) => {\n // Sửa lại cú pháp của hàm\n const date = new Date(dateString);\n\n // Lấy giờ, phút, giây\n const hours = String(date.getHours()).padStart(2, '0');\n const minutes = String(date.getMinutes()).padStart(2, '0');\n const seconds = String(date.getSeconds()).padStart(2, '0');\n\n // Lấy ngày, tháng, năm\n const day = String(date.getDate()).padStart(2, '0');\n const month = String(date.getMonth() + 1).padStart(2, '0'); // Tháng bắt đầu từ 0\n const year = date.getFullYear();\n\n // Trả về định dạng hh:mm:ss dd/mm/yyyy\n return `${hours}:${minutes}:${seconds} ${day}/${month}/${year}`;\n },\n formatDateFromYYYYMMDDToVietNamDate: (dateString) => {\n // Tạo đối tượng Date từ chuỗi theo định dạng 'YYYY-MM-DD'\n const date = new Date(dateString);\n\n // Lấy ngày, tháng, năm\n const day = String(date.getDate()).padStart(2, '0');\n const month = String(date.getMonth() + 1).padStart(2, '0'); // Tháng bắt đầu từ 0\n const year = date.getFullYear();\n\n // Trả về định dạng dd/mm/yyyy\n return `${day}/${month}/${year}`;\n },\n convertCategory: (category) => {\n const categoryMap = {\n dientu: 'Điện tử',\n thoitrang: 'Thời trang',\n giadung: 'Gia dụng',\n thucpham: 'Thực phẩm',\n mypham: 'Mỹ phẩm',\n thuocla: 'Thuốc lá',\n sua: 'Sữa',\n keocaosu: 'Kẹo cao su',\n nuocngot: 'Nước ngọt',\n thucphamanlien: 'Thực phẩm ăn liền',\n caphe: 'Cà phê',\n mitom: 'Mì tôm',\n pho: 'Phở',\n bun: 'Bún',\n chao: 'Cháo',\n mien: 'Miến',\n giavi: 'Gia vị',\n nuocmam: 'Nước mắm',\n thucphamdonghop: 'Thực phẩm đóng hộp',\n dauan: 'Dầu ăn',\n dodungnhabep: 'Đồ dùng nhà bếp',\n nguyenlieu: 'Nguyên liệu',\n banhkeo: 'Bánh, kẹo',\n thach: 'Thạch',\n };\n\n return categoryMap[category.toLowerCase()] || category;\n },\n removeSpecialChars: (input, key) => {\n const specialChars = '!@#$%^&*()_+={}[]|\\\\:;\"\\'<>,.?/~`-';\n // Nếu trường là 'name', không loại bỏ dấu ( và )\n if (key === 'name') {\n return input\n .split('')\n .filter(\n (char) =>\n !specialChars.includes(char) ||\n char === '(' ||\n char === ')' ||\n char === '-' ||\n char === ',',\n )\n .join('');\n }\n // Nếu trường là 'unit'\n if (key === 'unit') {\n return input\n .split('')\n .filter((char) => !specialChars.includes(char) || char === ',')\n .join('');\n }\n // Nếu trường là 'purchasePrice', chỉ chấp nhận số\n // if (key === 'purchasePrice') {\n // return input\n // .split('')\n // .filter((char) => !isNaN(char) && char !== ' ') // Chỉ giữ số\n // .join('');\n // }\n // Nếu không phải là 'name', 'unit', hay 'purchasePrice', loại bỏ tất cả các ký tự đặc biệt\n return input\n .split('')\n .filter((char) => !specialChars.includes(char))\n .join('');\n },\n };\n\n const contextValue = {\n fetchProductList,\n product_list,\n cartItems,\n setCartItems,\n addToCart,\n removeFromCart,\n updateCartItemQuantity,\n getTotalCartAmount,\n getTotalCartQuantity,\n utilityFunctions,\n url,\n urlImage,\n token,\n setToken,\n };\n return {props.children};\n};\n\nexport default StoreContextProvider;\n", "text_preview": "import axios from 'axios'; import { createContext, useEffect, useState } from 'react'; // import { product_list } from '../assets/assets_vn'; // import { products as product_list } from '../assets/products.js'; // Tạo Context: Context này s", "input_sha256": "b60ebebf254008a40a211661de1bbc5943dec6af320608ea437a06e6188255f0", "text_sha256": "5340f52af54cd2daaecf272f751e85656c4171e37fa8aa3617f639c4f11b8520", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8007, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5478912885736215, "noise_severity": 0.3743820319141635, "missing_content_severity": 0.022425138709912907, "factual_density": 3.3216841455467403, "pretraining_utility": 0.5829930779120714, "math_density": 0.8196060528254434, "code_density": 3.708984519873639, "reasoning_density": 0.35650456826628557, "education_level": 2.0685051212489234, "creative_writing_quality": -0.32092317476674265}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.017624033225967506, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9973346238808356, "secondary": 0.9420561155972883, "tertiary": 0.9604464111374283, "structured_records": 0.1974547334235232, "procedural_steps": 0.006440061055405273, "worked_solution": 0.0023608036823540442, "contextual_dialogue": 4.532027803492324e-06, "content_quality": 0.44707452527748004, "noise_severity": 0.6880149734048637, "missing_content_severity": 0.981312384408406, "factual_density": 0.4347367879556171, "pretraining_utility": 0.5252530592289075, "math_density": 0.5324200645110196, "code_density": 0.7574870998946992, "reasoning_density": 0.7029128597780954, "education_level": 0.8319492334762345, "creative_writing_quality": 0.9992640944004917, "primary_topic": 0.9894827456205099, "secondary_topic": 0.9076032551053967}, "probability_margin": {"primary": 0.9964076662607496, "secondary": 0.9169177995466908, "tertiary": 0.9504510690136927, "education_level": 0.760863503991611, "creative_writing_quality": 0.9990924198511228, "primary_topic": 0.9859446722767562, 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"master", "commit_count": 57, "repo_created_at": "2024-09-28T14:29:32.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "21edb11da80a46366006a1d2d2e510cf42b77a03", "size_bytes": 10280, "file_timestamp": 1735202894, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cb08d64855cc894de29c0ea5201d2e1dde5651fe", "url": "https://github.com/haduc25/Projects-2024/blob/cb08d64855cc894de29c0ea5201d2e1dde5651fe/MERNStack-2024/QLCH6688/frontend/src/context/StoreContext.jsx"}, "gpt4o_tokens": 2341, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.5478912885736215, "noise_severity": 0.3743820319141635, "missing_content_severity": 0.022425138709912907, "factual_density": 3.3216841455467407, "pretraining_utility": 0.5829930779120714, "math_density": 0.8196060528254434, "code_density": 3.708984519873639, "reasoning_density": 0.35650456826628557, "education_level": 2.0685051212489234, "creative_writing_quality": -0.32092317476674265}, "score_applicability": {"education_level": 0.9989501833740994, "creative_writing_quality": 0.0006132546662570602}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/fractalplatform/fractal-web3.js/blob/48c497066af0c27bc1d73a252a8d373510650407/ft/index.js", "id": "javascript-0-ee521ccb076bf3a09ebb9615a80b990a", "text": "import * as EthUtil from 'ethereumjs-util';\nimport * as EthCrypto from 'eth-crypto';\nimport { encode } from 'rlp';\nimport * as utils from '../utils';\nimport * as account from '../account';\n\nlet chainId = 1;\n\nexport function getChainId() {\n return chainId;\n}\n\nexport function setChainId(newChainId) {\n chainId = newChainId;\n}\n\n\nexport async function getCurrentBlock(bFullTx) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getCurrentBlock',\n params: [bFullTx],\n id: 1 });\n return utils.postToNode({ data: dataToSrv, });\n}\n\nexport async function getBlockByHash(blockHash, bFullTx) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getBlockByHash',\n params: [blockHash, bFullTx],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getBlockByNum(blockNum, bFullTx) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getBlockByNumber',\n params: [blockNum, bFullTx],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getTransactionByHash(blockHash) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getTransactionByHash',\n params: [blockHash],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getTransactionReceipt(txHash) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getTransactionReceipt',\n params: [txHash],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getSuggestionGasPrice() {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_gasPrice',\n params: [],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\nexport async function getTxsByAccount(accountName, blockNum, lookbackNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getTxsByAccount',\n params: [accountName, blockNum, lookbackNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getTxsByBloom(bloom, blockNum, lookbackNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getTxsByBloom',\n params: [bloom, blockNum, lookbackNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getInternalTxsByAccount(accountName, blockNum, lookbackNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getInternalTxByAccount',\n params: [accountName, blockNum, lookbackNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getInternalTxsByBloom(bloom, blockNum, lookbackNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getInternalTxByBloom',\n params: [bloom, blockNum, lookbackNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getInternalTxByHash(txHash) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getInternalTxByHash',\n params: [txHash],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport async function getChainConfig() {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_getChainConfig',\n params: [],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\n//{\"actionType\":0,\"from\":\"testtest31\",\"to\":\"testtest32\",\n// \"assetId\":1,\"gas\":200000,\"gasPrice\":1,\"value\":0,\"data\":\"\"}\nexport async function call(callInfo, blockNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_call',\n params: [callInfo, blockNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\n//{\"actionType\":0,\"from\":\"testtest31\",\"to\":\"testtest32\",\n// \"assetId\":1,\"gas\":200000,\"gasPrice\":1,\"value\":0,\"data\":\"\"}\nexport async function estimateGas(txInfo, blockNum) {\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_estimateGas',\n params: [txInfo, blockNum],\n id: 1 });\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nfunction getRSV(signature) {\n const r = signature.slice(0, 66);\n const s = '0x' + signature.slice(66, 130);\n let v = '0x' + signature.slice(130, 132);\n if (chainId !== 0) {\n v = (v === '0x1c') ? 1 : 0;\n v += chainId * 2 + 35;\n v = '0x' + v.toString(16);\n }\n return { r, s, v };\n}\n\nexport async function packTx(txInfo) {\n if (utils.isEmptyObj(txInfo.chainId)) {\n txInfo.chainId = chainId;\n }\n if (utils.isEmptyObj(txInfo.gasAssetId)) {\n const chainConfig = await getChainConfig();\n const assetInfo = await account.getAssetInfoByName(chainConfig.systemToken);\n txInfo.gasAssetId = assetInfo.assetId == null ? 0 : assetInfo.assetId;\n }\n for (let i = 0; i < txInfo.actions.length; i++) {\n let action = txInfo.actions[i];\n if (utils.isEmptyObj(action.nonce)) {\n action.nonce = await account.getNonce(action.accountName);\n }\n if (utils.isEmptyObj(action.payload)) {\n action.payload = '0x';\n }\n if (action.remark == null) {\n action.remark = '';\n }\n }\n return txInfo;\n}\n/**\n txInfo = {chainId, gasAssetId, gasPrice, actions:[{actionType, accountName, nonce, gasLimit, toAccountName, assetId, amount, payload, remark}]}\n*/\nexport async function signTx(txInfo, privateKey) {\n await packTx(txInfo);\n \n const actionHashs = [];\n for (const action of txInfo.actions) {\n const { accountName, actionType, nonce, gasLimit, toAccountName, assetId, amount, remark } = action;\n let { payload } = action;\n payload = utils.hex2Bytes(payload);\n const actionHash = EthUtil.rlphash([accountName, actionType, nonce, toAccountName, gasLimit, amount, payload, assetId, remark, txInfo.chainId, 0, 0]);\n\n actionHashs.push(actionHash);\n }\n const merkleRoot = EthUtil.keccak(actionHashs[0]);\n\n const txHash = EthUtil.rlphash([merkleRoot, txInfo.gasAssetId, txInfo.gasPrice]).toString('hex');\n\n const signature = EthCrypto.sign(privateKey, txHash);\n return signature;\n}\n\nexport async function recoverSignedTx(txInfo, signature) {\n const actionHashs = [];\n for (const action of txInfo.actions) {\n const { accountName, actionType, nonce, gasLimit, toAccountName, assetId, amount, remark } = action;\n let { payload } = action;\n payload = utils.hex2Bytes(payload);\n const actionHash = EthUtil.rlphash([accountName, actionType, nonce, toAccountName, gasLimit, amount, payload, assetId, remark, txInfo.chainId, 0, 0]);\n\n actionHashs.push(actionHash);\n }\n const merkleRoot = EthUtil.keccak(actionHashs[0]);\n\n const txHash = EthUtil.rlphash([merkleRoot, txInfo.gasAssetId, txInfo.gasPrice]).toString('hex');\n\n const address = EthCrypto.recover(signature, txHash);\n return address;\n}\n\n/* \ntxInfo: object of transaction info: {chainId, gasAssetId, gasPrice, actions:[{actionType, accountName, nonce, gasLimit, toAccountName, assetId, amount, payload, remark}]},\n if you don't pass chainId, gasAssetId, nonce, payload and remark, it will use default value.\n*/\nexport async function sendSingleSigTransaction(txInfo, signInfo) {\n const multiSigInfos = [{ signInfo, indexes: [0]}];\n return sendSeniorSigTransaction(txInfo, multiSigInfos, 0);\n}\n\n/* sendSeniorSigTransaction: send senior signed tx to node, you can use multiple or/and inherited signature\ntxInfo: object of transaction info: {chainId, gasAssetId, gasPrice, actions:[{actionType, accountName, nonce, gasLimit, toAccountName, assetId, amount, payload, remark}]},\n if you don't pass chainId, gasAssetId, nonce, payload and remark, it will use default value.\nmultiSigInfos: [{signInfo, indexes}], signInfo is generated by signTx function, and indexes are indexes of signatures, eg: [0] [0,1] [1] [1,2,3]\n*/\nexport async function sendSeniorSigTransaction(txInfo, multiSignInfos, parentIndex) {\n await packTx(txInfo);\n\n txInfo.actions[0].sign = {parentIndex, signData: []};\n //txInfo.actions[0].signData = [];\n for (const signInfo of multiSignInfos) {\n const rsv = getRSV(signInfo.signInfo);\n txInfo.actions[0].sign.signData.push([rsv.v, rsv.r, rsv.s, signInfo.indexes]);\n }\n //Object.assign(tx.actions[0], rsv);\n const action = txInfo.actions[0];\n let rlpData = encode([txInfo.gasAssetId, txInfo.gasPrice, [[action.actionType, action.nonce, action.assetId, action.accountName, action.toAccountName, action.gasLimit, action.amount, action.payload, action.remark, [action.sign.parentIndex, [...action.sign.signData]]]]]);\n rlpData = '0x' + rlpData.toString('hex');\n const dataToSrv = JSON.stringify({ jsonrpc: '2.0',\n method: 'ft_sendRawTransaction',\n params: [rlpData],\n id: 1 });\n\n return utils.postToNode({\n data: dataToSrv,\n });\n}\n\nexport default { getChainId, setChainId, getCurrentBlock, getBlockByHash, getBlockByNum, getTransactionByHash,\n getTransactionReceipt, getSuggestionGasPrice,\n getTxsByAccount, getTxsByBloom, getChainConfig,\n getInternalTxsByAccount, getInternalTxsByBloom, getInternalTxByHash, packTx,\n signTx, recoverSignedTx, sendSingleSigTransaction, sendSeniorSigTransaction, call, estimateGas};", "text_preview": "import * as EthUtil from 'ethereumjs-util'; import * as EthCrypto from 'eth-crypto'; import { encode } from 'rlp'; import * as utils from '../utils'; import * as account from '../account'; let chainId = 1; export function getChainId() { ret", "input_sha256": "f71de56f59bf9d77bdb2a4992eed19a40e1436c5251d57a429b286f1d7b5cad1", "text_sha256": "ee521ccb076bf3a09ebb9615a80b990a2d94b9c8eda730bdbce813808e6281d2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8009, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8250617562514045, "noise_severity": 0.07477624275375214, "missing_content_severity": 0.0134761563806854, "factual_density": 3.282461144036553, "pretraining_utility": 0.4833969451339529, "math_density": 0.6021856701666162, "code_density": 3.870423566431467, "reasoning_density": 0.3491738002925436, "education_level": 2.9366085763434526, "creative_writing_quality": -0.2780130916953048}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.029109876337622187, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9965399742015597, "secondary": 0.9427496055438686, "tertiary": 0.9731251286787789, "structured_records": 0.07309775130123176, "procedural_steps": 0.013208382606732006, "worked_solution": 0.0021319877686569464, "contextual_dialogue": 4.702645115268949e-06, "content_quality": 0.542155012811911, "noise_severity": 0.9376864643718732, "missing_content_severity": 0.9887698696827621, "factual_density": 0.40205095336379426, "pretraining_utility": 0.44421358710868664, "math_density": 0.49817860819448645, "code_density": 0.8920196386928886, "reasoning_density": 0.709021833089547, "education_level": 0.4638792225796009, "creative_writing_quality": 0.9994883959448395, "primary_topic": 0.9883717093661651, "secondary_topic": 0.8430283928855249}, "probability_margin": {"primary": 0.9952431642738724, "secondary": 0.9161897932479831, "tertiary": 0.9600319691716986, "education_level": 0.307822843431335, "creative_writing_quality": 0.9993812998108446, "primary_topic": 0.9825988829465369, "secondary_topic": 0.7967228306298519}, "raw_probabilities": {"primary": {"Academic Writing": 3.253573401356022e-05, "Content Listing": 3.1041077424028665e-05, "Creative Writing": 1.3088169980630272e-05, "Customer Support Page": 1.2647890672130143e-05, "Q&A Forum": 1.0528983488732124e-05, "Discussion Thread": 6.717003807950602e-06, "Comment Section": 1.2479407523633566e-05, "Missing Content": 2.6945072501355976e-05, "Knowledge Article": 9.665703463399606e-05, "Legal Notices": 2.7977856475554768e-05, "News Article": 4.9033841797996455e-06, "Organizational Announcement": 1.0822901131312633e-05, "Nonfiction Writing": 9.660468786299271e-06, "Organizational About Page": 1.2393603972969793e-05, "Personal About Page": 9.04194103296009e-06, "Personal Blog": 6.801722288956228e-06, "Product Page": 8.242247362829777e-06, "Spam / Ads": 9.062660095641181e-06, "Structured Data": 0.0011863472767911, "Technical Writing": 0.9966841419431614, "Transcript / Interview": 1.5666878828332974e-05, "Tutorial / How-To Guide": 0.0003248620477246493, "User Reviews": 6.457024833167834e-06, "Other / Unknown": 0.0014409776692889914}, "secondary": {"Academic Writing": 0.0002435273039653178, "Content Listing": 0.00026203683442517495, "Creative Writing": 0.00018940854980025576, "Customer Support Page": 0.00015872926374258493, "Q&A Forum": 9.735092502837029e-05, "Discussion Thread": 8.259091790912677e-05, "Comment Section": 0.00029468576091517434, "Missing Content": 0.00039493924081518677, "Knowledge Article": 0.0011087426004992985, "Legal Notices": 0.0003055883212524613, "News Article": 0.00014449287309829687, "Organizational Announcement": 0.0001273738301491703, "Nonfiction Writing": 0.0001260887929892723, "Organizational About Page": 0.00014901638658567127, "Personal About Page": 0.00010352851705679618, "Personal Blog": 0.00010465711395999351, "Product Page": 8.940646301912883e-05, "Spam / Ads": 0.00012525117676238663, "Structured Data": 0.028945245398224316, "Technical Writing": 0.02015772605566095, "Transcript / Interview": 0.00014287411868760578, "Tutorial / How-To Guide": 0.0014555547667996977, "User Reviews": 5.614614244629191e-05, "NONE": 0.9451350386462074}, "tertiary": {"Academic Writing": 0.00011699899320419896, "Content Listing": 0.00021527649602097042, "Creative Writing": 9.92958032193147e-05, "Customer Support Page": 0.00011224166510025268, "Q&A Forum": 3.960337453728239e-05, "Discussion Thread": 3.594604672955651e-05, "Comment Section": 0.00020997139246737824, "Missing Content": 0.00032467286321317967, "Knowledge Article": 0.0006022454379490314, "Legal Notices": 0.0002962802797781858, "News Article": 6.248458607278085e-05, "Organizational Announcement": 5.5834026259144864e-05, "Nonfiction Writing": 7.311279713520181e-05, "Organizational About Page": 8.541518147221027e-05, "Personal About Page": 4.136514406895715e-05, "Personal Blog": 4.457477152492712e-05, "Product Page": 4.197161569052922e-05, "Spam / Ads": 6.78736448433019e-05, "Structured Data": 0.0142129458121311, "Technical Writing": 0.007264096245490328, "Transcript / Interview": 5.758686145318646e-05, "Tutorial / How-To Guide": 0.0016695299620309663, "User Reviews": 2.576201577832257e-05, "NONE": 0.9742449149838297}, "structured_records": {"false": 0.9269022486987682, "true": 0.07309775130123176}, "procedural_steps": {"false": 0.986791617393268, "true": 0.013208382606732006}, "worked_solution": {"false": 0.997868012231343, "true": 0.0021319877686569464}, "contextual_dialogue": {"false": 0.9999952973548847, "true": 4.702645115268949e-06}, "content_quality": {"-2": 0.007368813372482146, "-1": 0.03483422771103301, "0": 0.27040121864763866, "1": 0.5001578698302905, "2": 0.18723787043855564}, "noise_severity": {"0": 0.9339809341920635, "1": 0.0595925494187891, "2": 0.004762918341879405, "3": 0.0009965355378678945, "4": 0.0006670625094001348}, "missing_content_severity": {"0": 0.9922835100113905, "1": 0.003722259075213363, "2": 0.0025242116018519565, "3": 0.001174603144409018, "4": 0.00029541616713526733}, "factual_density": {"0": 0.0046067056850709704, "1": 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"factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11399, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11399, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:13.244138+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "68c7b9b3-e67d-4ab4-a756-eb3df1af41c6", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "928076f24f42828ddbd59a00435f7e369fe098a2bb92477d8bd2e0e5264512b1", "source_metadata": {"uuid": "68c7b9b3-e67d-4ab4-a756-eb3df1af41c6", "repo_name": "fractalplatform/fractal-web3.js", "relative_path": "ft/index.js", "category": "TOOL", "algo_rel_score": 0.9117810130119324, "quality_score": 6.539189338684082, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 17, "repo_created_at": "2019-05-13T05:50:19.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4, "forks": 6, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "e1b5bbdf4022343b82298543acf710f922cf712b", "size_bytes": 9051, "file_timestamp": 1579177009, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "48c497066af0c27bc1d73a252a8d373510650407"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 17, "repo_created_at": "2019-05-13T05:50:19.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 4, "forks": 6, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "e1b5bbdf4022343b82298543acf710f922cf712b", "size_bytes": 9051, "file_timestamp": 1579177009, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "48c497066af0c27bc1d73a252a8d373510650407", "url": "https://github.com/fractalplatform/fractal-web3.js/blob/48c497066af0c27bc1d73a252a8d373510650407/ft/index.js"}, "gpt4o_tokens": 2609, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8250617562514045, "noise_severity": 0.07477624275375214, "missing_content_severity": 0.0134761563806854, "factual_density": 3.282461144036553, "pretraining_utility": 0.4833969451339529, "math_density": 0.6021856701666162, "code_density": 3.870423566431467, "reasoning_density": 0.34917380029254363, "education_level": 2.9366085763434526, "creative_writing_quality": -0.2780130916953048}, "score_applicability": {"education_level": 0.9988613843932478, "creative_writing_quality": 0.0004263367126337219}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/vishvash/Sales-Analytics/blob/87706064b85c664b6793fc6e70956d4834bb0a1c/salespitch-frontend/src/components/KPIDashboard.jsx", "id": "javascript-0-2f48fbf82c798966322436a9217526fc", "text": "import React, { useState } from 'react';\nimport {\n LineChart,\n Line,\n XAxis,\n YAxis,\n CartesianGrid,\n Tooltip,\n ResponsiveContainer,\n PieChart,\n Pie,\n Cell,\n BarChart,\n Bar,\n Legend,\n} from 'recharts';\nimport { useFiles } from '../context/FileContext';\nimport { Typography, TextField, MenuItem } from '@mui/material';\nimport './KPIDashboard.css';\nimport { useNavigate } from 'react-router-dom';\n\nconst KPIDashboard = () => {\n const { files } = useFiles();\n const [selectedUser, setSelectedUser] = useState('');\n const [searchTerm, setSearchTerm] = useState('');\n const [startDate, setStartDate] = useState('');\n const [endDate, setEndDate] = useState('');\n const navigate = useNavigate()\n\n // Helper to get YYYY-MM-DD from a date string\n const getDateString = (date) => {\n if (!date) return '';\n const d = new Date(date);\n if (isNaN(d)) return '';\n return d.toISOString().slice(0, 10);\n };\n\n // Filter files by date range (compare only date part, using uploaded_at)\n const filteredFiles = files.filter(file => {\n if (!file.uploaded_at) return true; // If no uploaded_at, include by default\n const fileDateStr = getDateString(file.uploaded_at);\n const afterStart = !startDate || fileDateStr >= startDate;\n const beforeEnd = !endDate || fileDateStr <= endDate;\n return afterStart && beforeEnd;\n });\n\n const userMap = {};\n filteredFiles.forEach((file) => {\n const user = file.username || file.file_name?.split('_')[0] || 'Unknown';\n if (!userMap[user]) userMap[user] = [];\n userMap[user].push(file);\n });\n\n const totalInterviews = filteredFiles.length;\n\n // Dynamically filter files by selected user as well\n const userFilteredFiles = selectedUser\n ? filteredFiles.filter(file => {\n const user = file.username || file.file_name?.split('_')[0] || 'Unknown';\n return user === selectedUser;\n })\n : filteredFiles;\n\n // Calculate average rating using all ratings in all result_data arrays\n const allRatings = userFilteredFiles.flatMap(f => {\n if (Array.isArray(f.result_data)) {\n return f.result_data.map(r => (r && typeof r.rating === 'number' ? r.rating : 0));\n }\n return [];\n }).filter(r => typeof r === 'number' && !isNaN(r));\n const averageRating = allRatings.length > 0 ? (allRatings.reduce((a, b) => a + b, 0) / allRatings.length).toFixed(2) : 0;\n\n const totalPauses = filteredFiles.reduce((sum, f) => {\n const pauses = JSON.parse(f.pauses || '{}');\n return sum + (pauses.total_pauses || 0);\n }, 0);\n\n const averageLongPauses = (\n filteredFiles.reduce((sum, f) => {\n const pauses = JSON.parse(f.pauses || '{}');\n return sum + (pauses.long_pauses || 0);\n }, 0) / filteredFiles.length\n ).toFixed(2);\n\n const averageTempo = (\n filteredFiles.reduce((sum, f) => {\n const tone = JSON.parse(f.speaking_rate_and_tone || '{}');\n return sum + (tone.tempo_value || 0);\n }, 0) / filteredFiles.length\n ).toFixed(2);\n\n const averagePitch = (\n filteredFiles.reduce((sum, f) => {\n const tone = JSON.parse(f.speaking_rate_and_tone || '{}');\n return sum + (tone.average_pitch || 0);\n }, 0) / filteredFiles.length\n ).toFixed(2);\n\n // Audio Length Metrics\n const totalAudioLength = userFilteredFiles.reduce((sum, f) => sum + (Number(f.audio_length) || 0), 0).toFixed(2);\n const averageAudioLength = (userFilteredFiles.length > 0 ? (totalAudioLength / userFilteredFiles.length).toFixed(2) : '0');\n\n const kpiCards = [\n { label: 'Total Calls', value: userFilteredFiles.length },\n {\n label: 'Average Rating',\n value: averageRating\n },\n {\n label: 'Total Pauses',\n value: userFilteredFiles.reduce((acc, f) => {\n const pauses = JSON.parse(f.pauses || '{}');\n return acc + (pauses.total_pauses || 0);\n }, 0)\n },\n {\n label: 'Avg. Long Pauses',\n value: userFilteredFiles.length > 0\n ? (\n userFilteredFiles.reduce((acc, f) => {\n const pauses = JSON.parse(f.pauses || '{}');\n return acc + (pauses.long_pauses || 0);\n }, 0) / userFilteredFiles.length\n ).toFixed(2)\n : 0\n },\n {\n label: 'Avg. Tempo',\n value: userFilteredFiles.length > 0\n ? (\n userFilteredFiles.reduce((acc, f) => {\n const tone = JSON.parse(f.speaking_rate_and_tone || '{}');\n return acc + (tone.tempo_value || 0);\n }, 0) / userFilteredFiles.length\n ).toFixed(2)\n : 0\n },\n {\n label: 'Avg. Pitch',\n value: userFilteredFiles.length > 0\n ? (\n userFilteredFiles.reduce((acc, f) => {\n const tone = JSON.parse(f.speaking_rate_and_tone || '{}');\n return acc + (tone.average_pitch || 0);\n }, 0) / userFilteredFiles.length\n ).toFixed(2)\n : 0\n },\n // New audio length metrics\n {\n label: 'Total Audio Length (min)',\n value: totalAudioLength\n },\n {\n label: 'Avg. Audio Length (min)',\n value: averageAudioLength\n }\n ];\n\n const userCharts = Object.entries(userMap).map(([user, userFiles]) => {\n const data = userFiles.map((file, i) => ({\n name: file.file_name || `Mock ${i + 1}`,\n rating: Array.isArray(file.result_data) && file.result_data[0] ? file.result_data[0].rating || 0 : 0,\n }));\n return { user, data };\n });\n\n const multiInterviewUsers = userCharts.filter(chart => chart.data.length > 1);\n\n // Only show users with at least one file (not just multi-interview users)\n const allUsers = Object.keys(userMap);\n\n return (\n
\n {/* Title at the very top */}\n
\n {/* Filters Row: interviewer search, date filters, and button */}\n
\n setSelectedUser(e.target.value)}\n sx={{ minWidth: 180, maxWidth: 220, height: 40 }}\n size=\"small\"\n SelectProps={{\n native: false,\n MenuProps: {\n PaperProps: {\n style: { maxHeight: 300, maxWidth: 220, overflowX: 'auto' }\n }\n }\n }}\n InputProps={{\n style: { height: 40, background: '#f5f6fa', borderRadius: 8 },\n startAdornment: (\n setSearchTerm(e.target.value)}\n style={{ marginRight: 8, padding: 4, border: 'none', outline: 'none', background: 'transparent', fontSize: '1rem', width: 80 }}\n />\n )\n }}\n inputProps={{\n style: { textOverflow: 'ellipsis', whiteSpace: 'nowrap', overflow: 'hidden', height: 40, padding: 0 }\n }}\n >\n All Agents\n {allUsers\n .filter(user => user.toLowerCase().includes(searchTerm.toLowerCase()))\n .map(user => (\n \n {user}\n \n ))}\n \n setStartDate(e.target.value)}\n InputLabelProps={{ shrink: true }}\n size=\"small\"\n sx={{ minWidth: 140, maxWidth: 180, height: 40 }}\n InputProps={{ style: { height: 40, background: '#f5f6fa', borderRadius: 8 } }}\n inputProps={{ style: { height: 40, padding: '0 8px' } }}\n />\n setEndDate(e.target.value)}\n InputLabelProps={{ shrink: true }}\n size=\"small\"\n sx={{ minWidth: 140, maxWidth: 180, height: 40 }}\n InputProps={{ style: { height: 40, background: '#f5f6fa', borderRadius: 8 } }}\n inputProps={{ style: { height: 40, padding: '0 8px' } }}\n />\n {/* navigate('/results')}\n >\n View Results Dashboard\n */}\n
\n
\n {/* Remove grid from dashboard-grid, use flex column for stacking */}\n
\n
\n {kpiCards.map((card, i) => (\n
\n
{card.label}
\n
{card.value}
\n
\n ))}\n
\n {/* Charts grid: 4 charts per row, full width */}\n {/* First: Move the bottom 4 charts UP */}\n
\n {/* Bar Chart: Calls vs Agents */}\n
\n

Calls vs Agents

\n \n ({\n agent: user,\n calls: userMap[user]?.length || 0\n }))}\n margin={{ left: 40 }}\n >\n \n \n \n \n \n \n \n \n
\n\n {/* Bar Chart: Day Wise Calls */}\n
\n

Day Wise Calls

\n \n {\n const dayMap = {};\n userFilteredFiles.forEach(f => {\n const day = getDateString(f.uploaded_at);\n if (!dayMap[day]) dayMap[day] = 0;\n dayMap[day]++;\n });\n return Object.entries(dayMap).map(([day, count]) => ({ day, calls: count }));\n })()}\n >\n \n \n \n \n \n \n \n \n
\n\n {/* Bar Chart: Top 20 Audio Length Calls */}\n
\n

Top 10 Audio Length Calls

\n \n ({\n name: f.username || f.file_name?.split('_')[0] || 'Unknown',\n audio_length: Number(f.audio_length) || 0\n }))\n .sort((a, b) => b.audio_length - a.audio_length)\n .slice(0, 10)\n }\n >\n \n \n `${val} min`} />\n `${value.toFixed(2)} min`} />\n \n \n \n
\n {/* Pie Chart: First Call vs Follow Up */}\n
\n

First Call vs Follow Up

\n \n \n {\n let first = 0, follow = 0;\n userFilteredFiles.forEach(f => {\n const user = f.username || f.file_name?.split('_')[0] || 'Unknown';\n if (userMap[user]?.length === 1) first++;\n else follow++;\n });\n return [\n { name: 'First Call', value: first },\n { name: 'Follow Up', value: follow }\n ];\n })()}\n dataKey=\"value\"\n nameKey=\"name\"\n cx=\"50%\"\n cy=\"50%\"\n outerRadius={80}\n fill=\"#8884d8\"\n label\n >\n \n \n \n \n \n \n \n
\n
\n\n {/* Second: Move the top 4 charts DOWN */}\n
\n {/* Combined Line Chart: Pitch and Tempo */}\n
\n

Pitch & Tempo per call

\n \n ({\n name: file.file_name || `Mock ${i + 1}`,\n pitch: (() => {\n try {\n return JSON.parse(file.speaking_rate_and_tone || '{}').average_pitch || 0;\n } catch {\n return 0;\n }\n })(),\n tempo: (() => {\n try {\n return JSON.parse(file.speaking_rate_and_tone || '{}').tempo_value || 0;\n } catch {\n return 0;\n }\n })(),\n }))}>\n \n \n \n \n \n \n \n \n \n
\n\n {/* Pie Chart: Rating Distribution */}\n
\n

Rating Distribution

\n \n \n {\n const buckets = [\n { name: '6', value: 0 },\n { name: '7', value: 0 },\n { name: '8', value: 0 },\n { name: '9-10', value: 0 },\n ];\n userFilteredFiles.forEach(f => {\n const rating = Array.isArray(f.result_data) && f.result_data[0] ? f.result_data[0].rating || 0 : 0;\n if (rating === 6) buckets[0].value++;\n else if (rating === 7) buckets[1].value++;\n else if (rating === 8) buckets[2].value++;\n else if (rating >= 9 && rating <= 10) buckets[3].value++;\n });\n return buckets;\n })()}\n dataKey=\"value\"\n nameKey=\"name\"\n cx=\"50%\"\n cy=\"50%\"\n outerRadius={80}\n fill=\"#8884d8\"\n label\n >\n \n \n \n \n \n \n \n \n \n
\n\n {/* Bar Chart: Pauses per Interview */}\n
\n

Pauses per Call

\n \n ({\n name: file.file_name || `Mock ${i + 1}`,\n pauses: (() => {\n try {\n return JSON.parse(file.pauses || '{}').total_pauses || 0;\n } catch {\n return 0;\n }\n })(),\n }))}>\n \n \n \n \n \n \n \n \n
\n\n {/* Bar Chart: Audio Length per Interview */}\n
\n

Audio Length per Call

\n \n ({\n name: file.file_name || `Mock ${i + 1}`,\n audio_length: Number(file.audio_length) || 0,\n }))}>\n \n \n \n \n \n \n \n \n
\n
\n\n
\n
\n );\n};\n\nexport default KPIDashboard;\n", "text_preview": "import React, { useState } from 'react'; import { LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, ResponsiveContainer, PieChart, Pie, Cell, BarChart, Bar, Legend, } from 'recharts'; import { useFiles } from '../context/FileContext'; ", "input_sha256": "19283122137514e722c2fd2ba067f5b7c77c2db16d3735b9a2c25a7e02d29321", "text_sha256": "2f48fbf82c798966322436a9217526fc5d1d255886828ecbc44e06c92895bbc5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8010, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.43666643218489765, "noise_severity": 0.26778683737026554, "missing_content_severity": 0.16623823100059948, "factual_density": 2.711786869964551, "pretraining_utility": 0.5230762356115034, "math_density": 1.2587354587330988, "code_density": 3.7547495603276446, "reasoning_density": 0.6178257728422437, "education_level": 2.154327256850012, "creative_writing_quality": -0.4287885620484025}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.012285775876782368, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.995052632571818, "secondary": 0.9517298017196363, "tertiary": 0.9645497417649743, "structured_records": 0.11376955214687952, "procedural_steps": 0.002186300224172825, "worked_solution": 0.01557906140807858, "contextual_dialogue": 8.051555965397155e-06, "content_quality": 0.3303569872230997, "noise_severity": 0.7768443021914454, "missing_content_severity": 0.8614681408328337, "factual_density": 0.2574796196401322, "pretraining_utility": 0.4646952256654566, "math_density": 0.0, "code_density": 0.7956246336063706, "reasoning_density": 0.48514518929813033, "education_level": 0.7727902219215687, "creative_writing_quality": 0.9990228416952516, "primary_topic": 0.9395794933945115, "secondary_topic": 0.39923897946536524}, "probability_margin": {"primary": 0.9924700193056454, "secondary": 0.9327167236794046, "tertiary": 0.9511881914241799, "education_level": 0.6485129030172545, "creative_writing_quality": 0.9987962721669676, "primary_topic": 0.8975709072084851, "secondary_topic": 0.026787751810776195}, "raw_probabilities": {"primary": {"Academic Writing": 3.604405754737798e-05, "Content Listing": 4.572643606498536e-05, "Creative Writing": 2.355225550143023e-05, "Customer Support Page": 1.1083736969032657e-05, "Q&A Forum": 1.6086773867430706e-05, "Discussion Thread": 9.96333159561829e-06, "Comment Section": 1.819423651220499e-05, "Missing Content": 5.043124111365892e-05, "Knowledge Article": 7.898777981411532e-05, "Legal Notices": 4.788519158312261e-05, "News Article": 1.0656005372927478e-05, "Organizational Announcement": 1.5842069501037963e-05, "Nonfiction Writing": 1.6702045476228835e-05, "Organizational About Page": 1.955858582769904e-05, "Personal About Page": 1.7804015769263053e-05, "Personal Blog": 1.0365107614562435e-05, "Product Page": 1.5612101330928117e-05, "Spam / Ads": 1.6805452304720288e-05, "Structured Data": 0.0009278560702049241, "Technical Writing": 0.9952587728813256, "Transcript / Interview": 3.1097933193340206e-05, "Tutorial / How-To Guide": 0.0005214697897506019, "User Reviews": 1.074932607906456e-05, "Other / Unknown": 0.002788753575680207}, "secondary": {"Academic Writing": 0.00025878519244301387, "Content Listing": 0.0017734902982800575, "Creative Writing": 0.00032515089909173543, "Customer Support Page": 0.00013802633184588727, "Q&A Forum": 0.00011139031350040146, "Discussion Thread": 8.288813538250487e-05, "Comment Section": 0.0003185172592768833, "Missing Content": 0.000672282279259802, "Knowledge Article": 0.0008034701766141472, "Legal Notices": 0.0002737862307839848, "News Article": 0.00022536703748916328, "Organizational Announcement": 0.00014265052239984072, "Nonfiction Writing": 0.00017968357993675295, "Organizational About Page": 0.00016958725241354784, "Personal About Page": 0.0001523281744063796, "Personal Blog": 0.00013381339401798462, "Product Page": 0.00011661926579817717, "Spam / Ads": 0.00014692966665851722, "Structured Data": 0.01700157439221842, "Technical Writing": 0.021024336301913474, "Transcript / Interview": 0.0002677606895344744, "Tutorial / How-To Guide": 0.0018603672372495307, "User Reviews": 8.013538816731244e-05, "NONE": 0.9537410599813181}, "tertiary": {"Academic Writing": 0.00015514872365197897, "Content Listing": 0.0019252131848633636, "Creative Writing": 0.00019480471854299304, "Customer Support Page": 7.916479149475703e-05, "Q&A Forum": 6.512334221536261e-05, "Discussion Thread": 5.208068947838818e-05, "Comment Section": 0.00035691778604283596, "Missing Content": 0.000882969823165908, "Knowledge Article": 0.0007532114067496036, "Legal Notices": 0.00027968642396028703, "News Article": 0.00012972906492208347, "Organizational Announcement": 8.7404760143941e-05, "Nonfiction Writing": 0.00013260825865224376, "Organizational About Page": 0.00012665629497197897, "Personal About Page": 0.00010098936270477473, "Personal Blog": 7.799600962040727e-05, "Product Page": 8.066241899157456e-05, "Spam / Ads": 0.0001131942259953943, "Structured Data": 0.011303156098214317, "Technical Writing": 0.01483864443392049, "Transcript / Interview": 0.00028825175760537075, "Tutorial / How-To Guide": 0.0018947573741426558, "User Reviews": 5.479319184888903e-05, "NONE": 0.9660268358581003}, "structured_records": {"false": 0.8862304478531204, "true": 0.11376955214687952}, "procedural_steps": {"false": 0.9978136997758271, "true": 0.002186300224172825}, "worked_solution": {"false": 0.9844209385919214, "true": 0.01557906140807858}, "contextual_dialogue": {"false": 0.9999919484440346, "true": 8.051555965397155e-06}, "content_quality": {"-2": 0.01756110282774796, "-1": 0.14779112972519354, "0": 0.33518702364006037, "1": 0.3793417200484091, "2": 0.12011902375858897}, "noise_severity": {"0": 0.8070790381713046, "1": 0.1316869613131666, "2": 0.050771250404686045, "3": 0.007293625195644185, "4": 0.00316912491519857}, "missing_content_severity": {"0": 0.8889870518215669, "1": 0.06798581329634984, "2": 0.032742748974937015, "3": 0.00837062387420866, "4": 0.0019137620329373994}, "factual_density": {"0": 0.04986041761293308, "1": 0.11178284017556019, "2": 0.21647186004372562, "3": 0.32047921896958514, "4": 0.3014056631981959}, "pretraining_utility": {"-2": 0.01023170521955261, "-1": 0.08034994964881921, "0": 0.36824973183867804, "1": 0.4584476308864726, "2": 0.08272098240647759}, "math_density": {"0": 0.3251043339133827, "1": 0.3082391116392761, "2": 0.22699555183482087, "3": 0.06213876702590049, "4": 0.07752223558661987}, "code_density": {"0": 0.0003382964096508699, "1": 0.0006839894449619688, "2": 0.0236703478382561, "3": 0.19450459002235368, "4": 0.7808027762847773}, "reasoning_density": {"0": 0.6779314318682849, "1": 0.120750021770359, "2": 0.13101542084405388, "3": 0.04616759268543204, "4": 0.024135532831870206}, "education_level": {"none": 0.001451791710775432, "0": 0.0017124715100294975, "1": 0.017560224311297983, "2": 0.8106585182679739, "3": 0.16214561525071933, "4": 0.006471378949203975}, "creative_writing_quality": {"not_applicable": 0.9991857014127097, "-2": 0.00013127015343291428, "-1": 0.0002024969506272366, "0": 0.0003894292457420469, "1": 6.632913780562025e-05, "2": 2.4773099682575713e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.549812693982138e-05, "Art & Design": 0.0010521864387833374, "Law & Crime": 0.00018515584748614363, "Education & Careers": 0.0007932717626679679, "Entertainment & Media": 0.0001057022037666245, "Fashion & Beauty": 0.00012403479982567992, "Business, Economics & Finance": 0.044332451824699026, "Food & Dining": 0.00013874014036952066, "Games & Gaming": 0.0001463968751354668, "Electronics & Hardware": 0.0001571159008656201, "Health, Healthcare & Wellness": 0.0004096993434492874, "History & Geography": 0.00012075744523068862, "Home, Pets & Hobbies": 8.582794920210163e-05, "Industry & Production": 0.000127905468510292, "Language, Literature & Philosophy": 0.00019019974619682777, "Politics & Government": 8.113176644573631e-05, "Religion & Spirituality": 0.00010535695563938007, "Science, Mathematics & Engineering": 0.002775095328346587, "Relationships & Community": 0.00031200148395549906, "Software Usage & Applications": 0.0016995701801612443, "Programming & Software Engineering": 0.9419033590331841, "Sports & Fitness": 0.00013457174936612226, "Vehicles & Transportation": 5.6974145545250155e-05, "Travel & Tourism": 0.00014191829448392967, "Other / Uncovered Subject": 0.004491005097113699, "Unknown / Insufficient Content": 0.0002340720926301708}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013794299845450487, "Art & Design": 0.04926898744034221, "Law & Crime": 0.0011455737114800778, "Education & Careers": 0.002152191169315937, "Entertainment & Media": 0.0007708557024304161, "Fashion & Beauty": 0.0006512151519231485, "Business, Economics & Finance": 0.39555742075207506, "Food & Dining": 0.000595742131968186, "Games & Gaming": 0.0005943206299498224, "Electronics & Hardware": 0.0007797943011871698, "Health, Healthcare & Wellness": 0.001971178543819917, "History & Geography": 0.0005396809667817172, "Home, Pets & Hobbies": 0.00047286045932402556, "Industry & Production": 0.0005568996388723221, "Language, Literature & Philosophy": 0.0006215437866923224, "Politics & Government": 0.00045215706813641065, "Religion & Spirituality": 0.0007089708213659628, "Science, Mathematics & Engineering": 0.029110816012486614, "Relationships & Community": 0.0007515883566195917, "Software Usage & Applications": 0.005200257639617625, "Programming & Software Engineering": 0.07548992712879546, "Sports & Fitness": 0.0005187240614908744, "Vehicles & Transportation": 0.00027090061307612906, "Travel & Tourism": 0.0006698434620435726, "Other / Uncovered Subject": 0.007423947902809317, "NONE": 0.42234517256285126}}, "probabilities": {"primary": {"Academic Writing": 3.604405754737798e-05, "Content Listing": 4.572643606498536e-05, "Creative Writing": 2.355225550143023e-05, "Customer Support Page": 1.1083736969032657e-05, "Q&A Forum": 1.6086773867430706e-05, "Discussion Thread": 9.96333159561829e-06, "Comment Section": 1.819423651220499e-05, "Missing Content": 5.043124111365892e-05, "Knowledge Article": 7.898777981411532e-05, "Legal Notices": 4.788519158312261e-05, "News Article": 1.0656005372927478e-05, "Organizational Announcement": 1.5842069501037963e-05, "Nonfiction Writing": 1.6702045476228835e-05, "Organizational About Page": 1.955858582769904e-05, "Personal About Page": 1.7804015769263053e-05, "Personal Blog": 1.0365107614562435e-05, "Product Page": 1.5612101330928117e-05, "Spam / Ads": 1.6805452304720288e-05, "Structured Data": 0.0009278560702049241, "Technical Writing": 0.9952587728813256, "Transcript / Interview": 3.1097933193340206e-05, "Tutorial / How-To Guide": 0.0005214697897506019, "User Reviews": 1.074932607906456e-05, "Other / Unknown": 0.002788753575680207}, "secondary": {"Academic Writing": 0.00025878519244301387, "Content Listing": 0.0017734902982800575, "Creative Writing": 0.00032515089909173543, "Customer Support Page": 0.00013802633184588727, "Q&A Forum": 0.00011139031350040146, "Discussion Thread": 8.288813538250487e-05, "Comment Section": 0.0003185172592768833, "Missing Content": 0.000672282279259802, "Knowledge Article": 0.0008034701766141472, "Legal Notices": 0.0002737862307839848, "News Article": 0.00022536703748916328, "Organizational Announcement": 0.00014265052239984072, "Nonfiction Writing": 0.00017968357993675295, "Organizational About Page": 0.00016958725241354784, "Personal About Page": 0.0001523281744063796, "Personal Blog": 0.00013381339401798462, "Product Page": 0.00011661926579817717, "Spam / Ads": 0.00014692966665851722, "Structured Data": 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0.00011319422599539431, "Structured Data": 0.011303156098214319, "Technical Writing": 0.014838644433920491, "Transcript / Interview": 0.0002882517576053708, "Tutorial / How-To Guide": 0.001894757374142656, "User Reviews": 5.479319184888904e-05, "NONE": 0.9660268358581005}, "structured_records": {"false": 0.8862304478531204, "true": 0.11376955214687952}, "procedural_steps": {"false": 0.9978136997758271, "true": 0.002186300224172825}, "worked_solution": {"false": 0.9844209385919214, "true": 0.01557906140807858}, "contextual_dialogue": {"false": 0.9999919484440346, "true": 8.051555965397155e-06}, "content_quality": {"-2": 0.017561102827747962, "-1": 0.14779112972519357, "0": 0.3351870236400604, "1": 0.37934172004840916, "2": 0.12011902375858899}, "noise_severity": {"0": 0.8070790381713046, "1": 0.1316869613131666, "2": 0.050771250404686045, "3": 0.007293625195644185, "4": 0.00316912491519857}, "missing_content_severity": {"0": 0.8889870518215671, "1": 0.06798581329634985, "2": 0.03274274897493702, "3": 0.008370623874208661, "4": 0.0019137620329373998}, "factual_density": {"0": 0.04986041761293309, "1": 0.1117828401755602, "2": 0.21647186004372565, "3": 0.3204792189695852, "4": 0.30140566319819595}, "pretraining_utility": {"-2": 0.01023170521955261, "-1": 0.08034994964881921, "0": 0.36824973183867804, "1": 0.4584476308864726, "2": 0.08272098240647759}, "math_density": {"0": 0.3251043339133827, "1": 0.3082391116392761, "2": 0.22699555183482087, "3": 0.06213876702590049, "4": 0.07752223558661987}, "code_density": {"0": 0.0003382964096508699, "1": 0.0006839894449619688, "2": 0.0236703478382561, "3": 0.19450459002235368, "4": 0.7808027762847773}, "reasoning_density": {"0": 0.6779314318682849, "1": 0.120750021770359, "2": 0.13101542084405388, "3": 0.04616759268543204, "4": 0.024135532831870206}, "education_level": {"none": 0.001451791710775432, "0": 0.0017124715100294975, "1": 0.017560224311297983, "2": 0.8106585182679739, "3": 0.16214561525071933, "4": 0.006471378949203975}, "creative_writing_quality": {"not_applicable": 0.9991857014127097, "-2": 0.00013127015343291428, "-1": 0.0002024969506272366, "0": 0.0003894292457420469, "1": 6.632913780562025e-05, "2": 2.4773099682575713e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.549812693982135e-05, "Art & Design": 0.0010521864387833372, "Law & Crime": 0.00018515584748614357, "Education & Careers": 0.0007932717626679676, "Entertainment & Media": 0.00010570220376662448, "Fashion & Beauty": 0.0001240347998256799, "Business, Economics & Finance": 0.04433245182469902, "Food & Dining": 0.00013874014036952063, "Games & Gaming": 0.00014639687513546678, "Electronics & Hardware": 0.00015711590086562007, "Health, Healthcare & Wellness": 0.0004096993434492873, "History & Geography": 0.00012075744523068859, "Home, Pets & Hobbies": 8.582794920210162e-05, "Industry & Production": 0.00012790546851029197, "Language, Literature & Philosophy": 0.00019019974619682772, "Politics & Government": 8.11317664457363e-05, "Religion & Spirituality": 0.00010535695563938005, "Science, Mathematics & Engineering": 0.0027750953283465865, "Relationships & Community": 0.000312001483955499, "Software Usage & Applications": 0.0016995701801612439, "Programming & Software Engineering": 0.9419033590331839, "Sports & Fitness": 0.00013457174936612224, "Vehicles & Transportation": 5.697414554525014e-05, "Travel & Tourism": 0.00014191829448392964, "Other / Uncovered Subject": 0.004491005097113698, "Unknown / Insufficient Content": 0.00023407209263017075}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013794299845450485, "Art & Design": 0.04926898744034219, "Law & Crime": 0.0011455737114800775, "Education & Careers": 0.0021521911693159366, "Entertainment & Media": 0.0007708557024304159, "Fashion & Beauty": 0.0006512151519231483, "Business, Economics & Finance": 0.39555742075207495, "Food & Dining": 0.0005957421319681859, "Games & Gaming": 0.0005943206299498223, "Electronics & Hardware": 0.0007797943011871696, "Health, Healthcare & Wellness": 0.0019711785438199167, "History & Geography": 0.0005396809667817171, "Home, Pets & Hobbies": 0.00047286045932402545, "Industry & Production": 0.000556899638872322, "Language, Literature & Philosophy": 0.0006215437866923223, "Politics & Government": 0.00045215706813641054, "Religion & Spirituality": 0.0007089708213659627, "Science, Mathematics & Engineering": 0.029110816012486607, "Relationships & Community": 0.0007515883566195915, "Software Usage & Applications": 0.005200257639617624, "Programming & Software Engineering": 0.07548992712879544, "Sports & Fitness": 0.0005187240614908743, "Vehicles & Transportation": 0.000270900613076129, "Travel & Tourism": 0.0006698434620435725, "Other / Uncovered Subject": 0.007423947902809315, "NONE": 0.42234517256285115}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13830, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13830, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:14.774026+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "c1a90eea-6f58-4197-84b2-2546960a83d7", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "27fcbd8016a83c89af40a695a26beaef9263c6d3490282636636013dcd0b65e3", "source_metadata": {"uuid": "c1a90eea-6f58-4197-84b2-2546960a83d7", "repo_name": "vishvash/Sales-Analytics", "relative_path": "salespitch-frontend/src/components/KPIDashboard.jsx", "category": "WEB", "algo_rel_score": 0.9520040154457092, "quality_score": 5.579074859619141, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2025-07-02T05:13:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2796369dfc395283e61888795f1258adca8b0c18", "size_bytes": 20017, "file_timestamp": 1751526892, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "87706064b85c664b6793fc6e70956d4834bb0a1c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2025-07-02T05:13:08.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2796369dfc395283e61888795f1258adca8b0c18", "size_bytes": 20017, "file_timestamp": 1751526892, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "87706064b85c664b6793fc6e70956d4834bb0a1c", "url": "https://github.com/vishvash/Sales-Analytics/blob/87706064b85c664b6793fc6e70956d4834bb0a1c/salespitch-frontend/src/components/KPIDashboard.jsx"}, "gpt4o_tokens": 4786, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.43666643218489765, "noise_severity": 0.26778683737026554, "missing_content_severity": 0.16623823100059948, "factual_density": 2.711786869964551, "pretraining_utility": 0.5230762356115034, "math_density": 1.2587354587330988, "code_density": 3.7547495603276446, "reasoning_density": 0.6178257728422437, "education_level": 2.154327256850012, "creative_writing_quality": -0.4287885620484025}, "score_applicability": {"education_level": 0.9985482082892246, "creative_writing_quality": 0.0008142985872903937}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Shivam-Kasera/viewtube/blob/bfdeda516a3a51d99084e7b5642e88ea26176781/frontend/src/pages/profile/MyProfile.jsx", "id": "javascript-0-a0bf2c2689b75cc4b26e603d62c5211a", "text": "import React, { Fragment, useRef, useState } from 'react';\nimport { Link } from 'react-router-dom';\nimport { FaChevronLeft, FaChevronRight } from 'react-icons/fa6';\nimport { ChannelDetailsModal, HorizontalScrollVideoCard, NotItem, SubscriptionCard } from '../../components';\nimport { useSelector } from 'react-redux';\nimport { formatValue } from '../../logics';\n\nconst MyProfile = () => {\n const { user } = useSelector(state => state.user);\n\n const videosDivRef = useRef(null);\n const subscriptionsDivRef = useRef(null);\n const watchLaterDivRef = useRef(null);\n const buttonRef = useRef(null);\n\n const [channelDetailsModalOpen, setChannelDetailsModalOpen] = useState(null);\n\n const scrollLeft = (scrollValue, div_1, div_2, div_3) => {\n const divRef = div_1 ? videosDivRef.current : div_2 ? subscriptionsDivRef.current : watchLaterDivRef.current;\n if (divRef) divRef.scrollLeft -= scrollValue;\n };\n\n const scrollRight = (scrollValue, div_1, div_2, div_3) => {\n const divRef = div_1 ? videosDivRef.current : div_2 ? subscriptionsDivRef.current : watchLaterDivRef.current;\n if (divRef) divRef.scrollLeft += scrollValue;\n };\n\n return (\n
\n
\n
\n \n profile pic\n \n
\n
{user ? user.channelName : \"example channel name\"}
\n
\n {user ? user.username : \"@username\"}\n •\n {user ? formatValue(user.subscribers) : 0} subscribers\n •\n {user ? user.videos && formatValue(user.videos.length) : 0} videos\n
\n \n {\n channelDetailsModalOpen &&\n \n }\n
\n customise channel\n manage videos\n
\n
\n
\n
\n
\n

Your Videos

\n {(user && user.videos && user.videos.length > 0) ? (\n \n
\n
scrollLeft(700, true)}>\n \n
\n
\n {user.videos.map(video => (\n \n ))}\n
\n
scrollRight(700, true)}>\n \n
\n
\n \n ) : (\n \n )}\n

Your subscriptions

\n {(user && user.subscriptions && user.subscriptions.length > 0) ? (\n \n
\n
scrollLeft(700, false, true, false)}>\n \n
\n
\n {user.subscriptions.map(subscription => (\n \n ))}\n
\n
scrollRight(700, false, true, false)}>\n \n
\n
\n \n ) : (\n \n )}\n

watch later videos

\n {(user && user.watch_later && user.watch_later.length > 0) ? (\n \n
\n
scrollLeft(700, false, false, true)}>\n \n
\n
\n {user.watch_later.map(video => (\n \n ))}\n
\n
scrollRight(700, false, false, true)}>\n \n
\n
\n \n ) : (\n \n )}\n
\n
\n
\n );\n};\n\nexport default MyProfile;", "text_preview": "import React, { Fragment, useRef, useState } from 'react'; import { Link } from 'react-router-dom'; import { FaChevronLeft, FaChevronRight } from 'react-icons/fa6'; import { ChannelDetailsModal, HorizontalScrollVideoCard, NotItem, Subscript", "input_sha256": "79275750bd4f920c6b55407e8b5b1345ee4c8a98a2bf76da46d66c377ae35eb8", "text_sha256": "a0bf2c2689b75cc4b26e603d62c5211a55e119dbd97ddce49378534c55ad3d75", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8012, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9959785910718076, "noise_severity": 0.1147047439714116, "missing_content_severity": 0.02877023276889287, "factual_density": 2.354336887083785, "pretraining_utility": 0.38723982882461927, "math_density": 0.08425299774572466, "code_density": 3.7966681135135913, "reasoning_density": 0.08516657684963361, "education_level": 2.0317654001306984, "creative_writing_quality": -0.21122824583296315}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.024853021565523048, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.988358478742652, "secondary": 0.9158939817014506, "tertiary": 0.8899603939809049, "structured_records": 0.053680342725815164, "procedural_steps": 0.0003966903382216671, "worked_solution": 0.002377189463120594, "contextual_dialogue": 9.065938048742438e-06, "content_quality": 0.5845352829202499, "noise_severity": 0.904412713357157, "missing_content_severity": 0.9760248060259226, "factual_density": 0.061185710636115775, "pretraining_utility": 0.4004013797167697, "math_density": 0.9297891685452294, "code_density": 0.8305567612613263, "reasoning_density": 0.9290278526253053, "education_level": 0.8326287630213514, "creative_writing_quality": 0.997322025290933, "primary_topic": 0.9860528058929102, 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"file_timestamp": 1708858853, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfdeda516a3a51d99084e7b5642e88ea26176781"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 16, "repo_created_at": "2024-02-24T16:40:47.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "747de53a13800939659191f049cb15c621a056fe", "size_bytes": 9599, "file_timestamp": 1708858853, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bfdeda516a3a51d99084e7b5642e88ea26176781", "url": "https://github.com/Shivam-Kasera/viewtube/blob/bfdeda516a3a51d99084e7b5642e88ea26176781/frontend/src/pages/profile/MyProfile.jsx"}, "gpt4o_tokens": 1833, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9959785910718076, "noise_severity": 0.1147047439714116, "missing_content_severity": 0.02877023276889287, "factual_density": 2.354336887083785, "pretraining_utility": 0.38723982882461927, "math_density": 0.08425299774572466, "code_density": 3.7966681135135913, "reasoning_density": 0.08516657684963361, "education_level": 2.0317654001306984, "creative_writing_quality": -0.21122824583296315}, "score_applicability": {"education_level": 0.9973009748600692, "creative_writing_quality": 0.002231645590888985}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/TheMisinformationGame/MisinformationGame/blob/ea15005852f997c3fa591b2c2fb5ec6de7f0ce5c/src/utils/odiff.js", "id": "javascript-0-cbdf4ed630b05652dc668bc4d1fed2ac", "text": "/* Copyright (c) 2013 - Free to use for any purpose: MIT License */\n\n\nexport function odiff(a, b) {\n const results = [];\n diffInternal(a, b, results, []);\n return results;\n}\n\n\n/**\n * adds a 'set' type to the changeList\n */\nfunction set(changeList, property, value) {\n changeList.push({\n type:'set',\n path: property,\n val: value\n });\n}\n\n\n/**\n * adds an 'unset' type to the changeList\n */\nfunction unset(changeList, property) {\n changeList.push({\n type:'unset',\n path: property\n });\n}\n\n\n/**\n * Adds an 'rm' type to the changeList.\n * index: The index to remove at.\n * count: The number of items to remove from that index. The indexes to remove count up from the index.\n */\nfunction rm(changeList, property, index, count, a) {\n changeList.push({\n type:'rm',\n path: property,\n index: index,\n num: count,\n vals: a.slice(index, index + count)\n });\n}\n\n\n/**\n * adds an 'add' type to the changeList\n */\nfunction add(changeList, property, index, values) {\n changeList.push({\n type:'add',\n path: property,\n index: index,\n vals: values\n })\n}\n\n\nfunction diffInternal(a, b, acc, base) {\n if (a === b || (Number.isNaN(a) && Number.isNaN(b)))\n return;\n\n if (a instanceof Array && b instanceof Array) {\n diffInternalArray(a, b, acc, base);\n } else if (a instanceof Date && b instanceof Date) {\n diffInternalDate(a, b, acc, base);\n } else if (a instanceof Object && b instanceof Object) {\n diffInternalObject(a, b, acc, base);\n } else {\n set(acc, base, b)\n }\n}\n\n\nfunction diffInternalArray(a, b, acc, base) {\n let an = a.length - 1;\n let bn = b.length - 1;\n\n // Loop backwards (so that making changes in order will work correctly).\n while(an >= 0 && bn >= 0) {\n if (equal(a[an], b[bn])) {\n an--;\n bn--;\n } else {\n const indexes = findMatchIndexes(equal, a,b, an,bn, 0, 0)\n\n // Loop backwards from the point at which the lists are not equal and find which elements can be matched\n // as similar or can be expressed as additions, changes, or removals.\n let anInner=an;\n let bnInner=bn;\n while(anInner > indexes.a && bnInner > indexes.b) {\n if (similar(a[anInner], b[bnInner])) {\n // get change for that element\n diffInternal(a[anInner],b[bnInner],acc, base.concat([anInner]));\n anInner--;\n bnInner--;\n } else {\n const indexesInner = findMatchIndexes(similar, a, b, anInner, bnInner, indexes.a+1, indexes.b+1);\n const numberPulled = anInner - indexesInner.a;\n const numberPushed = bnInner - indexesInner.b;\n\n if (numberPulled === 1 && numberPushed === 1) {\n // set the one\n set(acc, base.concat(indexesInner.a + 1), b[indexesInner.b + 1]);\n } else if (numberPulled === 1 && numberPushed === 2) {\n // set one, push the other\n add(acc, base,indexesInner.a + 2, b.slice(indexesInner.b + 2, bnInner + 1));\n set(acc, base.concat(indexesInner.a + 1), b[indexesInner.b + 1]);\n } else if (numberPulled === 2 && numberPushed === 1) {\n // set one, pull the other\n rm(acc, base, indexesInner.a + 2, 1, a);\n set(acc, base.concat(indexesInner.a + 1), b[indexesInner.b+1]);\n } else if (numberPulled === 2 && numberPushed === 2) {\n set(acc, base.concat(indexesInner.a + 2), b[indexesInner.b+2]);\n set(acc, base.concat(indexesInner.a + 1), b[indexesInner.b+1]);\n } else {\n if (numberPulled > 0) { // if there were some elements pulled\n rm(acc, base, indexesInner.a+1, numberPulled, a);\n }\n if (numberPushed > 0) { // if there were some elements pushed\n add(acc, base,indexesInner.a+1, b.slice(indexesInner.b + 1, bnInner + 1));\n }\n }\n\n anInner = indexesInner.a\n bnInner = indexesInner.b\n }\n }\n if (anInner > indexes.a) {\n // more to pull\n rm(acc, base, indexes.a+1, anInner-indexes.a, a);\n } else if (bnInner > indexes.b) {\n // more to push\n add(acc, base, anInner+1, b.slice(indexes.b+1, bnInner+1));\n }\n\n an = indexes.a;\n bn = indexes.b;\n }\n }\n\n if (an >= 0) {\n // more to pull\n rm(acc, base, 0, an+1, a);\n } else if (bn >= 0) {\n // more to push\n add(acc, base,0, b.slice(0, bn+1));\n }\n}\n\n\nfunction diffInternalDate(a, b, acc, base) {\n if (a.getTime() !== b.getTime()) {\n set(acc, base, b);\n }\n}\n\n\nfunction diffInternalObject(a, b, acc, base) {\n if (a.constructor !== b.constructor) {\n set(acc, base, b);\n } else {\n const keyMap = merge(arrayToMap(Object.keys(a)), arrayToMap(Object.keys(b)));\n for (let key in keyMap) {\n const path = base.concat([key]);\n if (key in a && !(key in b)) {\n unset(acc, path);\n } else {\n diffInternal(a[key], b[key], acc, path);\n }\n }\n }\n}\n\n\n/**\n * Finds and returns the closest indexes in a and b that match starting with divergenceIndex\n * Note: loops backwards like the rest of this stuff\n * compareFn: determines what matches (returns true if the arguments match)\n * a,b: two arrays to compare\n * divergenceIndexA,divergenceIndexB: the two positions of a and b to start comparing from\n * aSubMin,bSubMin: the two positions to compare until\n * Returns the index beyond the first element (aSubMin-1 or bSubMin-1) for each if there is no match\n */\nfunction findMatchIndexes(compareFn, a,b, divergenceIndexA,divergenceIndexB, aSubMin, bSubMin) {\n const maxNForA = divergenceIndexA - aSubMin;\n const maxNForB = divergenceIndexB - bSubMin;\n const maxN = Math.max(maxNForA, maxNForB);\n for (let n = 1; n <= maxN; n++) {\n // the current item farthest from the divergence index being compared\n const newestA = a[divergenceIndexA-n];\n const newestB = b[divergenceIndexB-n];\n\n if (n<=maxNForB && n<=maxNForA && compareFn(newestA, newestB))\n return {a: divergenceIndexA - n, b: divergenceIndexB - n};\n\n for (let j = 0; j < n; j++) {\n // an element between the divergence index and the newest items\n const elemA = a[divergenceIndexA-j];\n const elemB = b[divergenceIndexB-j];\n\n if (n<=maxNForB && compareFn(elemA, newestB)) {\n return {a: divergenceIndexA - j, b: divergenceIndexB - n};\n } else if (n<=maxNForA && compareFn(newestA, elemB)) {\n return {a: divergenceIndexA - n, b: divergenceIndexB - j};\n }\n }\n }\n // else\n return {a: aSubMin - 1, b: bSubMin - 1};\n}\n\n\n/**\n * compares arrays and objects and returns true if they're similar meaning:\n * less than 2 changes, or\n * less than 10% different members\n */\nfunction similar(a,b) {\n if (a instanceof Array) {\n if (!(b instanceof Array))\n return false;\n\n return similarArrays(a, b);\n\n } else if (a instanceof Object) {\n if (!(b instanceof Object))\n return false;\n\n return similarObjects(a, b);\n\n } else {\n return a===b || (Number.isNaN(a) && Number.isNaN(b));\n }\n}\n\nfunction similarArrays(a, b) {\n const tenPercent = a.length / 10;\n let notEqual = Math.abs(a.length - b.length);\n for (let n=0; n= 2 && notEqual > tenPercent) || notEqual === a.length)\n return false\n\n notEqual++;\n }\n }\n // else\n return true;\n}\n\nfunction similarObjects(a, b) {\n const keyMap = merge(arrayToMap(Object.keys(a)), arrayToMap(Object.keys(b)));\n const keyLength = Object.keys(keyMap).length;\n const tenPercent = keyLength / 10;\n let notEqual = 0;\n for (let key in keyMap) {\n const aVal = a[key];\n const bVal = b[key];\n\n if (!equal(aVal, bVal)) {\n if ((notEqual >= 2 && notEqual > tenPercent) || notEqual + 1 === keyLength)\n return false\n\n notEqual++;\n }\n }\n // else\n return true;\n}\n\n/**\n * compares arrays and objects for value equality (all elements and members must match)\n */\nfunction equal(a, b) {\n if (a instanceof Array) {\n if (!(b instanceof Array))\n return false;\n\n if (a.length !== b.length)\n return false;\n\n for (let n = 0; n < a.length; n++) {\n if (!equal(a[n], b[n]))\n return false;\n }\n // else\n return true;\n } else if (a instanceof Object) {\n if (!(b instanceof Object))\n return false;\n\n const aKeys = Object.keys(a);\n const bKeys = Object.keys(b);\n\n if (aKeys.length !== bKeys.length)\n return false;\n\n for (let n = 0; n < aKeys.length; n++) {\n const key = aKeys[n];\n const aVal = a[key];\n const bVal = b[key];\n if (!equal(aVal, bVal))\n return false;\n }\n // else\n return true;\n } else {\n return a===b || (Number.isNaN(a) && Number.isNaN(b));\n }\n}\n\n\n/**\n * turns an array of values into an object where those values are all keys that point to 'true'\n */\nfunction arrayToMap(array) {\n const result = {};\n array.forEach((v) => {\n result[v] = true;\n })\n return result;\n}\n\n/**\n * Overwrites obj1's values with obj2's and adds obj2's if non existent in obj1\n * returns obj1 (now mutated)\n */\nfunction merge(obj1, obj2){\n for (let key in obj2){\n if (obj2.hasOwnProperty(key)) {\n obj1[key] = obj2[key];\n }\n }\n return obj1;\n}\n", "text_preview": "/* Copyright (c) 2013 - Free to use for any purpose: MIT License */ export function odiff(a, b) { const results = []; diffInternal(a, b, results, []); return results; } /** * adds a 'set' type to the changeList */ function set(change", "input_sha256": "0a7f9574c5fd195b06e017d003aaa060889d4987ce7060122f60e86804fcb8e6", "text_sha256": "cbdf4ed630b05652dc668bc4d1fed2aca153c40c45945388e1fb6440da04f6da", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8011, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 4, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8133000673305566, "noise_severity": 0.16088761459899711, "missing_content_severity": 0.008591203777317678, "factual_density": 3.3365337218566173, "pretraining_utility": 0.5268089445274242, "math_density": 1.1549306066265597, "code_density": 3.82624536111349, "reasoning_density": 2.1609477128601458, "education_level": 2.0337038193521417, "creative_writing_quality": -0.1563709900424191}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.19974989508420782, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9969783905913765, "secondary": 0.5595858857996513, "tertiary": 0.7680205589309986, "structured_records": 0.0020152146375370175, "procedural_steps": 0.0050827900115021545, "worked_solution": 0.0012647296863154908, "contextual_dialogue": 6.5848354001872634e-06, "content_quality": 0.529257311373579, "noise_severity": 0.8659269878341691, "missing_content_severity": 0.992840663518902, "factual_density": 0.4471114348805144, "pretraining_utility": 0.44960444156809065, "math_density": 0.037557827811200206, "code_density": 0.8552044675945747, "reasoning_density": 0.0, "education_level": 0.7279545939570352, "creative_writing_quality": 0.9991553119489762, "primary_topic": 0.9746220184798313, "secondary_topic": 0.900133004270125}, "probability_margin": {"primary": 0.9959652445572116, "secondary": 0.18934466951526657, "tertiary": 0.5695951906556904, "education_level": 0.6561394613784427, "creative_writing_quality": 0.998949388727718, "primary_topic": 0.9578069810780765, "secondary_topic": 0.8499394318299851}, "raw_probabilities": {"primary": {"Academic Writing": 3.2083590834112745e-05, "Content Listing": 1.566760992458435e-05, "Creative Writing": 1.454754928701662e-05, "Customer Support Page": 9.025898533575387e-06, "Q&A Forum": 1.0093325983347127e-05, "Discussion Thread": 6.996054711258312e-06, "Comment Section": 1.3366524803581786e-05, "Missing Content": 2.932070767960168e-05, "Knowledge Article": 0.00011016325993738873, "Legal Notices": 0.0006254902312993942, "News Article": 4.184155760367526e-06, "Organizational Announcement": 1.1921431977832155e-05, "Nonfiction Writing": 1.1994766018100382e-05, "Organizational About Page": 1.2624095890986972e-05, "Personal About Page": 1.2198055028334703e-05, "Personal Blog": 7.484463404018001e-06, "Product Page": 6.318434988867007e-06, "Spam / Ads": 8.662243172653903e-06, "Structured Data": 0.0005640754019921947, "Technical Writing": 0.9971042909834025, "Transcript / Interview": 1.3227037298411496e-05, "Tutorial / How-To Guide": 0.0002313751271680432, "User Reviews": 5.842624712712091e-06, "Other / Unknown": 0.0011390464261909073}, "secondary": {"Academic Writing": 0.0006301643869973849, "Content Listing": 0.0004621289302927695, "Creative Writing": 0.0005632387952854261, "Customer Support Page": 0.00030779102689037493, "Q&A Forum": 0.0002945402525083516, "Discussion Thread": 0.00029773636294495187, "Comment Section": 0.00048710866087916556, "Missing Content": 0.0005906999838648702, "Knowledge Article": 0.0010298189085547892, "Legal Notices": 0.38859180437606594, "News Article": 0.0004957505989378274, "Organizational Announcement": 0.0005070239452364493, "Nonfiction Writing": 0.00046615027151781493, "Organizational About Page": 0.0005130101232404751, "Personal About Page": 0.0004099042796992087, "Personal Blog": 0.00028412904985401316, "Product Page": 0.000352836618287962, "Spam / Ads": 0.00036951332088634535, "Structured Data": 0.0014145406974964084, "Technical Writing": 0.022562116457039073, "Transcript / Interview": 0.0005971262922287065, "Tutorial / How-To Guide": 0.0005826489573869571, "User Reviews": 0.000253743812572206, "NONE": 0.5779364738913325}, "tertiary": {"Academic Writing": 0.00032462719765757934, "Content Listing": 0.00021008706669473984, "Creative Writing": 0.0003175791869305495, "Customer Support Page": 0.00017926830079903667, "Q&A Forum": 0.0001201090931490241, "Discussion Thread": 0.00013667751732160833, "Comment Section": 0.00025609347289257166, "Missing Content": 0.00031342012872799795, "Knowledge Article": 0.0006578473608740507, "Legal Notices": 0.20809117831984986, "News Article": 0.0002620475713581121, "Organizational Announcement": 0.0002436195147111448, "Nonfiction Writing": 0.0002141300655843022, "Organizational About Page": 0.00029934150600074007, "Personal About Page": 0.0001638625390522447, "Personal Blog": 0.0001379305709207899, "Product Page": 0.00018751258758327457, "Spam / Ads": 0.00019336766768322455, "Structured Data": 0.0004876701999804462, "Technical Writing": 0.008794466313109053, "Transcript / Interview": 0.00025859578571167804, "Tutorial / How-To Guide": 0.00036709593799281705, "User Reviews": 9.710311987492923e-05, "NONE": 0.7776863689755403}, "structured_records": {"false": 0.9979847853624629, "true": 0.0020152146375370175}, "procedural_steps": {"false": 0.9949172099884979, "true": 0.0050827900115021545}, "worked_solution": {"false": 0.9987352703136845, "true": 0.0012647296863154908}, "contextual_dialogue": {"false": 0.9999934151645998, "true": 6.5848354001872634e-06}, "content_quality": {"-2": 0.010580783039040172, "-1": 0.03676517136664303, "0": 0.27052288766016774, "1": 0.4930355110930181, "2": 0.18909564684113092}, "noise_severity": {"0": 0.8454082314128659, "1": 0.15091288504466008, "2": 0.0019108602666190642, "3": 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"file_timestamp": 1752117445, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["CC-BY-4.0"]}, "commit_id": "ea15005852f997c3fa591b2c2fb5ec6de7f0ce5c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 912, "repo_created_at": "2021-10-13T05:02:08.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 29, "forks": 6, "issues": 3, "pull_requests": 0}, "file_meta": {"content_id": "761292a5ad0556caa951ab66dbcdde8d35dc2229", "size_bytes": 10310, "file_timestamp": 1752117445, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["CC-BY-4.0"]}, "commit_id": "ea15005852f997c3fa591b2c2fb5ec6de7f0ce5c", "url": "https://github.com/TheMisinformationGame/MisinformationGame/blob/ea15005852f997c3fa591b2c2fb5ec6de7f0ce5c/src/utils/odiff.js"}, "gpt4o_tokens": 2616, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8133000673305566, "noise_severity": 0.16088761459899714, "missing_content_severity": 0.00859120377731768, "factual_density": 3.3365337218566173, "pretraining_utility": 0.5268089445274242, "math_density": 1.1549306066265597, "code_density": 3.82624536111349, "reasoning_density": 2.1609477128601458, "education_level": 2.0337038193521417, "creative_writing_quality": -0.1563709900424191}, "score_applicability": {"education_level": 0.9988583400957721, "creative_writing_quality": 0.0007039067091866771}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/hpkid/GoSharp/blob/73b07fac4b7ba5b84fa8775618bdda874ec59de4/UI/screens/View2.js", "id": "javascript-0-82c3047b54d814a457c54fff7440a038", "text": "import * as React from 'react';\nimport {\n View,\n Text,\n StyleSheet,\n Button,\n ScrollView,\n TouchableOpacity,\n} from 'react-native';\nimport Accordion from 'react-native-collapsible/Accordion';\nimport { Constants } from 'expo';\nimport axios from 'axios';\nimport { Ionicons } from '@expo/vector-icons';\nimport { AsyncStorage } from 'react-native';\n// import { getData } from '../services/helpers';\n\nimport d from './d';\nimport { turku } from './data';\n\nconst styles = StyleSheet.create({\n container: {\n paddingTop: Constants.statusBarHeight,\n paddingHorizontal: 20,\n },\n welcome: {\n fontSize: 25,\n textAlign: 'center',\n marginBottom: 50,\n color: '#f7c744',\n },\n header: {\n backgroundColor: '#B3D6C6',\n padding: 10,\n },\n headerText: {\n textAlign: 'center',\n fontSize: 16,\n fontWeight: '500',\n },\n content: {\n padding: 20,\n backgroundColor: '#F7C744',\n },\n loginScreenButton: {\n marginRight: 40,\n marginLeft: 40,\n marginTop: 50,\n paddingTop: 15,\n paddingBottom: 15,\n paddingLeft: 40,\n paddingRight: 40,\n backgroundColor: '#f7c744',\n borderRadius: 10,\n borderWidth: 1,\n borderColor: '#fff',\n },\n loginText: {\n color: '#203546',\n textAlign: 'center',\n paddingLeft: 10,\n paddingRight: 10,\n },\n});\n// TODO: list 3 routes(least money, least time, medium money-medium time)\nconst SECTIONS = [\n {\n title: 'First route',\n content: 'Lorem ipsum...',\n totalTime: d.totalTime\n },\n {\n title: 'Second route - Least time',\n content: 'Lorem ipsum...',\n },\n {\n title: 'Third route - Medium Choice',\n content: 'Lorem ipsum...',\n },\n];\n\nexport default class View2 extends React.Component {\n static navigationOptions = {\n header: null,\n };\n state = {\n activeSections: [],\n shopping_list: []\n };\n\n async componentWillMount() {\n try {\n const value = await AsyncStorage.getItem('shopping_list');\n console.log(value, this.props.navigation.getParam('origin').city)\n // getData(value, this.props.navigation.getParam('origin').city)\n this.props.navigation.getParam('origin')\n if (value !== null) {\n // We have data!!\n this.setState((state, props) => {\n return { shopping_list: JSON.parse(value) };\n });\n }\n } catch (error) {\n // Error retrieving data\n }\n }\n _renderHeader = section => {\n return (\n \n {section.title}\n {`${section.totalTime ? Math.round(section.totalTime/60000): 'x'} minutes - y euros`}\n \n );\n };\n\n _renderContent = section => {\n return (\n \n Total time: {section.totalTime ? Math.round(section.totalTime/60000): 'x'} minutes\n Total cost: y euros\n this.props.navigation.navigate('View3')}>Choose this route\n \n );\n };\n\n _updateSections = activeSections => {\n this.setState({ activeSections });\n };\n\n render() {\n // console.log(this.props.navigation.getParam('origin'), this.props.navigation.getParam('destination'))\n return (\n \n this.props.navigation.goBack()}\n />\n Suggested Routes\n \n \n this.props.navigation.navigate('View3')}\n underlayColor=\"#fff\">\n NEXT\n \n \n \n );\n }\n}\n", "text_preview": "import * as React from 'react'; import { View, Text, StyleSheet, Button, ScrollView, TouchableOpacity, } from 'react-native'; import Accordion from 'react-native-collapsible/Accordion'; import { Constants } from 'expo'; import axios from 'a", "input_sha256": "4eff4f22a827a73e927e4e7f1472bbd3deb39689cc7a9840ce00a6cd52f8b125", "text_sha256": "82c3047b54d814a457c54fff7440a03801c5258f45a587e76c73df0c2bb3d0ad", "run_sha256": 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"missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9722, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9722, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:29:18.203880+00:00", "scoring_key_slot": "primary", "dataset_uid": "427b4b99-9754-4024-a7c4-59bacf84fa6d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f56fab3e5cbb7b23b72949dda4e39b174c7a589bffb55bb8b1e044b0ec221ee5", "source_metadata": {"uuid": "427b4b99-9754-4024-a7c4-59bacf84fa6d", "repo_name": "hpkid/GoSharp", "relative_path": "UI/screens/View2.js", "category": "WEB", "algo_rel_score": 0.90330570936203, "quality_score": 4.335634708404541, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 20, "repo_created_at": "2018-11-24T12:19:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fa068309653267651a3f7875e04ac92572c5aa47", "size_bytes": 4400, "file_timestamp": 1543666030, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "73b07fac4b7ba5b84fa8775618bdda874ec59de4"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 20, "repo_created_at": "2018-11-24T12:19:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fa068309653267651a3f7875e04ac92572c5aa47", "size_bytes": 4400, "file_timestamp": 1543666030, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "73b07fac4b7ba5b84fa8775618bdda874ec59de4", "url": "https://github.com/hpkid/GoSharp/blob/73b07fac4b7ba5b84fa8775618bdda874ec59de4/UI/screens/View2.js"}, "gpt4o_tokens": 1087, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.32313041092486894, "noise_severity": 0.4615552993130266, "missing_content_severity": 0.369014385520591, "factual_density": 2.60598675935381, "pretraining_utility": 0.2288157936425892, "math_density": 0.43680781050675316, "code_density": 3.849787531631565, "reasoning_density": 0.15981426704768803, "education_level": 2.0672304175717353, "creative_writing_quality": -0.5997539043251771}, "score_applicability": {"education_level": 0.9974712088019643, "creative_writing_quality": 0.002001793516435533}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/som-31/car_rental_app/blob/432bac8276c697fc8d360341b7cf2b47993c713b/app/screens/ReturnVehicle.js", "id": "javascript-0-098901f045425ca3e6f59e42b09a70cd", "text": "import React from 'react';\nimport { Text, View, StyleSheet, TextInput, TouchableOpacity, Alert,\n ScrollView} from 'react-native';\n\nclass ReturnVehicle extends React.Component {\n\n\n constructor(props) {\n super(props)\n \n this.state = {\n customerName: '',\n custId: '',\n vin: '',\n returnDate: '',\n Description: '',\n PaymentStatus: false,\n loading: false,\n errorMessage: \"\",\n paymentDueRecords: [],\n disableStatus: false,\n totalAmount: '',\n buttonText: 'Pay'\n }\n }\n\n // componentDidMount() {\n // this.getData();\n // }\n \n updatePaymentStatus = () => {\n // const data = JSON.stringify({\n // Custid: this.state.custId,\n // Name: this.state.customerName,\n // VehicleID: this.state.vin,\n // Description: this.state.Description,\n // ReturnDate: this.state.returnDate,\n // TotalAmount: this.state.totalAmount,\n // PaymentStatus: true\n // });\n // console.log(data);\n fetch('http://10.182.153.235:8000/api/payment', {\n method: 'PUT',\n headers: {\n 'Accept': 'application/json',\n 'Content-Type': `application/json`\n // 'Content-Type': `multipart/form-data; charset=utf-8; boundary= ${Math.random().toString().substr(2)}`\n },\n body: JSON.stringify({\n Custid: this.state.custId,\n Name: this.state.customerName,\n VehicleID: this.state.vin,\n Description: this.state.Description,\n ReturnDate: this.state.returnDate,\n TotalAmount: this.state.totalAmount,\n PaymentStatus: true\n })\n }).then((response) => response.text())\n .then((responseJson) => {\n let parsedJson = JSON.parse(responseJson);\n console.log(parsedJson);\n this.setState({\n buttonText: 'Paid',\n disableStatus: true\n });\n // Showing response message coming from server after inserting records.\n Alert.alert(parsedJson.message);\n }).catch((error) => {\n console.error(error)\n });\n }\n \n getPaymentDues = () =>{\n console.log('in herer get Payment Dues');\n const query = `?customerName=${this.state.customerName}&vehicleID=${this.state.vin}&returnDate=${this.state.returnDate}`\n fetch(`http://10.182.153.235:8000/api/returnVehicle${query}`, {\n method: 'GET',\n headers: {\n 'Accept': 'application/json',\n 'Content-Type': `application/json`\n },\n }).then((response) => response.text())\n .then((responseJson) => {\n let parsedJson = JSON.parse(responseJson);\n console.log(parsedJson.data);\n this.setState({\n paymentDueRecords: parsedJson.data,\n totalAmount: parsedJson.data[0].TotalAmount,\n Description: parsedJson.data[0].Description,\n custId: parsedJson.data[0].Custid\n });\n // Showing response message coming from server after inserting records.\n Alert.alert(`${parsedJson.data.length} records retrieved successfully`);\n }).catch((error) => {\n console.error(error);\n });\n }\n\n render(){\n return (\n \n Retrieve Customer Information\n this.setState({ customerName : TextInputValue }) }\n underlineColorAndroid='transparent'\n style={styles.TextInputStyleClass}\n />\n this.setState({ vin : TextInputValue }) }\n underlineColorAndroid='transparent'\n style={styles.TextInputStyleClass}\n />\n this.setState({ returnDate : TextInputValue }) }\n underlineColorAndroid='transparent'\n style={styles.TextInputStyleClass}\n />\n \n Submit \n \n\n {/* Code for listing starts from here */}\n \n \n Payment Dues:\n {this.state.paymentDueRecords.map((data, index) => \n {index + 1}.\n {/* {data.CustomerName} */}\n Customer ID: {data.Custid}\n Customer Name: {data.Name}\n VIN: {data.VehicleID}\n Description: {data.Description}\n Return Date: {data.ReturnDate}\n Amount Due: {data.TotalAmount}\n\n \n {\n this.updatePaymentStatus();\n // this.setState({ PaymentStatus: true })\n }}\n style={{ ...styles.button, marginVertical: 0 }}>\n {this.state.buttonText}\n \n\n {/* {\n this.toggleDeleteEmployeeModal();\n this.setState({ selectedEmployee: data })\n }}\n style={{ ...styles.button, marginVertical: 0, marginLeft: 10, backgroundColor: \"tomato\" }}>\n Delete\n */}\n \n )}\n\n {this.state.loading ? Please Wait... : this.state.errorMessage ? {this.state.errorMessage} : null}\n\n \n \n\n \n // \n );\n }\n}\n\n\nexport default ReturnVehicle;\n\n\nconst styles = StyleSheet.create({\n container: {\n flex: 1,\n backgroundColor: '#fff',\n alignItems: 'center',\n justifyContent: 'center',\n },\n MainContainer: {\n alignItems: 'center',\n flex: 1,\n paddingTop: 30,\n backgroundColor: '#fff'\n },\n MainContainer_For_Show_StudentList_Activity: {\n flex: 1,\n paddingTop: (Platform.OS == 'ios') ? 20 : 0,\n marginLeft: 5,\n marginRight: 5\n },\n TextInputStyleClass: {\n textAlign: 'center',\n width: '90%',\n marginBottom: 7,\n height: 40,\n borderWidth: 1,\n borderColor: '#FF5722',\n borderRadius: 5,\n },\n TouchableOpacityStyle: {\n paddingTop: 10,\n paddingBottom: 10,\n borderRadius: 5,\n marginBottom: 7,\n width: '90%',\n backgroundColor: '#00BCD4'\n },\n TextStyle: {\n color: '#fff',\n textAlign: 'center',\n },\n \n rowViewContainer: {\n fontSize: 20,\n paddingRight: 10,\n paddingTop: 10,\n paddingBottom: 10,\n },\n listContainer: {\n paddingHorizontal: 20\n },\n button: {\n borderRadius: 5,\n marginVertical: 20,\n alignSelf: 'flex-start',\n backgroundColor: \"gray\",\n },\n buttonText: {\n color: \"white\",\n paddingVertical: 6,\n paddingHorizontal: 10,\n fontSize: 16\n },\n title: {\n fontWeight: \"bold\",\n fontSize: 20,\n marginBottom: 10\n },\n employeeListContainer: {\n marginBottom: 25,\n elevation: 4,\n backgroundColor: \"white\",\n padding: 10,\n borderRadius: 6,\n borderTopWidth: 1,\n borderColor: \"rgba(0,0,0,0.1)\"\n },\n name: {\n fontWeight: \"bold\",\n fontSize: 16\n },\n listItem: {\n fontSize: 16\n },\n buttonContainer: {\n marginTop: 10,\n flexDirection: \"row\",\n alignItems: \"center\"\n },\n message: {\n color: \"tomato\",\n fontSize: 17\n }\n });", "text_preview": "import React from 'react'; import { Text, View, StyleSheet, TextInput, TouchableOpacity, Alert, ScrollView} from 'react-native'; class ReturnVehicle extends React.Component { constructor(props) { super(props) this.state = { customerName: ''", "input_sha256": "60e7d0a2aea0b16b3345489e973d0a74044afb0e0b0f8fe6989ea3c5d6546b9f", "text_sha256": "098901f045425ca3e6f59e42b09a70cd4852b208c1fe8d6e51762de37b5ae50d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8013, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3115352590876515, "noise_severity": 0.37028675180291554, "missing_content_severity": 0.049031820706306246, "factual_density": 2.9779818447832427, "pretraining_utility": 0.36855603011276317, "math_density": 0.07811869782181394, "code_density": 3.8987305046383938, "reasoning_density": 0.14147969944769598, "education_level": 2.0693880745979154, "creative_writing_quality": -0.5565848139611208}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004097903130052316, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9951386635327162, "secondary": 0.9564390215749506, "tertiary": 0.9607150944063096, "structured_records": 0.16635655208461073, "procedural_steps": 0.003948481185238804, "worked_solution": 0.0035376859726354635, "contextual_dialogue": 9.73267192733175e-06, "content_quality": 0.42451825338728466, "noise_severity": 0.6914277068309037, "missing_content_severity": 0.9591401494114115, "factual_density": 0.38024050983246316, "pretraining_utility": 0.4627482457928247, "math_density": 0.9349010851484884, "code_density": 0.9156087538653281, "reasoning_density": 0.8821002504602533, "education_level": 0.8358617207500738, "creative_writing_quality": 0.999126202627592, "primary_topic": 0.9488416494168205, "secondary_topic": 0.3089214108672896}, "probability_margin": {"primary": 0.9931597352635609, "secondary": 0.9376947857684373, "tertiary": 0.9440548521661289, "education_level": 0.766327065326642, "creative_writing_quality": 0.9989788171330081, "primary_topic": 0.9201624382629197, "secondary_topic": 0.0016959612216428388}, "raw_probabilities": {"primary": {"Academic Writing": 3.459168175835429e-05, "Content Listing": 3.6015069665005433e-05, "Creative Writing": 1.7726257494647152e-05, "Customer Support Page": 9.872934395569541e-06, "Q&A Forum": 1.568226384722583e-05, "Discussion Thread": 8.704981210177702e-06, "Comment Section": 2.013519637867866e-05, "Missing Content": 4.923176370703079e-05, "Knowledge Article": 7.72561688666433e-05, "Legal Notices": 6.2856698231816e-05, "News Article": 5.7918342075041306e-06, "Organizational Announcement": 1.3057353750446073e-05, "Nonfiction Writing": 1.250545333859388e-05, "Organizational About Page": 1.5233714438118212e-05, "Personal About Page": 1.590443549628346e-05, "Personal Blog": 9.70039894995175e-06, "Product Page": 1.594129002152863e-05, "Spam / Ads": 1.3797924774341426e-05, "Structured Data": 0.0014241642449102458, "Technical Writing": 0.995341219218853, "Transcript / Interview": 1.6466413233443257e-05, "Tutorial / How-To Guide": 0.000595261721673273, "User Reviews": 7.399025506276395e-06, "Other / Unknown": 0.0021814839552920795}, "secondary": {"Academic Writing": 0.00019793357772865806, "Content Listing": 0.0004420816645237203, "Creative Writing": 0.00021796710721886045, "Customer Support Page": 0.00011572287889554745, "Q&A Forum": 8.18051218932058e-05, "Discussion Thread": 6.548336494298535e-05, "Comment Section": 0.0009853231657730077, "Missing Content": 0.0004809031610909795, "Knowledge Article": 0.00037363409667515087, "Legal Notices": 0.000561596127075887, "News Article": 9.56960784283154e-05, "Organizational Announcement": 0.00010180015789899486, "Nonfiction Writing": 8.402675363153863e-05, "Organizational About Page": 0.00011685997003172308, "Personal About Page": 0.0001009796261228584, "Personal Blog": 0.00010299646244019825, "Product Page": 0.00010880301243106641, "Spam / Ads": 0.00010832427034248587, "Structured Data": 0.020559276574223698, "Technical Writing": 0.015366456068072609, "Transcript / Interview": 0.00011808923554731493, "Tutorial / How-To Guide": 0.0013061885216170998, "User Reviews": 5.399066073302437e-05, "NONE": 0.9582540623426611}, "tertiary": {"Academic Writing": 0.00013308328796447303, "Content Listing": 0.0008814759984018888, "Creative Writing": 0.00019349760729497515, "Customer Support Page": 0.00011066892955186942, "Q&A Forum": 7.017964141944602e-05, "Discussion Thread": 5.166043943405512e-05, "Comment Section": 0.0014814761726890818, "Missing Content": 0.0007829332730359452, "Knowledge Article": 0.0006882621745234837, "Legal Notices": 0.0008951501837890857, "News Article": 8.45263144398288e-05, "Organizational Announcement": 8.03489419557446e-05, "Nonfiction Writing": 9.458660626667257e-05, "Organizational About Page": 0.0001218176436739263, "Personal About Page": 9.933486290785211e-05, "Personal Blog": 7.710949670807382e-05, "Product Page": 0.00012048595045079998, "Spam / Ads": 0.00010998408369832557, "Structured Data": 0.01829711330658441, "Technical Writing": 0.011351102415342798, "Transcript / Interview": 0.00010431829889413683, "Tutorial / How-To Guide": 0.0017709680279658902, "User Reviews": 4.7950870293805216e-05, "NONE": 0.9623519654727134}, "structured_records": {"false": 0.8336434479153892, "true": 0.16635655208461073}, "procedural_steps": {"false": 0.9960515188147612, "true": 0.003948481185238804}, "worked_solution": {"false": 0.9964623140273645, "true": 0.0035376859726354635}, "contextual_dialogue": {"false": 0.9999902673280726, "true": 9.73267192733175e-06}, "content_quality": {"-2": 0.017617663393031016, "-1": 0.15428609163774143, "0": 0.4005878396959605, "1": 0.35396013303507917, "2": 0.0735482722381879}, "noise_severity": {"0": 0.723734402216808, "1": 0.19506281018011196, "2": 0.07148358280916742, "3": 0.006620043171181762, "4": 0.0030991616227308673}, "missing_content_severity": {"0": 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\n
\n
`,\n\n mounted() {\n createP5({\n w: 400,\n h: 200,\n el: this.$refs.p5,\n draw: (p) => this.draw({p}),\n\n startDrag: (mouse) => {\n // record where we start dragging\n \tthis.dragPos = {\n \t\tstart:this.start,\n \t\thandle: this.xToT(mouse.x)\n \t}\n \tthis.$emit(\"focus\")\n },\n\n drag: (mouse) => {\n \t// The frame under the mouse should always stay the same\n \t// Calculate the new start so that the \n \t\n \tlet dFrame = mouse.dragOffset.x/this.frameWidth\n \tthis.start = this.dragPos.start - dFrame\n \tthis.constrainTimespan()\n \t\n\n \t// Clamp to window\n \tlet newActive = Math.round(this.start + 2)\n \tlet constrainedActive = constrain(newActive, 0, this.frames.length - 1)\n \t// console.log(\"Drag to new frame, mouseX\", mouse.dragOffset.x, newActive, constrainedActive)\n\n \tthis.$emit(\"setActiveFrame\", constrainedActive)\n \tthis.$emit(\"setTimespan\", {start:this.start,end:this.end})\n \t\n },\n\n stopDrag: () => {\n // End the dragging action\n this.dragPos = null;\n this.$emit(\"blur\")\n },\n });\n\n \n },\n\n watch: {\n \tactiveFrameIndex() {\n \t\t\n \t\tthis.constrainTimespan()\n\n \t}\n },\n computed: {\n \tframeWidth() {\n \t\treturn this.box.w/this.frameCount\n \t},\n end() {\n // The last visible frame\n return this.start + this.frameCount\n },\n },\n\n methods: {\n \tconstrainTimespan() {\n \t\n \t\tlet range = 2\n\n \t\t// Constrain it *if* we arent dragging\n \t\tif (this.keepActiveFrameInView && !this.dragPos) {\n \t\t\tlet constrainedStart = constrain(this.start, this.activeFrameIndex - (this.frameCount - 4), this.activeFrameIndex - 4)\n \t\t\tthis.start = constrainedStart\n \t\t\t// console.log(constrainedStart, this.start, this.activeFrameIndex)\n \t\t}\n\n \t\tlet startRange = this.frameCount - 3\n \tthis.start = constrain(this.start, - startRange, this.frames.length + startRange - this.frameCount)\n },\n\n \tdraw({p}) {\n\n \t\t// Clear and redraw the timeline\n p.background(50);\n p.fill(40);\n \n let border = 2\n\n for (let i = 0; i < this.frames.length; i++) {\n \n // Just draw the frames in the scene\n if (i >= this.start - border && i < this.end + border) {\n let x = this.tToX(i);\n let fr = this.frames[i]\n // console.log(i, x)\n\n // this.drawFrame({\n // p,\n // x,\n // frameIndex: fr.index,\n // data: fr.data,\n // isActive: this.activeFrameIndex === i,\n // });\n }\n }\n \t},\n drawFrame({ p, frameIndex, x, y = 40, isActive, data }) {\n // Draw individual frame on the timeline\n drawData({\n p,\n x,\n y,\n data,\n size: 10,\n horizontal: true,\n });\n\n if (isActive) {\n p.fill(150, 100, 50, 0.3);\n p.stroke(150, 100, 50, 0.3);\n p.rect(x, 0, 10, 120);\n }\n\n p.noStroke();\n p.fill(0);\n p.rect(x, 10, 1, 10);\n p.textSize(10)\n p.text(frameIndex, x, 30);\n },\n\n tToX(t) {\n // Convert frame index to X coordinate\n return remap(t, this.start, this.start + this.frameCount, \n \t\tthis.box.x, this.box.x1);\n },\n\n xToT(x) {\n // Convert X coordinate to frame index\n return remap(x, this.box.x, this.box.x1, \n \t\tthis.start, this.start + this.frameCount);\n },\n },\n\n data() {\n return {\n start: 0, // The starting frame, managed internally\n frameCount: 30, // Number of visible frames in the timeline\n dragStartPos: null, // Tracks the state of dragging\n };\n },\n\n props: {\n \tkeepActiveFrameInView: {\n \tdefault: true,\n \t},\n activeFrameIndex: {\n type: Number,\n required: true,\n },\n frames: {\n type: Array,\n required: true,\n },\n box: {\n type: Object,\n default() {\n return new Box({ x: 0, y: 0, w: 400, h: 150 });\n },\n },\n },\n});\n", "text_preview": "Vue.component(\"timeline\", { template: `
{{activeFrameIndex}}/{{frames.length}}
{{start}} - {{end}}
`, mounted() { createP5({ w: 400, h: 200, el: this.$refs", "input_sha256": "645498e073887c4326ac0729ec92d8ba8c64a6375ca5552c896ccb87da00fd40", "text_sha256": "09d231668473f2c5e71214f27199945d9a9647d967c0d1189d659833d1f6560d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8015, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6482595295766196, "noise_severity": 0.05348093660749388, "missing_content_severity": 0.1250905634688873, "factual_density": 3.3581534337934813, "pretraining_utility": 0.5194459569473335, "math_density": 1.5823795488688446, "code_density": 3.8492159036575693, "reasoning_density": 0.32252427837176406, "education_level": 2.2559067470089236, "creative_writing_quality": -0.2932204224604101}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.05221689499782811, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9940637842024165, "secondary": 0.9168031714374731, "tertiary": 0.9712903662178155, "structured_records": 0.027045063073107115, "procedural_steps": 0.0016696061563817068, "worked_solution": 0.0019001349748986925, "contextual_dialogue": 6.9541141289697e-06, "content_quality": 0.4445279763341323, "noise_severity": 0.9554325528270884, "missing_content_severity": 0.8957578637759273, "factual_density": 0.46512786149456786, "pretraining_utility": 0.4482847043200259, "math_density": 0.0825725372996785, "code_density": 0.8743465863813076, "reasoning_density": 0.73122976802353, "education_level": 0.6779729853819211, "creative_writing_quality": 0.9993828903252391, "primary_topic": 0.99082823813461, "secondary_topic": 0.8641996003291109}, "probability_margin": {"primary": 0.99057582939598, "secondary": 0.8743566020712413, "tertiary": 0.9564952929792712, "education_level": 0.5080935692786201, "creative_writing_quality": 0.9992104002491471, "primary_topic": 0.9882485207489725, "secondary_topic": 0.7853955838185365}, "raw_probabilities": {"primary": {"Academic Writing": 2.689818985480851e-05, "Content Listing": 1.7126202462554834e-05, "Creative Writing": 1.6119870602312046e-05, "Customer Support Page": 9.394619654440117e-06, "Q&A Forum": 9.933844862338607e-06, "Discussion Thread": 7.612175193647871e-06, "Comment Section": 1.3210592536974285e-05, "Missing Content": 3.135877784539132e-05, "Knowledge Article": 6.687929921269737e-05, "Legal Notices": 3.2250427345716524e-05, "News Article": 4.29708160816779e-06, "Organizational Announcement": 9.354232591881973e-06, "Nonfiction Writing": 1.0525971131227192e-05, "Organizational About Page": 1.1826831634751432e-05, "Personal About Page": 1.300504951047396e-05, "Personal Blog": 7.290330992339124e-06, "Product Page": 7.49975396079793e-06, "Spam / Ads": 8.624897858511085e-06, "Structured Data": 0.0037352971313357884, "Technical Writing": 0.9943111265273158, "Transcript / Interview": 1.3642972506600775e-05, "Tutorial / How-To Guide": 0.0005059134535545283, "User Reviews": 6.462082188647593e-06, "Other / Unknown": 0.0011243496842397885}, "secondary": {"Academic Writing": 0.000339523596539864, "Content Listing": 0.0002684152465029336, "Creative Writing": 0.000474176134744012, "Customer Support Page": 0.00019614180068492463, "Q&A Forum": 0.00015703334731657264, "Discussion Thread": 0.00013321268446896798, "Comment Section": 0.00028126899903033214, "Missing Content": 0.0011777743102408499, "Knowledge Article": 0.0007958305836901669, "Legal Notices": 0.0003191144607144946, "News Article": 0.00019476367090215594, "Organizational Announcement": 0.00018687204232997536, "Nonfiction Writing": 0.0002233152181364449, "Organizational About Page": 0.00021677784234024042, "Personal About Page": 0.00017883354187091905, "Personal Blog": 0.00020675146283491278, "Product Page": 0.00013657952194782628, "Spam / Ads": 0.00018192107511492303, "Structured Data": 0.045913103889670376, "Technical Writing": 0.023335405831340604, "Transcript / Interview": 0.00026580055082768897, "Tutorial / How-To Guide": 0.004458331653038588, "User Reviews": 8.934657480057334e-05, "NONE": 0.9202697059609117}, "tertiary": {"Academic Writing": 0.00010403221586531767, "Content Listing": 0.00010635615748979946, "Creative Writing": 0.00016332480504338545, "Customer Support Page": 6.255895824657488e-05, "Q&A Forum": 3.812065351011436e-05, "Discussion Thread": 4.130699093680871e-05, "Comment Section": 0.00014973497212547818, "Missing Content": 0.0006915868101720271, "Knowledge Article": 0.0005756140423086463, "Legal Notices": 0.00015071016269375663, "News Article": 5.7067612661901586e-05, "Organizational Announcement": 5.098145315643911e-05, "Nonfiction Writing": 8.286075568330519e-05, "Organizational About Page": 8.432164487481293e-05, "Personal About Page": 6.062658898765555e-05, "Personal Blog": 6.284490807252831e-05, "Product Page": 4.492940259595685e-05, "Spam / Ads": 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0.031098533454255377, "3": 0.006873697151730898, "4": 0.0011962991715321356}, "factual_density": {"0": 0.009933353248297446, "1": 0.02214099472348817, "2": 0.10120325348711215, "3": 0.33328366206864, "4": 0.5334387364724622}, "pretraining_utility": {"-2": 0.009802517837585517, "-1": 0.08440480834218113, "0": 0.37308902873719896, "1": 0.4419514892013832, "2": 0.09075215588165121}, "math_density": {"0": 0.2592667031857706, "1": 0.2779971663795825, "2": 0.2193416330351515, "3": 0.10787887317902282, "4": 0.13551562422047267}, "code_density": {"0": 0.00023473850271221836, "1": 0.0005119708996610264, "2": 0.01732976527922171, "3": 0.11364969907415554, "4": 0.8682738262442495}, "reasoning_density": {"0": 0.8379436625338338, "1": 0.05581468833967519, "2": 0.06452943315789694, "3": 0.02919814015808135, "4": 0.012514075810512731}, "education_level": {"none": 0.0012961826546278757, "0": 0.001569789219273323, "1": 0.0162381795050733, "2": 0.7316441544849343, "3": 0.22355058520631416, "4": 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0.0005741629679772122, "Programming & Software Engineering": 0.9911809982063561, "Sports & Fitness": 7.68720594724377e-05, "Vehicles & Transportation": 1.8869585463166254e-05, "Travel & Tourism": 3.862782977277709e-05, "Other / Uncovered Subject": 0.00031256885440810215, "Unknown / Insufficient Content": 0.00010896451609080455}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005870133658311393, "Art & Design": 0.08402710880560856, "Law & Crime": 0.00044133051617445694, "Education & Careers": 0.0005571419708044819, "Entertainment & Media": 0.0017719288389357794, "Fashion & Beauty": 0.00024112471150814, "Business, Economics & Finance": 0.0005028315787295773, "Food & Dining": 0.0001915734604155928, "Games & Gaming": 0.007638423285066956, "Electronics & Hardware": 0.00036486814377879786, "Health, Healthcare & Wellness": 0.0002723711665568586, "History & Geography": 0.000607277008432745, "Home, Pets & Hobbies": 0.00021119374948444327, "Industry & Production": 0.0001520912584587465, "Language, Literature & Philosophy": 0.00018584952567497884, "Politics & Government": 0.00021484919680163245, "Religion & Spirituality": 0.00025334308698175654, "Science, Mathematics & Engineering": 0.007679531054109736, "Relationships & Community": 0.00015321587265968453, "Software Usage & Applications": 0.0040229480588079855, "Programming & Software Engineering": 0.018765815274397878, "Sports & Fitness": 0.00025436484335762287, "Vehicles & Transportation": 0.00010658856251527869, "Travel & Tourism": 0.0003026741700000309, "Other / Uncovered Subject": 0.00107184987076205, "NONE": 0.8694226926241451}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9786, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9786, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:06.013288+00:00", "scoring_key_slot": "primary", "dataset_uid": "9723155c-daa5-4092-8098-514db9dc1541", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "1181b6a6b8295b2d0fafa2872f729b49ad313688971edaae2c7f6f7dbf32926e", "source_metadata": {"uuid": "9723155c-daa5-4092-8098-514db9dc1541", "repo_name": "galaxykate/kates-basics", "relative_path": "basic-timeseries/v-timeline.js", "category": "WEB", "algo_rel_score": 0.9176552891731262, "quality_score": 4.892580509185791, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2024-06-04T06:24:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9c2a18b095a0dcb75d12486a7fbe7cadd3d6f2a6", "size_bytes": 4202, "file_timestamp": 1740135646, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "45ae88f0eae8cba85f639a319a083da5a4198f62"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 21, "repo_created_at": "2024-06-04T06:24:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9c2a18b095a0dcb75d12486a7fbe7cadd3d6f2a6", "size_bytes": 4202, "file_timestamp": 1740135646, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "45ae88f0eae8cba85f639a319a083da5a4198f62", "url": "https://github.com/galaxykate/kates-basics/blob/45ae88f0eae8cba85f639a319a083da5a4198f62/basic-timeseries/v-timeline.js"}, "gpt4o_tokens": 1141, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6482595295766196, "noise_severity": 0.05348093660749388, "missing_content_severity": 0.1250905634688873, "factual_density": 3.3581534337934813, "pretraining_utility": 0.5194459569473335, "math_density": 1.5823795488688446, "code_density": 3.8492159036575693, "reasoning_density": 0.32252427837176406, "education_level": 2.2559067470089236, "creative_writing_quality": -0.2932204224604101}, "score_applicability": {"education_level": 0.9987038173453721, "creative_writing_quality": 0.0005142580623010053}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/saranshbalyan-1234/automation/blob/96c3ac2fb76a1a323a69450e68a14122130b2a19/FrontEnd/src/Components/TestCase/ExecutionHistory/TestStepTable.jsx", "id": "javascript-0-7754c0f54a22d20f94af34e41c2ddd69", "text": "import React, { useState } from 'react'\nimport { connect } from 'react-redux'\nimport { Table, Tag, Tooltip } from 'antd'\nimport { CameraFilled, EyeOutlined } from '@ant-design/icons'\nimport ViewObjectModal from '../../Common/TestStep/ViewObjectModal'\nimport ViewParameterModal from '../../Common/TestStep/ViewParameterModal'\nimport ViewCommentModal from '../../Common/TestStep/ViewCommentModal'\nimport ViewScreenShotModal from './ViewScreenShotModal'\nimport ViewFailedLogModal from './ViewFailedLogModal'\nconst TestStepTable = ({ testSteps, currentExecutionHistory, process }) => {\n const [viewParameterModal, setViewParameterModal] = useState(false)\n const [parameters, setParameters] = useState([])\n const [viewObjectModal, setViewObjectModal] = useState(false)\n const [object, setObject] = useState({})\n const [viewCommentModal, setViewCommentModal] = useState(false)\n const [viewFailedLogModal, setViewFailedLogModal] = useState('')\n const [comment, setComment] = useState('')\n const [screenShotKey, setScreenshotKey] = useState('')\n const columns = [\n {\n title:
Action Event
,\n dataIndex: 'actionEvent',\n },\n {\n title:
Test Object
,\n dataIndex: 'object',\n render: (text, record) =>\n text?.name ? (\n
\n {\n setObject(text)\n setViewObjectModal(true)\n }}\n >\n {text.name}\n \n
\n ) : (\n 'N/A'\n ),\n },\n {\n title:
Test Parameters
,\n dataIndex: 'testParameters',\n render: (text, record) =>\n text?.length ? (\n
\n {\n setParameters(text)\n setViewParameterModal(true)\n }}\n >\n View\n \n
\n ) : (\n 'N/A'\n ),\n },\n\n {\n title:
Comment
,\n dataIndex: 'comment',\n render: (text, record) =>\n text.length ? (\n
\n {\n setComment(text)\n setViewCommentModal(true)\n }}\n >\n View\n \n
\n ) : (\n 'N/A'\n ),\n },\n {\n title: ,\n width: 50,\n dataIndex: 'options',\n render: (text, record) => (\n <>\n {record.screenshot && (\n {\n setScreenshotKey(\n `${currentExecutionHistory.id}/${process.processId}_${record.testStepId}`\n )\n }}\n >\n \n \n )}\n \n ),\n },\n {\n title:
Result
,\n width: 100,\n dataIndex: 'result',\n render: (text, record) =>\n text === true ? (\n \n PASS\n \n ) : text === false ? (\n \n {\n setViewFailedLogModal(record.failedLog)\n }}\n >\n FAIL\n \n \n ) : (\n
N/A
\n ),\n },\n ]\n\n return (\n <>\n \n {viewObjectModal && (\n \n )}\n\n {viewParameterModal && (\n \n )}\n\n {viewCommentModal && (\n \n )}\n {screenShotKey && (\n \n )}\n {viewFailedLogModal && (\n \n )}\n \n )\n}\n\nconst mapStateToProps = (state) => ({\n currentExecutionHistory: state.executionHistory.currentExecutionHistory,\n})\n\nconst mapDispatchToProps = {}\n\nexport default connect(mapStateToProps, mapDispatchToProps)(TestStepTable)\n", "text_preview": "import React, { useState } from 'react' import { connect } from 'react-redux' import { Table, Tag, Tooltip } from 'antd' import { CameraFilled, EyeOutlined } from '@ant-design/icons' import ViewObjectModal from '../../Common/TestStep/ViewOb", "input_sha256": "fe8fcbc8326a39062befd1759b0ed197e58015453198890add126b42ae119c02", "text_sha256": "7754c0f54a22d20f94af34e41c2ddd696f49e915429949b10fb5ffe375183196", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8017, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.024110633703998, "noise_severity": 0.037847578314145446, "missing_content_severity": 0.025724809055282703, "factual_density": 2.9965619394699865, "pretraining_utility": 0.3549769013207846, "math_density": 0.011492306353388689, "code_density": 3.8747959582524216, "reasoning_density": 0.07629160037913285, "education_level": 2.2883290639228204, "creative_writing_quality": -0.2556989692124935}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008067501786449327, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9951043411469782, "secondary": 0.9627178311092277, "tertiary": 0.971136093842914, "structured_records": 0.1658233141354971, "procedural_steps": 0.0005152838568925018, "worked_solution": 0.0011765407489940812, "contextual_dialogue": 7.038556194620349e-06, "content_quality": 0.5556589512604606, "noise_severity": 0.9684603514048788, "missing_content_severity": 0.9785626591205977, "factual_density": 0.16380161622498857, "pretraining_utility": 0.48431852092581473, "math_density": 0.9904230780388428, "code_density": 0.8956632985436849, "reasoning_density": 0.9364236663507226, "education_level": 0.6429890876853046, "creative_writing_quality": 0.9994014026936329, "primary_topic": 0.9941394276783582, "secondary_topic": 0.8910687714703935}, "probability_margin": {"primary": 0.9930930382290993, "secondary": 0.9397299205782887, "tertiary": 0.9557077062548042, "education_level": 0.4323839285148986, "creative_writing_quality": 0.9992274766391303, "primary_topic": 0.9923095857476899, "secondary_topic": 0.8404408968664098}, "raw_probabilities": {"primary": {"Academic Writing": 3.198301098999546e-05, "Content Listing": 3.3897841235903654e-05, "Creative Writing": 1.7599793334876805e-05, "Customer Support Page": 8.74613305413803e-06, "Q&A Forum": 1.1927028319915873e-05, "Discussion Thread": 7.0516659871444845e-06, "Comment Section": 1.3656290455820117e-05, "Missing Content": 4.4452951005963195e-05, "Knowledge Article": 6.723018935117892e-05, "Legal Notices": 4.283891444162729e-05, "News Article": 4.77534608589944e-06, "Organizational Announcement": 9.997231674392624e-06, "Nonfiction Writing": 1.0376260927724026e-05, "Organizational About Page": 1.2685744033046214e-05, "Personal About Page": 1.1882733162837987e-05, "Personal Blog": 7.1248071223724854e-06, "Product Page": 7.36306168529792e-06, "Spam / Ads": 1.0974236513075447e-05, "Structured Data": 0.002215288703421417, "Technical Writing": 0.9953083269325208, "Transcript / Interview": 1.2189189543593907e-05, "Tutorial / How-To Guide": 0.00017562295431636235, "User Reviews": 6.882667117345483e-06, "Other / Unknown": 0.0019271263136991848}, "secondary": {"Academic Writing": 0.00017858320428788536, "Content Listing": 0.0003663864869956687, "Creative Writing": 0.00014558226832740302, "Customer Support Page": 8.125353019094276e-05, "Q&A Forum": 5.825526291012136e-05, "Discussion Thread": 4.695004768529844e-05, "Comment Section": 9.122398955483456e-05, "Missing Content": 0.00022862584177486145, "Knowledge Article": 0.00022783389770230302, "Legal Notices": 0.00014902186113233864, "News Article": 7.766971970286042e-05, "Organizational Announcement": 6.739489180077606e-05, "Nonfiction Writing": 6.562492075901106e-05, "Organizational About Page": 8.824581079544271e-05, "Personal About Page": 6.886499642784465e-05, "Personal Blog": 7.685801797506783e-05, "Product Page": 5.2213903632300486e-05, "Spam / Ads": 8.011550658239875e-05, "Structured Data": 0.024541334234721168, "Technical Writing": 0.008440389418576433, "Transcript / Interview": 9.152881663613817e-05, "Tutorial / How-To Guide": 0.0004688342644211377, "User Reviews": 3.595429439781242e-05, "NONE": 0.9642712548130099}, "tertiary": {"Academic Writing": 0.00010091970602427, "Content Listing": 0.00046027567863477994, "Creative Writing": 0.00010051480164756095, "Customer Support Page": 6.0150949942820245e-05, "Q&A Forum": 3.458522881133154e-05, "Discussion Thread": 3.316412842666185e-05, "Comment Section": 7.67682689224052e-05, "Missing Content": 0.0002732138792216552, "Knowledge Article": 0.000198586284891697, "Legal Notices": 0.00021093474832281671, "News Article": 5.132667777658925e-05, "Organizational Announcement": 4.795978862867824e-05, "Nonfiction Writing": 5.229789250566815e-05, "Organizational About Page": 8.606658187994157e-05, "Personal About Page": 5.022774131323337e-05, "Personal Blog": 4.7093696528313547e-05, "Product Page": 4.430949086013625e-05, "Spam / Ads": 6.91712163685925e-05, "Structured Data": 0.016631050344655084, "Technical Writing": 0.008527481942708122, "Transcript / Interview": 6.347198898762319e-05, "Tutorial / How-To Guide": 0.0004133970472918078, "User Reviews": 2.8275316191021194e-05, "NONE": 0.9723387565994592}, "structured_records": {"false": 0.8341766858645029, "true": 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"creative_writing_quality": 0.0004988310886390511}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Govindchaudhary/crud/blob/c8a1b0d7d3bed733733e6261fdad2ef91fc924d4/src/dragDemo.js", "id": "javascript-0-9023faf1742922f66c63b36a9003963e", "text": "import React, { Component } from 'react';\nimport {List,Form,Button,Modal,Input,Card,Grid,Header} from 'semantic-ui-react'\nimport './Demo.css';\n\nlet bays=['bay1','bay2']\nlet lines=['line1','line2','line3']\nexport default class AppDragDropDemo extends Component {\n \n state = {\n tasks: [\n {name:\" Angular\",category:\"wip\", icon: \"file alternate\"},\n {name:\"React\", category:\"wip\", icon:\"file alternate\"},\n {name:\"Java\", category:\"wip\", icon:\"file alternate\"},\n {name:\"Python\", category:\"wip\", icon:\"file alternate\"},\n {name:\"Lua\", category:\"wip\", icon:\"file alternate\"},\n {name:\"Nodejs\", category:\"wip\", icon:\"file alternate\"},\n {name:\"Front end\", category:\"complete\", icon:\"folder\",showDetail:false},\n {name:\"back end\", category:\"complete\", icon:\"folder\",showDetail:false}\n \n ],\n showForm:false,\n selectedTech:'',\n folder:'',\n sub:[],\n target:'',\n location:'',\n bay:'',\n line:''\n \n \n }\n\n \n\n handleClose = () =>{\n this.setState({showForm:false})\n }\n\n handleChange = (e, { name, value }) => this.setState({ [name]: value })\n formSubmit = () =>{\n let sub=this.state.sub\n sub.push({...this.state.selectedTech,id:this.state.target,bay:this.state.bay,line:this.state.line})\n console.log(sub)\n this.setState({showForm:false,sub:sub})\n \n \n }\n\n onDragStart = (ev, id) => {\n console.log('dragstart:',id);\n ev.dataTransfer.setData(\"id\", id);\n }\n\n onDragOver = (ev) => {\n ev.preventDefault();\n \n }\n showDetail =(t) =>{\n let arr=this.state.tasks\n for(let i=0;i{\n let arr=this.state.sub;\n for(let i=0;i{arr[i].name})\n }\n return ({line})\n \n }\n\n onDrop = (ev, cat) => {\n console.log('sghjkjh')\n let target=ev.target.innerText\n let id = ev.dataTransfer.getData(\"id\");\n \n let arr=this.state.tasks\n let task=''\n for(var i=0;i {\n if(t.category==='wip')\n {\n tasks[t.category].push(\n this.onDragStart(e, t.name)}\n draggable>\n \n {t.name}\n \n \n \n );\n }\n else{\n \n tasks[t.category].push(\n \n this.showDetail(t)}/>\n \n {t.name}\n \n {bays.map((bay) => {\n let display=(!t.showDetail)?\"none\":null\n return(\n \n \n \n \n \n {bay}\n \n {lines.map((line) => {\n return(\n \n \n \n \n {this.renderContent(t,bay,line)}\n \n \n )\n })}\n \n\n )\n })}\n \n \n );\n }\n \n });\n \n\n return (\n \n \n

Drag And Drop Demo

\n \n \n\n \n\n\n\n \n Technologies\n \n \n this.onDragOver(e)}\n onDrop={(e)=>{this.onDrop(e, \"wip\")}}>\n \n {tasks.wip}\n \n \n \n \n\n\n \n \n \n \n {\"Adding \"+ this.state.selectedTech.name +\" to \" + this.state.target} \n\n
this.formSubmit()} >\n\n \n \n\n\n \n \n\n\n\n\n\n
\n\n\n\n\n \n Classification \n \n\n \n this.onDragOver(e)}\n onDrop={(e)=>this.onDrop(e, \"complete\")}\n >\n \n \n {tasks.complete}\n \n \n \n\n \n \n\n\n\n\n \n \n \n \n \n );\n }\n}", "text_preview": "import React, { Component } from 'react'; import {List,Form,Button,Modal,Input,Card,Grid,Header} from 'semantic-ui-react' import './Demo.css'; let bays=['bay1','bay2'] let lines=['line1','line2','line3'] export default class AppDragDropDemo", "input_sha256": "90722fb7b2912823ce74bc959fa6f4faecd8a7287ea0b4ee82b5f56351ac5403", "text_sha256": "9023faf1742922f66c63b36a9003963e9680d80111bfd9a97b91c35c606881cd", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8016, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.23173049179921426, "noise_severity": 0.16778155804491673, "missing_content_severity": 0.03779806967643667, "factual_density": 2.6049648797550984, "pretraining_utility": 0.33289499599678424, "math_density": 0.023830921276321002, "code_density": 3.843770175839866, "reasoning_density": 0.24611830874018753, "education_level": 2.0778061053113723, "creative_writing_quality": -0.6093051190588928}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.04289381249821245, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9884660217519651, "secondary": 0.8793639452604474, "tertiary": 0.9241227061281472, "structured_records": 0.5530112327581538, "procedural_steps": 0.0011997729504025548, "worked_solution": 0.0038459804432886212, "contextual_dialogue": 9.591230865135232e-06, "content_quality": 0.3965166232285323, "noise_severity": 0.8601820349625694, "missing_content_severity": 0.9685016086029694, "factual_density": 0.21348969111165828, "pretraining_utility": 0.4865879920125542, "math_density": 0.9801408989363991, "code_density": 0.8698084798665549, "reasoning_density": 0.794901409383177, "education_level": 0.7947291348856445, "creative_writing_quality": 0.9985875743987989, "primary_topic": 0.9867794238434021, "secondary_topic": 0.9102845269876022}, "probability_margin": {"primary": 0.9845148105696342, "secondary": 0.8115952490478758, "tertiary": 0.8905521233030953, "education_level": 0.7145735542043536, "creative_writing_quality": 0.9983009989130415, "primary_topic": 0.9830056410739918, "secondary_topic": 0.8633470446691529}, "raw_probabilities": {"primary": {"Academic Writing": 5.4663323967478923e-05, "Content Listing": 0.00019290988977008734, "Creative Writing": 4.712879798575751e-05, "Customer Support Page": 1.9932327132145186e-05, "Q&A Forum": 2.7240558775016668e-05, "Discussion Thread": 1.9441075019425915e-05, "Comment Section": 3.559709873285547e-05, "Missing Content": 7.412325627829886e-05, "Knowledge Article": 0.00011140618760245325, "Legal Notices": 7.50153455869651e-05, "News Article": 1.3551939267493926e-05, "Organizational Announcement": 2.4411165903030308e-05, "Nonfiction Writing": 2.9670148249468778e-05, "Organizational About Page": 3.416747822227806e-05, "Personal About Page": 3.448633126511578e-05, "Personal Blog": 2.1102483534808126e-05, "Product Page": 2.020570876542205e-05, "Spam / Ads": 2.8402778902743444e-05, "Structured Data": 0.003894570554051199, "Technical Writing": 0.9889466041789665, "Transcript / Interview": 3.501957508297195e-05, "Tutorial / How-To Guide": 0.004431793609332365, "User Reviews": 1.8598906700763482e-05, "Other / Unknown": 0.0018099572809052934}, "secondary": {"Academic Writing": 0.0004782195637221895, "Content Listing": 0.008167161248068908, "Creative Writing": 0.000507845706121327, "Customer Support Page": 0.0002107380245477193, "Q&A Forum": 0.00018668324495408454, "Discussion Thread": 0.00016367574565543964, "Comment Section": 0.0003064222141868879, "Missing Content": 0.0006476261510306656, "Knowledge Article": 0.000593709134196327, "Legal Notices": 0.0003458637153681909, "News Article": 0.0002637328504703493, "Organizational Announcement": 0.00023308460622795197, "Nonfiction Writing": 0.00027015296267905275, "Organizational About Page": 0.0002907433152431692, "Personal About Page": 0.000256729014554867, "Personal Blog": 0.00024774416140190426, "Product Page": 0.0001837392181920708, "Spam / Ads": 0.0002887972905357646, "Structured Data": 0.07279519849338621, "Technical Writing": 0.019751294562304865, "Transcript / Interview": 0.0003325038324707417, "Tutorial / How-To Guide": 0.008948579741915732, "User Reviews": 0.00013930766150361556, "NONE": 0.8843904475412621}, "tertiary": {"Academic Writing": 0.0002307240952077628, "Content Listing": 0.010162181825585027, "Creative Writing": 0.00026228475482042144, "Customer Support Page": 0.0001285006036720394, "Q&A Forum": 8.451916184745626e-05, "Discussion Thread": 8.671657047428008e-05, "Comment Section": 0.00019216156066011614, "Missing Content": 0.000532704526996894, "Knowledge Article": 0.0005144202290154488, "Legal Notices": 0.00024151400067934739, "News Article": 0.00014137298004925666, "Organizational Announcement": 0.00011635818925862474, "Nonfiction Writing": 0.00016717471639024245, "Organizational About Page": 0.0001879435435759698, "Personal About Page": 0.00014843204609440677, "Personal Blog": 0.00011561338144127167, "Product Page": 0.00010834290458727734, "Spam / Ads": 0.00016364619186355162, "Structured Data": 0.03673213673637907, "Technical Writing": 0.013547114155352225, "Transcript / Interview": 0.0001563414535658904, "Tutorial / How-To Guide": 0.008621620542708975, "User Reviews": 7.391579029997353e-05, "NONE": 0.9272842600394744}, "structured_records": {"false": 0.4469887672418462, "true": 0.5530112327581538}, "procedural_steps": {"false": 0.9988002270495975, "true": 0.0011997729504025548}, "worked_solution": {"false": 0.9961540195567113, "true": 0.0038459804432886212}, "contextual_dialogue": {"false": 0.9999904087691349, "true": 9.591230865135232e-06}, "content_quality": {"-2": 0.03300443756717584, "-1": 0.1802159050289218, "0": 0.39356541563803826, "1": 0.3084732115692403, "2": 0.0847410301966237}, "noise_severity": {"0": 0.8686416999938578, "1": 0.102974618014659, "2": 0.02249302144600282, "3": 0.0037417450436695466, "4": 0.00214891550181086}, "missing_content_severity": {"0": 0.9804839855636508, "1": 0.005530275043842987, "2": 0.010259236827463018, "3": 0.0031566892825049334, "4": 0.000569813282538209}, "factual_density": {"0": 0.06666400592456236, "1": 0.13319279152262134, "2": 0.2030461446365262, "3": 0.32270843270573607, "4": 0.27438862521055407}, "pretraining_utility": {"-2": 0.012077342682696333, "-1": 0.11744502142868259, "0": 0.44247098553339415, "1": 0.3815185979195947, "2": 0.046488052435632456}, "math_density": {"0": 0.9816113242065995, "1": 0.015534819773617268, "2": 0.001252233011354777, "3": 0.0006148565537206263, "4": 0.000986766454708077}, "code_density": {"0": 0.0002995773907757238, "1": 0.0005878519264741123, "2": 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"url": "https://github.com/Govindchaudhary/crud/blob/c8a1b0d7d3bed733733e6261fdad2ef91fc924d4/src/dragDemo.js"}, "gpt4o_tokens": 1481, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.23173049179921426, "noise_severity": 0.16778155804491673, "missing_content_severity": 0.03779806967643667, "factual_density": 2.6049648797550984, "pretraining_utility": 0.33289499599678424, "math_density": 0.023830921276321002, "code_density": 3.843770175839866, "reasoning_density": 0.24611830874018753, "education_level": 2.0778061053113723, "creative_writing_quality": -0.6093051190588928}, "score_applicability": {"education_level": 0.9981380588394486, "creative_writing_quality": 0.0011770213343341581}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/vaibhavj-11/enotebook-mern/blob/dbb6d35fbae3aba51d75cbc1a955528d2a12bd06/src/components/Notes.js", "id": "javascript-0-6fab79ab241464200b89f4dc3a565909", "text": "import React, { useContext, useEffect, useRef, useState } from 'react'\nimport noteContext from \"../context/notes/NoteContext\"\nimport Noteitem from './Noteitem';\nimport AddNote from './AddNote';\nimport { useNavigate } from 'react-router-dom'\n\nconst Notes = (props) => {\n const context = useContext(noteContext);\n let history = useNavigate();\n const { notes, getNotes, editNote } = context;\n useEffect(() => {\n if (localStorage.getItem('token')) {\n getNotes()\n }\n else{\n history(\"/login\")\n }\n // eslint-disable-next-line\n }, [])\n const ref = useRef(null)\n const refClose = useRef(null)\n const [note, setNote] = useState({id: \"\", etitle: \"\", edescription: \"\", etag: \"\"})\n\n const updateNote = (currentNote) => {\n ref.current.click();\n setNote({id: currentNote._id, etitle: currentNote.title, edescription: currentNote.description, etag:currentNote.tag})\n }\n\n const handleClick = (e)=>{\n console.log(\"Updating the note...\", note)\n editNote(note.id, note.etitle, note.edescription, note.etag)\n refClose.current.click();\n props.showAlert(\"Updated Successfully\", \"success\")\n }\n\n const onChange = (e)=>{\n setNote({...note, [e.target.name]: e.target.value})\n }\n\n return (\n <>\n \n \n
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Edit Note
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Your Notes

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\n {notes.length === 0 && 'No notes to display'}\n
\n {notes.map((note) => {\n return \n })}\n
\n \n )\n}\n\nexport default Notes", "text_preview": "import React, { useContext, useEffect, useRef, useState } from 'react' import noteContext from \"../context/notes/NoteContext\" import Noteitem from './Noteitem'; import AddNote from './AddNote'; import { useNavigate } from 'react-router-dom'", "input_sha256": "0799519321c433a891ea3eea00e26db014851ffadc844dbcce1520a5c9c342d1", "text_sha256": "6fab79ab241464200b89f4dc3a565909623c941c621d69bd224a28585e8e1cbc", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8018, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8471732735756137, "noise_severity": 0.05864226882569974, "missing_content_severity": 0.028465674715258828, "factual_density": 3.092062216587157, "pretraining_utility": 0.41385377182556105, "math_density": 0.012840611070339751, "code_density": 3.8543496775639894, "reasoning_density": 0.17237325898809505, "education_level": 2.0775101907840092, "creative_writing_quality": -0.3181060647892512}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004004479548089801, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9952311122776711, "secondary": 0.9845902944142332, "tertiary": 0.9887688817687617, "structured_records": 0.015552829699776435, "procedural_steps": 0.0015070502984278786, "worked_solution": 0.0027530680059749124, "contextual_dialogue": 1.0141242179556082e-05, "content_quality": 0.5354016624404545, "noise_severity": 0.9511314426452502, "missing_content_severity": 0.976278604403951, "factual_density": 0.24338518048929725, "pretraining_utility": 0.41171182868548784, "math_density": 0.9892994907747169, "code_density": 0.8786247313033244, "reasoning_density": 0.8563556175099207, "education_level": 0.8061912949179186, "creative_writing_quality": 0.9994959292449568, "primary_topic": 0.9927887677056079, "secondary_topic": 0.9575048757325783}, "probability_margin": {"primary": 0.9925905976089673, "secondary": 0.9776494632144952, "tertiary": 0.9850849290638333, "education_level": 0.7265534252942942, "creative_writing_quality": 0.9993554994557912, "primary_topic": 0.9899974042874876, "secondary_topic": 0.9415150171428894}, "raw_probabilities": {"primary": {"Academic Writing": 4.17642623256265e-05, "Content Listing": 6.857109324118443e-05, "Creative Writing": 2.88697967425757e-05, "Customer Support Page": 8.686528073870701e-06, "Q&A Forum": 1.5177628435374113e-05, "Discussion Thread": 9.42682627641373e-06, "Comment Section": 1.9166797455475734e-05, "Missing Content": 5.304489342495931e-05, "Knowledge Article": 9.473993993038437e-05, "Legal Notices": 4.872572999247723e-05, "News Article": 6.007235034704508e-06, "Organizational Announcement": 1.3199549177342694e-05, "Nonfiction Writing": 1.5820550305401072e-05, "Organizational About Page": 1.750413073074148e-05, "Personal About Page": 2.4655748069939192e-05, "Personal Blog": 1.1079379601415917e-05, "Product Page": 1.0424569257487496e-05, "Spam / Ads": 1.602957804771106e-05, "Structured Data": 0.0005921099760566047, "Technical Writing": 0.9954298159327681, "Transcript / Interview": 1.6716987983919405e-05, "Tutorial / How-To Guide": 0.0006107622603045264, "User Reviews": 8.48228296282938e-06, "Other / Unknown": 0.002839218323800864}, "secondary": {"Academic Writing": 0.00010163260878274837, "Content Listing": 0.0018093194128738078, "Creative Writing": 0.00012920668371900075, "Customer Support Page": 5.1357630803440234e-05, "Q&A Forum": 3.685801689415835e-05, "Discussion Thread": 2.6910987781367553e-05, "Comment Section": 0.00015343878198077334, "Missing Content": 0.0003208009820508063, "Knowledge Article": 0.0002628023930318393, "Legal Notices": 0.00019645099158648038, "News Article": 5.279747716246682e-05, "Organizational Announcement": 4.9569510902430014e-05, "Nonfiction Writing": 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3.9057871795123446e-05, "Nonfiction Writing": 4.9020227822007985e-05, "Organizational About Page": 6.833513227291998e-05, "Personal About Page": 5.859024501707174e-05, "Personal Blog": 4.072573475468078e-05, "Product Page": 5.071984964384322e-05, "Spam / Ads": 6.312768209085069e-05, "Structured Data": 0.0014926512929500516, "Technical Writing": 0.004151915964563362, "Transcript / Interview": 4.531552935503018e-05, "Tutorial / How-To Guide": 0.0015909280869480253, "User Reviews": 2.7503179646759797e-05, "NONE": 0.9892368450283966}, "structured_records": {"false": 0.9844471703002236, "true": 0.015552829699776435}, "procedural_steps": {"false": 0.9984929497015721, "true": 0.0015070502984278786}, "worked_solution": {"false": 0.9972469319940251, "true": 0.0027530680059749124}, "contextual_dialogue": {"false": 0.9999898587578204, "true": 1.0141242179556082e-05}, "content_quality": {"-2": 0.01069145299329938, "-1": 0.02965239346040304, "0": 0.2637932198472162, "1": 0.49351729437554726, "2": 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"missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9633, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9633, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:06.507285+00:00", "scoring_key_slot": "primary", "dataset_uid": "46b5b84f-0d0c-4f79-b400-0ce809567e45", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "4977420580e228d449fe2a3c3d585eb954f50066ec9229c93df3e2973a70cdb2", "source_metadata": {"uuid": "46b5b84f-0d0c-4f79-b400-0ce809567e45", "repo_name": "vaibhavj-11/enotebook-mern", "relative_path": "src/components/Notes.js", "category": "WEB", "algo_rel_score": 0.9274868965148926, "quality_score": 5.32893705368042, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 34, "repo_created_at": "2023-08-19T06:31:03.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cbaa4474017bde8d46929e904d3e7df42634e84a", "size_bytes": 4890, "file_timestamp": 1693123337, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dbb6d35fbae3aba51d75cbc1a955528d2a12bd06"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 34, "repo_created_at": "2023-08-19T06:31:03.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "cbaa4474017bde8d46929e904d3e7df42634e84a", "size_bytes": 4890, "file_timestamp": 1693123337, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dbb6d35fbae3aba51d75cbc1a955528d2a12bd06", "url": "https://github.com/vaibhavj-11/enotebook-mern/blob/dbb6d35fbae3aba51d75cbc1a955528d2a12bd06/src/components/Notes.js"}, "gpt4o_tokens": 1103, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8471732735756137, "noise_severity": 0.05864226882569974, "missing_content_severity": 0.028465674715258828, "factual_density": 3.092062216587157, "pretraining_utility": 0.41385377182556105, "math_density": 0.012840611070339751, "code_density": 3.8543496775639894, "reasoning_density": 0.17237325898809505, "education_level": 2.0775101907840092, "creative_writing_quality": -0.3181060647892512}, "score_applicability": {"education_level": 0.9983319412935558, "creative_writing_quality": 0.000420058962535889}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Markel-Bradford/BudgetApp/blob/212b7386ea7263d98ef0dc5f8e5a968ee854c097/src/Components/Signin.jsx", "id": "javascript-0-d6a1f69cc4fa5cba910b45563889331e", "text": "import React, { useEffect, useRef, useState } from \"react\";\nimport { Form, useFetcher, useNavigate } from \"react-router-dom\";\nimport { ArrowRightEndOnRectangleIcon, UserPlusIcon } from \"@heroicons/react/24/solid\";\nimport { loginUser, newUser } from \"../helpers\";\nimport axios from \"axios\";\nimport { toast } from \"react-toastify\";\n\nconst Signin = () => {\n const [usernameInput, setUsernameInput] = useState(\"Guest\");\n const [emailInput, setEmailInput] = useState(\"\");\n const navigate = useNavigate();\n\n // Auto-populate and clear the input when clicked\n const handleFocus = (e) => {\n if (e.target.value === e.target.defaultValue) {\n e.target.value = \"\";\n }\n };\n \n useEffect(() => {\n const handleLoad = () => {\n toast.info(\n \"For guests, please press sign in to use the current credentials. Otherwise, input your name and email to create a new account or sign in if returning user.\",\n { autoClose: 12000 } // Optional: Prevent toast from auto-closing\n );\n };\n\n const timeout = setTimeout(handleLoad, 1500);\n \n\n return () => {\n clearTimeout(timeout); // Cleanup timeout on unmount\n }\n }, [])\n\n const handleFormSubmit = async (e) => {\n const action = e.nativeEvent.submitter.getAttribute(\"data-action\");\n\n if (!usernameInput.trim() || !emailInput.trim()) {\n toast.error(\"Name and email are required.\");\n return;\n }\n\n if (action === \"create\") {\n // Handle create account\n try {\n if (usernameInput.trim(), emailInput.trim()) {\n newUser({name: usernameInput.trim(), email: emailInput.trim()});\n \n navigate(\"/\"); // Redirect to baseroute\n\n \n\n console.log(\"User created:\", usernameInput, emailInput);\n } else {\n console.error(\"Error signing in:\", error);\n toast.error(\"Sign-in failed. Please try again.\");\n }\n } catch (error) {\n console.error(\"Error creating account:\", error);\n toast.error(\"Failed to create account. Please try again.\");\n }\n } else if (action === \"signin\") {\n if (!usernameInput.trim() || !emailInput.trim()) {\n toast.error(\"Name and email required.\")\n console.log(\"Please enter a name and email.\")\n return\n }\n \n // Handle sign in\n try {\n // Check if user exists\n if (usernameInput.trim(), emailInput.trim()) {\n await loginUser({name: usernameInput.trim(), email: emailInput.trim()});\n \n navigate(\"/\"); // Redirect to baseroute\n }\n } catch (error) {\n if (error.response && error.response.status === 404) {\n toast.error(\"User not found. Please create an account.\");\n } else {\n console.error(\"Error signing in:\", error);\n toast.error(\"Sign-in failed. Please try again.\");\n }\n }\n }\n };\n\n return (\n
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Sign in and let's get started!

\n setUsernameInput(e.target.value)} // Update state dunamically\n />\n \"\n autoComplete=\"email\"\n value={ emailInput }\n onFocus={handleFocus} // Clear the input on focus if it's still the default value\n onChange={(e) => setEmailInput(e.target.value)} />\n \n
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\n );\n};\n\nexport default Signin;\n", "text_preview": "import React, { useEffect, useRef, useState } from \"react\"; import { Form, useFetcher, useNavigate } from \"react-router-dom\"; import { ArrowRightEndOnRectangleIcon, UserPlusIcon } from \"@heroicons/react/24/solid\"; import { loginUser, newUse", "input_sha256": "b5fdfd4d53e6c6f4e239d4c14923d2632d956ae4111539698d2554e80b87e2bf", "text_sha256": "d6a1f69cc4fa5cba910b45563889331e7693130e9962aaa1098261fac92886c0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8019, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.030115448966307995, "noise_severity": 0.19293282700333633, "missing_content_severity": 0.11430473339451476, "factual_density": 2.983393998550521, "pretraining_utility": 0.36917175592362295, "math_density": 0.007116370838188553, "code_density": 3.8095020962059047, "reasoning_density": 0.41227223918026307, "education_level": 2.0234294226932055, "creative_writing_quality": -0.42129582732137644}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007505756621849269, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9933290982079274, "secondary": 0.9769876977775238, "tertiary": 0.9691556039112463, "structured_records": 0.010947499288353573, "procedural_steps": 0.020193294128397446, "worked_solution": 0.003185530170504141, "contextual_dialogue": 7.207572565030886e-06, "content_quality": 0.4667589327732167, "noise_severity": 0.8392226441638864, "missing_content_severity": 0.904746055504571, "factual_density": 0.15282833212543367, "pretraining_utility": 0.45975906191573157, "math_density": 0.9940696909681762, "code_density": 0.8412517468382538, "reasoning_density": 0.656439800683114, "education_level": 0.8302377443694787, "creative_writing_quality": 0.9966804467108107, "primary_topic": 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{\n\t\treturn function() {\n\t\t\treturn func.apply(scope, arguments);\n\t\t};\n\t};\n\n\tlet merge = function () {\n\t\tlet args = arguments, target = args[0], key, i;\n\t\tfor (i = 1; i < args.length; i++) {\n\t\t\tfor (key in args[i]) {\n\t\t\t\tif (!(key in target) && args[i].hasOwnProperty(key)) {\n\t\t\t\t\ttarget[key] = args[i][key];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\treturn target;\n\t};\n\n\tlet defineLogLevel = function(value, name) {\n\t\treturn { value: value, name: name };\n\t};\n\n\tLogger.TRACE = defineLogLevel(1, 'TRACE');\n\tLogger.DEBUG = defineLogLevel(2, 'DEBUG');\n\tLogger.INFO = defineLogLevel(3, 'INFO');\n\tLogger.TIME = defineLogLevel(4, 'TIME');\n\tLogger.WARN = defineLogLevel(5, 'WARN');\n\tLogger.ERROR = defineLogLevel(8, 'ERROR');\n\tLogger.OFF = defineLogLevel(99, 'OFF');\n\n\tlet ContextualLogger = function(defaultContext) {\n\t\tthis.context = defaultContext;\n\t\tthis.setLevel(defaultContext.filterLevel);\n\t\tthis.log = this.info; 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console, messages);\n\t\t};\n\n\t\tif (typeof console === \"undefined\") {\n\t\t\treturn function () { };\n\t\t}\n\n\t\treturn function(messages, context) {\n\t\t\tmessages = Array.prototype.slice.call(messages);\n\n\t\t\tlet hdlr = console.log;\n\t\t\tlet timerLabel;\n\n\t\t\tif (context.level === Logger.TIME) {\n\t\t\t\ttimerLabel = (context.name ? '[' + context.name + '] ' : '') + messages[0];\n\n\t\t\t\tif (messages[1] === 'start') {\n\t\t\t\t\tif (console.time) {\n\t\t\t\t\t\tconsole.time(timerLabel);\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\ttimerStartTimeByLabelMap[timerLabel] = new Date().getTime();\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tif (console.timeEnd) {\n\t\t\t\t\t\tconsole.timeEnd(timerLabel);\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tinvokeConsoleMethod(hdlr, [ timerLabel + ': ' +\n\t\t\t\t\t\t\t(new Date().getTime() - timerStartTimeByLabelMap[timerLabel]) + 'ms' ]);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\telse {\n\t\t\t\tif (context.level === Logger.WARN && console.warn) {\n\t\t\t\t\thdlr = console.warn;\n\t\t\t\t} else if (context.level === Logger.ERROR && console.error) {\n\t\t\t\t\thdlr = console.error;\n\t\t\t\t} else if (context.level === Logger.INFO && console.info) {\n\t\t\t\t\thdlr = console.info;\n\t\t\t\t} else if (context.level === Logger.DEBUG && console.debug) {\n\t\t\t\t\thdlr = console.debug;\n\t\t\t\t} else if (context.level === Logger.TRACE && console.trace) {\n\t\t\t\t\thdlr = console.trace;\n\t\t\t\t}\n\n\t\t\t\toptions.formatter(messages, context);\n\t\t\t\tinvokeConsoleMethod(hdlr, messages);\n\t\t\t}\n\t\t};\n\t};\n\n\tLogger.useDefaults = function(options) {\n\t\tLogger.setLevel(options && options.defaultLevel || Logger.DEBUG);\n\t\tLogger.setHandler(Logger.createDefaultHandler(options));\n\t};\n\n\tif (typeof define === 'function' && define.amd) {\n\t\tdefine(Logger);\n\t}\n\telse if (typeof module !== 'undefined' && module.exports) {\n\t\tmodule.exports = Logger;\n\t}\n\telse {\n\t\tLogger._prevLogger = global.Logger;\n\n\t\tLogger.noConflict = function () {\n\t\t\tglobal.Logger = Logger._prevLogger;\n\t\t\treturn Logger;\n\t\t};\n\n\t\tglobal.Logger = Logger;\n\t}\n}(this));\n", "text_preview": "(function (global) { \"use strict\"; let Logger = { }; Logger.VERSION = \"1.7.0-next\"; let logHandler; let contextualLoggersByNameMap = {}; let bind = 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"math_density": 0.16414457251122533, "code_density": 3.913942326514876, "reasoning_density": 0.19424719627981504, "education_level": 2.103297335802805, "creative_writing_quality": -0.2532156022972157}, "score_applicability": {"education_level": 0.9983906650382747, "creative_writing_quality": 0.00039542080143656696}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/sameem786-blip/LinkedIn-Clone-API/blob/452d21d70b2e49961df42f42df66026666ed1c35/routes/api/profile.js", "id": "javascript-0-3c085f8943c4c7983a64bab77c4d61cc", "text": "const express = require(\"express\");\nconst request = require('request');\nconst router = express.Router();\nconst { check, validationResult } = require(\"express-validator\");\n\nconst auth = require('../middleware/auth')\nconst Profile = require('../models/Profile');\nconst User = require('../models/User');\n\nconst config = require('config');\n\n/*\n@route GET api/Profile/Me\n@desc Get current users profile\n@access Private\n*/\nrouter.get(\"/me\", auth, async(req, res) => {\n try {\n const profile = await Profile.findOne({ user: req.user.id }).populate('user', ['name', 'avatar']);\n\n if (!profile) {\n return res.status(400).json({ msg: 'No profile for this user' });\n } else {\n res.json(profile);\n }\n } catch (err) {\n console.error(err.message);\n res.status(500).send('Server Error');\n }\n});\n\n/*\n@route GET api/Profile\n@desc Create or Update user profile\n@access Private\n*/\nrouter.post('/', [auth, [\n check('status', 'Status is required').not().isEmpty(),\n check('skills', 'skills is required').not().isEmpty()\n ]],\n async(req, res) => {\n const errors = validationResult(req);\n if (!errors.isEmpty()) {\n return res.status(400).json({ errors: errors.array() })\n }\n const {\n company,\n website,\n location,\n bio,\n status,\n githubusername,\n skills,\n youtube,\n facebook,\n twitter,\n instagram,\n linkedin\n } = req.body;\n\n //build profile object\n const profileFields = {};\n\n profileFields.user = req.user.id;\n company ? profileFields.company = company : profileFields.company = null;\n website ? profileFields.website = website : profileFields.website = null;\n location ? profileFields.location = location : profileFields.location = null;\n bio ? profileFields.bio = bio : profileFields.bio = null;\n status ? profileFields.status = status : profileFields.status = null;\n githubusername ? profileFields.githubusername = githubusername : profileFields.githubusername = null;\n skills ? profileFields.skills = skills.split(',').map(skill => skill.trim()) : profileFields.skills = null;\n\n //build social object\n profileFields.social = {};\n youtube ? profileFields.social.youtube = youtube : profileFields.social.youtube = null;\n twitter ? profileFields.social.twitter = twitter : profileFields.social.twitter = null;\n facebook ? profileFields.social.facebook = facebook : profileFields.social.facebook = null;\n linkedin ? profileFields.social.linkedin = linkedin : profileFields.social.linkedin = null;\n instagram ? profileFields.social.instagram = instagram : profileFields.social.instagram = null;\n\n\n try {\n let profile = await Profile.findOne({ user: req.user.id });\n if (profile) {\n //update\n profile = await Profile.findOneAndUpdate({ user: req.user.id }, { $set: profileFields }, { new: true });\n return res.json(profile);\n } else {\n //create\n profile = new Profile(profileFields);\n await profile.save();\n res.json(profile);\n }\n } catch (err) {\n console.error(err.message);\n res.status(500).send('Server Error')\n }\n });\n\n/*\n @route GET api/Profile/\n @desc Get all profiles\n @access Private\n*/\n\nrouter.get('/', async(req, res) => {\n try {\n const profiles = await Profile.find().populate('user', ['name', 'avatar']);\n res.json(profiles);\n } catch (err) {\n console.error(err.message);\n res.status(500).send('Server Error');\n }\n});\n\n\n/*\n @route GET api/Profile/user/:user_id\n @desc Get profile by user ID\n @access Private\n*/\n\nrouter.get('/user/:user_id', async(req, res) => {\n try {\n const profile = await Profile.findOne({ user: req.params.user_id }).populate('user', ['name', 'avatar']);\n if (!profile) {\n return res.status(400).json({ msg: \"No Profile found\" })\n } else {\n res.json(profile);\n }\n\n } catch (err) {\n console.error(err.message);\n if (err.kind === 'ObjectId') {\n return res.status(400).json({ msg: 'Profile Not Found' });\n }\n res.status(500).send('Server Error');\n }\n})\n\n/*\n @route DELETE api/Profile\n @desc delete profile, user & posts\n @access Private\n*/\n\nrouter.delete('/', auth, async(req, res) => {\n try {\n //Remove user posts\n\n //Remove Profile\n await Profile.findOneAndRemove({ user: req.user.id });\n\n //Remove user\n await User.findOneAndRemove({ _id: req.user.id });\n\n res.json({ msg: 'User Deleted' });\n } catch (err) {\n console.error(err.message);\n res.status(500).send('Server Error');\n }\n});\n\n/*\n @route PUT api/Profile\n @desc add profile experience\n @access Private\n*/\n\nrouter.put('/experience', [auth, [\n check('title', 'Title is required').not().isEmpty(),\n check('company', 'Company is required').not().isEmpty(),\n check('from', 'From date is required').not().isEmpty()\n ]],\n async(req, res) => {\n const errors = validationResult(req);\n if (!errors.isEmpty()) {\n return res.status(400).json({ errors: errors.array() });\n }\n\n const {\n title,\n company,\n location,\n from,\n to,\n current,\n description\n } = req.body;\n\n const newExp = {\n title,\n company,\n location,\n from,\n to,\n current,\n description\n }\n\n try {\n const profile = await Profile.findOne({ user: req.user.id });\n\n profile.experience.unshift(newExp);\n\n await profile.save();\n\n res.json(profile);\n } catch (err) {\n console.log('123');\n console.error(err.message);\n res.status(500).send('Server Error')\n }\n });\n\n/*\n @route DELETE api/Profile/experience/:exp_id\n @desc delete experience from profile\n @access Private\n*/\n\nrouter.delete('/experience/:exp_id', auth, async(req, res) => {\n try {\n const profile = await Profile.findOne({ user: req.user.id });\n\n //get remove index\n const removeIndex = profile.experience.map(item => item.id).indexOf(req.params.exp_id);\n\n profile.experience.splice(removeIndex, 1);\n\n await profile.save();\n\n res.json(profile);\n } catch (err) {\n\n }\n})\n\n/*\n @route PUT api/Profile\n @desc add profile experience\n @access Private\n*/\n\nrouter.put('/education', [auth, [\n check('school', 'School is required').not().isEmpty(),\n check('degree', 'Degree is required').not().isEmpty(),\n check('fieldofstudy', 'Field of Study is required').not().isEmpty(),\n check('from', 'from date is required').not().isEmpty()\n ]],\n async(req, res) => {\n const errors = validationResult(req);\n if (!errors.isEmpty()) {\n return res.status(400).json({ errors: errors.array() });\n }\n\n const {\n school,\n degree,\n fieldofstudy,\n from,\n to,\n current,\n decription\n } = req.body;\n\n const newEdu = {\n school,\n degree,\n fieldofstudy,\n from,\n to,\n current,\n decription\n }\n\n try {\n const profile = await Profile.findOne({ user: req.user.id });\n\n profile.education.unshift(newEdu);\n\n await profile.save();\n\n res.json(profile);\n } catch (err) {\n console.log('123');\n console.error(err.message);\n res.status(500).send('Server Error')\n }\n });\n\n/*\n@route DELETE api/Profile/education/:edu_id\n@desc delete education from profile\n@access Private\n*/\n\nrouter.delete('/education/:edu_id', auth, async(req, res) => {\n try {\n const profile = await Profile.findOne({ user: req.user.id });\n\n //get remove index\n const removeIndex = profile.education.map(item => item.id).indexOf(req.params.edu_id);\n\n profile.education.splice(removeIndex, 1);\n\n await profile.save();\n\n res.json(profile);\n } catch (err) {\n console.log('123');\n console.error(err.message);\n res.status(500).send('Server Error')\n }\n});\n\n/*\n@route GET api/Profile/github/:username\n@desc Get user repos from Github\n@access Private\n*/\n\nrouter.get('/github/:username', (req, res) => {\n try {\n const options = {\n uri: `https://api.github.com/users/${req.params.username}/repos?per_page=5&sort=created:asc&client_id=${config.get('githubClientId')}&client_secret=${config.get('githubSecret')}`,\n method: 'GET',\n headers: { 'user-agent': 'node.js' }\n };\n\n request(options, (error, response, body) => {\n if (error) {\n console.error(error);\n }\n if (response.statusCode != 200) {\n return (res.status(404).json({ msg: 'No Github profile found' }));\n }\n\n res.json(JSON.parse(body));\n\n })\n } catch (err) {\n console.error(err.message);\n res.status(500).send('Server Error');\n }\n})\n\nmodule.exports = router;", "text_preview": "const express = require(\"express\"); const request = require('request'); const router = express.Router(); const { check, validationResult } = require(\"express-validator\"); const auth = require('../middleware/auth') const Profile = require('.", "input_sha256": "eb72c7eaa78d515d0b98de7941c687a22af930bc51e13bae93c845f3e0d4f849", "text_sha256": "3c085f8943c4c7983a64bab77c4d61cc8401747279aa2b6e82434fda146e8b6d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8021, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.26446035098682497, "noise_severity": 0.0778530059521494, "missing_content_severity": 0.05402109971762075, "factual_density": 3.2899377658030162, "pretraining_utility": 0.6388985846717804, "math_density": 0.029887502892043387, "code_density": 3.8438570273685753, "reasoning_density": 0.49244387322416966, "education_level": 2.223134085629373, "creative_writing_quality": -0.38617435334346206}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.02931808206353892, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9977501389180679, "secondary": 0.9493941326658293, "tertiary": 0.9799869139495221, "structured_records": 0.05613459471769131, "procedural_steps": 0.016528502370066776, "worked_solution": 0.01091847987835126, "contextual_dialogue": 1.0157541807789844e-05, "content_quality": 0.4505195583264613, "noise_severity": 0.9351224950398755, "missing_content_severity": 0.9549824169019827, "factual_density": 0.40828147150251326, "pretraining_utility": 0.5318535198738557, "math_density": 0.9750937475899638, "code_density": 0.869880856140479, "reasoning_density": 0.5896301056465254, "education_level": 0.7144728920735405, "creative_writing_quality": 0.99900549975494, "primary_topic": 0.9943787711659342, "secondary_topic": 0.9542020329648452}, "probability_margin": {"primary": 0.9969152665836319, "secondary": 0.920021550332223, "tertiary": 0.9724535769673475, "education_level": 0.5461528135087589, 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Writing": 0.997843883129815, "Transcript / Interview": 8.911883086225767e-06, "Tutorial / How-To Guide": 0.0004187810693049751, "User Reviews": 4.184524811438392e-06, "Other / Unknown": 0.0009286165461830044}, "secondary": {"Academic Writing": 0.00025900596944386734, "Content Listing": 0.00036888906329049026, "Creative Writing": 0.0002096615302286489, "Customer Support Page": 0.000154990122064783, "Q&A Forum": 9.67236217848688e-05, "Discussion Thread": 8.667851860514148e-05, "Comment Section": 0.001541190264704516, "Missing Content": 0.0005297566470603229, "Knowledge Article": 0.0010357428254969005, "Legal Notices": 0.00032742405996589525, "News Article": 0.00011990319541973143, "Organizational Announcement": 0.00013192893189786328, "Nonfiction Writing": 0.00010858689673792519, "Organizational About Page": 0.00014655534156503294, "Personal About Page": 0.00012471366629643207, "Personal Blog": 0.00011329072507382274, "Product Page": 8.567456587935644e-05, "Spam / Ads": 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4.161478965814794e-05, "Spam / Ads": 6.623097258391226e-05, "Structured Data": 0.004288097446570983, "Technical Writing": 0.008367215567611212, "Transcript / Interview": 6.413677907751547e-05, "Tutorial / How-To Guide": 0.0030881294790654186, "User Reviews": 2.7847473759291576e-05, "NONE": 0.9808207925349587}, "structured_records": {"false": 0.9438654052823087, "true": 0.05613459471769131}, "procedural_steps": {"false": 0.9834714976299332, "true": 0.016528502370066776}, "worked_solution": {"false": 0.9890815201216487, "true": 0.01091847987835126}, "contextual_dialogue": {"false": 0.9999898424581922, "true": 1.0157541807789844e-05}, "content_quality": {"-2": 0.012037380152519565, "-1": 0.17338332920567168, "0": 0.4162063097118418, "1": 0.33482752136239813, "2": 0.06354545956756881}, "noise_severity": {"0": 0.9297523338048685, "1": 0.06473034695602244, "2": 0.004063576879418576, "3": 0.0008194642014716037, "4": 0.0006342781582187477}, "missing_content_severity": {"0": 0.960428082168683, 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0.382656819138932, "4": 0.47012369025651035}, "pretraining_utility": {"-2": 0.008051761157553142, "-1": 0.05269034934591003, "0": 0.3319026135753169, "1": 0.5070180955096432, "2": 0.10033718041157678}, "math_density": {"0": 0.9751318032323971, "1": 0.02218169671489697, "2": 0.0012978416348256696, "3": 0.0004445107640258745, "4": 0.0009441476538543634}, "code_density": {"0": 0.00031934440390090095, "1": 0.0006089301606226755, "2": 0.01618391959062934, "3": 0.12067096535269485, "4": 0.8622168404921523}, "reasoning_density": {"0": 0.7095093415281856, "1": 0.12845918435186793, "2": 0.1317213942231125, "3": 0.02069841916125911, "4": 0.009611660735574856}, "education_level": {"none": 0.0009680700872331269, "0": 0.0012935444593172875, "1": 0.009980729705846396, "2": 0.762060743394617, "3": 0.21590792988585814, "4": 0.009788982467128017}, "creative_writing_quality": {"not_applicable": 0.9991712497957833, "-2": 0.00010431464427068843, "-1": 0.00023666206890408652, "0": 0.0003895291004145482, "1": 7.123949800616507e-05, "2": 2.700489262130754e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.984063376745079e-05, "Art & Design": 3.08631494273308e-05, "Law & Crime": 2.5011039009005148e-05, "Education & Careers": 0.00231860275074726, "Entertainment & Media": 2.6719268809684313e-05, "Fashion & Beauty": 2.935878421695187e-05, "Business, Economics & Finance": 0.00017110086740189116, "Food & Dining": 2.0960877074271716e-05, "Games & Gaming": 3.7240368409490776e-05, "Electronics & Hardware": 5.3225555877995286e-05, "Health, Healthcare & Wellness": 1.7217529876744676e-05, "History & Geography": 2.2824546456330436e-05, "Home, Pets & Hobbies": 1.6980059493553455e-05, "Industry & Production": 1.6571198416222134e-05, "Language, Literature & Philosophy": 2.4776342426666295e-05, "Politics & Government": 1.71453518603484e-05, "Religion & Spirituality": 2.1461879377196888e-05, "Science, Mathematics & Engineering": 4.952582655480262e-05, "Relationships & Community": 0.0014186181049285766, "Software Usage & Applications": 0.0005075586151174305, "Programming & Software Engineering": 0.9945949722749365, "Sports & Fitness": 3.6877724365999386e-05, "Vehicles & Transportation": 1.5044582160246844e-05, "Travel & Tourism": 3.0933634467506694e-05, "Other / Uncovered Subject": 0.0004292197956057601, "Unknown / Insufficient Content": 3.7349239214658333e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000374295853753982, "Art & Design": 0.0003049280054003083, "Law & Crime": 0.0003377942232445027, "Education & Careers": 0.019255932103597984, "Entertainment & Media": 0.0005682390910240207, "Fashion & Beauty": 0.0001641131272514636, "Business, Economics & Finance": 0.0013629033385537978, "Food & Dining": 0.00011166244329269685, "Games & Gaming": 0.00023245545475891996, "Electronics & Hardware": 0.00022155959446979882, "Health, Healthcare & Wellness": 0.00014064548395553695, "History & Geography": 0.0001379550158760875, "Home, Pets & Hobbies": 0.00012876191592039185, "Industry & Production": 9.906452507792958e-05, "Language, Literature & Philosophy": 0.00010628203880624714, "Politics & Government": 0.0001629448852015553, "Religion & Spirituality": 0.00014984368139702808, "Science, Mathematics & Engineering": 0.00023320707710268438, "Relationships & Community": 0.003890493277335051, "Software Usage & Applications": 0.00658289086579664, "Programming & Software Engineering": 0.008121919636887904, "Sports & Fitness": 0.00013474926746307473, "Vehicles & Transportation": 6.073118528477959e-05, "Travel & Tourism": 0.0001687178518924739, "Other / Uncovered Subject": 0.0009844168212271, "NONE": 0.9559634932354281}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10859, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10859, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:09.793119+00:00", "scoring_key_slot": "primary", "dataset_uid": "650e7d66-2e0e-47b1-a6b3-3aa76eedd9f5", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "e50be52dcc29fc5b4f284046575a04cb7d122bcdb2db7438c953a5efe785560f", "source_metadata": {"uuid": "650e7d66-2e0e-47b1-a6b3-3aa76eedd9f5", "repo_name": "sameem786-blip/LinkedIn-Clone-API", "relative_path": "routes/api/profile.js", "category": "WEB", "algo_rel_score": 0.9060690999031067, "quality_score": 5.731666564941406, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2021-10-01T11:12:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "faa2242a19f5e7d5dba16cd55a4b1efd90cbef40", "size_bytes": 9641, "file_timestamp": 1633112845, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "452d21d70b2e49961df42f42df66026666ed1c35"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4, "repo_created_at": "2021-10-01T11:12:22.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "faa2242a19f5e7d5dba16cd55a4b1efd90cbef40", "size_bytes": 9641, "file_timestamp": 1633112845, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "452d21d70b2e49961df42f42df66026666ed1c35", "url": "https://github.com/sameem786-blip/LinkedIn-Clone-API/blob/452d21d70b2e49961df42f42df66026666ed1c35/routes/api/profile.js"}, "gpt4o_tokens": 2083, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.26446035098682497, "noise_severity": 0.0778530059521494, "missing_content_severity": 0.05402109971762075, "factual_density": 3.2899377658030162, "pretraining_utility": 0.6388985846717804, "math_density": 0.029887502892043387, "code_density": 3.8438570273685753, "reasoning_density": 0.49244387322416966, "education_level": 2.223134085629373, "creative_writing_quality": -0.38617435334346206}, "score_applicability": {"education_level": 0.9990319299127669, "creative_writing_quality": 0.0008287502042167958}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/do-/node-xml-toolkit/blob/f95ea90cbb48b02f630ef279e5e98f465a87d033/lib/XMLLexer.js", "id": "javascript-0-d97dfd875b64232f86b4924c3793b31a", "text": "const assert = require ('assert')\nconst {Transform} = require ('stream')\nconst {StringDecoder} = require('string_decoder')\n\nconst ST_TEXT = 0\nconst ST_LT\t = 1\nconst ST_LT_X = 2\nconst ST_TAG = 3\nconst ST_TAG_X = 4\n\nconst CH_EXCLAMATION = '!'.charCodeAt (0)\nconst CH_QUESTION = '?'.charCodeAt (0)\nconst CH_MINUS = '-'.charCodeAt (0)\nconst CH_SQUARE = '['.charCodeAt (0)\nconst CH_GT = '>'.charCodeAt (0)\n\nconst CL_DEAFULT = ''\nconst CL_PI = '?'\nconst CL_COMMENT = '--'\nconst CL_SQ_1 = ']'\n\nconst S_ENCODING = Symbol ('encoding')\n\nconst XMLLexer = class extends Transform {\n\n\tconstructor (options = {}) {\n\n\t\toptions.readableObjectMode = true\n\t\toptions.decodeStrings = false\n\t\t\n\t\tlet encoding = options.encoding; delete options.encoding\n\t\tif (encoding == null) encoding = 'utf8'\n\t\t\n\t\tlet maxLength = options.maxLength; delete options.maxLength\n\t\tif (maxLength == null) maxLength = 1 << 20\n\t\t\n\t\tassert (Number.isInteger (maxLength), 'maxLength must be integer, not ' + maxLength)\n\t\tassert (maxLength > 0, 'maxLength must be positive, not ' + maxLength)\n\t\t\n\t\tsuper (options)\n\t\t\n\t\tthis.decoder = new StringDecoder ('utf8')\n\n\t\tthis.body\t\t = ''\n\t\tthis.start = 0\n\t\tthis.setState (ST_TEXT)\n\t\tthis.beforeBody = 0n\n\t\t\n\t\tthis [S_ENCODING] = encoding\n\t\tthis.maxLength = maxLength\n\n\t}\n\n\tisClosing (pos) {\n\n\t\tconst {awaited, body} = this, {length} = awaited; if (length === 0) return true\n\n\t\tconst from = pos - length; if (from < this.start) return false\n\n\t\tfor (let i = 0; i < length; i ++) if (body.charCodeAt (from + i) !== awaited.charCodeAt (i)) return false\n\t\t\n\t\treturn true\n\t\n\t}\n\t\n\tsetState (state, awaited = null) {\n\n\t\tswitch (state) {\n\n\t\t\tcase ST_TAG:\n\t\t\tcase ST_TAG_X:\n\t\t\t\tassert (awaited != null, 'ST_TAG[_X]: awaited is mandatory')\n\t\t\t\tbreak\n\n\t\t\tdefault:\n\t\t\t\tassert (awaited === null, 'not ST_TAG[_X]: awaited must be null')\n\t\t\t\tthis.position = 0\n\t\t\t\tbreak\n\n\t\t}\n\n\t\tthis.state = state\n\t\tthis.awaited\t= awaited\n\n\t}\n\n\tparse () {\t\n\t\n\t\touter: while (true) {\n\t\t\n\t\t\tconst {start, body} = this\n\n\t\t\tswitch (this.state) {\n\t\t\t\n\t\t\t\tcase ST_TEXT:\n\t\t\t\t\n\t\t\t\t\t{\n\n\t\t\t\t\t\tconst pos = body.indexOf ('<', start); switch (pos) {\n\n\t\t\t\t\t\t\tcase -1:\n\t\t\t\t\t\t\t\treturn\n\n\t\t\t\t\t\t\tcase 0:\n\t\t\t\t\t\t\t\tthis.setState (ST_LT)\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\t\tthis.publishTo (pos - 1)\n\t\t\t\t\t\t\t\tthis.setState (ST_LT)\n\n\t\t\t\t\t\t}\n\n\t\t\t\t\t} \n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t\tcase ST_LT: \n\t\t\t\t\n\t\t\t\t\t{\n\n\t\t\t\t\t\tconst pos = start + 1; if (pos >= body.length) return null\n\n\t\t\t\t\t\tswitch (body.charCodeAt (pos)) {\n\n\t\t\t\t\t\t\tcase CH_EXCLAMATION:\n\t\t\t\t\t\t\t\tthis.setState (ST_LT_X)\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\tcase CH_QUESTION:\n\t\t\t\t\t\t\t\tthis.setState (ST_TAG, CL_PI)\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\t\tthis.setState (ST_TAG, CL_DEAFULT)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t\tcase ST_LT_X:\n\t\t\t\t\n\t\t\t\t\t{\n\n\t\t\t\t\t\tconst pos = start + 2; if (pos >= body.length) return null\n\n\t\t\t\t\t\tswitch (body.charCodeAt (pos)) {\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tcase CH_MINUS:\n\t\t\t\t\t\t\t\tthis.setState (ST_TAG, CL_COMMENT)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tdefault:\n\t\t\t\t\t\t\t\tthis.setState (ST_TAG_X, CL_DEAFULT)\n\t\t\t\t\t\t\t\tthis.position = pos\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\t\t\t\t\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t\tcase ST_TAG:\n\t\t\t\t\n\t\t\t\t\tlet pos = start; while (true) {\n\n\t\t\t\t\t\tpos = body.indexOf ('>', pos + 1); if (pos === -1) return\n\t\t\t\t\t\t\n\t\t\t\t\t\tif (!this.isClosing (pos)) continue\n\n\t\t\t\t\t\tthis.publishTo (pos)\n\n\t\t\t\t\t\tthis.setState (ST_TEXT)\n\n\t\t\t\t\t\tcontinue outer\t\t\t\t\t\t\t\n\n\t\t\t\t\t}\n\t\t\t\t\n\t\t\t\tcase ST_TAG_X:\n\t\t\t\t\n\t\t\t\t\tfor (let pos = this.position + 1; pos < body.length; pos ++) {\n\n\t\t\t\t\t\tswitch (body.charCodeAt (pos)) {\n\t\t\t\t\t\t\n\t\t\t\t\t\t\tcase CH_SQUARE:\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tswitch (this.awaited) {\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tcase CL_DEAFULT:\n\t\t\t\t\t\t\t\t\t\tthis.awaited = CL_SQ_1\n\t\t\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tthis.position = pos\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\tcase CH_GT:\n\t\t\t\t\t\t\t\tif (!this.isClosing (pos)) break\n\t\t\t\t\t\t\t\tthis.publishTo (pos)\n\t\t\t\t\t\t\t\tthis.setState (ST_TEXT)\n\t\t\t\t\t\t\t\tcontinue outer\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t\treturn\n\n\t\t\t\tdefault:\n\t\t\t\t\tthrow new Error ('Invalid state: ' + this.state)\n\t\t\t\t\t\n\t\t\t}\n\n\t\t}\t\n\t\t\n\t}\n\n\tpublishTo (pos) {\n\t\n\t\tlet lexeme = this.body.slice (this.start, ++ pos)\n\n\t\tthis.start = pos\n\t\t\n\t\tif (lexeme.length !== 0) this.push (lexeme)\n\n\t}\n\n\t_flush (callback) {\n\t\n\t\tthis.publishTo (this.body.length - 1)\n\t\t\n\t\tcallback ()\n\n\t}\n\t\n\tgetPosition () {\n\t\n\t\tlet {beforeBody, start} = this\n\t\t\n\t\treturn beforeBody + BigInt (start)\n\t\n\t}\n\t\n\tcheckMaxLength () {\n\t\n\t\tconst {body, maxLength} = this, size = body.length - this.start; \n\t\t\n\t\tif (size <= maxLength) return\n\n\t\tconst s = body.slice (0, size - 1), dump = JSON.stringify ([s])\n\n\t\tthis.destroy (new Error (`The fragment ${dump} at position ${this.getPosition ()} exceeds maxLength=${maxLength}`))\n\t\n\t}\n\t\n\t_transform (chunk, _, callback) {\n\t\n\t\tif (Buffer.isBuffer (chunk)) chunk = this.decoder.write (chunk)\n\n\t\tthis.body = this.body.slice (this.start) + chunk\n\t\tthis.start = 0\n\t\t\n\t\tthis.parse ()\n\t\t\n\t\tif (this.start > 0) this.beforeBody += BigInt (this.start)\t\t\n\t\tthis.checkMaxLength ()\n\t\t\n\t\tcallback ()\n\n\t}\n\t\n}\n\nmodule.exports = XMLLexer", "text_preview": "const assert = require ('assert') const {Transform} = require ('stream') const {StringDecoder} = require('string_decoder') const ST_TEXT = 0 const ST_LT = 1 const ST_LT_X = 2 const ST_TAG = 3 const ST_TAG_X = 4 const CH_EXCLAMATION = '!'.ch", "input_sha256": "4ba8053cd2ddd272b04d4ebb8ffe37dfaf9c921074cfa883aee0f79769cf493c", "text_sha256": "d97dfd875b64232f86b4924c3793b31ad6e5475d907c0e141efc6758eaee2d8e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8022, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8471358435153773, "noise_severity": 0.006097964122588396, "missing_content_severity": 0.006050341885538563, "factual_density": 3.471917115691623, "pretraining_utility": 0.5574465537508377, "math_density": 0.35606402854631825, "code_density": 3.938201967847202, "reasoning_density": 0.9968912079589731, "education_level": 2.546691356377007, "creative_writing_quality": -0.20729962863288284}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007094696521843802, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9965846880203628, "secondary": 0.9898579640365522, "tertiary": 0.9972611256245631, "structured_records": 0.000653914010633326, "procedural_steps": 0.003303326395075365, "worked_solution": 0.0015875666676588365, "contextual_dialogue": 4.9168388252385454e-06, "content_quality": 0.48842708850952954, "noise_severity": 0.9949183632311763, "missing_content_severity": 0.9949580484287178, "factual_density": 0.5599309297430191, "pretraining_utility": 0.4392058240715798, "math_density": 0.7032799762114015, "code_density": 0.9485016398726681, "reasoning_density": 0.16925732670085591, "education_level": 0.4307242134447103, "creative_writing_quality": 0.9993590835295743, "primary_topic": 0.9919208657440644, "secondary_topic": 0.9510491610189876}, "probability_margin": {"primary": 0.9950663371544115, "secondary": 0.9858590625494923, "tertiary": 0.9962793336952492, "education_level": 0.1446365774504162, "creative_writing_quality": 0.9992257215598996, "primary_topic": 0.986214698668778, "secondary_topic": 0.9289547940636642}, "raw_probabilities": {"primary": {"Academic Writing": 2.42888034585079e-05, "Content Listing": 1.4055457878215056e-05, "Creative Writing": 1.394969551078472e-05, "Customer Support Page": 7.090958403071061e-06, "Q&A Forum": 9.846238587539945e-06, "Discussion Thread": 6.412497853506726e-06, "Comment Section": 1.034201975717668e-05, "Missing Content": 2.3553878024279483e-05, "Knowledge Article": 6.651686811210112e-05, "Legal Notices": 3.8366970063153847e-05, "News Article": 3.661819694260044e-06, "Organizational Announcement": 8.34818186760969e-06, "Nonfiction Writing": 8.573749665135672e-06, "Organizational About Page": 9.6816640863708e-06, "Personal About Page": 8.03230442175844e-06, "Personal Blog": 5.449232590960184e-06, "Product Page": 6.22775785885683e-06, "Spam / Ads": 6.967262242381083e-06, "Structured Data": 0.0016606555317695377, "Technical Writing": 0.996726992686181, "Transcript / Interview": 1.2417124307769029e-05, "Tutorial / How-To Guide": 0.0001365810165003086, "User Reviews": 5.58547362802772e-06, "Other / Unknown": 0.0011864028075376026}, "secondary": {"Academic Writing": 6.54673044930048e-05, "Content Listing": 3.561215525768294e-05, "Creative Writing": 5.138178427765033e-05, "Customer Support Page": 2.585141813222895e-05, "Q&A Forum": 2.0284548372021153e-05, "Discussion Thread": 1.800830874240678e-05, "Comment Section": 4.713546940782009e-05, "Missing Content": 0.00015284933094174657, "Knowledge Article": 0.00017429542249590023, "Legal Notices": 0.0001563846133807989, "News Article": 3.0871657430728255e-05, "Organizational Announcement": 2.9226690885416874e-05, "Nonfiction Writing": 2.5037266993482107e-05, "Organizational About Page": 3.729787703400836e-05, "Personal About Page": 2.617581007910368e-05, "Personal Blog": 2.100829697665991e-05, "Product Page": 2.001087292635884e-05, "Spam / Ads": 2.8879633938842016e-05, "Structured Data": 0.004038105964058331, "Technical Writing": 0.004421486318870298, "Transcript / Interview": 3.4034464095794584e-05, "Tutorial / How-To Guide": 0.0002458080149463468, "User Reviews": 1.4237907900816206e-05, "NONE": 0.9902805488683626}, "tertiary": {"Academic Writing": 3.2120417523698345e-05, "Content Listing": 2.013403711272803e-05, "Creative Writing": 3.0419818028862603e-05, "Customer Support Page": 1.285444782781417e-05, "Q&A Forum": 9.8140106379374e-06, "Discussion Thread": 7.789935131519633e-06, "Comment Section": 1.942371009139466e-05, "Missing Content": 9.887559688320765e-05, "Knowledge Article": 8.43737966574646e-05, "Legal Notices": 0.00013026435595063382, "News Article": 1.7096826576285415e-05, "Organizational Announcement": 1.4647257916890145e-05, "Nonfiction Writing": 1.4214417265481954e-05, "Organizational About Page": 2.331877942086516e-05, "Personal About Page": 1.3155103896905177e-05, "Personal Blog": 1.1795100084980459e-05, "Product Page": 1.164267830809752e-05, "Spam / Ads": 1.6415708036317982e-05, "Structured Data": 0.0008197806105352147, "Technical Writing": 0.001095911694957122, "Transcript / Interview": 1.6127118736817704e-05, "Tutorial / How-To Guide": 0.00011625098749917781, "User Reviews": 8.328200714149457e-06, "NONE": 0.9973752453902064}, "structured_records": {"false": 0.9993460859893667, "true": 0.000653914010633326}, "procedural_steps": {"false": 0.9966966736049246, "true": 0.003303326395075365}, "worked_solution": {"false": 0.9984124333323412, "true": 0.0015875666676588365}, "contextual_dialogue": {"false": 0.9999950831611748, "true": 4.9168388252385454e-06}, "content_quality": {"-2": 0.011280760118798534, "-1": 0.04301363308583049, "0": 0.26350627860853704, "1": 0.45168765953486306, "2": 0.2305116686519709}, "noise_severity": {"0": 0.995040524882335, "1": 0.0042283773247863915, "2": 0.00043892817054155803, "3": 0.00017694803262958525, "4": 0.00011522158970753325}, "missing_content_severity": {"0": 0.9966080470824463, "1": 0.001441829170045975, "2": 0.0013737635965320413, "3": 0.0004444550814741878, "4": 0.0001319050695014855}, "factual_density": {"0": 0.006942297980655594, "1": 0.01326306563356649, "2": 0.08106548430523175, "3": 0.29839352687459175, "4": 0.6003356252059544}, "pretraining_utility": {"-2": 0.009275645431458718, "-1": 0.08141216316158192, "0": 0.36710196606409323, "1": 0.4270104429103951, "2": 0.11519978243247096}, "math_density": {"0": 0.7724259251188978, "1": 0.17370908817214648, "2": 0.012941455371397572, "3": 0.007222095718855801, "4": 0.0337014356187023}, "code_density": {"0": 0.00023198437856735966, "1": 0.0004072485605871599, "2": 0.006371466131191652, "3": 0.046905416694384076, "4": 0.9460838842352698}, "reasoning_density": {"0": 0.6310304088858117, "1": 0.05621349754815142, "2": 0.10629543166703928, "3": 0.09775580051924662, "4": 0.10870486137975079}, "education_level": {"none": 0.0008820356414125526, "0": 0.001316918555904327, "1": 0.00486171440545595, "2": 0.5256035112039252, "3": 0.38096693375350904, "4": 0.08636888643979301}, "creative_writing_quality": {"not_applicable": 0.9994659029413119, "-2": 7.099260589084727e-05, "-1": 0.00011273802504290372, "0": 0.0002401813814121961, "1": 7.636497777960772e-05, "2": 3.382006856251892e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 3.473206900617904e-05, "Art & Design": 4.24850163395335e-05, "Law & Crime": 2.8617445981600815e-05, "Education & Careers": 2.7800948297058037e-05, "Entertainment & Media": 2.2060356524614155e-05, "Fashion & Beauty": 1.6993406418094465e-05, "Business, Economics & Finance": 3.779949851402839e-05, "Food & Dining": 2.310353852191232e-05, "Games & Gaming": 4.260466876146992e-05, "Electronics & Hardware": 0.00010120639818085449, "Health, Healthcare & Wellness": 2.594035845849921e-05, "History & Geography": 3.213513402441201e-05, "Home, Pets & Hobbies": 1.9954366823943415e-05, "Industry & Production": 3.225826158106067e-05, "Language, Literature & Philosophy": 0.00039098349631371844, "Politics & Government": 1.664056094710484e-05, "Religion & Spirituality": 2.5675115374083047e-05, "Science, Mathematics & Engineering": 0.00601690300820698, "Relationships & Community": 1.650741036551514e-05, "Software Usage & Applications": 0.0003083215478529232, "Programming & Software Engineering": 0.992231601676985, "Sports & Fitness": 3.7441049233571995e-05, "Vehicles & Transportation": 1.77345672701233e-05, "Travel & Tourism": 5.516296496569107e-05, "Other / Uncovered Subject": 0.00030312128713487095, "Unknown / Insufficient Content": 9.221584791713079e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0002724965543110375, "Art & Design": 0.00018703621652175112, "Law & Crime": 0.0002213691743139545, "Education & Careers": 0.00013270407017986114, "Entertainment & Media": 0.00016265810685085335, "Fashion & Beauty": 9.060978785747709e-05, "Business, Economics & Finance": 0.0001950155703419863, "Food & Dining": 8.475461644626313e-05, "Games & Gaming": 0.00016019108537278023, "Electronics & Hardware": 0.00023103776563286572, "Health, Healthcare & Wellness": 0.00010995428264771147, "History & Geography": 0.00011887169386434203, "Home, Pets & Hobbies": 6.827867788077415e-05, "Industry & Production": 8.558284319755114e-05, "Language, Literature & Philosophy": 0.0009618351618955376, "Politics & Government": 9.969833908424719e-05, "Religion & Spirituality": 0.00013972527874779874, "Science, Mathematics & Engineering": 0.017357927347788116, "Relationships & Community": 6.615452176967326e-05, "Software Usage & Applications": 0.0013570292770827361, "Programming & Software Engineering": 0.023977091531516153, "Sports & Fitness": 9.432203115357383e-05, "Vehicles & Transportation": 4.586512660821424e-05, "Travel & Tourism": 0.00019944317796485425, "Other / Uncovered Subject": 0.0006484621657895274, "NONE": 0.9529318855951804}}, "probabilities": {"primary": {"Academic Writing": 2.42888034585079e-05, "Content Listing": 1.4055457878215056e-05, "Creative Writing": 1.394969551078472e-05, "Customer Support Page": 7.090958403071061e-06, "Q&A Forum": 9.846238587539945e-06, "Discussion Thread": 6.412497853506726e-06, "Comment Section": 1.034201975717668e-05, "Missing Content": 2.3553878024279483e-05, "Knowledge Article": 6.651686811210112e-05, "Legal Notices": 3.8366970063153847e-05, "News Article": 3.661819694260044e-06, "Organizational Announcement": 8.34818186760969e-06, "Nonfiction Writing": 8.573749665135672e-06, "Organizational About Page": 9.6816640863708e-06, "Personal About Page": 8.03230442175844e-06, "Personal Blog": 5.449232590960184e-06, "Product Page": 6.22775785885683e-06, "Spam / Ads": 6.967262242381083e-06, "Structured Data": 0.0016606555317695377, "Technical Writing": 0.996726992686181, "Transcript / Interview": 1.2417124307769029e-05, "Tutorial / How-To Guide": 0.0001365810165003086, "User Reviews": 5.58547362802772e-06, "Other 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"repo_created_at": "2021-11-14T11:57:37.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 3, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c71f732cc79e22e5a2847d3960ac4d4f7e3ba7d6", "size_bytes": 5344, "file_timestamp": 1752656162, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f95ea90cbb48b02f630ef279e5e98f465a87d033", "url": "https://github.com/do-/node-xml-toolkit/blob/f95ea90cbb48b02f630ef279e5e98f465a87d033/lib/XMLLexer.js"}, "gpt4o_tokens": 1421, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8471358435153773, "noise_severity": 0.006097964122588396, "missing_content_severity": 0.006050341885538563, "factual_density": 3.471917115691623, "pretraining_utility": 0.5574465537508377, "math_density": 0.35606402854631825, "code_density": 3.938201967847202, "reasoning_density": 0.9968912079589731, "education_level": 2.546691356377007, "creative_writing_quality": -0.20729962863288284}, "score_applicability": {"education_level": 0.9991179643585874, "creative_writing_quality": 0.0005340970586880737}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/KnechtNoobrecht/gt-db_public/blob/8b663e859e037f2d18ce4de3529060af6ce1da4a/app_extension.js", "id": "javascript-0-fa374e4006ad9c0e3b8590e6d75260a4", "text": "const mongoose = require('mongoose')\nconst User = require('./models/user')\nconst Book = require(\"./models/book\")\nconst BookHistory = require('./models/bookhistory')\nconst dotenv = require(\"dotenv\")\ndotenv.config()\n\nfunction sleep(ms) {\n return new Promise((resolve) => setTimeout(resolve, ms))\n}\n\nvar url = 'mongodb://'+process.env.DBUSER+':'+process.env.DBPASSWORDLOCAL+'@'+process.env.IPADDRESS+':27017/gt-db?authSource=admin'\nasync function connectdb(callback) {\n console.log('Connecting to database...')\n try {\n await mongoose.connect(url, {useUnifiedTopology: true, useNewUrlParser: true })\n .then(() => console.log('Successfully connected to database.'))\n callback(null)\n }\n catch (err) {\n console.log(err)\n console.log('Connection failed. Retrying.')\n await sleep(5000)\n callback(err)\n }\n}\n\nasync function getAllBooks() {\n var books = await Book.find()\n return books\n}\n\nasync function getAllUsers() {\n var users = await User.find()\n return users\n}\n\nfunction updatedate(date) {\n var dateoc = Date.now()\n dateoc = new Date(dateoc)\n dateoc = formatDate(dateoc)\n var date = date\n var date0 = date.substring(5,15)\n date = date0 +\" - \"+dateoc\n return date\n}\n\nasync function getexpsoon() {\n var date = Date.now() + (1000*60*60*24*4) //look for books expiring in next 4 days\n var docs = await BookHistory.find({ expiring_at: { $gt: Date.now(), $lt: date } })\n //for(let i=0i {\n //console.log(book)\n //const book = await Book.findOne({_id:id})\n return {name: book.bname,id: book.bookid, exp: book.expiring_at}\n })\n \n return Promise.all(promises)\n}\n\nasync function getexprd() {\n var datenow = Date.now()\n var docs = await BookHistory.find({ expiring_at: { $lt: datenow, $gt:0 } })\n //for(let i=0;i {\n //console.log(book)\n //const book = await Book.findOne({_id:id})\n return {name: book.bname,id: book.bookid, exp: book.expiring_at}\n })\n \n return Promise.all(promises)\n //return b_names\n}\n\nfunction createid(len) {\n var result = \"\"\n var characters =\n \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789\"\n var charactersLength = characters.length\n for (var i = 0 ; i < len; i++) {\n result += characters.charAt(Math.floor(Math.random() * charactersLength))\n }\n return result\n}\n\nfunction ensureAuthenticated(req, res, next) {\n if (req.isAuthenticated()) {\n //console.log(\"logged in \")\n return next()\n }\n if(req.method == \"GET\") {\n req.session.dest = req.url\n }\n req.flash('error','noauth')\n res.redirect(\"/\")\n}\n\nfunction ensureManager(req, res, next) {\n if (req.user.manager === true) {\n return next()\n } else {\n req.logout()\n req.flash('error','access denied')\n res.redirect('/')\n }\n}\n\nfunction ensureAdmin(req, res, next) {\n if (req.user.admin === true) {\n return next()\n } else {\n res.end()\n }\n}\n\nasync function userExists(usermail) {\n var useremail = usermail.toLowerCase()\n console.log(\"Checking for mail: \", useremail)\n await User.findOne({ email: useremail }).then(function (docs) {\n if (!docs) {\n return false\n } else {\n return true\n }\n })\n}\n\nfunction aufdroeselisierung(mail) {\n var mail = mail.toLowerCase()\n var name\n var username\n var firstname\n var lastname\n var userdetailsstring\n var userdetails = mail.split(\"@\")\n userdetails.remove(1)\n username = userdetails\n //console.log(username)\n userdetailsstring = userdetails.toString()\n username = userdetailsstring.toLowerCase()\n name = userdetailsstring.split(\".\")\n firstname = name[0]\n lastname = name[1]\n //console.log(firstname, lastname)\n firstname = firstname[0].toUpperCase() + firstname.substring(1)\n lastname = lastname[0].toUpperCase() + lastname.substring(1)\n //console.log(firstname,lastname)\n name[0] = firstname\n name[1] = lastname\n name[2] = username\n return name\n}\n\nfunction mailvalidation(mail,req) {\n var mailat = mail.split(\"@\")\n if (mail.includes(\"@gym-trittau.de\") && mailat[0].includes(\".\")) {\n return true\n }\n return false\n}\n\nfunction formatDate(date) {\n var d = new Date(date),\n month = '' + (d.getMonth() + 1),\n day = '' + d.getDate(),\n year = d.getFullYear()\n\n if (month.length < 2)\n month = '0' + month\n if (day.length < 2)\n day = '0' + day\n\n return [day,month,year].join('.')\n}\n\nfunction sortName(a,b) {\n let xa = a.name.toLowerCase()\n let xb = b.name.toLowerCase()\n\n if (xa < xb) {\n return -1\n }\n if (xa > xb) {\n return 1\n }\n return 0\n}\n\nfunction sortGenre(a,b) {\n let xa = a.genre.toLowerCase()\n let xb = b.genre.toLowerCase()\n\n if (xa < xb) {\n return -1\n }\n if (xa > xb) {\n return 1\n }\n return 0\n}\n\nfunction sortAuthor(a,b) {\n let xa = a.author.toLowerCase()\n let xb = b.author.toLowerCase()\n\n if (xa < xb) {\n return -1\n }\n if (xa > xb) {\n return 1\n }\n return 0\n}\n\nfunction sortPublisher(a,b) {\n let xa = a.publisher.toLowerCase()\n let xb = b.publisher.toLowerCase()\n\n if (xa < xb) {\n return -1\n }\n if (xa > xb) {\n return 1\n }\n return 0\n}\n\nfunction sortISBN(a,b) {\n let xa = a.ISBN.toLowerCase()\n let xb = b.ISBN.toLowerCase()\n\n if (xa < xb) {\n return -1\n }\n if (xa > xb) {\n return 1\n }\n return 0\n}\n\nasync function updatebookhiring(bid) {\n var book = await Book.findById(bid)\n var available = book.available\n var available = available + 1\n await Book.updateOne(\n { _id: bid },\n {available: available}\n )\n}\n\nasync function updatebhistory(bhistory,id,req) {\n var updateddate = updatedate(bhistory.date)\n await BookHistory.updateOne(\n { _id: id },\n { date: updateddate, closed_at: Date.now(), closed_by: req.user.username, expiring_at: \"0\" })\n}\n\nasync function searchbookbyid(params) {\n var book = await Book.findById(params)\n return book\n}\n\nasync function userExists(usermail) {\n var useremail = usermail.toLowerCase()\n console.log(\"Checking for mail: \", useremail)\n return Boolean(await User.findOne({ email: useremail }))\n}\n\n// Array Remove - By (MIT Licensed)\nArray.prototype.remove = function (from, to) {\n var rest = this.slice((to || from) + 1 || this.length)\n this.length = from < 0 ? this.length + from : from\n return this.push.apply(this, rest)\n}\n\nexports.connectdb = connectdb\nexports.getAllBooks = getAllBooks\nexports.updatedate = updatedate\nexports.getexpsoon = getexpsoon\nexports.getexprd = getexprd\nexports.createid = createid\nexports.userExists = userExists\nexports.aufdroeselisierung = aufdroeselisierung\nexports.mailvalidation = mailvalidation\nexports.formatDate = formatDate\nexports.ensureAuthenticated = ensureAuthenticated\nexports.ensureManager = ensureManager\nexports.ensureAdmin = ensureAdmin\nexports.getAllUsers = getAllUsers\nexports.userExists = userExists\nexports.sortName = sortName\nexports.sortGenre = sortGenre\nexports.sortAuthor = sortAuthor\nexports.sortPublisher = sortPublisher\nexports.sortISBN = sortISBN\nexports.updatebookhiring = updatebookhiring\nexports.updatebhistory = updatebhistory\nexports.searchbookbyid = searchbookbyid\n", "text_preview": "const mongoose = require('mongoose') const User = require('./models/user') const Book = require(\"./models/book\") const BookHistory = require('./models/bookhistory') const dotenv = require(\"dotenv\") dotenv.config() function sleep(ms) { retur", "input_sha256": "a949c415ec6a93873c7b68269e8d782954551f85ac213de32c290133e03a96b9", "text_sha256": "fa374e4006ad9c0e3b8590e6d75260a41a182cecf1f41a0cb82dc8e70a155534", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8023, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.08038563332007553, "noise_severity": 0.18386277718282268, "missing_content_severity": 0.040692779117839285, "factual_density": 3.2039053011205203, "pretraining_utility": 0.2562323545863741, "math_density": 0.3985905397874973, "code_density": 3.8919723824716934, "reasoning_density": 0.546456410235117, "education_level": 2.105332308026833, "creative_writing_quality": -0.4700668278485617}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.011265673119074374, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967215937657069, "secondary": 0.9665176158593065, "tertiary": 0.9619408762497076, "structured_records": 0.00344747642080898, "procedural_steps": 0.0037070556249447447, "worked_solution": 0.0013499323628079173, "contextual_dialogue": 7.344094917971261e-06, "content_quality": 0.5064423541832985, "noise_severity": 0.8467810190143145, "missing_content_severity": 0.9660893507351339, "factual_density": 0.3365877509337668, "pretraining_utility": 0.4741222339796044, "math_density": 0.6678412168437522, "code_density": 0.9099769853930777, "reasoning_density": 0.5446196581374024, "education_level": 0.7900236363845382, "creative_writing_quality": 0.9992457350548182, "primary_topic": 0.9881721113038733, "secondary_topic": 0.9204459658213437}, "probability_margin": {"primary": 0.9949985519866047, "secondary": 0.9546110846440656, "tertiary": 0.9389593693991256, "education_level": 0.6955013137354329, "creative_writing_quality": 0.9991080099709699, "primary_topic": 0.9839243187165706, "secondary_topic": 0.9010887076937194}, "raw_probabilities": {"primary": {"Academic Writing": 2.6331676358005382e-05, "Content Listing": 2.5004870256445326e-05, "Creative Writing": 1.938986431400683e-05, "Customer Support Page": 7.477080680172665e-06, "Q&A Forum": 9.547525722787716e-06, "Discussion Thread": 7.310470131344608e-06, "Comment Section": 1.2840899809831582e-05, "Missing Content": 2.614003299086679e-05, "Knowledge Article": 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"input_tokens": 10658, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:12.373275+00:00", "scoring_key_slot": "primary", "dataset_uid": "cbc10ed7-4ea2-4e6d-bdc1-8915132621f8", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ae9269c8edf7215a48b0db8dfb54b2249961e1eb27a98116133d7fad93e11179", "source_metadata": {"uuid": "cbc10ed7-4ea2-4e6d-bdc1-8915132621f8", "repo_name": "KnechtNoobrecht/gt-db_public", "relative_path": "app_extension.js", "category": "TOOL", "algo_rel_score": 0.9564253687858582, "quality_score": 4.9523491859436035, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 24, "repo_created_at": "2021-06-28T15:39:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3058308286b430c60ea2f131bf11dd39f6bcb578", "size_bytes": 7271, "file_timestamp": 1665359019, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8b663e859e037f2d18ce4de3529060af6ce1da4a"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 24, "repo_created_at": "2021-06-28T15:39:41.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "3058308286b430c60ea2f131bf11dd39f6bcb578", "size_bytes": 7271, "file_timestamp": 1665359019, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8b663e859e037f2d18ce4de3529060af6ce1da4a", "url": "https://github.com/KnechtNoobrecht/gt-db_public/blob/8b663e859e037f2d18ce4de3529060af6ce1da4a/app_extension.js"}, "gpt4o_tokens": 2001, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": -0.08038563332007553, "noise_severity": 0.18386277718282268, "missing_content_severity": 0.040692779117839285, "factual_density": 3.2039053011205203, "pretraining_utility": 0.2562323545863741, "math_density": 0.3985905397874973, "code_density": 3.8919723824716934, "reasoning_density": 0.546456410235117, "education_level": 2.105332308026833, "creative_writing_quality": -0.4700668278485617}, "score_applicability": {"education_level": 0.9983182572727519, "creative_writing_quality": 0.0006285541209849158}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/YUETANG2/CSE316-Final-Project-Makeup/blob/e6808c2a1c6e9ea6a74f903df822ecfb678fb920/client/src/components/mainPage/qstnList.js", "id": "javascript-0-83da62e27726d6a7f36f4a9a38b2f5b7", "text": "import React from \"react\";\nimport QstnStat from \"./qstnStat.js\";\nimport QstnsDisplayBlock from \"./qstnsDisplayBlock.js\";\n\nimport { useState, useEffect } from \"react\";\nimport axios from \"axios\";\n\n//Main (Questions) Page for FakeStackOverflow\nexport default function QstnList(props) {\n const [qstnsList, setQstnsList] = useState([]);\n const [currentPage, setCurrentPage] = useState(1);\n const searchTerm = props.searchInput;\n\n async function updateQstnsListHelper(input) {\n if (input !== \"\") {\n let qstnSet = new Set();\n let search_terms = input.split(\" \");\n\n let tags = search_terms.filter((term) => {\n var pattern = /\\[.*\\]/;\n return pattern.test(term);\n });\n\n for (let tag of tags) {\n try {\n tag = tag.slice(1, -1).toLowerCase();\n console.log(tag);\n\n let qstnList = await axios.get(\n \"http://127.0.0.1:8000/question/getQstnsByTag?tagName=\" + tag\n );\n\n qstnList = qstnList.data;\n\n console.log(qstnList);\n\n qstnList.forEach((qstn) => {\n let contains = false;\n qstnSet.forEach((q) => {\n if (q._id === qstn._id) {\n contains = true;\n }\n });\n if (!contains) {\n qstnSet.add(qstn);\n }\n });\n } catch (err) {\n console.error(err);\n }\n }\n\n let keyWords = search_terms.filter((term) => {\n var pattern = /\\[.*\\]/;\n return !pattern.test(term);\n });\n\n for (let word of keyWords) {\n try {\n let qstnList = await axios.get(\n \"http://127.0.0.1:8000/question/getQstnsByKeyWord?keyword=\" + word\n );\n\n qstnList = qstnList.data;\n qstnList.forEach((qstn) => {\n let contains = false;\n qstnSet.forEach((q) => {\n if (q._id === qstn._id) {\n contains = true;\n }\n });\n if (!contains) {\n qstnSet.add(qstn);\n }\n });\n } catch (err) {\n console.error(err);\n }\n }\n \n setQstnsList(Array.from(qstnSet));\n } else {\n let qstnsbynewest = await axios.get(\n \"http://127.0.0.1:8000/question/getAllQstns?filter=newest\"\n );\n setQstnsList(qstnsbynewest.data)\n console.log(qstnsbynewest.data);\n }\n setCurrentPage(1);\n }\n\n useEffect(() => {\n console.log(searchTerm);\n updateQstnsListHelper(searchTerm);\n }, [searchTerm]);\n\n /**Sort questions by Newest */\n let filterByNewest = async () => {\n console.log(\"Newest\");\n\n let qstnsbynewest = await axios.get(\n \"http://127.0.0.1:8000/question/getAllQstns?filter=newest\"\n );\n\n setQstnsList(qstnsbynewest.data);\n setCurrentPage(1);\n //setRender(render + 1);\n };\n\n let filterByActive = async () => {\n console.log(\"Active\");\n\n let qstnsbyactive = await axios.get(\n \"http://127.0.0.1:8000/question/getAllQstns?filter=active\"\n );\n\n setQstnsList(qstnsbyactive.data);\n setCurrentPage(1);\n //setRender(render + 1);\n };\n\n /** filter questions by unanswered */\n let filterByUnanswered = async () => {\n console.log(\"Unanswered\");\n\n let qstnsbyunanswered = await axios.get(\n \"http://127.0.0.1:8000/question/getAllQstns?filter=unanswered\"\n );\n\n setQstnsList(qstnsbyunanswered.data);\n setCurrentPage(1);\n //setRender(render + 1);\n };\n\n let incrementPageNum = () => {\n let num = currentPage; \n if(num >= Math.ceil(qstnsList.length/5)){\n setCurrentPage(1);\n }else{\n setCurrentPage(++num);\n }\n }\n\n let decrementPageNum = () => {\n let num = currentPage;\n if(num != 1){\n setCurrentPage(--num);\n }\n }\n\n //console.log(\"Inside qstnList: \" + props.userStatus);\n return (\n
\n \n \n
\n );\n}\n", "text_preview": "import React from \"react\"; import QstnStat from \"./qstnStat.js\"; import QstnsDisplayBlock from \"./qstnsDisplayBlock.js\"; import { useState, useEffect } from \"react\"; import axios from \"axios\"; //Main (Questions) Page for FakeStackOverflow e", "input_sha256": "25fb5445206d6ffd7c7eee020308609f45885755e5ed01cf1a0efcef2046c3db", "text_sha256": "83da62e27726d6a7f36f4a9a38b2f5b71f6e0818e5d317a0e3f64e7a9154ef62", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8025, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5439194842366105, "noise_severity": 0.08500077293835727, "missing_content_severity": 0.04533967402654411, "factual_density": 2.991939276885765, "pretraining_utility": 0.3662452635113879, "math_density": 0.27587658253434877, "code_density": 3.8924018268061378, "reasoning_density": 0.3899642868388355, "education_level": 2.0639525151755396, "creative_writing_quality": -0.37829909646560234}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005769335901062211, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964057252370624, "secondary": 0.9840261768060844, "tertiary": 0.9900463533984972, "structured_records": 0.0017648905329173544, "procedural_steps": 0.0019650448198862787, "worked_solution": 0.005805674274171462, "contextual_dialogue": 1.0788841105534225e-05, "content_quality": 0.4392353987574952, "noise_severity": 0.9291660225513689, "missing_content_severity": 0.9622169383112132, "factual_density": 0.15994939740480407, "pretraining_utility": 0.4939733382804472, "math_density": 0.7701028478880427, "code_density": 0.9103348556717812, "reasoning_density": 0.675029760967637, "education_level": 0.8537347506739099, "creative_writing_quality": 0.9991582458742692, "primary_topic": 0.9923793611223001, "secondary_topic": 0.9627738911297463}, "probability_margin": {"primary": 0.9949845756897548, "secondary": 0.9767775329055471, "tertiary": 0.9868831321743016, "education_level": 0.7898978820350484, "creative_writing_quality": 0.9989776492248753, "primary_topic": 0.9900752103399668, "secondary_topic": 0.9532056994145757}, "raw_probabilities": {"primary": {"Academic Writing": 6.271843383907249e-05, "Content Listing": 0.0001307655666863625, "Creative Writing": 3.6786568758177006e-05, "Customer Support Page": 1.3432151841141077e-05, "Q&A Forum": 0.0001592951927198357, "Discussion Thread": 1.2796768113886775e-05, "Comment Section": 2.1497028263634886e-05, "Missing Content": 6.119160926040666e-05, "Knowledge Article": 0.00010598427351018743, "Legal Notices": 4.971638811675732e-05, "News Article": 7.801948293049417e-06, "Organizational Announcement": 1.8103029188840624e-05, "Nonfiction Writing": 1.7262075286948125e-05, "Organizational About Page": 1.986267276453951e-05, "Personal About Page": 2.09808941805942e-05, "Personal Blog": 1.260470597499669e-05, "Product Page": 1.4191564156967063e-05, "Spam / Ads": 1.8011501098598126e-05, "Structured Data": 0.00032668329794567635, "Technical Writing": 0.9965554866855181, "Transcript / Interview": 2.03104360246707e-05, "Tutorial / How-To Guide": 0.0007334461802291347, "User Reviews": 1.01600324648524e-05, "Other / Unknown": 0.001570910995763274}, "secondary": {"Academic Writing": 0.0002358050391958849, "Content Listing": 0.000996324546222617, "Creative Writing": 0.00017394213618700103, "Customer Support Page": 6.607071574605975e-05, "Q&A Forum": 0.0003159587642746773, "Discussion Thread": 4.3637811970154656e-05, "Comment Section": 0.0001703302155359752, "Missing Content": 0.0002923855248329408, "Knowledge Article": 0.00041557075479468436, "Legal Notices": 0.00018532588376373485, "News Article": 6.0074883218821844e-05, "Organizational Announcement": 6.792940191064152e-05, "Nonfiction Writing": 6.407055392372571e-05, "Organizational About Page": 7.759597720579623e-05, "Personal About Page": 7.587596349450881e-05, "Personal Blog": 5.9714143838259985e-05, "Product Page": 4.117180605018409e-05, "Spam / Ads": 7.23864029542192e-05, "Structured Data": 0.001964952122076296, "Technical Writing": 0.007914219866950381, "Transcript / Interview": 7.240135015329509e-05, "Tutorial / How-To Guide": 0.0019086206588314206, "User Reviews": 3.388270437110424e-05, "NONE": 0.9846917527724975}, "tertiary": {"Academic Writing": 0.00018143954319754834, "Content Listing": 0.001258546759956988, "Creative Writing": 0.0001411054171222994, "Customer Support Page": 4.512826705875768e-05, "Q&A Forum": 0.0003167021567994846, "Discussion Thread": 2.9528515983603466e-05, "Comment Section": 0.00016672740964914054, "Missing Content": 0.00039864297234597034, "Knowledge Article": 0.00032389452830680545, "Legal Notices": 0.0002401194086710359, "News Article": 4.500223540906374e-05, "Organizational Announcement": 4.611321295040539e-05, "Nonfiction Writing": 5.5879664232806085e-05, "Organizational About Page": 6.494821090292704e-05, "Personal About Page": 4.808668905003333e-05, "Personal Blog": 4.0882708613624944e-05, "Product Page": 3.1958752326144966e-05, "Spam / Ads": 6.755845064869141e-05, "Structured Data": 0.0013836359356160393, "Technical Writing": 0.0035779564992583137, "Transcript / Interview": 4.612607801332497e-05, "Tutorial / How-To Guide": 0.0010052667687006245, "User Reviews": 2.3661141626581362e-05, "NONE": 0.9904610886735599}, "structured_records": {"false": 0.9982351094670826, "true": 0.0017648905329173544}, "procedural_steps": {"false": 0.9980349551801138, "true": 0.0019650448198862787}, "worked_solution": {"false": 0.9941943257258286, "true": 0.005805674274171462}, "contextual_dialogue": {"false": 0.9999892111588945, "true": 1.0788841105534225e-05}, "content_quality": {"-2": 0.014285613313624023, "-1": 0.07733471499688607, "0": 0.3669727486925534, "1": 0.43298842013312844, "2": 0.10841850286380808}, "noise_severity": {"0": 0.9259315263281984, "1": 0.06582498982386162, "2": 0.006364522643047914, "3": 0.0010691069911679216, "4": 0.0008098542137240098}, "missing_content_severity": {"0": 0.97570222920389, "1": 0.00849593719992316, "2": 0.01120744186158781, "3": 0.003948713834950982, "4": 0.0006456778996480958}, "factual_density": {"0": 0.028905384099098, "1": 0.05841565620247004, "2": 0.1815695181281888, "3": 0.3540531818540552, "4": 0.37705625971618795}, "pretraining_utility": {"-2": 0.009567085077215131, "-1": 0.10135919512160742, "0": 0.4471576098071486, "1": 0.39709359120063226, "2": 0.04482251879339668}, "math_density": {"0": 0.7543509708020744, "1": 0.23077426238385906, "2": 0.006102826155776727, "3": 0.0021910947942226093, "4": 0.006580845864067101}, "code_density": {"0": 0.00021669179031808602, "1": 0.00040738389351264943, "2": 0.009509861619256961, "3": 0.0864895311135384, "4": 0.9033765315833738}, "reasoning_density": {"0": 0.8094345512701571, "1": 0.05465468089149093, "2": 0.08790716307686608, "3": 0.03251913925233152, "4": 0.015484465509154466}, "education_level": {"none": 0.0010672264140880413, "0": 0.0016594845910542127, "1": 0.027634783598594524, "2": 0.8781122922282583, "3": 0.0882144101932098, "4": 0.0033118029747950983}, "creative_writing_quality": {"not_applicable": 0.9992985382285576, "-2": 9.350281364259723e-05, "-1": 0.0001974935741330741, "0": 0.00032088900368235307, "1": 6.001591289224671e-05, "2": 2.9560467092105047e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 6.933149385735774e-05, "Art & Design": 0.0001980493166038097, "Law & Crime": 6.0817917170527416e-05, "Education & Careers": 0.0004303390200563845, "Entertainment & Media": 6.868405057901257e-05, "Fashion & Beauty": 3.7555620587149535e-05, "Business, Economics & Finance": 9.377853782682501e-05, "Food & Dining": 4.0210726458776973e-05, "Games & Gaming": 0.00025553339601359763, "Electronics & Hardware": 0.00010359090669372694, "Health, Healthcare & Wellness": 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"id": "javascript-0-093d7e305146682a193977ad814af4a0", "text": "// LETTER\nlet btnYear = document.querySelector('#btnYear');\nconst answerLetter = document.querySelector('#answerLetter');\nbtnYear.addEventListener('click', findLetterByYear);\nlet whichOne\n\nfunction findLetterByYear(){\n whichOne = true\n answerLetter.style.color = 'blue'\n answerLetter.style.fontWeight = 800\n\n let year\n year = parseInt(document.querySelector('#year').value);\n\n findLetter(year)\n}\n\nfunction findLetter(year){\n let text = ` est la première lettre correspondant à l'année ${year}.`\n switch(year){\n case 1926:\n case 1951:\n case 1985:\n case 2005:\n case 2025:\n if(whichOne === true){\n answerLetter.textContent = 'A' + text\n } else{\n return 'A'\n }\n break;\n case 1927:\n case 1952:\n case 1986:\n case 2006:\n case 2026:\n if(whichOne === true){\n answerLetter.textContent = 'B' + text\n } else{\n return 'B'\n }\n break;\n case 1928:\n case 1953:\n case 1987:\n case 2007:\n case 2027:\n if(whichOne === true){\n answerLetter.textContent = 'C' + text\n } else{\n return 'C'\n }\n break;\n case 1929:\n case 1954:\n case 1988:\n case 2008:\n case 2028:\n if(whichOne === true){\n answerLetter.textContent = 'D' + text\n } else{\n return 'D'\n }\n break;\n case 1930:\n case 1955:\n case 1989:\n case 2009:\n case 2029:\n if(whichOne === true){\n answerLetter.textContent = 'E' + text\n } else{\n return 'E'\n }\n break;\n case 1931:\n case 1956:\n case 1990:\n case 2010:\n case 2030:\n if(whichOne === true){\n answerLetter.textContent = 'F' + text\n } else{\n return 'F'\n }\n break;\n case 1932:\n case 1957:\n case 1991:\n case 2011:\n case 2031:\n if(whichOne === true){\n answerLetter.textContent = 'G' + text\n } else{\n return 'G'\n }\n break;\n case 1933:\n case 1958:\n case 1992:\n case 2012:\n case 2032:\n if(whichOne === true){\n answerLetter.textContent = 'H' + text\n } else{\n return 'H'\n }\n break;\n case 1934:\n case 1959:\n case 1973:\n case 1993:\n case 2013:\n case 2033:\n if(whichOne === true){\n answerLetter.textContent = 'I' + text\n } else{\n return 'I'\n }\n break;\n case 1935:\n case 1960:\n case 1974:\n case 1994:\n case 2014:\n case 2034:\n if(whichOne === true){\n answerLetter.innerHTML = 'J, (K sera une alternative possible à partir de 2034)'\n } else if(randYr === 2034){\n return 'J K'\n }else{\n return 'J'\n }\n break;\n case 1936:\n case 1961:\n if(whichOne === true){\n answerLetter.textContent = 'K' + text\n } else{\n return 'K'\n }\n break;\n case 1937:\n case 1962:\n case 1975:\n case 1995:\n case 2015:\n if(whichOne === true){\n answerLetter.textContent = 'L' + text\n } else{\n return 'L'\n }\n break;\n case 1938:\n case 1963:\n case 1976:\n case 1996:\n case 2016:\n if(whichOne === true){\n answerLetter.textContent = 'M' + text\n } else{\n return 'M'\n }\n break;\n case 1939:\n case 1964:\n case 1977:\n case 1997:\n case 2017:\n if(whichOne === true){\n answerLetter.textContent = 'N' + text\n } else{\n return 'N'\n }\n break;\n case 1940:\n case 1965:\n case 1978:\n case 1998:\n case 2018:\n if(whichOne === true){\n answerLetter.textContent = 'O' + text\n } else{\n return 'O'\n }\n break;\n case 1941:\n case 1966:\n case 1979:\n case 1999:\n case 2019:\n if(whichOne === true){\n answerLetter.textContent = 'P (Q est également une alternative officielle depuis 2019)'\n } else if(randYr === 2019){\n return 'P Q'\n } else{\n return 'P'\n }\n break;\n case 1942:\n case 1967:\n if(whichOne === true){\n answerLetter.textContent = 'Q' + text\n } else{\n return 'Q'\n }\n break;\n case 1943:\n case 1968:\n case 1980:\n case 2000:\n case 2020:\n if(whichOne === true){\n answerLetter.textContent = 'R' + text\n } else{\n return 'R'\n }\n break;\n case 1944:\n case 1969:\n case 1981:\n case 2001:\n case 2021:\n if(whichOne === true){\n answerLetter.textContent = 'S' + text\n } else{\n return 'S'\n }\n break;\n case 1945:\n case 1970:\n case 1982:\n case 2002:\n case 2022:\n if(whichOne === true){\n answerLetter.textContent = 'T' + text\n } else{\n return 'T'\n }\n break;\n case 1946:\n case 1971:\n case 1983:\n case 2003:\n case 2023:\n if(whichOne === true){\n answerLetter.textContent = 'U' + text\n } else{\n return 'U'\n }\n break;\n case 1947:\n case 1972:\n case 1984:\n case 2004:\n case 2024:\n if(whichOne === true){\n answerLetter.textContent = 'V (alternatives officielles à partir de 2024 : W, X, Y et Z )'\n } else if(randYr === 2024){\n return 'V W X Y Z'\n } else{\n return 'V'\n }\n break;\n case 1948:\n if(whichOne === true){\n answerLetter.textContent = 'W' + text\n } else{\n return 'W'\n }\n break;\n case 1949:\n if(whichOne === true){\n answerLetter.textContent = 'X' + text\n } else{\n return 'X'\n }\n break;\n case 1950:\n if(whichOne === true){\n answerLetter.textContent = `Y ou Z sont les lettres correspondantes à ${year}.`\n } else{\n return 'Y Z'\n }\n break;\n default:\n if(whichOne === true){\n answerLetter.style.color = 'red'\n answerLetter.style.fontWeight = 700\n answerLetter.textContent = `Lettre inconnue pour cette année ou erreur de saisie. (${year})`\n } else{\n return ''\n }\n\n }\n}\n\n\n\n\n\n// YEAR\nlet btnLetter = document.querySelector('#btnLetter');\nconst answerYear = document.querySelector('#answerYear');\nbtnLetter.addEventListener('click', findYearByLetter);\n\nfunction findYearByLetter(){\n answerYear.style.color = 'blue'\n answerYear.style.fontWeight = 800\n\n let letter = document.querySelector('#letter').value;\n letter = letter.toUpperCase()\n let text = ` sont les années correspondantes à la lettre ${letter}.`\n\n switch(letter){\n case 'A':\n answerYear.textContent = '1926, 1951, 1985, 2005 et 2025' + text\n break;\n case 'B':\n answerYear.textContent = '1927, 1952, 1986, 2006 et 2026' + text\n break;\n case 'C':\n answerYear.textContent = '1928, 1953, 1987, 2007 et 2027' + text\n break;\n case 'D':\n answerYear.textContent = '1929, 1954, 1988, 2008 et 2028' + text\n break;\n case 'E':\n answerYear.textContent = '1930, 1955, 1989, 2009 et 2029' + text\n break;\n case 'F':\n answerYear.textContent = '1931, 1956, 1990, 2010 et 2030' + text\n break;\n case 'G':\n answerYear.textContent = '1932, 1957, 1991, 2011 et 2031' + text\n break;\n case 'H':\n answerYear.textContent = '1933, 1958, 1992, 2012 et 2032' + text\n break;\n case 'I':\n answerYear.textContent = '1934, 1959, 1973, 1993, 2013 et 2033' + text\n break;\n case 'J':\n answerYear.innerHTML = '1935, 1960, 1974, 1994, 2014, et 2034, sont les années correspondantes à la lettre J. K sera une alternative possible à partir de 2034'\n break;\n case 'K':\n answerYear.textContent = '1936 et 1961' + text + ' Notez que J sera une alternative possible à partir de 2034.'\n break;\n case 'L':\n answerYear.textContent = '1937, 1962, 1975, 1995 et 2015' + text\n break;\n case 'M':\n answerYear.textContent = '1938, 1963, 1976, 1996 et 2016' + text\n break;\n case 'N':\n answerYear.textContent = '1939, 1964, 1977, 1997 et 2017' + text\n break;\n case 'O':\n answerYear.textContent = '1940, 1965, 1978, 1998 et 2018' + text\n break;\n case 'P':\n answerYear.textContent = '1941, 1966, 1979, 1999 et 2019' + text + ' Notez que Q est également une alternative officielle depuis 2019.'\n break;\n case 'Q':\n answerYear.textContent = '1942 et 1967' + text + ' Notez que P est également une alternative officielle depuis 2019.'\n break;\n case 'R':\n answerYear.textContent = '1943, 1968, 1980, 2000 et 2020' + text\n break;\n case 'S':\n answerYear.textContent = '1944, 1969, 1981, 2001, 2021' + text\n break;\n case 'T':\n answerYear.textContent = '1945, 1970, 1982, 2002, 2022' + text\n break;\n case 'U':\n answerYear.textContent = '1946, 1971, 1983, 2003 et 2023' + text\n break;\n case 'V':\n answerYear.textContent = `1947, 1972, 1984, 2004 et 2024 ${text} Notez les alternatives officielles à ${letter} partir de 2024 : W, X, Y et Z.`\n break;\n case 'W':\n answerYear.textContent = '1948 est l\\'unique année pour la lettre W. Notez ses alternatives officielles à partir de 2024 : V, X, Y et Z.'\n break;\n case 'X':\n answerYear.textContent = '1949 est l\\'unique année pour la lettre X. Notez ses alternatives officielles à partir de 2024 : V, W, Y et Z.'\n break;\n case 'Y':\n case 'Z':\n answerYear.textContent = '1950 est l\\'unique année pour les lettres Y et Z. Notez leurs alternatives officielles à partir de 2024 : V, W et X.'\n break;\n default:\n answerYear.style.color = 'red'\n answerYear.style.fontWeight = 700\n answerYear.textContent = 'Veuillez taper la première lettre de son nom, sans accent.'\n }\n}\n\n\n\n// DOG\nlet btnDog = document.querySelector('#btnDog');\nconst answerDog = document.querySelector('#answerDog');\nbtnDog.addEventListener('click', dogAge);\n\nfunction dogAge(){\n answerDog.style.color = 'blue'\n answerDog.style.fontWeight = 800\n\n let ageDog = parseFloat(document.querySelector('#ageDog').value);\n switch(ageDog){\n case 0.5:\n answerDog.textContent = '10 ans'\n break;\n case 1:\n answerDog.textContent = '18 ans'\n break;\n case 2:\n answerDog.textContent = '27 ans'\n break;\n case 3:\n answerDog.textContent = '33 ans'\n break;\n case 4:\n answerDog.textContent = '39 ans'\n break;\n case 5:\n answerDog.textContent = '45 ans'\n break;\n case 6:\n answerDog.textContent = '51 ans'\n break;\n case 7:\n answerDog.textContent = '57 ans'\n break;\n case 8:\n answerDog.textContent = '63 ans'\n break;\n case 9:\n answerDog.textContent = '69 ans'\n break;\n case 10:\n answerDog.textContent = '75 ans'\n break;\n case 11:\n answerDog.textContent = '80 ans'\n break;\n case 12:\n answerDog.textContent = '85 ans'\n break;\n case 13:\n answerDog.textContent = '90 ans'\n break;\n case 14:\n answerDog.textContent = '96 ans'\n break;\n case 15:\n answerDog.textContent = '102 ans'\n break;\n case 16:\n answerDog.textContent = '110 ans'\n break;\n case 17:\n case 18:\n case 19:\n answerDog.textContent = '♥'\n break;\n default:\n answerDog.style.color = 'red'\n answerDog.style.fontWeight = 700\n answerDog.textContent = 'Âge non pris en compte'\n }\n}\n\n\n\n// CAT\nlet btnCat = document.querySelector('#btnCat');\nconst answerCat = document.querySelector('#answerCat');\nbtnCat.addEventListener('click', catAge);\n\nfunction catAge(){\n answerCat.style.color = 'blue'\n answerCat.style.fontWeight = 800\n\n let ageCat = parseFloat(document.querySelector('#ageCat').value);\n\n if (ageCat < 0.5 || ageCat === 0.5 || ageCat === 1 || ageCat === 1.5 || ageCat === 2 || ageCat > 40){\n if(ageCat < 0.5){\n answerCat.style.color = 'red'\n answerCat.style.fontWeight = 700\n answerCat.textContent = 'Âge non pris en compte'\n } else if(ageCat === 0.5){\n answerCat.textContent = '10 ans'\n } else if(ageCat === 1){\n answerCat.textContent = '15 ans'\n }else if(ageCat === 1.5){\n answerCat.textContent = '21 ans'\n }else if(ageCat === 2){\n answerCat.textContent = '24 ans'\n }else{\n answerCat.style.color = 'yellow'\n answerCat.style.fontWeight = 700\n answerCat.textContent = 'Valeur en années terrestre requise'\n }\n } else if(Number.isInteger(ageCat)){\n let catsAge = 24 + (ageCat - 2) * 4\n answerCat.textContent = `${catsAge} ans`\n }else{\n answerCat.style.color = 'red'\n answerCat.style.fontWeight = 700\n answerCat.textContent = 'Âge non pris en compte'\n }\n}\n\n\n\n// DEGUS\nconst answerDegus = document.querySelector('#answerDegus');\n\nlet degusAge = () => {\n let ageDegus = parseInt(document.querySelector('#ageDegus').value)\n if (isNaN(ageDegus) || ageDegus > 18){\n answerDegus.style.color = 'red'\n answerDegus.style.fontWeight = 700\n return answerDegus.textContent = 'Âge non pris en compte'\n }\n let ageDegusAnswer = ageDegus * 8\n answerDegus.style.color = 'blue'\n answerDegus.style.fontWeight = 800\n answerDegus.textContent = `${ageDegusAnswer} ans`\n}\n\nlet btnDegus = document.querySelector('#btnDegus');\nbtnDegus.addEventListener('click', degusAge);\n\n\n\n// GAME\nlet userAnswer = document.querySelector('#userAnswer')\nlet programAnswer\n\nconst start = document.querySelector('#start');\nstart.addEventListener('click', startGame)\nlet randYear = document.querySelector('#randYear')\nlet submit = document.querySelector('#submit')\nsubmit.addEventListener('click', testInput)\nlet randYr\nlet count = 0\nlet result = document.querySelector('#result')\nlet score = document.querySelector('#score')\nlet scoreNb = document.querySelector('#scoreNb')\nlet chances\nlet rest = document.querySelector('#rest')\nlet restNb = document.querySelector('#restNb')\n\nlet preGame = () => alert('Vous avez 5 chances par année.\\n\\nBonne chance !')\n\ndocument.addEventListener('DOMContentLoaded', blockGame)\n\nfunction blockGame() {\n userAnswer.disabled = true\n typeof userAnswer !== 'string' || userAnswer === ' ' || userAnswer === '' ? submit.disabled = true : ''\n}\n\nfunction allowSubmit(){\n submit.disabled = false\n}\n\nfunction blockSubmit(){\n submit.disabled = true\n}\n\nfunction startGame(){\n answerLetter.classList.add('hiddenVisibility')\n submit.disabled === false ? submit.disabled = true : ''\n result.classList.add(\"hidden\");\n rest.classList.remove(\"hidden\");\n score.classList.remove(\"hidden\");\n chances = 5\n count = 0\n restNb.textContent = chances\n scoreNb.textContent = count\n document.querySelector(\"#userAnswer\").value = '';\n userAnswer.disabled = false\n preGame()\n game()\n}\n\nfunction game(){\n randYear.textContent = Math.floor(Math.random() * 109 + 1926);\n return randYr = parseInt(randYear.textContent)\n}\n\nfunction testInput(){\n whichOne = false\n userAnswer = document.querySelector('#userAnswer').value.toUpperCase().charAt(0)\n\n programAnswer = findLetter(randYr)\n if(programAnswer.includes(userAnswer) && chances !== 0){\n count++\n chances = 5\n restNb.textContent = chances\n scoreNb.textContent = count\n game()\n } else{\n chances--\n restNb.textContent = chances\n if(chances === 0){\n answerLetter.classList.toggle('hiddenVisibility')\n blockGame()\n submit.disabled = true\n randYear.textContent = ''\n rest.classList.add(\"hidden\")\n score.classList.add(\"hidden\")\n result.classList.remove(\"hidden\")\n count > 4 ? result.textContent = `Wouf Wouf ! Votre score est de ${count} points !` : result.textContent = `Fin de partie, votre score est de ${count} points.`\n setTimeout(function(){ result.textContent = 'Tentez de dépasser votre propre score !' ; }, 2500);\n }\n }\n}\n\nuserAnswer.addEventListener('focusin', clearInput)\n\nfunction clearInput(){\n blockSubmit()\n document.querySelector(\"#userAnswer\").value = '';\n}\n", "text_preview": "// LETTER let btnYear = document.querySelector('#btnYear'); const answerLetter = document.querySelector('#answerLetter'); btnYear.addEventListener('click', findLetterByYear); let whichOne function findLetterByYear(){ whichOne = true answerL", "input_sha256": "71826667b552d15558c3abc2cfd91eb90a542d85a178aff7f55a4bb40f094bc3", "text_sha256": "093d7e305146682a193977ad814af4a016617d4b8235b2d90bad04ef00ef48cb", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8026, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Home, Pets & Hobbies", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5309007071439836, "noise_severity": 0.207171169676974, "missing_content_severity": 0.011879116751579284, "factual_density": 2.5348347998372596, "pretraining_utility": -0.03538299215148712, "math_density": 1.0758071400820732, "code_density": 3.764512791747371, "reasoning_density": 0.6772226123969337, "education_level": 1.5047105798428568, "creative_writing_quality": -0.5846304513497134}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Home, Pets & Hobbies"}, "review_reasons": ["secondary_topic probability 0.5073 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.012827841788231709, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9901173482656819, "secondary": 0.9209432868066202, "tertiary": 0.9075577127667263, "structured_records": 0.6146147232041479, "procedural_steps": 0.004399421517042647, "worked_solution": 0.03216331650430068, "contextual_dialogue": 1.5217996616230115e-05, "content_quality": 0.3946106625081566, "noise_severity": 0.8273573586025216, "missing_content_severity": 0.9901007360403506, "factual_density": 0.28522510566044523, "pretraining_utility": 0.5241665659721033, "math_density": 0.1034940499316056, "code_density": 0.8037606597894758, "reasoning_density": 0.4356478230025552, "education_level": 0.4291652090139041, "creative_writing_quality": 0.994160990721745, "primary_topic": 0.8087900254981715, "secondary_topic": 0.48759034081720903}, "probability_margin": {"primary": 0.9857993398269366, "secondary": 0.8899997934928356, "tertiary": 0.8844817356663258, "education_level": 0.11376332629789682, "creative_writing_quality": 0.9929691727917032, "primary_topic": 0.7228303527358014, "secondary_topic": 0.2952949023302456}, "raw_probabilities": {"primary": {"Academic Writing": 7.192273126844163e-05, "Content Listing": 6.651999983684354e-05, "Creative Writing": 0.00010151285229055068, "Customer Support Page": 2.521442061106259e-05, "Q&A Forum": 2.7680121287915464e-05, "Discussion Thread": 1.987899486274233e-05, "Comment Section": 3.4541055426364934e-05, "Missing Content": 6.225432488076199e-05, "Knowledge Article": 0.0006753619803288479, "Legal Notices": 5.9238016805514484e-05, "News Article": 1.1685068556118198e-05, "Organizational Announcement": 2.468353113186295e-05, "Nonfiction Writing": 4.236940921812188e-05, "Organizational About Page": 3.624989545846108e-05, "Personal About Page": 5.892685005705759e-05, "Personal Blog": 2.3351361912407036e-05, "Product Page": 2.2009277774159287e-05, "Spam / Ads": 3.602272092931049e-05, "Structured Data": 0.0022939641534397894, "Technical Writing": 0.9905291254212785, "Transcript / Interview": 3.482685734190509e-05, "Tutorial / How-To Guide": 0.000995904882104695, "User Reviews": 1.697047885657294e-05, "Other / Unknown": 0.004729785594341856}, "secondary": {"Academic Writing": 0.00044595543555963534, "Content Listing": 0.0006024490742829629, 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0.0004398075818133942, "Content Listing": 0.0007115797280212746, "Creative Writing": 0.005502071742651535, "Customer Support Page": 0.0003021646477202916, "Q&A Forum": 0.00020080466687145775, "Discussion Thread": 0.00015030555684932177, "Comment Section": 0.00030817941323187694, "Missing Content": 0.0004268356720675222, "Knowledge Article": 0.024138467817481052, "Legal Notices": 0.00028997806365770073, "News Article": 0.000190021242179176, "Organizational Announcement": 0.0002152140857748126, "Nonfiction Writing": 0.000535262877888834, "Organizational About Page": 0.0003147514002860959, "Personal About Page": 0.0003628450785718758, "Personal Blog": 0.0002842583472081201, "Product Page": 0.0001529297980468593, "Spam / Ads": 0.0003223624321073961, "Structured Data": 0.023077699188953166, "Technical Writing": 0.02692773906845349, "Transcript / Interview": 0.0003288281636057507, "Tutorial / How-To Guide": 0.0032967911566470867, "User Reviews": 0.00011162753513270316, "NONE": 0.9114094747347793}, "structured_records": {"false": 0.3853852767958521, "true": 0.6146147232041479}, "procedural_steps": {"false": 0.9956005784829574, "true": 0.004399421517042647}, "worked_solution": {"false": 0.9678366834956993, "true": 0.03216331650430068}, "contextual_dialogue": {"false": 0.9999847820033838, "true": 1.5217996616230115e-05}, "content_quality": {"-2": 0.02165721979097175, "-1": 0.11433496623595799, "0": 0.30414165707828306, "1": 0.4311822008276894, "2": 0.12868395606709787}, "noise_severity": {"0": 0.852638485075921, "1": 0.10092780260139878, "2": 0.03724425573484752, "3": 0.005002970745451767, "4": 0.004186485842381229}, "missing_content_severity": {"0": 0.9941173099538823, "1": 0.0016232379833710823, "2": 0.002834847437526272, "3": 0.0011122346077251494, "4": 0.0003123700174950517}, "factual_density": {"0": 0.041997039217451126, "1": 0.1490615031224886, "2": 0.23733341278777265, "3": 0.37532570834992507, "4": 0.1962823365223625}, "pretraining_utility": {"-2": 0.027849412137325055, "-1": 0.24749273221783147, "0": 0.49264231961483435, "1": 0.19622250771902391, "2": 0.03579302831098527}, "math_density": {"0": 0.38033551152675327, "1": 0.3570496230030198, "2": 0.14667673784329208, "3": 0.03834846911527049, "4": 0.07758965851166448}, "code_density": {"0": 0.00041817490012278826, "1": 0.0008080177451101815, "2": 0.03214247455814654, "3": 0.16710550630051435, "4": 0.7995258264961062}, "reasoning_density": {"0": 0.6743090427489116, "1": 0.10238730025201698, "2": 0.1365626893159532, "3": 0.045253937219463004, "4": 0.04148703046365534}, "education_level": {"none": 0.0053847793753718015, "0": 0.04812886251258842, "1": 0.41054101454702324, "2": 0.5243043408449201, "3": 0.009105661770634205, "4": 0.0025353409494623213}, "creative_writing_quality": {"not_applicable": 0.9951341589347875, "-2": 0.0009334196862497546, "-1": 0.0014167135502821066, "0": 0.002164986143084323, "1": 0.0002626093065619536, "2": 8.811237903427844e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.000245571100824167, "Art & Design": 0.0005860100979475234, "Law & Crime": 0.00036088387583561215, "Education & Careers": 0.0004019873469528852, "Entertainment & Media": 0.000954839637933929, "Fashion & Beauty": 0.00036293211771225, "Business, Economics & Finance": 0.0003012703522710232, "Food & Dining": 0.0002839903662109624, "Games & Gaming": 0.06774276341748597, "Electronics & Hardware": 0.0003783792891171862, "Health, Healthcare & Wellness": 0.0017507038570636318, "History & Geography": 0.0012731006777917005, "Home, Pets & Hobbies": 0.09331390255090191, "Industry & Production": 0.0002466218326813948, "Language, Literature & Philosophy": 0.0006752603528072067, "Politics & Government": 0.00021011541106715655, "Religion & Spirituality": 0.0005992162089649211, "Science, Mathematics & Engineering": 0.002039568697001008, "Relationships & Community": 0.00023995383917745045, "Software Usage & Applications": 0.0007213964937886298, "Programming & Software 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"excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 13961, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 13961, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:16.253190+00:00", "scoring_key_slot": "primary", "dataset_uid": "3e62dadc-6511-46df-ae08-bb6498a65131", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8cac53f9b04ac8f0907666d170af374bc9ab9af2fde05a3cd2e7a2c828d3cd38", "source_metadata": {"uuid": "3e62dadc-6511-46df-ae08-bb6498a65131", "repo_name": "undefined-giter/spa", "relative_path": "js/pets.js", "category": "WEB", "algo_rel_score": 0.9048079252243042, "quality_score": 3.616164445877075, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2023-08-25T11:06:05.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "616d9020ac31f18ce02d2532dd96e831b052cd74", "size_bytes": 19140, "file_timestamp": 1692961314, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8fe83dff9d9f624b5c176ce09974d79328e5d0ec"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2023-08-25T11:06:05.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "616d9020ac31f18ce02d2532dd96e831b052cd74", "size_bytes": 19140, "file_timestamp": 1692961314, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8fe83dff9d9f624b5c176ce09974d79328e5d0ec", "url": "https://github.com/undefined-giter/spa/blob/8fe83dff9d9f624b5c176ce09974d79328e5d0ec/js/pets.js"}, "gpt4o_tokens": 4573, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.5309007071439836, "noise_severity": 0.207171169676974, "missing_content_severity": 0.011879116751579284, "factual_density": 2.5348347998372596, "pretraining_utility": -0.03538299215148712, "math_density": 1.0758071400820732, "code_density": 3.764512791747371, "reasoning_density": 0.6772226123969337, "education_level": 1.5047105798428568, "creative_writing_quality": -0.5846304513497134}, "score_applicability": {"education_level": 0.9946152206246283, "creative_writing_quality": 0.004865841065212417}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/HarshPariya/React-Form/blob/afe70b130c09454dc22104d8fb3ab53d45b141a4/src/components/Todo.jsx", "id": "javascript-0-9f4c6312ef430dcb2c1db3d0b04319e4", "text": "import React, { useState, useEffect } from \"react\";\n\nconst Todo = () => {\n const [todo, setTodo] = useState(\"\");\n const [todoList, setTodolist] = useState([]);\n const [search, setSearch] = useState(\"\");\n const [darkMode, setDarkMode] = useState(false);\n\n // Load todo list from localStorage\n useEffect(() => {\n const savedTodos = JSON.parse(localStorage.getItem(\"todos\"));\n if (savedTodos) setTodolist(savedTodos);\n }, []);\n\n // Save todo list to localStorage\n useEffect(() => {\n localStorage.setItem(\"todos\", JSON.stringify(todoList));\n }, [todoList]);\n\n const addTodo = () => {\n if (todo.trim() !== \"\") {\n setTodolist([...todoList, { text: todo, completed: false, priority: \"Medium\", dueDate: \"\", tags: [], id: Date.now() }]);\n setTodo(\"\"); // Clear input after adding\n }\n };\n\n const removeTodo = (id) => {\n setTodolist(todoList.filter((item) => item.id !== id));\n };\n\n const toggleComplete = (id) => {\n setTodolist(todoList.map(item =>\n item.id === id ? { ...item, completed: !item.completed } : item\n ));\n };\n\n const filteredTodos = todoList.filter(item => item.text.toLowerCase().includes(search.toLowerCase()));\n const completedTasks = todoList.filter((item) => item.completed).length;\n const progressPercentage = (completedTasks / todoList.length) * 100;\n\n return (\n
\n
\n

\n Todo List\n

\n\n {/* Dark Mode Toggle */}\n setDarkMode(!darkMode)}\n className={`absolute top-5 right-5 p-3 rounded-md ${darkMode ? 'bg-indigo-600' : 'bg-gray-600'} text-white hover:bg-indigo-700 transition-all`}\n >\n Toggle {darkMode ? \"Light\" : \"Dark\"} Mode\n \n\n {/* Input Area (Add Task) */}\n
\n setTodo(e.target.value)}\n placeholder=\"Enter a task\"\n className={`flex-1 p-3 border ${darkMode ? 'border-gray-600' : 'border-gray-300'} rounded-md focus:ring-2 focus:ring-indigo-500 transition-all text-lg`}\n />\n \n Add\n \n
\n\n {/* Search Bar (Move Below Add Task) */}\n setSearch(e.target.value)}\n placeholder=\"Search tasks\"\n className={`mb-4 w-full p-3 border ${darkMode ? 'border-gray-600' : 'border-gray-300'} rounded-md focus:ring-2 focus:ring-indigo-500 transition-all text-lg`}\n />\n\n {/* Todo List */}\n
    \n {filteredTodos.map((item) => (\n \n \n {item.text}\n \n
    \n toggleComplete(item.id)}\n className={`p-2 rounded-md ${item.completed ? 'bg-green-500' : 'bg-gray-500'} text-white hover:bg-green-600 transition-all`}\n >\n {item.completed ? \"Undo\" : \"Complete\"}\n \n removeTodo(item.id)}\n className=\"bg-red-600 text-white px-4 py-2 rounded-md hover:bg-red-700 transition-all\"\n >\n Delete\n \n
    \n \n ))}\n
\n\n {/* Enhanced Progress Bar */}\n
\n

\n Progress: {completedTasks} / {todoList.length} tasks completed\n

\n\n {/* Circular Progress */}\n
\n
\n
\n {progressPercentage.toFixed(0)}%\n
\n \n \n \n \n
\n
\n
\n
\n
\n );\n};\n\nexport default Todo;\n", "text_preview": "import React, { useState, useEffect } from \"react\"; const Todo = () => { const [todo, setTodo] = useState(\"\"); const [todoList, setTodolist] = useState([]); const [search, setSearch] = useState(\"\"); const [darkMode, setDarkMode] = useState(", "input_sha256": "6d7e6fc88fdbcd4277d891be276891b17eec07b710ba4f07d481c699afc19d53", "text_sha256": "9f4c6312ef430dcb2c1db3d0b04319e4766909fc8debfeb5126ac8b3173cc8d4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8024, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, 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null, "cache_write_tokens": null, "input_tokens": 10167, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:16.903198+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "7ab72b41-d395-482b-ab92-d313af476c07", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "4f11d8a9722b88197f8eb99c74bbd525a889e4b619739d90af941e3116833968", "source_metadata": {"uuid": "7ab72b41-d395-482b-ab92-d313af476c07", "repo_name": "HarshPariya/React-Form", "relative_path": "src/components/Todo.jsx", "category": "WEB", "algo_rel_score": 0.9604530930519104, "quality_score": 7.043966770172119, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 15, "repo_created_at": "2025-02-17T19:48:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "dd35bb73ce1f8de5cdaef1030afc52d4e12cf80a", "size_bytes": 5925, "file_timestamp": 1739907501, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "afe70b130c09454dc22104d8fb3ab53d45b141a4"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 15, "repo_created_at": "2025-02-17T19:48:34.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "dd35bb73ce1f8de5cdaef1030afc52d4e12cf80a", "size_bytes": 5925, "file_timestamp": 1739907501, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "afe70b130c09454dc22104d8fb3ab53d45b141a4", "url": "https://github.com/HarshPariya/React-Form/blob/afe70b130c09454dc22104d8fb3ab53d45b141a4/src/components/Todo.jsx"}, "gpt4o_tokens": 1473, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7882208531304669, "noise_severity": 0.05896605060504927, "missing_content_severity": 0.006363169021936355, "factual_density": 3.0074535309565538, "pretraining_utility": 0.6461464409339638, "math_density": 0.5808154631291617, "code_density": 3.702837669262031, "reasoning_density": 0.2695152505109329, "education_level": 1.9697252116217008, "creative_writing_quality": -0.1514519240862763}, "score_applicability": {"education_level": 0.9984886903043705, "creative_writing_quality": 0.0010433549881507561}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/dammymoses2001/oms2/blob/b0a451cdbdca47d8d81714ab12b2a3a1cb9de886/src/pages/product/ProductList/index.jsx", "id": "javascript-0-505b1c823950fcfecb4141f5b303daab", "text": "import React, { useState } from \"react\";\nimport { ProductSearchCard } from \"./shared\";\n//import ClientLogo from \"../../../assets/images/client-logo-1.jpg\";\n\nimport BackgroundImage1 from \"../../../assets/images/pages-background-shape-1.png\";\nimport BackgroundImage3 from \"../../../assets/images/pages-background-shape-3.png\";\n\n// import Product1 from \"../../../assets/images/product-list-1.jpg\";\n// import Product2 from \"../../../assets/images/product-list-2.jpg\";\n// import Product3 from \"../../../assets/images/product-list-3.jpg\";\n\nimport { AppLayout, Input } from \"../../../components\";\nimport { useAuth } from \"../../../hooks\";\n\n\nexport const ProductList = () => {\n const { getProductList,handleSearchProduct } =useAuth();\n const [searchText,setSearchText] =useState(\"\");\n\n //console.log(getProductList,\"getProductList\");\n\n // const results = [\n // {\n // brandLogo: ClientLogo,\n // brandName: \"glaxosmithkline\",\n // discount: 50,\n // id: 1,\n // image: Product1,\n // name: \"Granules or powder\",\n // oldPrice: 100,\n // price: 50\n // },\n // {\n // brandLogo: ClientLogo,\n // brandName: \"glaxosmithkline\",\n // discount: 50,\n // id: 1,\n // image: Product2,\n // name: \"Granules or powder\",\n // oldPrice: 100,\n // price: 50\n // },\n // {\n // brandLogo: ClientLogo,\n // brandName: \"glaxosmithkline\",\n // discount: 50,\n // id: 1,\n // image: Product3,\n // name: \"Granules or powder\",\n // oldPrice: 100,\n // price: 50\n // },\n // {\n // brandLogo: ClientLogo,\n // brandName: \"glaxosmithkline\",\n // discount: 50,\n // id: 1,\n // image: Product3,\n // name: \"Granules or powder\",\n // oldPrice: 100,\n // price: 50\n // },\n // {\n // brandLogo: ClientLogo,\n // brandName: \"glaxosmithkline\",\n // discount: 50,\n // id: 1,\n // image: Product3,\n // name: \"Granules or powder\",\n // oldPrice: 100,\n // price: 50\n // }\n // ];\n\n return (\n \n
\n
\n \n \n {\n handleSearchProduct(e.target.value);\n setSearchText(e.target.value);\n }}\n />\n
\n\n \n \n \n
\n \n \n \n \n
\n
{ getProductList?.find_products?.length>0 ?`Showing 1 -${getProductList?.find_products.length}` :\"No Product Found\"}
\n
{`Your results for \"${searchText?searchText:\"All Product\"}\"`}
\n
\n
\n
\n
\n {getProductList?.find_products?.map((result,index) => (\n \n \n
\n ))}\n
\n
\n \n \n \n \n );\n};\n", "text_preview": "import React, { useState } from \"react\"; import { ProductSearchCard } from \"./shared\"; //import ClientLogo from \"../../../assets/images/client-logo-1.jpg\"; import BackgroundImage1 from \"../../../assets/images/pages-background-shape-1.png\"; ", "input_sha256": "1c0aa4d0b76e1c3899f585934ea000e1344e79c5501533aec17c457ca0a26c5f", "text_sha256": "505b1c823950fcfecb4141f5b303daab69f82821e64bd4108cf5250ca81c4554", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8027, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6070334295522908, "noise_severity": 0.590156033161761, "missing_content_severity": 0.07582672376481167, "factual_density": 2.2616848865830876, "pretraining_utility": 0.1385745104264785, "math_density": 0.04083745205395977, "code_density": 3.750209303131654, "reasoning_density": 0.061820073835330336, "education_level": 1.9633212426218987, "creative_writing_quality": -0.35885004982647756}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010961864608583505, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9878594150349855, "secondary": 0.8943440279713598, "tertiary": 0.9057824953890121, "structured_records": 0.361713671736249, "procedural_steps": 0.000510054170674848, "worked_solution": 0.001962288014549816, "contextual_dialogue": 8.181133351393023e-06, "content_quality": 0.47106791142908644, "noise_severity": 0.5082033056985326, "missing_content_severity": 0.9368110635293236, "factual_density": 0.061779509566325164, "pretraining_utility": 0.5280601106732369, "math_density": 0.9659687899550335, "code_density": 0.7918410859430448, "reasoning_density": 0.9484832718038914, "education_level": 0.8317486971980365, "creative_writing_quality": 0.9982954566904096, "primary_topic": 0.9517248898017632, "secondary_topic": 0.771105883056147}, "probability_margin": {"primary": 0.9831571328970877, "secondary": 0.857080414657018, "tertiary": 0.877389240182713, "education_level": 0.7791250774452416, "creative_writing_quality": 0.9977826860114011, "primary_topic": 0.9421422140925217, "secondary_topic": 0.6956194939795572}, "raw_probabilities": {"primary": {"Academic Writing": 7.763358412847052e-05, "Content Listing": 0.005208139844773426, "Creative Writing": 5.3571743915351926e-05, "Customer Support Page": 2.6861771245873514e-05, "Q&A Forum": 3.230241141715771e-05, "Discussion Thread": 2.071631059421956e-05, "Comment Section": 3.774912339271317e-05, "Missing Content": 0.00023771139153595873, "Knowledge Article": 8.983488051119824e-05, "Legal Notices": 7.513394097170648e-05, "News Article": 1.5789127524796323e-05, "Organizational Announcement": 2.412525867304344e-05, "Nonfiction Writing": 3.2621929848019286e-05, "Organizational About Page": 4.41509825422092e-05, "Personal About Page": 3.9722263642961815e-05, "Personal Blog": 2.3431309025381873e-05, "Product Page": 0.0016045818217977044, "Spam / Ads": 5.2997501276013185e-05, "Structured Data": 0.0013133411716483951, "Technical Writing": 0.9883652727418611, "Transcript / Interview": 3.492260487138857e-05, "Tutorial / How-To Guide": 0.0003125226507710653, "User Reviews": 2.1903416393766757e-05, "Other / Unknown": 0.0022549622176380076}, "secondary": {"Academic Writing": 0.0004944644049482299, "Content Listing": 0.041665945482201816, "Creative Writing": 0.0006553713383206317, "Customer Support Page": 0.00028880263157414623, "Q&A Forum": 0.00023653712027402803, "Discussion Thread": 0.00017488939472939718, "Comment Section": 0.0005512007873036852, "Missing Content": 0.0020098830015416536, "Knowledge Article": 0.000489895498881709, "Legal Notices": 0.0004757887265444407, "News Article": 0.000320949195449206, "Organizational Announcement": 0.00026576974817559934, "Nonfiction Writing": 0.0002898547337119629, "Organizational About Page": 0.00035113221399868876, "Personal About Page": 0.0002824412192306382, "Personal Blog": 0.00031070579576697406, "Product Page": 0.011852346450010052, "Spam / Ads": 0.0009430725324264963, "Structured Data": 0.023882991169728293, "Technical Writing": 0.014541172338063733, "Transcript / Interview": 0.00027511746681311997, "Tutorial / How-To Guide": 0.0007314721052419954, "User Reviews": 0.0001638365058437805, "NONE": 0.8987463601392198}, "tertiary": {"Academic Writing": 0.0003044134368537364, "Content Listing": 0.03231898456509032, "Creative Writing": 0.0004478931816600659, "Customer Support Page": 0.0002373172045039361, "Q&A Forum": 0.00015026426323860807, "Discussion Thread": 0.0001270832712784094, "Comment Section": 0.0005587895236815432, "Missing Content": 0.0018681091695212561, "Knowledge Article": 0.0003805643475024906, "Legal Notices": 0.0005344642797620731, "News Article": 0.00024012412793461644, "Organizational Announcement": 0.00019082107611648964, "Nonfiction 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0.017836054313665733, "3": 0.0007500913332041506, "4": 0.00039541739757487245}, "education_level": {"none": 0.004409234124960007, "0": 0.004786536686733174, "1": 0.08066550355312203, "2": 0.8597905809983637, "3": 0.046974744496462044, "4": 0.0033734001403588616}, "creative_writing_quality": {"not_applicable": 0.998579547242008, "-2": 0.0001727341486597395, "-1": 0.00032607900209967764, "0": 0.0007968612306069356, "1": 8.773899681351279e-05, "2": 3.703937981204473e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00015137429893689712, "Art & Design": 0.0029804057186886127, "Law & Crime": 0.00016429182154812725, "Education & Careers": 0.00017278727138025045, "Entertainment & Media": 0.0001797227736045401, "Fashion & Beauty": 0.0024034272288324717, "Business, Economics & Finance": 0.011439410716865906, "Food & Dining": 0.0034369031068064207, "Games & Gaming": 0.00025971238602811973, "Electronics & Hardware": 0.00036229287218864987, "Health, Healthcare & Wellness": 0.009465515224437773, "History & Geography": 0.00016323614336546808, "Home, Pets & Hobbies": 0.0008104074553141888, "Industry & Production": 0.0011335843840638582, "Language, Literature & Philosophy": 0.00015554191753261863, "Politics & Government": 7.875436397669243e-05, "Religion & Spirituality": 0.00013841475745079662, "Science, Mathematics & Engineering": 0.0002243128655924832, "Relationships & Community": 8.097368603802002e-05, "Software Usage & Applications": 0.0016506096626857365, "Programming & Software Engineering": 0.9535816248093877, "Sports & Fitness": 0.0002198241080984549, "Vehicles & Transportation": 0.00012041898561649354, "Travel & Tourism": 0.00022661527216003675, "Other / Uncovered Subject": 0.0058915484941358155, "Unknown / Insufficient Content": 0.004508289675263638}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011123352518762617, "Art & Design": 0.08429000895904569, "Law & Crime": 0.0009486420162114186, "Education & Careers": 0.0005585920609285541, 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7.875436397669244e-05, "Religion & Spirituality": 0.00013841475745079664, "Science, Mathematics & Engineering": 0.00022431286559248325, "Relationships & Community": 8.097368603802004e-05, "Software Usage & Applications": 0.001650609662685737, "Programming & Software Engineering": 0.9535816248093879, "Sports & Fitness": 0.00021982410809845497, "Vehicles & Transportation": 0.00012041898561649357, "Travel & Tourism": 0.0002266152721600368, "Other / Uncovered Subject": 0.005891548494135817, "Unknown / Insufficient Content": 0.004508289675263639}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0011123352518762617, "Art & Design": 0.08429000895904569, "Law & Crime": 0.0009486420162114186, "Education & Careers": 0.0005585920609285541, "Entertainment & Media": 0.0009090779768515881, "Fashion & Beauty": 0.003013918555346298, "Business, Economics & Finance": 0.06037410568674254, "Food & Dining": 0.0023793948616009832, "Games & Gaming": 0.0007479469078612562, "Electronics & Hardware": 0.0009024147536322091, "Health, Healthcare & Wellness": 0.007396812414975944, "History & Geography": 0.000523715789029732, "Home, Pets & Hobbies": 0.0017448771082608655, "Industry & Production": 0.0008035019730172845, "Language, Literature & Philosophy": 0.0003888473133418225, "Politics & Government": 0.0003953956952766633, "Religion & Spirituality": 0.0004731729830957062, "Science, Mathematics & Engineering": 0.0006895727957012685, "Relationships & Community": 0.00029371111978029603, "Software Usage & Applications": 0.009333537337179183, "Programming & Software Engineering": 0.03291197304212396, "Sports & Fitness": 0.000397129522819753, "Vehicles & Transportation": 0.0002931476700111992, "Travel & Tourism": 0.0005315429644094174, "Other / Uncovered Subject": 0.008677122302277136, "NONE": 0.779909502938603}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, 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"valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9766, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9766, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:30:17.986510+00:00", "scoring_key_slot": "primary", "dataset_uid": "6a5c6aec-851b-4c99-9fc7-c676c7764e14", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b5eb0918067f7c3cfbbe79a62fc09f3a9dd0041dd8a0bb47d84537c3e30926b7", "source_metadata": {"uuid": "6a5c6aec-851b-4c99-9fc7-c676c7764e14", "repo_name": "dammymoses2001/oms2", "relative_path": "src/pages/product/ProductList/index.jsx", "category": "WEB", "algo_rel_score": 0.9562708735466003, "quality_score": 6.109536647796631, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 26, "repo_created_at": "2022-12-05T10:17:36.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0b4d9d3c99382038fe358bff59e962322cdbdcf3", "size_bytes": 5579, "file_timestamp": 1676432715, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b0a451cdbdca47d8d81714ab12b2a3a1cb9de886"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 26, "repo_created_at": "2022-12-05T10:17:36.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0b4d9d3c99382038fe358bff59e962322cdbdcf3", "size_bytes": 5579, "file_timestamp": 1676432715, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b0a451cdbdca47d8d81714ab12b2a3a1cb9de886", "url": "https://github.com/dammymoses2001/oms2/blob/b0a451cdbdca47d8d81714ab12b2a3a1cb9de886/src/pages/product/ProductList/index.jsx"}, "gpt4o_tokens": 1119, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6070334295522908, "noise_severity": 0.590156033161761, "missing_content_severity": 0.07582672376481167, "factual_density": 2.2616848865830876, "pretraining_utility": 0.13857451042647853, "math_density": 0.04083745205395978, "code_density": 3.750209303131654, "reasoning_density": 0.061820073835330336, "education_level": 1.9633212426218987, "creative_writing_quality": -0.35885004982647756}, "score_applicability": {"education_level": 0.99559076587504, "creative_writing_quality": 0.0014204527579919103}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Priyanshu2425/Daash-Frontend/blob/7faaefcef411f0109412b8c8a43d7aca82686325/src/components/Form.jsx", "id": "javascript-0-0b8116d151449f3ca50c40d2e2aeb5f9", "text": "import {useEffect, useState} from 'react'\nimport { useNavigate } from 'react-router-dom';\nimport '../assets/form.css'\n\nexport default function Form(props){\n const [loginData, setLoginData] = useState({\n email:\"\", password:\"\"\n })\n\n const [res, setRes] = useState(\"\");\n const navigate = useNavigate();\n\n function handleLoginChange(event){\n const {name, value} = event.target;\n setLoginData(function(prevData){\n return {\n ...prevData,\n [name] : value\n }\n })\n }\n\n function submitLoginForm(event){\n event.preventDefault();\n fetch('https://deadpool2411.pythonanywhere.com/auth/login', {\n method: \"POST\",\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(loginData),\n }).then((res) => {\n if (!res.ok) {\n throw new Error(`HTTP error! Status: ${res.status}`);\n }\n return res.json();\n })\n .then((data) => {\n if(data[0].success){\n setRes(data[0].success);\n props.login(data[0].currentUser);\n setTimeout(()=>navigate('/dashboard'), 2000);\n }\n else setRes(data[0].failure)\n\n })\n .catch((error) => {\n console.error('Error fetching data:', error);\n });\n }\n\n const [signUpData, setSignUpData] = useState({\n email:\"\", username:\"\", password:\"\", confirmPassword: \"\"\n })\n\n function handleSignUpChange(event){\n const {name, value} = event.target;\n setSignUpData(function(prevData){\n return {\n ...prevData,\n [name] : value\n }\n })\n }\n\n function submitSignupForm(event){\n event.preventDefault();\n console.log(event.form)\n fetch('https://deadpool2411.pythonanywhere.com/auth/signup', {\n method: \"POST\",\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(signUpData),\n }).then((res) => {\n if (!res.ok) {\n throw new Error(`HTTP error! Status: ${res.status}`);\n }\n return res.json();\n })\n .then((data) => {\n if(data[0].success){\n setRes(data[0].success)\n setTimeout(()=>navigate('/dashboard'), 2000);\n \n }else setRes(data[0].failure)\n \n })\n .catch((error) => {\n console.error('Error fetching data:', error);\n });\n }\n\n useEffect(()=>{\n const signUpButton = document.getElementById('signUp');\n const signInButton = document.getElementById('signIn');\n const container = document.getElementById('container');\n\n signUpButton.addEventListener('click', () => {\n container.classList.add(\"right-panel-active\");\n setRes(\"\");\n });\n\n signInButton.addEventListener('click', () => {\n container.classList.remove(\"right-panel-active\");\n setRes(\"\");\n });\n }, [])\n\n return (\n \n
\n
\n
\n

Create Account

\n
\n \n \n \n
\n {res}\n \n \n \" name=\"password\" value={signUpData.password} onChange={handleSignUpChange}/>\n \" name=\"confirmPassword\" value={signUpData.confirmPassword} onChange={handleSignUpChange}/>\n \n
\n
\n
\n
\n

Dashboard

\n
\n \n \n \n
\n {res}\n \n \" name=\"password\" value={loginData.password} onChange={handleLoginChange}/>\n Forgot your password?\n \n
\n
\n
\n
\n
\n

Welcome Back!

\n

To keep connected with us please login with your personal info

\n \n
\n
\n

Hello, Friend!

\n

Enter your personal details and start journey with us

\n \n
\n
\n
\n
\n\n \n\n )\n}", "text_preview": "import {useEffect, useState} from 'react' import { useNavigate } from 'react-router-dom'; import '../assets/form.css' export default function Form(props){ const [loginData, setLoginData] = useState({ email:\"\", password:\"\" }) const [res, set", "input_sha256": "09db097095603945a4b0881fae69bfafdd4f1c1ed2e41d94f4798ca927686864", "text_sha256": "0b8116d151449f3ca50c40d2e2aeb5f977c6b78d0510d4c55d328f791c9ca80a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8028, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4310414636464374, "noise_severity": 0.1754431279077119, "missing_content_severity": 0.08233136254383973, "factual_density": 3.0037259522441797, "pretraining_utility": 0.35427462838706675, "math_density": 0.014114652289154811, "code_density": 3.7967682658875535, "reasoning_density": 0.18076094421366315, "education_level": 2.0160235196682437, "creative_writing_quality": -0.20584475284522047}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", 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"stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "33c707f3de6150fbc5645def9b90edf714137c29", "size_bytes": 6639, "file_timestamp": 1705590472, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7faaefcef411f0109412b8c8a43d7aca82686325"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 3, "repo_created_at": "2024-01-18T14:50:18.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "33c707f3de6150fbc5645def9b90edf714137c29", "size_bytes": 6639, "file_timestamp": 1705590472, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7faaefcef411f0109412b8c8a43d7aca82686325", "url": "https://github.com/Priyanshu2425/Daash-Frontend/blob/7faaefcef411f0109412b8c8a43d7aca82686325/src/components/Form.jsx"}, "gpt4o_tokens": 1417, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.4310414636464374, "noise_severity": 0.1754431279077119, "missing_content_severity": 0.08233136254383973, "factual_density": 3.0037259522441797, "pretraining_utility": 0.35427462838706675, "math_density": 0.014114652289154811, "code_density": 3.7967682658875535, "reasoning_density": 0.18076094421366318, "education_level": 2.0160235196682437, "creative_writing_quality": -0.20584475284522047}, "score_applicability": {"education_level": 0.998307205696307, "creative_writing_quality": 0.007713831129949883}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/mdaiyaz940/helpdesk-app/blob/8f47729f8ecdd147756cdeff3d490700ab239f68/frontend/src/pages/user/CreateTicket.jsx", "id": "javascript-0-62dc3357f7d23bd9d95c2f173fbaa105", "text": "import { useState } from \"react\";\nimport { FiPaperclip } from \"react-icons/fi\";\nimport axios from \"axios\";\n\nconst CreateTicket = () => {\n const [formData, setFormData] = useState({\n ticketNo: \"\",\n date: new Date().toISOString().split(\"T\")[0],\n name: \"\",\n department: \"\",\n subject: \"\",\n category: \"\",\n type: \"\",\n priority: \"Low\",\n description: \"\",\n attachment: null,\n });\n\n const handleChange = (e) => {\n const { name, value, files } = e.target;\n setFormData({\n ...formData,\n [name]: files ? files[0] : value,\n });\n };\n\n const handleSubmit = async (e) => {\n e.preventDefault();\n try {\n const form = new FormData();\n Object.entries(formData).forEach(([key, value]) =>\n form.append(key, value)\n );\n\n const token = localStorage.getItem(\"token\");\nconsole.log(\"Sending token:\", token);\n\n const res = await axios.post(`${VITE_BACKEND_URL}/tickets/new-ticket`, form, {\n headers: {\n \"Content-Type\": \"multipart/form-data\",\n Authorization: `Bearer ${token}`,\n },\n });\n\n alert(`Ticket created! Ticket No: ${formData.ticketNo}`);\n\n // Reset form\n setFormData({\n ticketNo: \"\",\n date: new Date().toISOString().split(\"T\")[0],\n name: \"\",\n department: \"\",\n subject: \"\",\n category: \"\",\n type: \"\",\n priority: \"Low\",\n description: \"\",\n attachment: null,\n });\n } catch (err) {\n alert(\"Error creating ticket: \" + (err.response?.data?.msg || err.message));\n }\n };\n\n return (\n
\n

Create New Ticket

\n\n
\n {/* Ticket No & Date */}\n
\n
\n \n \n
\n
\n \n \n
\n
\n\n {/* Name & Department */}\n
\n
\n \n \n
\n
\n \n \n
\n
\n\n {/* Subject */}\n
\n \n \n
\n\n {/* Category, Type, Priority */}\n
\n
\n \n \n
\n
\n \n \n
\n
\n \n \n \n \n \n \n
\n
\n\n {/* Description + Attachment */}\n
\n \n \n \n
\n\n {/* reCAPTCHA Box */}\n
\n
\n \n I'm not a robot\n
\n
\n \n

Privacy - Terms

\n
\n
\n\n {/* Submit Button */}\n
\n \n Submit\n \n
\n
\n
\n );\n};\n\nexport default CreateTicket;\n", "text_preview": "import { useState } from \"react\"; import { FiPaperclip } from \"react-icons/fi\"; import axios from \"axios\"; const CreateTicket = () => { const [formData, setFormData] = useState({ ticketNo: \"\", date: new Date().toISOString().split(\"T\")[0], n", "input_sha256": "bdbf2f141c6d72aaf3f2ddf6e2c6b3a680d79e7f972b4dc911980e8f3c4ede3b", "text_sha256": "62dc3357f7d23bd9d95c2f173fbaa1059c92409f6bbcf4ef89e6296f3810d3e3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8030, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6440795691723553, "noise_severity": 0.26378597119979563, "missing_content_severity": 0.09186503746262896, "factual_density": 2.592279545414771, "pretraining_utility": 0.4157091458530956, "math_density": 0.018078782367267544, "code_density": 3.765106534539758, "reasoning_density": 0.14756349659968934, "education_level": 2.0101267624007755, "creative_writing_quality": -0.2394601808662146}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0034860535154156436, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9717054346225762, "secondary": 0.9085847687282155, "tertiary": 0.9122223897877796, "structured_records": 0.0961910172107858, "procedural_steps": 0.011627210842639055, "worked_solution": 0.004098373874678408, "contextual_dialogue": 5.6804819408518655e-06, "content_quality": 0.47948075999718376, "noise_severity": 0.7801783573335037, "missing_content_severity": 0.9234458021144759, "factual_density": 0.20714849588968443, "pretraining_utility": 0.42560437852275634, "math_density": 0.9849343480272771, "code_density": 0.8042554454497985, "reasoning_density": 0.8770304195002588, "education_level": 0.8601915395767772, "creative_writing_quality": 0.9982812125810134, "primary_topic": 0.9788592521108778, "secondary_topic": 0.8873300730387136}, "probability_margin": {"primary": 0.9515592898976046, "secondary": 0.8580697611182132, "tertiary": 0.8604663662066451, "education_level": 0.8240602928829872, "creative_writing_quality": 0.9976349847400765, "primary_topic": 0.971025609260199, "secondary_topic": 0.858410100362668}, "raw_probabilities": {"primary": {"Academic Writing": 5.792688430384177e-05, "Content Listing": 6.0094657808736685e-05, "Creative Writing": 5.588016468334741e-05, "Customer Support Page": 0.021325084949030827, "Q&A Forum": 3.426765849983307e-05, "Discussion Thread": 2.340805919345154e-05, "Comment Section": 5.490488197399681e-05, "Missing Content": 0.00013451665340877066, "Knowledge Article": 8.805418731812665e-05, "Legal Notices": 9.062615554087789e-05, "News Article": 1.4996852598109106e-05, "Organizational Announcement": 3.2971137518241166e-05, "Nonfiction Writing": 3.0109733308892497e-05, "Organizational About Page": 4.537322813240403e-05, "Personal About Page": 3.736443598271492e-05, "Personal Blog": 1.9744901061898705e-05, "Product Page": 3.6533435683150714e-05, "Spam / Ads": 3.219315923079861e-05, "Structured Data": 0.00043780648839346033, "Technical Writing": 0.9728843748466355, "Transcript / Interview": 5.054704066687199e-05, "Tutorial / How-To Guide": 0.0007631280252440714, "User Reviews": 1.8068096930883507e-05, "Other / Unknown": 0.003672024366851184}, "secondary": {"Academic Writing": 0.00038960845355188095, "Content Listing": 0.0004756142219455212, "Creative Writing": 0.00045985682197096517, "Customer Support Page": 0.054323975579659955, "Q&A Forum": 0.0001838682722726602, "Discussion Thread": 0.00014835210099149314, "Comment Section": 0.0003581641777675384, "Missing Content": 0.0015692284260916567, "Knowledge Article": 0.0005034059702911212, "Legal Notices": 0.001738857857081011, "News Article": 0.00021628777697010185, "Organizational Announcement": 0.00025328368753500363, "Nonfiction Writing": 0.00021761462759753287, "Organizational About Page": 0.0003151377408038092, "Personal About Page": 0.0002450196720501229, "Personal Blog": 0.0002114309663010855, "Product Page": 0.00020667277037171656, "Spam / Ads": 0.0003848775472983968, "Structured Data": 0.008135384406201915, "Technical Writing": 0.014439737855624437, "Transcript / Interview": 0.00032402027276787177, "Tutorial / How-To Guide": 0.0023893090411918807, "User Reviews": 0.0001165550557891556, "NONE": 0.9123937366978732}, "tertiary": {"Academic Writing": 0.0002495889133359952, "Content Listing": 0.0003058565406743884, "Creative Writing": 0.0003588537029746867, "Customer Support Page": 0.055413424006643695, "Q&A Forum": 0.00013688191968425035, "Discussion Thread": 0.00012221066625609802, "Comment Section": 0.00036845697290265825, "Missing Content": 0.0012459871824852056, "Knowledge Article": 0.0004201278478826057, "Legal Notices": 0.00403313309377438, "News Article": 0.00018382956621988905, "Organizational Announcement": 0.00019917075519094914, "Nonfiction Writing": 0.00018084515095578117, "Organizational About Page": 0.00028551954229290316, "Personal About Page": 0.00018584374079716684, "Personal Blog": 0.00015276224418728926, "Product Page": 0.00018197053838121344, "Spam / Ads": 0.0006050723718516888, "Structured Data": 0.006104566375778362, "Technical Writing": 0.011284265601401569, "Transcript / Interview": 0.0002588263469662497, "Tutorial / How-To Guide": 0.0017510871079517158, "User Reviews": 9.19295981224909e-05, "NONE": 0.9158797902132888}, "structured_records": {"false": 0.9038089827892142, "true": 0.0961910172107858}, "procedural_steps": {"false": 0.9883727891573609, "true": 0.011627210842639055}, "worked_solution": {"false": 0.9959016261253216, "true": 0.004098373874678408}, "contextual_dialogue": {"false": 0.9999943195180592, "true": 5.6804819408518655e-06}, "content_quality": {"-2": 0.009347645338724318, "-1": 0.07962672212939319, "0": 0.3029753791405528, "1": 0.4736989248034623, "2": 0.13435132858786739}, "noise_severity": {"0": 0.7841074423235275, "1": 0.17759274727378394, "2": 0.03138628833625318, "3": 0.004233441012235698, "4": 0.0026800810541995506}, "missing_content_severity": {"0": 0.9453270845887194, "1": 0.023933259131115252, "2": 0.02524935810309299, "3": 0.004528130582961433, "4": 0.0009621675941108534}, "factual_density": {"0": 0.0646450876353656, "1": 0.12143186300457143, "2": 0.24277214084356424, "3": 0.2993002333429241, "4": 0.2718506751735747}, "pretraining_utility": {"-2": 0.0100266934887818, "-1": 0.09835864355690227, "0": 0.40998543237964846, "1": 0.4291372847617736, "2": 0.052491945812894}, "math_density": {"0": 0.9850055903706165, "1": 0.013363345104862508, "2": 0.0007278413339693588, "3": 0.0003531381677397709, "4": 0.0005500850228117505}, "code_density": {"0": 0.0003747696213316905, "1": 0.0007783739103791614, "2": 0.026833292787594495, "3": 0.1773926796685885, "4": 0.7946208840121062}, "reasoning_density": {"0": 0.9091948498710878, "1": 0.03915598356589831, "2": 0.0475266343817479, "3": 0.00313588445476901, "4": 0.000986647726497049}, "education_level": {"none": 0.0016552574155257334, "0": 0.003281474028145756, "1": 0.04901167766506456, "2": 0.8834929496473143, "3": 0.05943265676432702, "4": 0.0031259844796227465}, "creative_writing_quality": {"not_applicable": 0.9985676771508445, "-2": 0.00010786807879338705, "-1": 0.0002767094470083055, "0": 0.0009326924107679637, "1": 8.064450909386638e-05, "2": 3.4408403491830307e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.172287064475667e-05, "Art & Design": 0.0012081767136100955, "Law & Crime": 9.489180317327845e-05, "Education & Careers": 0.00010767996582489845, "Entertainment & Media": 3.940978690446039e-05, "Fashion & Beauty": 5.293926910280192e-05, "Business, Economics & Finance": 0.004313730814618423, "Food & Dining": 6.257435268526894e-05, "Games & Gaming": 7.084790050424892e-05, "Electronics & Hardware": 0.00011442267313347769, "Health, Healthcare & Wellness": 0.0001525840780415489, "History & Geography": 5.027189779502305e-05, "Home, Pets & Hobbies": 4.84567011810039e-05, "Industry & Production": 5.2000037867628084e-05, "Language, Literature & Philosophy": 5.359439283848982e-05, "Politics & Government": 4.043623129463202e-05, "Religion & Spirituality": 4.9891240423337835e-05, "Science, Mathematics & Engineering": 6.953465047526868e-05, "Relationships & Community": 4.998289220360283e-05, "Software Usage & Applications": 0.008646748538721945, "Programming & Software Engineering": 0.9796723577989209, "Sports & Fitness": 6.522222408710085e-05, "Vehicles & Transportation": 2.931917185480109e-05, "Travel & Tourism": 6.181125816871328e-05, "Other / Uncovered Subject": 0.004744058819533235, "Unknown / Insufficient Content": 9.733391639123793e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006389741409413729, "Art & Design": 0.022377415097194327, "Law & Crime": 0.0012312544203433307, "Education & Careers": 0.00046929524403243965, "Entertainment & Media": 0.000412846323886397, "Fashion & Beauty": 0.0002627463845239963, "Business, Economics & Finance": 0.013889908723333086, "Food & Dining": 0.00025645630327452096, "Games & Gaming": 0.0003528757079820416, "Electronics & Hardware": 0.00043578946607236333, "Health, Healthcare & Wellness": 0.0006772574425607888, "History & Geography": 0.0002500469573966322, "Home, Pets & Hobbies": 0.0002574594310338102, "Industry & Production": 0.00020053417988467363, "Language, Literature & Philosophy": 0.00021543578141925833, "Politics & Government": 0.00026865806172357456, "Religion & Spirituality": 0.0003050822393625507, "Science, Mathematics & Engineering": 0.00047376780240506334, "Relationships & Community": 0.000216438883583662, "Software Usage & Applications": 0.033253431405325835, "Programming & Software Engineering": 0.02197037420342983, "Sports & Fitness": 0.00019454696473082545, "Vehicles & Transportation": 0.0001235972000742097, "Travel 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"3": 0.05943265676432702, "4": 0.0031259844796227465}, "creative_writing_quality": {"not_applicable": 0.9985676771508446, "-2": 0.00010786807879338706, "-1": 0.00027670944700830557, "0": 0.0009326924107679639, "1": 8.06445090938664e-05, "2": 3.440840349183031e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.172287064475666e-05, "Art & Design": 0.0012081767136100953, "Law & Crime": 9.489180317327842e-05, "Education & Careers": 0.00010767996582489842, "Entertainment & Media": 3.9409786904460384e-05, "Fashion & Beauty": 5.293926910280191e-05, "Business, Economics & Finance": 0.004313730814618422, "Food & Dining": 6.257435268526893e-05, "Games & Gaming": 7.084790050424891e-05, "Electronics & Hardware": 0.00011442267313347767, "Health, Healthcare & Wellness": 0.00015258407804154888, "History & Geography": 5.027189779502304e-05, "Home, Pets & Hobbies": 4.845670118100389e-05, "Industry & Production": 5.200003786762807e-05, "Language, Literature & Philosophy": 5.359439283848981e-05, "Politics & Government": 4.043623129463201e-05, "Religion & Spirituality": 4.989124042333782e-05, "Science, Mathematics & Engineering": 6.953465047526866e-05, "Relationships & Community": 4.998289220360282e-05, "Software Usage & Applications": 0.008646748538721943, "Programming & Software Engineering": 0.9796723577989207, "Sports & Fitness": 6.522222408710084e-05, "Vehicles & Transportation": 2.9319171854801082e-05, "Travel & Tourism": 6.181125816871326e-05, "Other / Uncovered Subject": 0.0047440588195332345, "Unknown / Insufficient Content": 9.73339163912379e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006389741409413729, "Art & Design": 0.022377415097194327, "Law & Crime": 0.0012312544203433307, "Education & Careers": 0.00046929524403243965, "Entertainment & Media": 0.000412846323886397, "Fashion & Beauty": 0.0002627463845239963, "Business, Economics & Finance": 0.013889908723333086, "Food & Dining": 0.00025645630327452096, "Games & Gaming": 0.0003528757079820416, "Electronics & Hardware": 0.00043578946607236333, "Health, Healthcare & Wellness": 0.0006772574425607888, "History & Geography": 0.0002500469573966322, "Home, Pets & Hobbies": 0.0002574594310338102, "Industry & Production": 0.00020053417988467363, "Language, Literature & Philosophy": 0.00021543578141925833, "Politics & Government": 0.00026865806172357456, "Religion & Spirituality": 0.0003050822393625507, "Science, Mathematics & Engineering": 0.00047376780240506334, "Relationships & Community": 0.000216438883583662, "Software Usage & Applications": 0.033253431405325835, "Programming & Software Engineering": 0.02197037420342983, "Sports & Fitness": 0.00019454696473082545, "Vehicles & Transportation": 0.0001235972000742097, "Travel & Tourism": 0.00030500398409387695, "Other / Uncovered Subject": 0.009297271883397662, "NONE": 0.8916635317679938}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 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"reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:02.322414+00:00", "scoring_key_slot": "primary", "dataset_uid": "1bbb6d01-9e73-4b8a-a1ab-6c84951843fb", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b2fe21a30b7828a20442a2a82dd3615d2137a2f13b80554e12acc9ce6bae53c4", "source_metadata": {"uuid": "1bbb6d01-9e73-4b8a-a1ab-6c84951843fb", "repo_name": "mdaiyaz940/helpdesk-app", "relative_path": "frontend/src/pages/user/CreateTicket.jsx", "category": "WEB", "algo_rel_score": 0.9253132939338684, "quality_score": 7.3732171058654785, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2025-07-03T11:19:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "d26119fad2c4709ed2a4c19fcace01d18657d0f5", "size_bytes": 6642, "file_timestamp": 1751561368, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8f47729f8ecdd147756cdeff3d490700ab239f68"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2025-07-03T11:19:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "d26119fad2c4709ed2a4c19fcace01d18657d0f5", "size_bytes": 6642, "file_timestamp": 1751561368, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8f47729f8ecdd147756cdeff3d490700ab239f68", "url": "https://github.com/mdaiyaz940/helpdesk-app/blob/8f47729f8ecdd147756cdeff3d490700ab239f68/frontend/src/pages/user/CreateTicket.jsx"}, "gpt4o_tokens": 1546, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6440795691723553, "noise_severity": 0.26378597119979563, "missing_content_severity": 0.09186503746262896, "factual_density": 2.592279545414771, "pretraining_utility": 0.41570914585309565, "math_density": 0.018078782367267544, "code_density": 3.765106534539758, "reasoning_density": 0.14756349659968934, "education_level": 2.0101267624007755, "creative_writing_quality": -0.2394601808662146}, "score_applicability": {"education_level": 0.9983447425844744, "creative_writing_quality": 0.0014323228491553532}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/andrew781026/findMask/blob/9f4877e626a211db7df3ed600ce18f8e02299ca6/src/layout/partial/map/Map.js", "id": "javascript-0-64f39b5c426f60a95ac2f39400d61d43", "text": "// react\nimport React from 'react';\nimport ReactDOMServer from 'react-dom/server';\nimport Styles from './Map.module.css';\n\n// leaflet\nimport \"leaflet/dist/leaflet.css\";\nimport L from \"leaflet\";\n\n// components\nimport Tooltip from \"./components/Tooltip\";\nimport GeoLocated from \"./GeoLocation\";\n\n// redux\nimport {connect} from \"react-redux\";\nimport {bindActionCreators} from 'redux';\nimport ReduxMap from \"redux/map/actionReducer\";\nimport ReduxMask from \"redux/mask/actionReducer\";\n\n// utils\nimport {getDebounceFunc} from \"../../../utils/throttleUtil\";\n\n// 參考資料 : https://leafletjs.com/examples/quick-start/ & https://juejin.im/post/5cc192976fb9a032092e8e0a\nclass SimpleExample extends React.Component {\n\n map = null;\n layerGroup = null;\n icons = {};\n\n state = {\n lat: this.props.center.lat, // 25.034180,\n lng: this.props.center.lng, //121.554517,\n zoom: 17\n };\n\n componentDidMount() {\n\n ReduxMask.AsyncMethods.getMaskData().then().catch(e => console.error(e));\n\n const map = L.map('mapid').setView([this.state.lat, this.state.lng], this.state.zoom);\n\n const OSMUrl = \"https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png\";\n\n L.tileLayer(OSMUrl, {\n id: 'mapbox/streets-v11',\n tileSize: 512,\n zoomOffset: -1,\n }).addTo(map);\n\n // 監聽 map 的移動事件\n map.on('moveend', (e) => {\n\n const latlng = map.getCenter();\n this.setState({lat: latlng.lat, lng: latlng.lng});\n this.props.actions.setMapCenter(latlng);\n });\n\n // 計算 5 km 內的藥局 , 並顯示之\n this.map = map;\n this.layerGroup = L.layerGroup().addTo(map); // add layerGroup for markers with clearLayers\n }\n\n componentDidUpdate(prevProps) {\n\n if (this.map) {\n if (this.props.center.fly) this.map.flyTo(this.props.center, 18);\n getDebounceFunc('renderMarker')(() => this.renderMarker(this.map))\n }\n }\n\n getIcon = (color = 'green') => {\n\n const icon = this.icons[color];\n\n if (icon) return icon;\n else {\n\n // 使用 leaflet-color-markers ( https://github.com/pointhi/leaflet-color-markers ) 當作 marker\n const newIcon = new L.Icon({\n iconUrl: `https://cdn.rawgit.com/pointhi/leaflet-color-markers/master/img/marker-icon-2x-${color}.png`,\n shadowUrl: 'https://cdnjs.cloudflare.com/ajax/libs/leaflet/0.7.7/images/marker-shadow.png',\n iconSize: [25, 41],\n iconAnchor: [12, 41],\n popupAnchor: [1, -34],\n shadowSize: [41, 41]\n });\n\n this.icons[color] = newIcon;\n return newIcon;\n }\n\n };\n\n getMedicalStores = () => {\n\n const medicalStores = this.props.nearByMasks.map(mask => ({\n lat: mask.geometry.coordinates[1],\n lng: mask.geometry.coordinates[0],\n ...mask.properties,\n name: mask.properties.name,\n address: mask.properties.address, // 401 - 郵遞區號 , 需要查詢郵局系統\n time: '08:00~21:30', // 只有 available : 星期一上午看診、星期二上午看診、星期三上午看診、星期四上午看診\n phone: mask.properties.phone,\n leftMask: {\n adult: mask.properties.mask_adult,\n children: mask.properties.mask_child,\n },\n }));\n\n return medicalStores;\n };\n\n getMarker = (store) => {\n\n // 使用 leaflet-color-markers ( https://github.com/pointhi/leaflet-color-markers ) 當作 marker\n const greenIcon = this.getIcon('green');\n const goldIcon = this.getIcon('gold');\n const redIcon = this.getIcon('red');\n\n // we can use differ color present the left amount of mask ( user can pick finding mask_adult or mask_child )\n\n // 0 => red\n // 1 ~ 30 => gold\n // 31 ~ => green\n\n const maskType = this.props.maskType;\n const mask_adult = store.leftMask.adult;\n const mask_child = store.leftMask.children;\n\n const getMaskAmount = (maskType) => {\n\n if (maskType === 'child') return mask_child;\n else if (maskType === 'adult') return mask_adult;\n else return mask_child + mask_adult;\n };\n\n\n const getIcon = (maskAmount) => {\n\n if (maskAmount > 30) return greenIcon;\n else if (maskAmount <= 30 && maskAmount > 0) return goldIcon;\n else return redIcon;\n };\n\n return L.marker([store.lat, store.lng], {icon: getIcon(getMaskAmount(maskType))});\n };\n\n renderMarker = () => {\n\n const medicalStores = this.getMedicalStores();\n\n // remove all the markers in one go\n this.layerGroup.clearLayers();\n\n medicalStores.forEach(store => {\n const marker = this.getMarker(store).addTo(this.layerGroup);\n const content = ReactDOMServer.renderToString(); // 將 react-element 轉換成 html-string\n\n marker.bindPopup(content, {className: 'popupCustom'});\n\n if (store.lat === this.props.center.lat && store.lng === this.props.center.lng /* && this.props.center.fly */) {\n\n marker.openPopup();\n }\n });\n\n };\n\n render() {\n\n return (\n <>\n \n
\n \n );\n }\n}\n\nconst mapStateToProps = state => ({\n center: ReduxMap.Selector.getMapCenter(state),\n maskType: ReduxMap.Selector.getMaskType(state),\n maskData: ReduxMask.Selector.getMaskData(state),\n nearByMasks: ReduxMask.Selector.getNearByMasks(state),\n});\n\nconst mapDispatchToProps = (dispatch) => {\n const actions = {\n setMapCenter: ReduxMap.ActionCreator.setMapCenter,\n };\n\n return {\n actions: bindActionCreators(actions, dispatch),\n dispatch,\n }\n};\n\nexport default connect(mapStateToProps, mapDispatchToProps)(SimpleExample);\n\n", "text_preview": "// react import React from 'react'; import ReactDOMServer from 'react-dom/server'; import Styles from './Map.module.css'; // leaflet import \"leaflet/dist/leaflet.css\"; import L from \"leaflet\"; // components import Tooltip from \"./components", "input_sha256": "60b537f7f324fc5fc763f2b2b373c135cf6ea99d75fdcd394a30f5db49f52ca4", "text_sha256": "64f39b5c426f60a95ac2f39400d61d43fd9e94e66cd56ff679cc3539a89379bd", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8032, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5312594356823324, "noise_severity": 0.08207842203732092, 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"file_timestamp": 1621917325, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9f4877e626a211db7df3ed600ce18f8e02299ca6", "url": "https://github.com/andrew781026/findMask/blob/9f4877e626a211db7df3ed600ce18f8e02299ca6/src/layout/partial/map/Map.js"}, "gpt4o_tokens": 1444, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.5312594356823324, "noise_severity": 0.08207842203732092, "missing_content_severity": 0.16985980342305684, "factual_density": 3.2523034497025036, "pretraining_utility": 0.4950416167091267, "math_density": 0.23897322582606229, "code_density": 3.832869186237372, "reasoning_density": 0.5271238569990209, "education_level": 2.290827794264836, "creative_writing_quality": -0.3488610435182079}, "score_applicability": {"education_level": 0.9985535125417694, "creative_writing_quality": 0.0020900446718772918}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/fedspendingtransparency/data-act-broker-web-app/blob/f90bb393999bc76d19001e82eca4e62b2157d57c/src/js/components/dashboard/graph/DashboardGraph.jsx", "id": "javascript-0-a1c6883550253caa62d2ebcb82669bfc", "text": "/**\n * DashboardGraph.jsx\n * Created by 11/14/19\n */\n\nimport React from 'react';\nimport PropTypes from 'prop-types';\nimport { throttle } from 'lodash';\nimport { significanceColors } from 'dataMapping/dashboard/fileLabels';\nimport NoResultsMessage from 'components/SharedComponents/NoResultsMessage';\nimport LoadingMessage from 'components/SharedComponents/LoadingMessage';\nimport ErrorMessageOverlay from 'components/SharedComponents/ErrorMessageOverlay';\nimport BarChartStacked from './historical/BarChartStacked';\nimport DashboardGraphTooltip from './DashboardGraphTooltip';\nimport SignificanceGraph from './active/SignificanceGraph';\nimport CategoryButton from './active/CategoryButton';\nimport HistoricalDashboardTooltip from './historical/HistoricalDashboardTooltip';\nimport ActiveDashboardTooltip from './active/ActiveDashboardTooltip';\n\nconst propTypes = {\n xSeries: PropTypes.arrayOf(PropTypes.oneOfType([PropTypes.string, PropTypes.number])),\n ySeries: PropTypes.arrayOf(PropTypes.object),\n allY: PropTypes.oneOfType([PropTypes.object, PropTypes.arrayOf(PropTypes.number)]),\n loading: PropTypes.bool,\n error: PropTypes.bool,\n type: PropTypes.oneOf(['historical', 'active']),\n description: PropTypes.string,\n errorLevel: PropTypes.oneOf(['error', 'warning']).isRequired\n};\n\nconst defaultProps = {\n description: null\n};\n\nconst graphHeight = 540;\nconst spaceBetweenStacks = 2;\n\nexport default class DashboardGraph extends React.Component {\n constructor(props) {\n super(props);\n\n this.state = {\n windowWidth: 0,\n visualizationWidth: 0,\n showTooltip: false,\n tooltipData: {},\n tooltipX: 0,\n tooltipY: 0,\n hovered: '',\n categories: ['accuracy', 'completeness', 'existence']\n };\n\n this.handleWindowResize = throttle(this.handleWindowResize.bind(this), 50);\n this.showTooltip = this.showTooltip.bind(this);\n this.hideTooltip = this.hideTooltip.bind(this);\n this.toggleTooltip = this.toggleTooltip.bind(this);\n this.filterCategory = this.filterCategory.bind(this);\n }\n\n componentDidMount() {\n this.handleWindowResize();\n window.addEventListener('resize', this.handleWindowResize);\n }\n\n componentWillUnmount() {\n window.removeEventListener('resize', this.handleWindowResize);\n }\n\n handleWindowResize() {\n // determine if the width changed\n const windowWidth = window.innerWidth;\n if (this.state.windowWidth !== windowWidth && this.graphDiv) {\n // width changed, update the visualization width\n this.setState({\n windowWidth,\n visualizationWidth: this.graphDiv.offsetWidth\n });\n }\n }\n\n showTooltip(data) {\n this.setState({\n showTooltip: true,\n tooltipData: data,\n hovered: data.xValue\n });\n }\n\n hideTooltip() {\n this.setState({\n showTooltip: false,\n hovered: ''\n });\n }\n\n toggleTooltip(data) {\n if (this.state.showTooltip) {\n this.hideTooltip();\n }\n else {\n this.showTooltip(data);\n }\n }\n\n filterCategory(category) {\n let categories = [...this.state.categories];\n if (categories.includes(category)) {\n // remove this category from the graph\n categories = categories.filter((cat) => cat !== category);\n }\n else {\n // add this category to the graph\n categories.push(category);\n }\n\n this.setState({\n categories\n });\n }\n\n generateCategoryButtons() {\n if (this.props.type === 'historical') {\n return null;\n }\n const categories = Object.keys(significanceColors);\n const buttons = categories.map((category) => {\n const rules = this.props.ySeries.filter((rule) => rule.category === category);\n return (\n \n );\n });\n return (\n
\n {buttons}\n
\n );\n }\n\n render() {\n let tooltip = null;\n if (this.state.showTooltip) {\n tooltip = this.props.type === 'historical' ? (\n \n \n \n ) :\n (\n \n \n \n );\n }\n const empty = (this.props.ySeries.length === 0);\n\n let graphContent = ;\n if (!this.props.loading) {\n if (this.props.error) {\n graphContent = ;\n }\n else if (empty) {\n graphContent = ;\n }\n else {\n graphContent = this.props.type === 'historical' ? (\n \n ) : (\n \n );\n }\n }\n const graphTitle = this.props.type === 'historical' ? (\n null\n ) : (\n

Significance

\n );\n\n return (\n
\n {graphTitle}\n

{this.props.description}

\n {this.generateCategoryButtons()}\n {\n this.graphDiv = div;\n }}>\n {tooltip}\n {graphContent}\n
\n
\n );\n }\n}\n\nDashboardGraph.propTypes = propTypes;\nDashboardGraph.defaultProps = defaultProps;\n", "text_preview": "/** * DashboardGraph.jsx * Created by 11/14/19 */ import React from 'react'; import PropTypes from 'prop-types'; import { throttle } from 'lodash'; import { significanceColors } from 'dataMapping/dashboard/fileLabels'; import NoResul", "input_sha256": "da10a08a09d289d773500f97e55d05d8edde0f58a3c7029df4e0fc9095f72f57", "text_sha256": "a1c6883550253caa62d2ebcb82669bfcd9dc4edefdf2825d99b052bf684b2b21", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8031, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1461873072971813, "noise_severity": 0.06602888332777924, "missing_content_severity": 0.04310139075989732, "factual_density": 3.1869710125026796, "pretraining_utility": 0.5126629113662914, "math_density": 0.1881431657416879, "code_density": 3.854702314421329, "reasoning_density": 0.34707407039407384, "education_level": 2.2695466758283196, "creative_writing_quality": -0.18829779526985627}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007583622067314999, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.99753175069295, "secondary": 0.9798499098848598, "tertiary": 0.9877632546507537, "structured_records": 0.010956393591313092, "procedural_steps": 0.0017680874042717521, "worked_solution": 0.0014448190739661142, "contextual_dialogue": 6.002504074950369e-06, "content_quality": 0.5899546277343266, "noise_severity": 0.9449759305601839, "missing_content_severity": 0.9640821743667523, "factual_density": 0.3224758437522326, "pretraining_utility": 0.4712037311235415, "math_density": 0.8432140285485934, "code_density": 0.8789185953511074, "reasoning_density": 0.7107716080049384, "education_level": 0.6609711061567437, "creative_writing_quality": 0.9992565675217313, "primary_topic": 0.9701717800950435, "secondary_topic": 0.788021804657794}, "probability_margin": {"primary": 0.9962561095284573, "secondary": 0.9725049253511624, "tertiary": 0.9836576767699585, "education_level": 0.4715976547142429, "creative_writing_quality": 0.9990290776521733, "primary_topic": 0.9550035640953237, "secondary_topic": 0.7314619686953796}, "raw_probabilities": {"primary": {"Academic Writing": 2.1813428815774535e-05, "Content Listing": 1.3998770049121753e-05, "Creative Writing": 9.290656355814157e-06, "Customer Support Page": 5.267462066450088e-06, "Q&A Forum": 6.522383651357699e-06, "Discussion Thread": 4.482837032981751e-06, "Comment Section": 9.472326171293644e-06, "Missing Content": 5.265335236136272e-05, "Knowledge Article": 4.468340064564996e-05, "Legal Notices": 3.440270181615034e-05, "News Article": 3.341226581381118e-06, "Organizational Announcement": 8.437263747377058e-06, "Nonfiction Writing": 7.263020332383135e-06, "Organizational About Page": 9.423486352483329e-06, "Personal About Page": 6.665405188892652e-06, "Personal Blog": 4.0866473213535465e-06, "Product Page": 4.866031798726818e-06, "Spam / Ads": 5.4530243468198145e-06, "Structured Data": 0.0005972361387241551, "Technical Writing": 0.9976345944140771, "Transcript / Interview": 8.708981521284295e-06, "Tutorial / How-To Guide": 0.00012487574412846375, "User Reviews": 3.976411293779435e-06, "Other / Unknown": 0.0013784848856198068}, "secondary": {"Academic Writing": 0.00015102131250414432, "Content Listing": 0.00016631906378821178, "Creative Writing": 0.0001314583362033104, "Customer Support Page": 7.529402955800752e-05, "Q&A Forum": 4.2433691102840786e-05, "Discussion Thread": 3.290245132212723e-05, "Comment Section": 9.633614999543855e-05, "Missing Content": 0.0008716873058669239, "Knowledge Article": 0.0003832396740292638, "Legal Notices": 0.0011994894756779916, "News Article": 7.236527525965263e-05, "Organizational Announcement": 9.17149631896477e-05, "Nonfiction Writing": 5.992838939773366e-05, "Organizational About Page": 8.290910084066487e-05, "Personal About Page": 6.47405013006049e-05, "Personal Blog": 5.8783030506759644e-05, "Product Page": 3.533519942443526e-05, "Spam / Ads": 6.753659324203621e-05, "Structured Data": 0.006968515758189923, "Technical Writing": 0.008184571621828169, "Transcript / Interview": 7.337889627512984e-05, "Tutorial / How-To Guide": 0.00036749155353347135, "User Reviews": 3.3050653972948796e-05, "NONE": 0.9806894969729906}, "tertiary": {"Academic Writing": 9.038122212708363e-05, "Content Listing": 0.000152965733506549, "Creative Writing": 9.129224479024858e-05, "Customer Support Page": 4.949318900239469e-05, "Q&A Forum": 2.756292785401418e-05, "Discussion Thread": 2.19148502680563e-05, "Comment Section": 7.69132871483155e-05, "Missing Content": 0.0009402638348728798, "Knowledge Article": 0.0002895908462105434, "Legal Notices": 0.0012205224769366871, "News Article": 5.263540642646277e-05, "Organizational Announcement": 7.435215951175744e-05, "Nonfiction Writing": 5.686312869942108e-05, "Organizational About Page": 7.226712919761078e-05, "Personal About Page": 4.227434081713202e-05, "Personal Blog": 4.379015547066799e-05, "Product Page": 3.236966077901996e-05, "Spam / Ads": 5.8274420169594245e-05, "Structured Data": 0.0033558272862666268, "Technical Writing": 0.004615442270346996, "Transcript / Interview": 4.6044839451828885e-05, "Tutorial / How-To Guide": 0.00029238449241894283, "User Reviews": 2.3455057421673595e-05, "NONE": 0.9882731190403056}, "structured_records": {"false": 0.9890436064086869, "true": 0.010956393591313092}, "procedural_steps": {"false": 0.9982319125957283, "true": 0.0017680874042717521}, "worked_solution": {"false": 0.9985551809260339, "true": 0.0014448190739661142}, "contextual_dialogue": {"false": 0.9999939974959251, "true": 6.002504074950369e-06}, "content_quality": {"-2": 0.003887426407786235, "-1": 0.008933831427064685, "0": 0.1434036276333254, "1": 0.524654237523829, "2": 0.3191208770079946}, "noise_severity": {"0": 0.9396528769442234, "1": 0.056671884286230004, "2": 0.0022782751913948056, "3": 0.0007874056538475201, "4": 0.0006095579243042666}, "missing_content_severity": {"0": 0.9660745119113517, "1": 0.027798662452908966, "2": 0.0038664615575115207, "3": 0.0014716511209446124, "4": 0.0007887129572828653}, "factual_density": {"0": 0.015735372795855688, "1": 0.03978257941413545, "2": 0.13032293601483766, "3": 0.3700938860418165, "4": 0.44406522573335483}, "pretraining_utility": {"-2": 0.008426345891116823, "-1": 0.07329001327994943, "0": 0.3890872414094801, "1": 0.45558718241043294, "2": 0.07360921700902075}, "math_density": {"0": 0.8645859550169745, "1": 0.1107652947253193, "2": 0.008695804522450504, "3": 0.003825520969554511, "4": 0.012127424765701006}, "code_density": {"0": 0.00021955569214405528, "1": 0.00041167989684012736, "2": 0.014041892543149184, "3": 0.1151006380332761, "4": 0.8702262338345905}, "reasoning_density": {"0": 0.7964326935056912, "1": 0.09611599975669838, "2": 0.08240782878018005, "3": 0.014031498752706362, "4": 0.011011979204724074}, "education_level": {"none": 0.001246826550485657, "0": 0.0014725657503087766, "1": 0.01385845844795869, "2": 0.7174759217972864, "3": 0.24587826708304344, "4": 0.020067960370917074}, "creative_writing_quality": {"not_applicable": 0.999380472934776, "-2": 6.123368421410718e-05, "-1": 0.0001126978610682261, "0": 0.00035139528260272286, "1": 6.989082567293197e-05, "2": 2.4309411665920987e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.4497476352073764e-05, "Art & Design": 0.0024043251775769012, "Law & Crime": 0.00010706039969049368, "Education & Careers": 6.69912874456231e-05, "Entertainment & Media": 4.1586376326802536e-05, "Fashion & Beauty": 4.572083125807254e-05, "Business, Economics & Finance": 0.004117941089492089, "Food & Dining": 5.054817346348473e-05, "Games & Gaming": 6.981730789066905e-05, "Electronics & Hardware": 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"missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10442, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10442, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:06.552333+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "f335527e-c4ce-43bf-a9ef-505c4fdc4a9d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d20e13c44aac28ad966680ac101f320f51e9e3762766f668af60cefb8b29d2a0", "source_metadata": {"uuid": "f335527e-c4ce-43bf-a9ef-505c4fdc4a9d", "repo_name": "voxxlr/server", "relative_path": "doc/client/gl/gl.js", "category": "TOOL", "algo_rel_score": 0.9507340788841248, "quality_score": 3.8668124675750732, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-06-06T19:51:36.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9084003936ab2d17f3f1107feaae3e5d1a2abfb2", "size_bytes": 6568, "file_timestamp": 1718630644, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f75f35be77d7b9b4db1845def4291c8f5c86987e"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2024-06-06T19:51:36.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9084003936ab2d17f3f1107feaae3e5d1a2abfb2", "size_bytes": 6568, "file_timestamp": 1718630644, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "f75f35be77d7b9b4db1845def4291c8f5c86987e", "url": "https://github.com/voxxlr/server/blob/f75f35be77d7b9b4db1845def4291c8f5c86987e/doc/client/gl/gl.js"}, "gpt4o_tokens": 1754, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.46675485668254957, "noise_severity": 0.06219288670018122, "missing_content_severity": 0.10708516042320913, "factual_density": 3.2486171461090136, "pretraining_utility": 0.3246690773628742, "math_density": 0.7552879752809544, "code_density": 3.9099998985394246, "reasoning_density": 0.1632237869465159, "education_level": 2.4113003642633277, "creative_writing_quality": -0.415460157584263}, "score_applicability": {"education_level": 0.9984210824376855, "creative_writing_quality": 0.0004831846852441422}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/WemellyNunes/app-disem/blob/46e29980eb1fdd4dfc6f3ce5e96017b4953e9a52/src/pages/form/index.jsx", "id": "javascript-0-9ed8b2dd5fd5657c667c9647b7d9efa6", "text": "import { useState, useEffect } from \"react\";\nimport { useNavigate, useParams } from \"react-router-dom\";\nimport { FaFilePen } from \"react-icons/fa6\";\nimport { useUser } from \"../../contexts/user\";\nimport InputSelect from \"../../components/inputs/inputSelect\";\nimport InputPrimary from \"../../components/inputs/inputPrimary\";\nimport RadioInput from \"../../components/inputs/radioInput\";\nimport ButtonPrimary from \"../../components/buttons/buttonPrimary\";\nimport ButtonSecondary from \"../../components/buttons/buttonSecondary\";\nimport MessageBox from \"../../components/box/message\";\nimport PageTitle from \"../../components/title\";\nimport Loading from \"../../components/modal/loading\";\nimport MessageCard from \"../../components/cards/menssegeCard\";\nimport UploadModalFiles from \"../../components/modal/uploadFiles\";\nimport DocumentList from \"../../components/documents\";\nimport ButtonUpload from \"../../components/buttons/buttonUpload\";\n\nimport { useNotifications } from \"../../contexts/notification/NotificationContext\";\nimport { origin, classification, options, system, indicesRisco, maintence } from \"../../utils/constants/selectOptions\";\nimport { calcularValorRisco, calcularPrioridade } from \"../../utils/matriz\";\nimport { createOrder, updateOrder, getOrderById, getAllInstitutes, getAllUnits, getDocumentsByOrderServiceId } from \"../../utils/api/api\";\n\nexport default function Form() {\n const { id } = useParams();\n const { user } = useUser();\n const navigate = useNavigate();\n const { addNotification } = useNotifications();\n\n\n const [orderId, setOrderId] = useState(null);\n const [emptyFields, setEmptyFields] = useState({});\n const [showMessageBox, setShowMessageBox] = useState(false);\n const [messageContent, setMessageContent] = useState({ type: '', title: '', message: '' });\n const [isCreating, setIsCreating] = useState(!id);\n const [isEditing, setIsEditing] = useState(!id);\n const [isSaved, setIsSaved] = useState(false);\n const [status, setStatus] = useState(\"A atender\");\n const [isLoading, setIsLoading] = useState(false);\n const [institutes, setInstitutes] = useState([]);\n const [units, setUnits] = useState([]);\n const [documents, setDocuments] = useState([]);\n const [uploadedFiles, setUploadedFiles] = useState([]); \n const [isModalOpen, setIsModalOpen] = useState(false);\n\n const [formData, setFormData] = useState({\n selectedOption: { value: 'comum', required: false },\n classe: { value: '', required: true },\n indiceRisco: { value: '', required: true },\n valorRisco: { value: '', required: false },\n prioridade: { value: '', required: false },\n requisicao: { value: '', required: true },\n solicitante: { value: '', required: true },\n contato: { value: '', required: false },\n unidade: { value: '', required: true },\n origem: { value: '', required: true },\n manutencao: { value: 'CORRETIVA', required: true },\n sistema: { value: '', required: true },\n unidadeManutencao: { value: '', required: true },\n campus: { value: '', required: true },\n observacao: { value: '', required: false },\n objetoPreparo: { value: '', required: true },\n });\n\n\n useEffect(() => {\n if (orderId) {\n fetchDocuments(orderId);\n }\n }, [orderId]);\n\n const fetchDocuments = async (orderId) => {\n try {\n console.log(`📂 Buscando documentos para OS ID: ${orderId}`);\n const response = await getDocumentsByOrderServiceId(orderId);\n setDocuments(response);\n console.log(\"📂 Documentos encontrados:\", response);\n } catch (error) {\n console.error(\"❌ Erro ao buscar documentos:\", error);\n }\n };\n\n \n useEffect(() => {\n const fetchInstitutes = async () => {\n try {\n const response = await getAllInstitutes(); \n const formattedInstitutes = response.map((institute) => ({\n label: `${institute.name.toUpperCase()} - ${institute.acronym.toUpperCase()}`,\n value: institute.name, \n }))\n .sort((a, b) => a.label.localeCompare(b.label));\n setInstitutes(formattedInstitutes);\n } catch (error) {\n console.error(\"Erro ao buscar institutos:\", error);\n }\n };\n \n fetchInstitutes();\n }, []);\n\n useEffect(() => {\n const fetchUnits = async () => {\n try {\n const response = await getAllUnits(); \n const formattedUnits = response.map((unit) => ({\n label: unit.unit.toUpperCase(),\n value: unit.unit,\n campus: unit.campus.toUpperCase(),\n }))\n .sort((a, b) => a.label.localeCompare(b.label));\n setUnits(formattedUnits);\n } catch (error) {\n console.error(\"Erro ao buscar unidades:\", error);\n }\n };\n \n fetchUnits();\n }, []);\n\n\n\n useEffect(() => {\n document.body.classList.add(\"bg-form-page\");\n\n return () => {\n document.body.classList.remove(\"bg-form-page\");\n };\n }, []);\n\n useEffect(() => {\n if (id) {\n setIsCreating(false);\n setIsEditing(true);\n fetchOrderData(id);\n }\n }, [id]);\n\n const fetchOrderData = async (id) => {\n try {\n const response = await getOrderById(id);\n setOrderId(id);\n\n setFormData((prevData) => ({\n ...prevData,\n origem: { ...prevData.origem, value: response.origin },\n requisicao: { ...prevData.requisicao, value: response.requisition },\n classe: { ...prevData.classe, value: response.classification },\n solicitante: { ...prevData.solicitante, value: response.requester },\n contato: { ...prevData.contato, value: response.contact },\n unidade: { ...prevData.unidade, value: response.unit },\n manutencao: { ...prevData.manutencao, value: response.typeMaintenance },\n sistema: { ...prevData.sistema, value: response.system },\n unidadeManutencao: { ...prevData.unidadeManutencao, value: response.maintenanceUnit },\n campus: { ...prevData.campus, value: response.campus },\n observacao: { ...prevData.observacao, value: response.observation },\n objetoPreparo: { ...prevData.objetoPreparo, value: response.preparationObject },\n indiceRisco: { ...prevData.indiceRisco, value: response.maintenanceIndicators },\n \n\n }));\n\n setEmptyFields({});\n } catch (error) {\n console.error(\"Erro ao carregar os dados da ordem de serviço:\", error);\n }\n };\n \n \n\n const handleFieldChange = (field) => (value) => {\n setFormData((prevData) => {\n const updatedData = {\n ...prevData,\n [field]: { ...prevData[field], value },\n };\n\n if (field === 'origem' && value === 'DISEM') {\n const randomNumber = Math.floor(Math.random() * 90000) + 10000;\n const requisitionNumber = randomNumber;\n updatedData.requisicao = { ...prevData.requisicao, value: requisitionNumber };\n\n setEmptyFields((prevEmptyFields) => {\n const updatedEmptyFields = { ...prevEmptyFields };\n delete updatedEmptyFields.requisicao;\n return updatedEmptyFields;\n });\n } else if (field === 'origem' && value === 'sipac') {\n updatedData.requisicao = { ...prevData.requisicao, value: '' };\n }\n\n if (field === 'unidadeManutencao') {\n const selectedUnit = units.find((unit) => unit.value === value); \n if (selectedUnit) {\n updatedData.campus = { ...prevData.campus, value: selectedUnit.campus }; \n }\n }\n\n if (updatedData[field].required && value.trim()) {\n setEmptyFields((prevEmptyFields) => {\n const updatedEmptyFields = { ...prevEmptyFields };\n delete updatedEmptyFields[field];\n return updatedEmptyFields;\n });\n }\n\n return updatedData;\n });\n };\n\n const validateFields = () => {\n const newEmptyFields = {};\n Object.keys(formData).forEach((field) => {\n const { value, required } = formData[field];\n if (required && (!value || value.toString().trim() === \"\")) {\n newEmptyFields[field] = true;\n }\n });\n\n setEmptyFields(newEmptyFields);\n return Object.keys(newEmptyFields).length === 0;\n };\n\n\n const getOrderData = () => {\n const valor = calcularValorRisco(formData.classe, formData.indiceRisco);\n const prioridadeCalculada = calcularPrioridade(valor);\n\n return {\n origin: formData.origem.value,\n requisition: formData.requisicao.value,\n classification: formData.classe.value,\n maintenanceIndicators: formData.indiceRisco.value,\n valorRisco: valor,\n prioridade: prioridadeCalculada,\n requester: formData.solicitante.value,\n contact: formData.contato.value,\n unit: formData.unidade.value,\n maintenanceUnit: formData.unidadeManutencao.value,\n campus: formData.campus.value,\n observation: formData.observacao.value,\n preparationObject: formData.objetoPreparo.value,\n system: formData.sistema.value,\n typeMaintenance: formData.manutencao.value,\n typeTreatment: formData.selectedOption.value,\n status,\n date: new Date().toISOString().split(\"T\")[0],\n modificationDate: new Date().toISOString().split(\"T\")[0]\n };\n };\n\n const handleSave = async () => {\n if (!validateFields()) {\n setMessageContent({ type: 'error', title: 'Erro.', message: 'Por favor, preencha todos os campos obrigatórios.' });\n setShowMessageBox(true);\n setTimeout(() => setShowMessageBox(false), 1500);\n return;\n }\n\n setIsLoading(true);\n\n try {\n const ordemDeServico = getOrderData();\n const response = await createOrder(ordemDeServico);\n\n if (response && response.id) {\n setOrderId(response.id);\n setIsSaved(true);\n setIsCreating(false);\n setIsEditing(false);\n setMessageContent({ type: 'success', title: 'Sucesso.', message: `Ordem de serviço criada com sucesso.` });\n setShowMessageBox(true);\n addNotification(`Nova OS cadastrada n°${response.requisition} em ${response.date}`);\n } else {\n throw new Error(\"Erro ao criar OS. Nenhum ID retornado.\");\n }\n } catch (error) {\n setMessageContent({ type: 'error', title: 'Erro.', message: 'Não foi possível salvar a ordem de serviço.' });\n setShowMessageBox(true);\n console.error(\"Erro ao salvar a ordem de serviço:\", error);\n } finally {\n setIsLoading(false);\n setTimeout(() => setShowMessageBox(false), 1500);\n }\n };\n\n const handleEdit = () => {\n setIsEditing(true);\n setIsSaved(true);\n };\n\n const handleUpdate = async () => {\n if (!orderId) {\n console.error(\"ID da ordem de serviço não está definido.\");\n return;\n }\n\n if (!validateFields()) {\n setMessageContent({ type: 'error', title: 'Erro.', message: 'Por favor, preencha todos os campos obrigatórios.' });\n setShowMessageBox(true);\n setTimeout(() => setShowMessageBox(false), 1500);\n return;\n }\n\n setIsLoading(true);\n\n try {\n const ordemDeServico = getOrderData();\n const response = await updateOrder(orderId, ordemDeServico);\n\n if (response) {\n setIsSaved(true);\n setIsEditing(false);\n \n\n setMessageContent({ type: 'success', title: 'Sucesso.', message: 'Ordem de serviço atualizada com sucesso.' });\n setShowMessageBox(true);\n setTimeout(() => setShowMessageBox(false), 1500);\n }\n } catch (error) {\n setMessageContent({ type: 'error', title: 'Erro.', message: 'Não foi possível atualizar a ordem de serviço.' });\n setShowMessageBox(true);\n console.error(\"Erro ao atualizar a ordem de serviço:\", error);\n } finally {\n setIsLoading(false);\n setTimeout(() => setShowMessageBox(false), 1500);\n }\n };\n\n const handleContinue = () => {\n navigate(\"../Listing\");\n };\n\n const handleRemoveDocument = (documentId) => {\n setDocuments((prevDocuments) => prevDocuments.filter(doc => doc.id !== documentId));\n };\n \n const handleUploadSuccess = () => {\n fetchDocuments(orderId); // Busca a lista de documentos atualizada diretamente da API\n };\n \n \n\n const handleKeyDown = (event) => {\n if (/\\d/.test(event.key)) {\n event.preventDefault(); // Bloqueia a entrada de números\n }\n };\n const handleKeyNumber = (event) => {\n if (!/[0-9]/.test(event.key) && event.key !== \"Backspace\") {\n event.preventDefault();\n }\n };\n\n return (\n <>\n {isLoading && }\n\n {showMessageBox && (\n setShowMessageBox(false)}\n />\n )}\n\n
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Formulário de cadastro

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1. Dados da ordem de serviço

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3. Dados do solicitante

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3. Dados da manutenção

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\n {isCreating ? (\n <>\n navigate(\"../Listing\")}>Cancelar\n Salvar\n \n ) : isEditing ? (\n <>\n {\n setIsEditing(false);\n setShowMessageBox(false);\n }}>Cancelar\n setIsModalOpen(true)}>\n Anexar documentos\n \n Atualizar\n \n ) : (\n <>\n Editar\n setIsModalOpen(true)}>\n Anexar documentos\n \n Continuar\n \n )}\n
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\n\n
\n setIsModalOpen(false)} \n orderId={orderId} \n onUploadSuccess={handleUploadSuccess} />\n\n
\n\n \n );\n}\n", "text_preview": "import { useState, useEffect } from \"react\"; import { useNavigate, useParams } from \"react-router-dom\"; import { FaFilePen } from \"react-icons/fa6\"; import { useUser } from \"../../contexts/user\"; import InputSelect from \"../../components/in", "input_sha256": "a3c636b59f6ecb10bc7c475a0e0b24acf26330931db57b343ed02ff8f049834a", "text_sha256": "9ed8b2dd5fd5657c667c9647b7d9efa65d8b52221c46913a80c2d87a129fea7a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8033, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": 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{"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:08.694699+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "67d5fa31-d990-49a7-a629-57f67870042b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "f582f5a56d7039482e873b895b20db00573979c5caddd3df669b9b9a8f9e793b", "source_metadata": {"uuid": "67d5fa31-d990-49a7-a629-57f67870042b", "repo_name": "WemellyNunes/app-disem", "relative_path": "src/pages/form/index.jsx", "category": "WEB", "algo_rel_score": 0.9470541477203369, "quality_score": 6.575560092926025, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 133, "repo_created_at": "2024-08-13T18:54:39.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "76f33db2874012ed7cd90567b6b43eb9ba488d77", "size_bytes": 27080, "file_timestamp": 1738860609, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "46e29980eb1fdd4dfc6f3ce5e96017b4953e9a52"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 133, "repo_created_at": "2024-08-13T18:54:39.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "76f33db2874012ed7cd90567b6b43eb9ba488d77", "size_bytes": 27080, "file_timestamp": 1738860609, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "46e29980eb1fdd4dfc6f3ce5e96017b4953e9a52", "url": "https://github.com/WemellyNunes/app-disem/blob/46e29980eb1fdd4dfc6f3ce5e96017b4953e9a52/src/pages/form/index.jsx"}, "gpt4o_tokens": 4927, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.4687615239381515, "noise_severity": 0.18519130756520516, "missing_content_severity": 0.09418643449898867, "factual_density": 2.397219611913211, "pretraining_utility": 0.45875574702900784, "math_density": 0.38501903082447186, "code_density": 3.78196487575058, "reasoning_density": 0.571295995413514, "education_level": 2.0550924748395603, "creative_writing_quality": -0.35872011564017564}, "score_applicability": {"education_level": 0.997622348094523, "creative_writing_quality": 0.0006925360229520246}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/DyszSSJ/Ecommerce_Perfumes/blob/a2af23805f5c3c4ae2644c72224a362acb9ac8f2/ecommerce-react/src/pages/Carrito.jsx", "id": "javascript-0-7b9036df2287f4e98c0a48ce4d74835e", "text": "import React, { useEffect, useState } from \"react\";\nimport styles from \"../styles/carrito.module.css\";\nimport usePerfumes from \"../hook/usePerfumes\";\nimport { ToastContainer } from 'react-toastify';\nimport 'react-toastify/dist/ReactToastify.css';\n\nfunction Carrito() {\n const [total, setTotal] = useState(0);\n const { carrito, actualizarCantidad, eliminarProducto } = usePerfumes();\n\n useEffect(() => {\n let total = 0;\n carrito?.forEach((producto) => {\n total += producto.precio * producto.cantidad;\n });\n setTotal(total);\n }, [carrito]);\n\n return (\n
\n

Carrito de compras

\n
\n
\n

Articulos

\n {carrito?.length === 0 ? (\n

No hay articulos en el carrito

\n ) : (\n carrito?.map((perfume) => (\n
\n
\n \n
\n
\n eliminarProducto(perfume.id)}\n >\n \n \n \n \n

{perfume.nombre}

\n

Cantidad: {perfume.cantidad}

\n \n actualizarCantidad({\n id: perfume.id,\n cantidad: Number(e.target.value),\n })\n }\n >\n \n \n \n \n \n \n \n \n \n \n \n\n

${perfume.precio}

\n

\n Subtotal: $ {perfume.cantidad * perfume.precio}\n

\n
\n
\n ))\n )}\n
\n\n \n
\n \n
\n );\n}\n\nexport default Carrito;\n", "text_preview": "import React, { useEffect, useState } from \"react\"; import styles from \"../styles/carrito.module.css\"; import usePerfumes from \"../hook/usePerfumes\"; import { ToastContainer } from 'react-toastify'; import 'react-toastify/dist/ReactToastify", "input_sha256": "b8b7b1b2e3d2911ffcedf91ce59be2326db567554c296700ee363c5a37111b42", "text_sha256": "7b9036df2287f4e98c0a48ce4d74835e4789f80cc931bbc03ae2b46ef7b6f765", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8034, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.562525773616034, "noise_severity": 0.1297630963203621, "missing_content_severity": 0.05247285640195437, "factual_density": 2.6109559307270964, "pretraining_utility": 0.4768678487633354, "math_density": 0.7595464164809628, "code_density": 3.7988052055321826, "reasoning_density": 0.18555719173190732, "education_level": 1.9375807213571732, "creative_writing_quality": -0.28108185204609454}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0059619988011763905, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9937628111383121, "secondary": 0.9608820180927825, "tertiary": 0.9587421211981993, "structured_records": 0.07098531912860394, "procedural_steps": 0.00040220121986150263, "worked_solution": 0.012664741036377864, "contextual_dialogue": 5.0061673069477835e-06, "content_quality": 0.42638791267368903, "noise_severity": 0.8918640863996983, "missing_content_severity": 0.9562726196650381, "factual_density": 0.21491354587526157, "pretraining_utility": 0.46863878004933057, "math_density": 0.6238350954317999, "code_density": 0.8323376712768189, "reasoning_density": 0.8453690068900772, "education_level": 0.822044533037547, "creative_writing_quality": 0.9983376621720474, "primary_topic": 0.9471972653085945, "secondary_topic": 0.5145531271000948}, "probability_margin": {"primary": 0.9907746287490078, "secondary": 0.9452714878739928, "tertiary": 0.9488977044862165, "education_level": 0.753749861220138, "creative_writing_quality": 0.9977899313765506, "primary_topic": 0.9155771680625484, "secondary_topic": 0.19989563974126678}, "raw_probabilities": {"primary": {"Academic Writing": 3.9389655813938445e-05, "Content Listing": 0.0003920474721699017, "Creative Writing": 4.832993404231259e-05, "Customer Support Page": 1.3958866730878921e-05, "Q&A Forum": 2.0153582136328723e-05, "Discussion Thread": 1.155655248173251e-05, "Comment Section": 1.943106373808295e-05, "Missing Content": 6.68571888265035e-05, "Knowledge Article": 9.410911355349556e-05, "Legal Notices": 5.0686252921493443e-05, "News Article": 8.868887066190136e-06, "Organizational Announcement": 1.6877630169559294e-05, "Nonfiction Writing": 1.854785606655878e-05, "Organizational About Page": 2.4400526594352156e-05, "Personal About Page": 2.5683848315656258e-05, "Personal Blog": 1.2094651854909702e-05, "Product Page": 0.0005379457473284134, "Spam / Ads": 2.0788695703033107e-05, "Structured Data": 0.0007820203866934836, "Technical Writing": 0.9940226940075491, "Transcript / Interview": 2.168743265549223e-05, "Tutorial / How-To Guide": 0.0004922169690300665, "User Reviews": 1.1588420017256338e-05, "Other / Unknown": 0.0032480652585412336}, "secondary": {"Academic Writing": 0.00021450986044274772, "Content Listing": 0.0059018296171137056, "Creative Writing": 0.0003072404208600568, "Customer Support Page": 0.00012853749837363413, "Q&A Forum": 8.77713568565472e-05, "Discussion Thread": 6.601876222527701e-05, "Comment Section": 0.0001565436161642095, "Missing Content": 0.0006518360201459002, "Knowledge Article": 0.00039072041828053694, "Legal Notices": 0.00030005795315829486, "News Article": 0.00013775174856911368, "Organizational Announcement": 0.00012106684189582821, "Nonfiction Writing": 0.00013025640893430215, "Organizational About Page": 0.00017095498785320344, "Personal About Page": 0.00013810492982482906, "Personal Blog": 0.00013933831027984706, "Product Page": 0.004147014199291855, "Spam / Ads": 0.00015322472073615496, "Structured Data": 0.005728487314976243, "Technical Writing": 0.017240446131590356, "Transcript / Interview": 0.000148250190503394, "Tutorial / How-To Guide": 0.0009600526606218537, "User Reviews": 6.805202571891238e-05, "NONE": 0.9625119340055832}, "tertiary": {"Academic Writing": 0.00016209999732548787, "Content Listing": 0.011563494995391183, "Creative Writing": 0.00025666761289103683, "Customer Support Page": 0.00010504811013290777, "Q&A Forum": 6.381173855459376e-05, "Discussion Thread": 5.657913106962075e-05, "Comment Section": 0.00015776501045130844, "Missing Content": 0.0008267817127467925, "Knowledge Article": 0.00042627194932169085, "Legal Notices": 0.0005447901651406135, "News Article": 0.000101459903786581, "Organizational Announcement": 0.00010311743844876393, "Nonfiction Writing": 0.00012630616569887077, "Organizational About Page": 0.0001854843392905091, "Personal About Page": 0.00013334706913929194, "Personal Blog": 0.0001024877283195217, "Product Page": 0.005889670769065345, "Spam / Ads": 0.00017700246196559867, "Structured Data": 0.0063265691965857145, "Technical Writing": 0.011278447330413966, "Transcript / Interview": 0.00011444142945203663, "Tutorial / How-To Guide": 0.0007789189350439774, "User Reviews": 5.823732815687204e-05, "NONE": 0.9604611994816077}, "structured_records": {"false": 0.929014680871396, "true": 0.07098531912860394}, "procedural_steps": {"false": 0.9995977987801385, "true": 0.00040220121986150263}, "worked_solution": {"false": 0.9873352589636222, "true": 0.012664741036377864}, "contextual_dialogue": {"false": 0.9999949938326931, "true": 5.0061673069477835e-06}, "content_quality": {"-2": 0.022603481938006532, "-1": 0.09745372331475628, "0": 0.3001864731442374, "1": 0.4543261823991961, "2": 0.12543013920380355}, "noise_severity": {"0": 0.8983514325409341, "1": 0.07992751159646938, "2": 0.017021379745831344, "3": 0.003005879234830489, "4": 0.0016937968819346306}, "missing_content_severity": {"0": 0.9736693136505534, "1": 0.005591296103189032, "2": 0.01606140722255014, "3": 0.003953186241164116, "4": 0.0007247967825431742}, "factual_density": {"0": 0.05815363572059687, "1": 0.13060276887703, "2": 0.22990746219544406, "3": 0.30480629536853765, "4": 0.27652983783839136}, "pretraining_utility": {"-2": 0.0102201604745195, "-1": 0.08159042785909122, "0": 0.38654147044699294, "1": 0.46439728486732684, "2": 0.057250656352069364}, "math_density": {"0": 0.3459257345004387, "1": 0.57711308159818, "2": 0.059579954161102205, "3": 0.006251492400537911, "4": 0.011129737339741135}, "code_density": {"0": 0.00035648491041226403, "1": 0.0007184404305680811, "2": 0.021343584811147124, "3": 0.15492636391216977, "4": 0.8226551259357028}, "reasoning_density": {"0": 0.891508958909577, "1": 0.0373412010415135, "2": 0.06637131489526847, "3": 0.003640739714706483, "4": 0.001137785438934347}, "education_level": {"none": 0.0028370514526134144, "0": 0.004985164713728298, "1": 0.09795391631115112, "2": 0.8517037775312891, "3": 0.03935812618151056, "4": 0.0031619638097075482}, "creative_writing_quality": {"not_applicable": 0.9986147184767062, "-2": 0.00010674367418164159, "-1": 0.00033020961460143836, "0": 0.0008247871001556671, "1": 9.276280191764045e-05, "2": 3.0778332437237036e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00010366152287359851, "Art & Design": 0.0008374691517550883, "Law & Crime": 0.00014652604594246474, "Education & Careers": 9.731414543160094e-05, "Entertainment & Media": 0.00010633303283753701, "Fashion & Beauty": 0.03365097165725397, "Business, Economics & Finance": 0.009132320497492671, "Food & Dining": 0.00017414160614689792, "Games & Gaming": 0.00015560547278073295, "Electronics & Hardware": 0.00015409394513650823, "Health, Healthcare & Wellness": 0.00011989814982073646, "History & Geography": 7.820932670369518e-05, "Home, Pets & Hobbies": 0.0001152592980739088, "Industry & Production": 0.00011424041957449822, "Language, Literature & Philosophy": 0.00010028561775918198, "Politics & Government": 3.8996272170437084e-05, "Religion & Spirituality": 7.440631580354996e-05, "Science, Mathematics & Engineering": 0.00010865779130774734, "Relationships & Community": 4.860162944793796e-05, "Software Usage & Applications": 0.0008233605890469898, "Programming & Software Engineering": 0.9492281397198024, "Sports & Fitness": 8.956077742651246e-05, "Vehicles & Transportation": 5.1026688902210846e-05, "Travel & Tourism": 9.505270411441118e-05, "Other / Uncovered Subject": 0.004177242900697594, "Unknown / Insufficient Content": 0.00017862472169734476}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0012409856400749824, "Art & Design": 0.01735481009236886, "Law & Crime": 0.0008997222000714545, "Education & Careers": 0.0005172798201355017, "Entertainment & Media": 0.0008067946329546812, "Fashion & Beauty": 0.33332852093190135, "Business, Economics & Finance": 0.04926936159902995, "Food & Dining": 0.0005928804473097846, "Games & Gaming": 0.0005820849630561804, "Electronics & Hardware": 0.0006484563975687096, "Health, Healthcare & Wellness": 0.0006772369958274515, "History & Geography": 0.0004686605228686412, "Home, Pets & Hobbies": 0.0005782360557941112, "Industry & Production": 0.00048081883269774506, "Language, Literature & Philosophy": 0.0003736989932924667, "Politics & Government": 0.00026487370591241245, "Religion & Spirituality": 0.0005210262476324253, "Science, Mathematics & Engineering": 0.0005592627065890154, "Relationships & Community": 0.0002560883201603853, "Software Usage & Applications": 0.0028987437127693453, "Programming & Software Engineering": 0.049255878196947066, "Sports & Fitness": 0.0004293558544943626, "Vehicles & Transportation": 0.0002584923267413486, "Travel & Tourism": 0.0004467505607147361, "Other / Uncovered Subject": 0.004065819569918975, "NONE": 0.5332241606731681}}, "probabilities": {"primary": {"Academic Writing": 3.938965581393845e-05, "Content Listing": 0.0003920474721699018, "Creative Writing": 4.8329934042312595e-05, "Customer Support Page": 1.3958866730878923e-05, "Q&A Forum": 2.0153582136328727e-05, "Discussion Thread": 1.1556552481732512e-05, "Comment Section": 1.9431063738082955e-05, "Missing Content": 6.685718882650352e-05, "Knowledge Article": 9.410911355349558e-05, "Legal Notices": 5.068625292149345e-05, "News Article": 8.868887066190138e-06, "Organizational Announcement": 1.6877630169559297e-05, "Nonfiction Writing": 1.8547856066558784e-05, "Organizational About Page": 2.440052659435216e-05, "Personal About Page": 2.568384831565626e-05, "Personal Blog": 1.2094651854909704e-05, "Product Page": 0.0005379457473284135, "Spam / Ads": 2.078869570303311e-05, "Structured Data": 0.0007820203866934837, "Technical Writing": 0.9940226940075492, "Transcript / Interview": 2.1687432655492234e-05, "Tutorial / How-To Guide": 0.0004922169690300667, "User Reviews": 1.158842001725634e-05, "Other / Unknown": 0.003248065258541234}, "secondary": {"Academic Writing": 0.00021450986044274772, "Content Listing": 0.0059018296171137056, "Creative Writing": 0.0003072404208600568, "Customer Support Page": 0.00012853749837363413, "Q&A Forum": 8.77713568565472e-05, "Discussion Thread": 6.601876222527701e-05, "Comment Section": 0.0001565436161642095, "Missing Content": 0.0006518360201459002, "Knowledge Article": 0.00039072041828053694, "Legal Notices": 0.00030005795315829486, "News Article": 0.00013775174856911368, "Organizational Announcement": 0.00012106684189582821, "Nonfiction Writing": 0.00013025640893430215, "Organizational About Page": 0.00017095498785320344, "Personal About Page": 0.00013810492982482906, "Personal Blog": 0.00013933831027984706, "Product Page": 0.004147014199291855, "Spam / Ads": 0.00015322472073615496, "Structured Data": 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3.899627217043708e-05, "Religion & Spirituality": 7.440631580354994e-05, "Science, Mathematics & Engineering": 0.00010865779130774731, "Relationships & Community": 4.860162944793795e-05, "Software Usage & Applications": 0.0008233605890469895, "Programming & Software Engineering": 0.9492281397198021, "Sports & Fitness": 8.956077742651245e-05, "Vehicles & Transportation": 5.102668890221083e-05, "Travel & Tourism": 9.505270411441116e-05, "Other / Uncovered Subject": 0.004177242900697593, "Unknown / Insufficient Content": 0.00017862472169734474}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0012409856400749824, "Art & Design": 0.01735481009236886, "Law & Crime": 0.0008997222000714545, "Education & Careers": 0.0005172798201355017, "Entertainment & Media": 0.0008067946329546812, "Fashion & Beauty": 0.33332852093190135, "Business, Economics & Finance": 0.04926936159902995, "Food & Dining": 0.0005928804473097846, "Games & Gaming": 0.0005820849630561804, "Electronics & Hardware": 0.0006484563975687096, "Health, Healthcare & Wellness": 0.0006772369958274515, "History & Geography": 0.0004686605228686412, "Home, Pets & Hobbies": 0.0005782360557941112, "Industry & Production": 0.00048081883269774506, "Language, Literature & Philosophy": 0.0003736989932924667, "Politics & Government": 0.00026487370591241245, "Religion & Spirituality": 0.0005210262476324253, "Science, Mathematics & Engineering": 0.0005592627065890154, "Relationships & Community": 0.0002560883201603853, "Software Usage & Applications": 0.0028987437127693453, "Programming & Software Engineering": 0.049255878196947066, "Sports & Fitness": 0.0004293558544943626, "Vehicles & Transportation": 0.0002584923267413486, "Travel & Tourism": 0.0004467505607147361, "Other / Uncovered Subject": 0.004065819569918975, "NONE": 0.5332241606731681}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, 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"cache_write_tokens": null, "input_tokens": 9840, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:08.738564+00:00", "scoring_key_slot": "primary", "dataset_uid": "b82edde6-c0d6-487e-88da-31996f42c2c9", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "b1fafa06dcb5a10f077e2de26fc31720d09dccda86892b63d5abe42814fa91ba", "source_metadata": {"uuid": "b82edde6-c0d6-487e-88da-31996f42c2c9", "repo_name": "DyszSSJ/Ecommerce_Perfumes", "relative_path": "ecommerce-react/src/pages/Carrito.jsx", "category": "WEB", "algo_rel_score": 0.9407702088356018, "quality_score": 7.2918267250061035, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-11-02T19:38:55.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c67f0dcbbee81377cae172fb21b85d0aec6f8a5a", "size_bytes": 4386, "file_timestamp": 1667417961, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2af23805f5c3c4ae2644c72224a362acb9ac8f2"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-11-02T19:38:55.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c67f0dcbbee81377cae172fb21b85d0aec6f8a5a", "size_bytes": 4386, "file_timestamp": 1667417961, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2af23805f5c3c4ae2644c72224a362acb9ac8f2", "url": "https://github.com/DyszSSJ/Ecommerce_Perfumes/blob/a2af23805f5c3c4ae2644c72224a362acb9ac8f2/ecommerce-react/src/pages/Carrito.jsx"}, "gpt4o_tokens": 1128, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.562525773616034, "noise_severity": 0.1297630963203621, "missing_content_severity": 0.05247285640195437, "factual_density": 2.6109559307270964, "pretraining_utility": 0.4768678487633354, "math_density": 0.7595464164809628, "code_density": 3.7988052055321826, "reasoning_density": 0.18555719173190732, "education_level": 1.9375807213571732, "creative_writing_quality": -0.28108185204609454}, "score_applicability": {"education_level": 0.9971629485473866, "creative_writing_quality": 0.0013852815232936247}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/leeluolee/puer/blob/c5ea5fc9d6f823ccbc66dfcc901a084d22f1d2ef/src/middleware/rewrite.js", "id": "javascript-0-43726af5c89396c94101e36deb5352ea", "text": "\nvar path2reg = require(\"path-to-regexp\");\nvar proxy = require('../util/proxy.js');\nvar jsonParser = require('body-parser').json();\nvar urlencoded = require('body-parser').urlencoded({ extended: false });\nvar notifier = require('node-notifier');\nvar helper = require('../util/helper');\nvar chokidar = require('chokidar');\nvar request = require('request');\nvar libPath = require('path');\nvar chalk = require('chalk');\nvar async = require('async');\nvar libUrl = require('url');\nvar glob = require('glob');\nvar fs = require('fs');\n\n\nfunction processHandle( handle, rulePath ){\n var type = typeof handle;\n var ruleDir;\n\n if(typeof rulePath === 'string'){\n ruleDir = libPath.dirname( rulePath );\n }else{\n ruleDir = process.cwd();\n }\n\n if(type === 'string' ){ // proxy or file send\n\n if( handle.indexOf('http') === 0 ) { // http or https\n return function( req, res ){\n // todo\n var relUrl = helper.encode( handle, req.params );\n\n // var target = relUrl !== handle? relUrl: libUrl.resolve(relUrl, req.url);\n // var stream = request({\n // uri: target,\n // method: req.method\n // }, function (error, response, body) {\n // if (error) {\n // return console.error('upload failed:', error);\n // }\n\n // });\n // stream.pipe(res);\n // req.pipe(stream);\n\n // fix target path\n if(relUrl !== handle){\n req.url = libUrl.parse(relUrl).path;\n }\n\n\n return proxy( req, res, {\n target: relUrl\n }) \n }\n }\n return function( req, res, next){\n var relativePath = helper.encode( handle, req.params)\n var filepath = libPath.resolve( ruleDir, relativePath );\n if( fs.existsSync( filepath ) ){\n return res.sendFile( filepath );\n }else{\n res.send(handle)\n }\n }\n }\n\n // {\n // handle: 'xx',\n // transform: xx\n // }\n if( helper.typeOf(handle) === 'object' ){\n var realHandle = processHandle(handle.handle, rulePath);\n return function(req, res, next){\n res.puer_transform = handle.transform\n realHandle(req, res, next);\n }\n }\n\n if(typeof handle !== 'function'){\n return function(req, res){\n res.send(handle)\n }\n }\n\n return function(req, res, next){\n // https://github.com/nodejitsu/node-http-proxy/issues/180#issuecomment-97702206\n // Fix body Parser error\n jsonParser(req, res, function(){\n urlencoded(req, res, function(){\n handle(req, res, next)\n })\n });\n }\n}\nfunction processRule(rules, rulePath){\n var rst = []\n for(var i in rules) if ( rules.hasOwnProperty(i) ){\n rst.push(createRule(i, rules[i], rulePath) )\n }\n return rst ;\n}\nfunction createRule( path, handle, rulePath){\n var tmp = path.split(/\\s+/), method = \"all\";\n if( tmp[0] && tmp[1] ) {\n method = tmp[0].toLowerCase();\n path = tmp[1];\n }\n var regexp = path2reg( path );\n handle = processHandle(handle, rulePath)\n return {\n method: method,\n path: path,\n regexp: regexp,\n keys: regexp.keys,\n handle: handle\n } \n}\n\nfunction rewriteMiddleware( options ){\n\n\n var ruleCache = {defaults: []};\n\n processRules( options.rules, ruleCache );\n\n\n return function rule( req, res, next ){\n\n var url = libUrl.parse( req.url );\n var method = req.method.toLowerCase();\n var applied = [];\n\n // checking ruleCache\n for(var i in ruleCache){\n var rules = ruleCache[i];\n for(var i = 0, len = rules.length; i < len; i++ ){\n\n var rule = rules[i];\n\n if((rule.method === 'all' || rule.method === method) && rule.regexp ){\n\n var params = helper.getParam( rule.regexp ,url.pathname);\n\n if(params && rule.handle) {\n\n applied.push({\n params: params,\n handle: rule.handle,\n path: rule.path\n })\n }\n\n }\n }\n }\n\n var alen = applied.length;\n if( !alen ) return next();\n\n var cursor = -1;\n function nextStep(){\n cursor++ \n if(cursor === alen) return next();\n var step = applied[cursor];\n req.params = step.params;\n try{\n step.handle(req, res, nextStep)\n }catch(e){\n notifier.notify({\n title: 'some error occurs in route: ' + step.path,\n message: e.message\n })\n helper.log( 'some error occurs in route: ' + step.path + '\\n' + e.message, 'error') \n next()\n }\n }\n nextStep();\n }\n}\n\nfunction processRules(rules, ruleCache){\n\n if( typeof rules === 'string' ){\n chokidar.watch(rules).on('all', function( event, path ){\n var logPath = chalk.underline.italic( path );\n switch(event){\n case 'add':\n case 'change':\n try{\n if( libPath.extname(path) === '.js'){\n delete require.cache[path];\n ruleCache[path] = processRule(require(path), path);\n }\n helper.log( 'rule ' + logPath + ' synchronized');\n }catch(e){ \n notifier.notify({\n title: 'some error occurs in ' + path,\n message: e.message\n })\n helper.log( logPath + '\\n\\t' + e.message, 'error') \n }\n break;\n case 'unlink':\n delete ruleCache[path]\n break;\n case 'error':\n helper.log('Some Error happend:' + path, 'error');\n }\n })\n }else{\n ruleCache['$defaults'] = processRules( options.rules, options.dir );\n }\n}\n\n\nmodule.exports = rewriteMiddleware;", "text_preview": "var path2reg = require(\"path-to-regexp\"); var proxy = require('../util/proxy.js'); var jsonParser = require('body-parser').json(); var urlencoded = require('body-parser').urlencoded({ extended: false }); var notifier = require('node-notifie", "input_sha256": "e5e7aebf7756d84e4dd3ed82e29c8e586d05abe4f753c69363c72976a238db5e", "text_sha256": "43726af5c89396c94101e36deb5352ea12cd424256d62271308925fbaa991e51", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8035, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4620252689875169, "noise_severity": 0.034581671979707566, "missing_content_severity": 0.06110803906161868, "factual_density": 3.313143107634005, "pretraining_utility": 0.3898658555073025, "math_density": 0.025029954188896284, "code_density": 3.862749645908673, "reasoning_density": 0.7040810878905721, "education_level": 2.365855372622355, "creative_writing_quality": -0.20948031494316108}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, 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require('../config/config').CADUCIDAD_TOKEN;\n\nvar app = express();\n\nvar Usuario = require('../models/usuario');\n\n//Google Sign-In\nconst { OAuth2Client } = require('google-auth-library');\nconst GOOGLE_CLIENT_ID = require('../config/config').GOOGLE_CLIENT_ID;\n// const GOOGLE_SECRET = require('../config/config').GOOGLE_SECRET;\nconst client = new OAuth2Client(GOOGLE_CLIENT_ID);\n\n// =====================================================================\n// Autenticación Google\n// =====================================================================\napp.post('/google', async (req, res) => {\n let token = req.body.token;\n const ticket = await client.verifyIdToken({\n idToken: token,\n audience: GOOGLE_CLIENT_ID,\n }).catch(e => {\n return res.status(403).json({\n ok: false,\n mensaje: 'Token no válido',\n err: e\n });\n })\n \n const googleUser = ticket.getPayload();\n \n /* res.status(200).json({\n ok: true,\n ticket: googleUser,\n email: googleUser.email\n }) */\n \n Usuario.findOne({ email: googleUser.email }, (err, usuarioDB) => {\n if (err) {\n return res.status(500).json({\n ok: false,\n err\n });\n };\n if (usuarioDB) {\n if (usuarioDB.google === false) {\n return res.status(400).json({\n ok: false,\n err: {\n message: 'Debe de usar su autenticación normal'\n }\n });\n } else {\n // console.log('No existe usuario');\n \n let token = jwt.sign({ usuario: usuarioDB }, SEED, { expiresIn: CADUCIDAD_TOKEN });\n return res.json({\n ok: true,\n usuario: usuarioDB,\n token: token,\n id: usuarioDB._id,\n menu: obtenerMenu(usuarioDB.role)\n });\n }\n } else {\n // Si el usuario no existe en nuestra base de datos\n let usuario = new Usuario();\n usuario.nombre = googleUser.name;\n usuario.email = googleUser.email;\n usuario.img = googleUser.picture;\n usuario.google = true;\n usuario.password = ':)';\n usuario.save((err, usuarioDB) => {\n if (err) {\n return res.status(500).json({\n ok: false,\n err\n });\n };\n let token = jwt.sign({ usuario: usuarioDB }, SEED, { expiresIn: CADUCIDAD_TOKEN });\n //var token = jwt.sign({ usuario: usuarioBD }, SEED, { expiresIn: 14400 }); // 4 horas\n \n return res.json({\n ok: true,\n usuario: usuarioDB,\n token: token,\n id: usuarioDB._id,\n menu: obtenerMenu(usuarioDB.role)\n });\n });\n }\n });\n});\n \n\n\n\n// =====================================================================\n// Autenticación normal\n// =====================================================================\napp.post('/', (req, res)=>{\n\n\tvar body = req.body;\n\n\tUsuario.findOne({email: body.email}, (err, usuarioDB)=>{\n\n\t\tif(err){\n\t\t\treturn res.status(500).json({\n\t\t\t\tok: false,\n\t\t\t\tmensaje: 'Error al buscar usuario!',\n\t\t\t\terrors: err\n\t\t\t});\n\t\t}\n\n\t\tif(!usuarioDB){\n\t\t\treturn res.status(400).json({\n\t\t\t\tok: false,\n\t\t\t\tmensaje: 'Nombre o usuario incorrecto - email',\n\t\t\t\terrors: err\n\t\t\t});\n\t\t}\n\n\t\tif(!bcrypt.compareSync(body.password, usuarioDB.password)){\n\t\t\treturn res.status(400).json({\n\t\t\t\tok: false,\n\t\t\t\tmensaje: 'Nombre o usuario incorrecto - password',\n\t\t\t\terrors: err\n\t\t\t});\n\t\t}\n\n\t\t//Crear un token!!!\n\t\tusuarioDB.password = ':)';\n\n\t\tvar token = jwt.sign({usuario: usuarioDB}, SEED, {expiresIn: 14400}); //4 Horas\n\n\t\tres.status(200).json({\n\t\t\tok: true,\n\t\t\tusuario: usuarioDB,\n\t\t\ttoken: token,\n\t\t\tid: usuarioDB._id,\n menu: obtenerMenu(usuarioDB.role)\n\t\t});\n\n\t});\t\n\n});\n\nfunction obtenerMenu( ROLE ){\n\n var menu = [\n {\n titulo: 'Principal',\n icono: 'mdi mdi-gauge',\n submenu: [\n { titulo: 'Dashboard', url: '/dashboard' },\n { titulo : 'ProgressBar', url: '/progress' },\n { titulo: 'Gráficas', url: '/graficas1' },\n { titulo: 'Promesas', url: '/promesas' },\n { titulo: 'RxJs', url: '/rxjs' }\n ]\n },\n {\n titulo: 'Mantenimientos',\n icono: 'mdi mdi-folder-lock-open',\n submenu: [\n // { titulo: 'Usuarios', url: '/usuarios' },\n { titulo: 'Hospitales', url: '/hospitales' },\n { titulo: 'Médicos', url: '/medicos' }\n ]\n }\n ];\n\n if( ROLE === 'ADMIN_ROLE' ){\n\n menu[1].submenu.unshift({ titulo: 'Usuarios', url: '/usuarios' });\n\n }\n\n return menu;\n\n}\n\n\nmodule.exports = app;", "text_preview": "var express = require('express'); var bcrypt = require('bcryptjs'); var jwt = require('jsonwebtoken'); var SEED = require('../config/config').SEED; var CADUCIDAD_TOKEN = require('../config/config').CADUCIDAD_TOKEN; var app = express(); var ", "input_sha256": "8368d96a647ac0f4fe5fc37198ebc2979b9bb350f156c03d0f71ff2124cc16ad", "text_sha256": "9701c050acb5c292837442f6a4e886ec16e76edf1390f8c96abc520c7be3e46a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8036, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", 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0.4072332628354982, "2": 0.09794695065079397}, "noise_severity": {"0": 0.8325186556653472, "1": 0.14831182256485645, "2": 0.015316201615060468, "3": 0.0024910190091510078, "4": 0.001362301145584724}, "missing_content_severity": {"0": 0.9343928503092582, "1": 0.052459068683477966, "2": 0.009514005568689414, "3": 0.002889708409582672, "4": 0.0007443670289916764}, "factual_density": {"0": 0.007088932974685431, "1": 0.018711776406009246, "2": 0.12551457585107006, "3": 0.3986099385900212, "4": 0.450074776178214}, "pretraining_utility": {"-2": 0.009113091479243888, "-1": 0.07292333319470892, "0": 0.36137886143818065, "1": 0.4920286016057538, "2": 0.06455611228211272}, "math_density": {"0": 0.962718055650742, "1": 0.03307423497961917, "2": 0.002320491075401405, "3": 0.0007376414823580197, "4": 0.0011495768118795226}, "code_density": {"0": 0.0003784441182300874, "1": 0.0007593945844214873, "2": 0.021497023182188955, "3": 0.17637571512832684, "4": 0.8009894229868326}, "reasoning_density": {"0": 0.5678860109769339, "1": 0.10118483991712111, "2": 0.17364827996527976, "3": 0.1008344868883586, "4": 0.056446382252306686}, "education_level": {"none": 0.0012205077135051595, "0": 0.0016026977302329463, "1": 0.009627486954576582, "2": 0.7174328270742037, "3": 0.25619918320808055, "4": 0.013917297319401096}, "creative_writing_quality": {"not_applicable": 0.9992683577974825, "-2": 7.079439092348266e-05, "-1": 0.0001865592939178759, "0": 0.0003819865120780986, "1": 6.801124097297027e-05, "2": 2.4290764625145978e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.523858458750666e-05, "Art & Design": 5.938870250219516e-05, "Law & Crime": 3.6968753031733794e-05, "Education & Careers": 6.233859340035834e-05, "Entertainment & Media": 1.9439981354060492e-05, "Fashion & Beauty": 1.52881411208152e-05, "Business, Economics & Finance": 0.0002026407484891467, "Food & Dining": 3.271076506560155e-05, "Games & Gaming": 9.149627377250183e-05, "Electronics & Hardware": 9.527536063642103e-05, "Health, Healthcare & Wellness": 0.0007490979393241439, "History & Geography": 2.6773397992272554e-05, "Home, Pets & Hobbies": 1.936897562735209e-05, "Industry & Production": 2.0789748847218583e-05, "Language, Literature & Philosophy": 2.5915303212925818e-05, "Politics & Government": 1.8711487879436084e-05, "Religion & Spirituality": 3.0674548063127675e-05, "Science, Mathematics & Engineering": 0.00021677603390889326, "Relationships & Community": 2.0290044743057475e-05, "Software Usage & Applications": 0.0008050831619816117, "Programming & Software Engineering": 0.9970471695893885, "Sports & Fitness": 3.691460402419279e-05, "Vehicles & Transportation": 1.783662896175814e-05, "Travel & Tourism": 3.499660564125851e-05, "Other / Uncovered Subject": 0.00022297435437826504, "Unknown / Insufficient Content": 6.584167206557991e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0004641730630614035, "Art & Design": 0.0009189391262328905, "Law & Crime": 0.0022374165943808096, "Education & Careers": 0.0004116655816236982, "Entertainment & Media": 0.00034545835576600545, "Fashion & Beauty": 0.00017183693793194565, "Business, Economics & Finance": 0.0013677568529800186, "Food & Dining": 0.00028199791875088443, "Games & Gaming": 0.0004007843759239088, "Electronics & Hardware": 0.0004390232587708111, "Health, Healthcare & Wellness": 0.005416477499901851, "History & Geography": 0.0001687435196005829, "Home, Pets & Hobbies": 0.00017289068584787064, "Industry & Production": 0.00014038785688892896, "Language, Literature & Philosophy": 0.00016835516907970995, "Politics & Government": 0.00017836292026971172, "Religion & Spirituality": 0.0002175906773763972, "Science, Mathematics & Engineering": 0.0007125969327359673, "Relationships & Community": 0.0001427939270157501, "Software Usage & Applications": 0.013340853296741732, "Programming & Software Engineering": 0.01605314621550853, "Sports & Fitness": 0.00023970096242797667, "Vehicles & Transportation": 8.888038179367559e-05, "Travel & Tourism": 0.00024304686615839822, "Other / Uncovered Subject": 0.001213971781473942, "NONE": 0.9544631492417566}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9802, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9802, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:09.613273+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "f100a253-6745-4e4d-86a1-dfabfc060858", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5d601566b8a75bacba0ea146197b93cb6213ee48e9e4fec6d0541c9c6be11aac", "source_metadata": {"uuid": "f100a253-6745-4e4d-86a1-dfabfc060858", "repo_name": "davidrodrigo/backend-server-udemy", "relative_path": "routes/login - copia.js", "category": "WEB", "algo_rel_score": 0.9458845257759094, "quality_score": 5.219698429107666, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 9, "repo_created_at": "2018-06-08T10:43:56.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e8327400f33c219a6587cb3547a1ed59b2b66b78", "size_bytes": 5182, "file_timestamp": 1553618972, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e09c397c600ec30ad11e8ece2f086c8be2514861"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 9, "repo_created_at": "2018-06-08T10:43:56.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e8327400f33c219a6587cb3547a1ed59b2b66b78", "size_bytes": 5182, "file_timestamp": 1553618972, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e09c397c600ec30ad11e8ece2f086c8be2514861", "url": "https://github.com/davidrodrigo/backend-server-udemy/blob/e09c397c600ec30ad11e8ece2f086c8be2514861/routes/login%20-%20copia.js"}, "gpt4o_tokens": 1130, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.47128061363846574, "noise_severity": 0.19186648740476933, "missing_content_severity": 0.08313367316557152, "factual_density": 3.265869848591069, "pretraining_utility": 0.5299913100167826, "math_density": 0.04452644882501413, "code_density": 3.7768382782811103, "reasoning_density": 0.9767703895219831, "education_level": 2.2715323027017535, "creative_writing_quality": -0.28915131578470915}, "score_applicability": {"education_level": 0.9987794922864949, "creative_writing_quality": 0.0007316422025175734}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/baguajoe/StreampireX/blob/14c19b87f43856dffc12c6764b0c8263bbd0d87f/src/front/js/pages/BrowseVideosPage.js", "id": "javascript-0-5e3535b93e021b7599b3b3fd103a35ce", "text": "import React, { useEffect, useState, useCallback, useMemo, useRef } from \"react\";\nimport { Link } from \"react-router-dom\";\nimport \"../../styles/BrowseVideos.css\";\nimport VideoCard from \"../component/VideoCard\";\nimport VideoUploadForm from \"../component/VideoUploadForm\";\n\n// Constants moved outside component to prevent recreation\nconst ITEMS_PER_PAGE = 20;\nconst DEBOUNCE_DELAY = 300;\n\nconst DEFAULT_CATEGORIES = [\n { id: 0, name: \"All\", video_count: 0 },\n // Music & Audio\n { id: 1, name: \"Music\", video_count: 0 },\n { id: 2, name: \"Podcasts\", video_count: 0 },\n { id: 3, name: \"Live Concerts\", video_count: 0 },\n { id: 4, name: \"Music Videos\", video_count: 0 },\n { id: 5, name: \"DJ Sets\", video_count: 0 },\n { id: 6, name: \"Karaoke\", video_count: 0 },\n // Health & Wellness \n { id: 7, name: \"Meditation\", video_count: 0 },\n { id: 8, name: \"Yoga\", video_count: 0 },\n { id: 9, name: \"Fitness\", video_count: 0 },\n { id: 10, name: \"Mental Health\", video_count: 0 },\n { id: 11, name: \"Nutrition\", video_count: 0 },\n { id: 12, name: \"Sleep & Relaxation\", video_count: 0 },\n // Education & Learning\n { id: 13, name: \"Education\", video_count: 0 },\n { id: 14, name: \"Tutorials\", video_count: 0 },\n { id: 15, name: \"Language Learning\", video_count: 0 },\n { id: 16, name: \"Science\", video_count: 0 },\n { id: 17, name: \"History\", video_count: 0 },\n { id: 18, name: \"Philosophy\", video_count: 0 },\n // Technology\n { id: 19, name: \"Tech\", video_count: 0 },\n { id: 20, name: \"Programming\", video_count: 0 },\n { id: 21, name: \"AI & Machine Learning\", video_count: 0 },\n { id: 22, name: \"Web Development\", video_count: 0 },\n { id: 23, name: \"Mobile Apps\", video_count: 0 },\n { id: 24, name: \"Cybersecurity\", video_count: 0 },\n // Entertainment\n { id: 25, name: \"Comedy\", video_count: 0 },\n { id: 26, name: \"Movies & TV\", video_count: 0 },\n { id: 27, name: \"Anime & Manga\", video_count: 0 },\n { id: 28, name: \"Celebrity News\", video_count: 0 },\n { id: 29, name: \"Reactions\", video_count: 0 },\n { id: 30, name: \"Memes\", video_count: 0 },\n // Gaming\n { id: 31, name: \"Gaming\", video_count: 0 },\n { id: 32, name: \"Game Reviews\", video_count: 0 },\n { id: 33, name: \"Esports\", video_count: 0 },\n { id: 34, name: \"Game Development\", video_count: 0 },\n { id: 35, name: \"Streaming Highlights\", video_count: 0 },\n // Lifestyle\n { id: 36, name: \"Lifestyle\", video_count: 0 },\n { id: 37, name: \"Fashion\", video_count: 0 },\n { id: 38, name: \"Beauty\", video_count: 0 },\n { id: 39, name: \"Travel\", video_count: 0 },\n { id: 40, name: \"Food & Cooking\", video_count: 0 },\n { id: 41, name: \"Home & Garden\", video_count: 0 },\n { id: 42, name: \"Parenting\", video_count: 0 },\n { id: 43, name: \"Relationships\", video_count: 0 },\n // Creative Arts\n { id: 44, name: \"Art\", video_count: 0 },\n { id: 45, name: \"Photography\", video_count: 0 },\n { id: 46, name: \"Design\", video_count: 0 },\n { id: 47, name: \"Writing\", video_count: 0 },\n { id: 48, name: \"Crafts & DIY\", video_count: 0 },\n { id: 49, name: \"Architecture\", video_count: 0 },\n // Business & Finance\n { id: 50, name: \"Business\", video_count: 0 },\n { id: 51, name: \"Entrepreneurship\", video_count: 0 },\n { id: 52, name: \"Investing\", video_count: 0 },\n { id: 53, name: \"Cryptocurrency\", video_count: 0 },\n { id: 54, name: \"Marketing\", video_count: 0 },\n { id: 55, name: \"Personal Finance\", video_count: 0 },\n // Sports & Activities\n { id: 56, name: \"Sports\", video_count: 0 },\n { id: 57, name: \"Basketball\", video_count: 0 },\n { id: 58, name: \"Football\", video_count: 0 },\n { id: 59, name: \"Soccer\", video_count: 0 },\n { id: 60, name: \"Extreme Sports\", video_count: 0 },\n { id: 61, name: \"Martial Arts\", video_count: 0 },\n // News & Politics\n { id: 62, name: \"News\", video_count: 0 },\n { id: 63, name: \"Politics\", video_count: 0 },\n { id: 64, name: \"Current Events\", video_count: 0 },\n { id: 65, name: \"Documentary\", video_count: 0 },\n // Spirituality & Personal Growth\n { id: 66, name: \"Spirituality\", video_count: 0 },\n { id: 67, name: \"Personal Development\", video_count: 0 },\n { id: 68, name: \"Motivation\", video_count: 0 },\n { id: 69, name: \"Life Coaching\", video_count: 0 },\n // Special Interest\n { id: 70, name: \"True Crime\", video_count: 0 },\n { id: 71, name: \"ASMR\", video_count: 0 },\n { id: 72, name: \"Animals\", video_count: 0 },\n { id: 73, name: \"Nature\", video_count: 0 },\n { id: 74, name: \"Space & Astronomy\", video_count: 0 },\n { id: 75, name: \"Product Reviews\", video_count: 0 },\n { id: 76, name: \"Vlogs\", video_count: 0 },\n { id: 77, name: \"Other\", video_count: 0 }\n];\n\n// Custom hook for debounced search\nconst useDebounce = (value, delay) => {\n const [debouncedValue, setDebouncedValue] = useState(value);\n\n useEffect(() => {\n const handler = setTimeout(() => {\n setDebouncedValue(value);\n }, delay);\n\n return () => {\n clearTimeout(handler);\n };\n }, [value, delay]);\n\n return debouncedValue;\n};\n\n// Custom hook for API calls\nconst useApi = () => {\n const baseUrl = process.env.BACKEND_URL;\n\n const apiCall = useCallback(async (endpoint, options = {}) => {\n const response = await fetch(`${baseUrl}${endpoint}`, {\n headers: {\n 'Content-Type': 'application/json',\n ...options.headers,\n },\n ...options,\n });\n\n if (!response.ok) {\n throw new Error(`HTTP error! status: ${response.status}`);\n }\n\n return response.json();\n }, [baseUrl]);\n\n return { apiCall };\n};\n\nconst BrowseVideosPage = () => {\n // State management\n const [videos, setVideos] = useState([]);\n const [searchTerm, setSearchTerm] = useState(\"\");\n const [selectedCategory, setSelectedCategory] = useState(\"All\");\n const [sortBy, setSortBy] = useState(\"newest\");\n const [isLoading, setIsLoading] = useState(true);\n const [categories, setCategories] = useState(DEFAULT_CATEGORIES);\n const [pagination, setPagination] = useState({});\n const [currentPage, setCurrentPage] = useState(1);\n const [error, setError] = useState(null);\n const [activeTab, setActiveTab] = useState(\"\"); // For navigation tabs\n const [showUploadForm, setShowUploadForm] = useState(false);\n\n // Refs for scrolling\n const scrollRef = useRef(null);\n\n // Custom hooks\n const debouncedSearchTerm = useDebounce(searchTerm, DEBOUNCE_DELAY);\n const { apiCall } = useApi();\n\n // Memoized values\n const hasActiveFilters = useMemo(() => {\n return searchTerm || selectedCategory !== \"All\" || sortBy !== \"newest\";\n }, [searchTerm, selectedCategory, sortBy]);\n\n const videoCountText = useMemo(() => {\n return pagination.total\n ? `${pagination.total.toLocaleString()} videos found`\n : `${videos.length.toLocaleString()} videos`;\n }, [pagination.total, videos.length]);\n\n // Scroll functions for categories\n const scrollLeft = () => {\n if (scrollRef.current) {\n scrollRef.current.scrollLeft -= 200;\n }\n };\n\n const scrollRight = () => {\n if (scrollRef.current) {\n scrollRef.current.scrollLeft += 200;\n }\n };\n\n // Drag and drop handlers\n const handleDragOver = useCallback((e) => {\n e.preventDefault();\n e.stopPropagation();\n }, []);\n\n const handleDragEnter = useCallback((e) => {\n e.preventDefault();\n e.stopPropagation();\n }, []);\n\n const handleDragLeave = useCallback((e) => {\n e.preventDefault();\n e.stopPropagation();\n }, []);\n\n const handleDrop = useCallback(async (e) => {\n e.preventDefault();\n e.stopPropagation();\n\n const files = Array.from(e.dataTransfer.files);\n const videoFiles = files.filter(file => file.type.startsWith('video/'));\n\n if (videoFiles.length === 0) {\n alert('Please drop video files only.');\n return;\n }\n\n // Handle the first video file dropped\n const videoFile = videoFiles[0];\n \n // Check file size (50MB limit)\n const maxSize = 50 * 1024 * 1024; // 50MB\n if (videoFile.size > maxSize) {\n alert('Video file is too large. Maximum size is 50MB.');\n return;\n }\n\n try {\n const token = localStorage.getItem(\"token\");\n if (!token) {\n alert(\"Please log in to upload videos\");\n return;\n }\n\n setIsLoading(true);\n \n const formData = new FormData();\n formData.append('video', videoFile);\n formData.append('title', videoFile.name.replace(/\\.[^/.]+$/, \"\")); // Remove file extension\n formData.append('description', `Uploaded via drag and drop: ${videoFile.name}`);\n\n const response = await fetch(`${process.env.BACKEND_URL}/api/upload_video`, {\n method: 'POST',\n headers: {\n 'Authorization': `Bearer ${token}`,\n },\n body: formData,\n });\n\n if (!response.ok) {\n const errorData = await response.json();\n throw new Error(errorData.error || 'Upload failed');\n }\n\n const result = await response.json();\n alert('Video uploaded successfully!');\n \n // Refresh the videos list\n fetchVideos();\n \n } catch (err) {\n console.error('Upload error:', err);\n alert(`Upload failed: ${err.message}`);\n } finally {\n setIsLoading(false);\n }\n }, []);\n\n // API functions\n const fetchCategories = useCallback(async () => {\n try {\n const data = await apiCall('/api/videos/categories');\n setCategories(data.categories || DEFAULT_CATEGORIES);\n } catch (err) {\n console.error(\"Failed to fetch categories:\", err);\n setCategories(DEFAULT_CATEGORIES);\n }\n }, [apiCall]);\n\n const fetchVideos = useCallback(async () => {\n setIsLoading(true);\n setError(null);\n\n try {\n const params = new URLSearchParams({\n search: debouncedSearchTerm,\n category: selectedCategory,\n sort_by: sortBy,\n page: currentPage.toString(),\n per_page: ITEMS_PER_PAGE.toString()\n });\n\n const data = await apiCall(`/api/videos?${params}`);\n\n // Handle both response formats\n if (data.videos) {\n setVideos(data.videos);\n setPagination(data.pagination || {});\n } else if (Array.isArray(data)) {\n setVideos(data);\n setPagination({\n page: 1,\n total: data.length,\n has_next: false,\n has_prev: false,\n pages: 1\n });\n }\n } catch (err) {\n console.error(\"Failed to fetch videos:\", err);\n setError(\"Failed to load videos. Please try again later.\");\n setVideos([]);\n } finally {\n setIsLoading(false);\n }\n }, [apiCall, debouncedSearchTerm, selectedCategory, sortBy, currentPage]);\n\n // Event handlers\n const handleLike = useCallback(async (videoId) => {\n try {\n const token = localStorage.getItem(\"token\");\n if (!token) {\n alert(\"Please log in to like videos\");\n return;\n }\n\n const data = await apiCall(`/api/videos/${videoId}/like`, {\n method: \"POST\",\n headers: {\n \"Authorization\": `Bearer ${token}`,\n }\n });\n\n setVideos(prev => prev.map(video =>\n video.id === videoId\n ? { ...video, likes: data.likes }\n : video\n ));\n } catch (err) {\n console.error(\"Failed to like video:\", err);\n if (err.message.includes('401')) {\n alert(\"Please log in to like videos\");\n } else {\n alert(\"Failed to like video. Please try again.\");\n }\n }\n }, [apiCall]);\n\n const handleVideoView = useCallback(async (videoId) => {\n try {\n await apiCall(`/api/videos/${videoId}/view`, {\n method: \"POST\"\n });\n } catch (err) {\n console.error(\"Failed to record view:\", err);\n // Silently fail for view tracking\n }\n }, [apiCall]);\n\n const handlePageChange = useCallback((newPage) => {\n setCurrentPage(newPage);\n window.scrollTo({ top: 0, behavior: 'smooth' });\n }, []);\n\n const handleClearFilters = useCallback(() => {\n setSearchTerm(\"\");\n setSelectedCategory(\"All\");\n setSortBy(\"newest\");\n setCurrentPage(1);\n }, []);\n\n const handleShare = useCallback(async (video) => {\n const shareData = {\n title: video.title,\n url: `${window.location.origin}${window.location.pathname}?video=${video.id}`\n };\n\n try {\n if (navigator.share && navigator.canShare(shareData)) {\n await navigator.share(shareData);\n } else {\n await navigator.clipboard.writeText(shareData.url);\n alert(\"Video link copied to clipboard!\");\n }\n } catch (err) {\n console.error(\"Failed to share:\", err);\n // Fallback to clipboard\n try {\n await navigator.clipboard.writeText(shareData.url);\n alert(\"Video link copied to clipboard!\");\n } catch (clipboardErr) {\n console.error(\"Failed to copy to clipboard:\", clipboardErr);\n alert(\"Unable to share or copy link\");\n }\n }\n }, []);\n\n // Utility functions\n const formatDuration = useCallback((seconds) => {\n if (!seconds) return \"0:00\";\n const minutes = Math.floor(seconds / 60);\n const remainingSeconds = seconds % 60;\n return `${minutes}:${remainingSeconds.toString().padStart(2, '0')}`;\n }, []);\n\n // Effects\n useEffect(() => {\n fetchCategories();\n }, [fetchCategories]);\n\n useEffect(() => {\n fetchVideos();\n }, [fetchVideos]);\n\n // Reset page when filters change\n useEffect(() => {\n if (currentPage !== 1) {\n setCurrentPage(1);\n }\n }, [debouncedSearchTerm, selectedCategory, sortBy]);\n\n // Loading state\n if (isLoading && videos.length === 0) {\n return (\n
\n

📹 Browse All Free Videos

\n
\n
\n

Loading videos...

\n
\n
\n );\n }\n\n return (\n
\n {/* Header with Persistent Upload Button */}\n
\n
\n
\n

📹 Browse All Free Videos

\n

{videoCountText}

\n
\n
\n setShowUploadForm(true)}\n className=\"btn-primary upload-header-btn\"\n >\n 📤 Upload Video\n \n
\n
\n
\n\n {/* Search Bar */}\n
\n
\n setSearchTerm(e.target.value)}\n className=\"search-input\"\n aria-label=\"Search videos\"\n />\n {searchTerm && (\n setSearchTerm(\"\")}\n className=\"clear-search-btn\"\n title=\"Clear search\"\n aria-label=\"Clear search\"\n >\n ✖️\n \n )}\n
\n
\n\n {/* Sort and Filter Controls with Navigation Tabs */}\n
\n {/* Navigation Tabs */}\n
\n setActiveTab('history')}\n >\n 🕐\n History\n
\n setActiveTab('playlists')}\n >\n 📋\n Playlists\n
\n setActiveTab('your-videos')}\n >\n 📹\n Your videos\n
\n setActiveTab('watch-later')}\n >\n 🕒\n Watch later\n \n setActiveTab('liked')}\n >\n 👍\n Liked videos\n \n setActiveTab('downloads')}\n >\n ⬇️\n Downloads\n \n \n\n {/* Sort Section */}\n
\n
\n \n setSortBy(e.target.value)}\n className=\"sort-select\"\n >\n \n \n \n \n \n
\n\n {hasActiveFilters && (\n \n 🔄 Clear All Filters\n \n )}\n
\n \n\n {/* Horizontal Category Navigation */}\n
\n \n
\n {categories.map((category) => (\n setSelectedCategory(category.name)}\n className={`category-pill ${selectedCategory === category.name ? \"active\" : \"\"}`}\n >\n {category.name}\n
\n ))}\n
\n \n \n\n {/* Error State */}\n {error && (\n
\n

⚠️ {error}

\n \n
\n )}\n\n {/* No Videos State with Drag & Drop */}\n {!isLoading && !error && videos.length === 0 && (\n
\n
\n
📁
\n

🎬 No videos found

\n

\n {hasActiveFilters\n ? \"Try adjusting your search or filters\"\n : \"No videos have been uploaded yet. Be the first!\"\n }\n

\n \n
\n
\n Drag & drop videos here\n or\n
\n \n {hasActiveFilters && (\n \n )}\n \n \n
\n \n
\n Supported formats: MP4, AVI, MOV, WMV • Max size: 50MB\n
\n
\n
\n )}\n\n {/* Video Grid */}\n {videos.length > 0 && (\n
\n
\n {videos.map((video) => (\n
\n \n
\n

{video.title}

\n\n \n \n {video.uploader_name || 'Unknown'}\n \n \n\n
\n {video.views || 0} views\n \n {formatDuration(video.duration)}\n \n
\n
\n
\n ))}\n
\n
\n )}\n\n {/* Pagination */}\n {pagination.pages > 1 && (\n
\n handlePageChange(currentPage - 1)}\n disabled={!pagination.has_prev}\n className=\"pagination-btn\"\n >\n ← Previous\n \n\n
\n Page {pagination.page} of {pagination.pages}\n \n ({pagination.total?.toLocaleString()} total videos)\n \n
\n\n handlePageChange(currentPage + 1)}\n disabled={!pagination.has_next}\n className=\"pagination-btn\"\n >\n Next →\n \n
\n )}\n\n {/* Loading overlay for pagination */}\n {isLoading && videos.length > 0 && (\n
\n
\n
\n )}\n\n {/* Upload Modal */}\n {showUploadForm && (\n setShowUploadForm(false)}\n onUploadSuccess={() => {\n setShowUploadForm(false);\n fetchVideos(); // Refresh the video list\n }}\n />\n )}\n \n );\n};\n\nexport default BrowseVideosPage;", "text_preview": "import React, { useEffect, useState, useCallback, useMemo, useRef } from \"react\"; import { Link } from \"react-router-dom\"; import \"../../styles/BrowseVideos.css\"; import VideoCard from \"../component/VideoCard\"; import VideoUploadForm from \"", "input_sha256": "aa85d922dab4bc38129d1212b259088cf0096a6c901e62175fa4a0432857316e", "text_sha256": "5e3535b93e021b7599b3b3fd103a35ce769ac1fd15f7765cbe3b243b216dcc7d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8038, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": ["structured_records"]}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8345021333360734, "noise_severity": 0.3181072090668443, "missing_content_severity": 0.1374627679514454, "factual_density": 2.764412006679548, "pretraining_utility": 0.5300620519216261, "math_density": 0.4182549646309461, "code_density": 3.6756562812556925, "reasoning_density": 0.5163403226583251, "education_level": 2.032163792654745, "creative_writing_quality": -0.09584123429455589}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.17060173810735368, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9865725262582875, "secondary": 0.5088421692113725, "tertiary": 0.686861374192959, "structured_records": 0.8565118096953886, "procedural_steps": 0.0219713171101495, "worked_solution": 0.0030701814046496835, "contextual_dialogue": 5.080528532349378e-06, "content_quality": 0.5194925444665707, "noise_severity": 0.7349106591109631, "missing_content_severity": 0.8854476933737955, "factual_density": 0.34915664842880967, "pretraining_utility": 0.4663763409178112, "math_density": 0.6514541961408782, "code_density": 0.7297135677130767, "reasoning_density": 0.569716397784729, "education_level": 0.8426951602636119, "creative_writing_quality": 0.9943439798284206, "primary_topic": 0.9581472867739351, "secondary_topic": 0.7687374612992989}, "probability_margin": {"primary": 0.9817521627823184, "secondary": 0.23669165172673867, "tertiary": 0.5651823039052373, "education_level": 0.7938843224627422, "creative_writing_quality": 0.9916619426201795, "primary_topic": 0.9310327123224156, "secondary_topic": 0.6387295900604119}, "raw_probabilities": {"primary": {"Academic Writing": 6.221901668116702e-05, "Content Listing": 0.004728552628110672, "Creative Writing": 3.566419976601894e-05, "Customer Support Page": 2.03549461703849e-05, "Q&A Forum": 1.99158193764301e-05, "Discussion Thread": 1.639745221277353e-05, "Comment Section": 2.9498488043812098e-05, "Missing Content": 0.00010040146404561539, "Knowledge Article": 6.09622269020431e-05, "Legal Notices": 5.365901453583801e-05, "News Article": 1.2214079934844006e-05, "Organizational Announcement": 2.478141875515726e-05, "Nonfiction Writing": 2.135732655560134e-05, "Organizational About Page": 3.216505651706354e-05, "Personal About Page": 3.471151965692832e-05, "Personal Blog": 1.65595366216533e-05, "Product Page": 2.756745091774099e-05, "Spam / Ads": 7.581034994090032e-05, "Structured Data": 0.0053798415485405504, "Technical Writing": 0.9871320043308589, "Transcript / Interview": 2.6224616060877983e-05, "Tutorial / How-To Guide": 0.00017639887491796468, "User Reviews": 1.400445985815649e-05, "Other / Unknown": 0.0018987341750190453}, "secondary": {"Academic Writing": 0.0008928939417972192, "Content Listing": 0.13151768223698654, "Creative Writing": 0.0008165082354297079, "Customer Support Page": 0.0007240253105042781, "Q&A Forum": 0.00040184452488878716, "Discussion Thread": 0.0003591153006964588, "Comment Section": 0.0006599515351797175, "Missing Content": 0.001241447520105172, "Knowledge Article": 0.0011002266261682055, "Legal Notices": 0.000686294355425712, "News Article": 0.0005611599631608917, "Organizational Announcement": 0.0006103501553912872, "Nonfiction Writing": 0.0007066748071413404, "Organizational About Page": 0.0008769567437444488, "Personal About Page": 0.0006056953864901081, "Personal Blog": 0.0004732546037976824, "Product Page": 0.0005308627904749454, "Spam / Ads": 0.0020062859471984334, "Structured Data": 0.29261542710082666, "Technical Writing": 0.031387348931866905, "Transcript / Interview": 0.0005842451537603593, "Tutorial / How-To Guide": 0.0010130152955710342, "User Reviews": 0.00032165470582876376, "NONE": 0.5293070788275653}, "tertiary": {"Academic Writing": 0.0009228611634647674, "Content Listing": 0.1347265130296818, "Creative Writing": 0.0008998236553520339, "Customer Support Page": 0.000898983072282821, "Q&A Forum": 0.0004172752855895712, "Discussion Thread": 0.0003296694140272919, "Comment Section": 0.0006267602394845297, "Missing Content": 0.0014655250984317763, "Knowledge Article": 0.0012091533937049607, "Legal Notices": 0.0006882698857193898, "News Article": 0.0005419475753103534, "Organizational Announcement": 0.0005905494451326558, "Nonfiction Writing": 0.0005998389486942659, "Organizational About Page": 0.0007751773378875109, "Personal About Page": 0.0005781871078448258, "Personal Blog": 0.00044319433995706293, "Product Page": 0.0006287355397515306, "Spam / Ads": 0.005354240613225202, "Structured Data": 0.12747222502925382, "Technical Writing": 0.018618367142371292, "Transcript / Interview": 0.0005386555145621895, "Tutorial / How-To Guide": 0.0014985387783771613, "User Reviews": 0.00026669145497427505, "NONE": 0.699908816934919}, "structured_records": {"false": 0.14348819030461135, "true": 0.8565118096953886}, "procedural_steps": {"false": 0.9780286828898505, "true": 0.0219713171101495}, "worked_solution": {"false": 0.9969298185953503, "true": 0.0030701814046496835}, "contextual_dialogue": {"false": 0.9999949194714677, "true": 5.080528532349378e-06}, "content_quality": {"-2": 0.006843661695125027, "-1": 0.04437461781957228, "0": 0.26177318592750126, "1": 0.48145299456970697, "2": 0.20555553998809434}, "noise_severity": {"0": 0.7724808415060583, "1": 0.15167071960563966, "2": 0.06506842340726311, "3": 0.006820419277476605, "4": 0.003959596203562134}, "missing_content_severity": {"0": 0.9268941673380415, "1": 0.02453890931486589, "2": 0.03485733679274302, "3": 0.011629161166304013, "4": 0.0020804253880453492}, "factual_density": {"0": 0.025731752932496795, "1": 0.09343910229890554, "2": 0.2442265442076387, "3": 0.36389058627847065, "4": 0.2727120142824883}, "pretraining_utility": {"-2": 0.009617526170467896, "-1": 0.09150850211519217, "0": 0.34327358674671216, "1": 0.4703951635575016, "2": 0.08520522141012633}, "math_density": {"0": 0.612926933140791, "1": 0.3689369917941004, "2": 0.010470928602345265, "3": 0.0022844702188978237, "4": 0.005380676243865419}, "code_density": {"0": 0.0003967940751226694, "1": 0.0008200474472745687, "2": 0.03163817834419608, "3": 0.2570200434136014, "4": 0.7101249367198054}, "reasoning_density": {"0": 0.6864896197399825, "1": 0.14853545199809765, "2": 0.13483263973457568, "3": 0.022429562918301078, "4": 0.007712725609043213}, "education_level": {"none": 0.0017418637040408445, "0": 0.002835419080008886, "1": 0.04673775012128642, "2": 0.86891263355301, "3": 0.07502831109026774, "4": 0.004744022451386115}, "creative_writing_quality": {"not_applicable": 0.9952866498570172, "-2": 0.0001904914631543602, "-1": 0.000523322762719702, "0": 0.0036247072368376697, "1": 0.00029708496687966713, "2": 7.774371339142973e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00036526259703192546, "Art & Design": 0.001142469390018667, "Law & Crime": 0.00010301132678183878, "Education & Careers": 0.0001776953013354782, "Entertainment & Media": 0.02872429419098353, "Fashion & Beauty": 0.00010347719610763593, "Business, Economics & 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9.543854571252252e-05, "Industry & Production": 8.412885191166902e-05, "Language, Literature & Philosophy": 0.00011126719373311312, "Politics & Government": 4.804730646601539e-05, "Religion & Spirituality": 8.238301032891927e-05, "Science, Mathematics & Engineering": 0.00012017596119051898, "Relationships & Community": 0.00016968065634562112, "Software Usage & Applications": 0.0031485833390198217, "Programming & Software Engineering": 0.9597570065133989, "Sports & Fitness": 0.00014133650885491742, "Vehicles & Transportation": 5.5285895519192076e-05, "Travel & Tourism": 0.00012880592031371644, "Other / Uncovered Subject": 0.0033284667960451976, "Unknown / Insufficient Content": 0.0006229675236205559}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0033870103477990987, "Art & Design": 0.0192441388705618, "Law & Crime": 0.0007796202918184521, "Education & Careers": 0.0009120584371264964, "Entertainment & Media": 0.13890258426583704, "Fashion & Beauty": 0.00042477865702750006, "Business, Economics & Finance": 0.001106023600185685, "Food & Dining": 0.000292311891648301, "Games & Gaming": 0.0017800040230388668, "Electronics & Hardware": 0.0004994680389831898, "Health, Healthcare & Wellness": 0.0005527811126649521, "History & Geography": 0.0003334779366965391, "Home, Pets & Hobbies": 0.0004905352936899038, "Industry & Production": 0.00027831985871244063, "Language, Literature & Philosophy": 0.0003256991884856943, "Politics & Government": 0.00032943960362676874, "Religion & Spirituality": 0.00038580828704120387, "Science, Mathematics & Engineering": 0.0005468427405861648, "Relationships & Community": 0.00040580816615603545, "Software Usage & Applications": 0.0210651218524704, "Programming & Software Engineering": 0.027359680120930638, "Sports & Fitness": 0.0004158081771881663, "Vehicles & Transportation": 0.00014847416798734108, "Travel & Tourism": 0.0004196721153209598, "Other / Uncovered Subject": 0.0019823586281675332, "NONE": 0.7776321743262489}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 14838, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 14838, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:11.469147+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "83bc7458-66c7-4315-9758-93ba18f19d71", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5b96bf6a1e7fcd00339ab8e5f830703862b0634e1ae8db43cfab8712d9e24466", "source_metadata": {"uuid": "83bc7458-66c7-4315-9758-93ba18f19d71", "repo_name": "baguajoe/StreampireX", "relative_path": "src/front/js/pages/BrowseVideosPage.js", "category": "WEB", "algo_rel_score": 0.9204055070877075, "quality_score": 7.333073616027832, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 87, "repo_created_at": "2024-12-22T04:30:48.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b6e44355e70e24dc525ba7b6dd069aec2ebd2d9e", "size_bytes": 22984, "file_timestamp": 1753494415, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "14c19b87f43856dffc12c6764b0c8263bbd0d87f"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 87, "repo_created_at": "2024-12-22T04:30:48.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "b6e44355e70e24dc525ba7b6dd069aec2ebd2d9e", "size_bytes": 22984, "file_timestamp": 1753494415, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "14c19b87f43856dffc12c6764b0c8263bbd0d87f", "url": "https://github.com/baguajoe/StreampireX/blob/14c19b87f43856dffc12c6764b0c8263bbd0d87f/src/front/js/pages/BrowseVideosPage.js"}, "gpt4o_tokens": 5822, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8345021333360734, "noise_severity": 0.3181072090668443, "missing_content_severity": 0.1374627679514454, "factual_density": 2.764412006679548, "pretraining_utility": 0.5300620519216261, "math_density": 0.4182549646309461, "code_density": 3.6756562812556925, "reasoning_density": 0.5163403226583251, "education_level": 2.032163792654745, "creative_writing_quality": -0.09584123429455589}, "score_applicability": {"education_level": 0.9982581362959592, "creative_writing_quality": 0.0047133501429828285}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/thethanh02/Bookstore/blob/c32d034c0384aab81431b975ee3a1d4d5bbff960/shopping_fe/src/components/admin/OrderManagerPage.js", "id": "javascript-0-243d35b21c97492c0ad9ec868981e1da", "text": "import React, { useEffect, useState } from 'react';\nimport { Link, Navigate } from 'react-router-dom'\nimport { storeApi } from '../misc/StoreApi';\nimport { useAuth } from '../context/AuthContext';\nimport { Button, Container, List, Table } from 'semantic-ui-react';\nimport { handleLogError } from '../utils/Helpers'\nimport { formatCurrency } from '../utils/formatCurrency';\nimport moment from 'moment';\n\nconst OrderManagerPage = () => {\n const { getUser } = useAuth()\n const user = getUser()\n const isAdmin = (user.data.rol[0] === 'ADMIN')\n const [orders, setOrders] = useState(null)\n\n useEffect(() => {\n storeApi.getAllOrders(user)\n .then(response => {\n setOrders(response.data)\n })\n .catch(error => { \n handleLogError(error)\n })\n }, [])\n\n if (!isAdmin) {\n return \n }\n\n const handleCancelOrder = async (event, order) => {\n event.preventDefault();\n\n if (order.status === 'Đang xác nhận') {\n const orderStatusReq = {id: order.id, status: 'Đã hủy đơn'}\n await storeApi.updateOrderStatus(user, orderStatusReq)\n\n setOrders(orders => {\n return orders.map(o => o.id !== order.id ? o : {\n ...order,\n canceledAt: moment(new Date()).format('HH:mm DD/MM/YYYY'),\n status: 'Đã hủy đơn'\n } )\n })\n }\n }\n const handelConfirmOrder = async (event, order) => {\n event.preventDefault();\n\n if (order.status === 'Đang xác nhận') {\n const orderStatusReq = {id: order.id, status: 'Đang giao hàng'}\n await storeApi.updateOrderStatus(user, orderStatusReq)\n\n setOrders(orders => {\n return orders.map(o => o.id !== order.id ? o : {\n ...order,\n confirmedAt: moment(new Date()).format('HH:mm DD/MM/YYYY'),\n status: 'Đang giao hàng'\n } )\n })\n }\n }\n const handleDeliveredOrder = async (event, order) => {\n event.preventDefault();\n\n if (order.status === 'Đang giao hàng') {\n const orderStatusReq = {id: order.id, status: 'Đã giao hàng'}\n await storeApi.updateOrderStatus(user, orderStatusReq)\n\n setOrders(orders => {\n return orders.map(o => o.id !== order.id ? o : {\n ...order,\n deliveredAt: moment(new Date()).format('HH:mm DD/MM/YYYY'),\n status: 'Đã giao hàng'\n } )\n })\n }\n }\n\n let orderList\n if (orders === null || orders.length === 0) {\n orderList = (\n \n No order\n \n )\n } else {\n orderList = orders.map(order => {\n return (\n \n {order.id}\n {order.user.name}\n \n \n {order.orderItems.map(orderItem => {\n return(\n {orderItem.quantity}× {orderItem.book.title}\n )\n })}\n \n \n \n {formatCurrency(order.orderItems.reduce((total, orderItem) => {\n return total + (orderItem?.book.price || 0) * orderItem.quantity\n }, 0)\n )}\n \n {order.status}\n \n \n {order.createdAt !== null && Đặt lúc: {moment(order.createdAt).format('HH:mm DD/MM/YYYY')}}\n {order.confirmedAt !== null && Xác nhận lúc: {moment(order.confirmedAt).format('HH:mm DD/MM/YYYY')}}\n {order.deliveredAt !== null && Đã giao lúc: {moment(order.deliveredAt).format('HH:mm DD/MM/YYYY')}}\n {order.canceledAt !== null && Hủy lúc: {moment(order.canceledAt).format('HH:mm DD/MM/YYYY')}}\n \n \n \n \n Chi tiết\n \n handelConfirmOrder(event, order)}\n >\n Xác nhận đơn\n \n handleDeliveredOrder(event, order)}\n >\n Xác nhận đã giao\n \n handleCancelOrder(event, order)}\n >\n Hủy đơn\n \n \n \n )\n })\n }\n\n return (\n \n

Đơn hàng

\n
\n \n \n \n ID\n Tên KH\n Sách\n Tổng tiền\n Trạng thái\n Khung thời gian\n \n \n \n \n {orderList}\n \n
\n
\n \n )\n};\n\nexport default OrderManagerPage;", "text_preview": "import React, { useEffect, useState } from 'react'; import { Link, Navigate } from 'react-router-dom' import { storeApi } from '../misc/StoreApi'; import { useAuth } from '../context/AuthContext'; import { Button, Container, List, Table } f", "input_sha256": "5ddcc472b1b2643c850bba616d47038d0a9e073c68fbda64db75c95fe8082dad", "text_sha256": "243d35b21c97492c0ad9ec868981e1da2dd53290ff310e3563b7e095e822320e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8039, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.568891190659242, "noise_severity": 0.05639964031620167, "missing_content_severity": 0.01235255999680579, "factual_density": 2.9321660321942695, "pretraining_utility": 0.4711595644498067, "math_density": 0.4265671851390698, "code_density": 3.8667912935079665, "reasoning_density": 0.2918803935117643, "education_level": 2.0660458161116995, "creative_writing_quality": -0.35379260168165616}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007869514207851425, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9942216341669213, "secondary": 0.9317201316702932, "tertiary": 0.9399317986697906, "structured_records": 0.22335572033926274, "procedural_steps": 0.0026285168834826278, "worked_solution": 0.003000908537005057, "contextual_dialogue": 8.992316381885803e-06, "content_quality": 0.44451399037531647, "noise_severity": 0.9530002997364986, "missing_content_severity": 0.9897062000026619, "factual_density": 0.36155555828182084, "pretraining_utility": 0.4603478367400685, "math_density": 0.6445273457174419, "code_density": 0.8889927445899719, "reasoning_density": 0.7567663387401964, "education_level": 0.8239031503368792, "creative_writing_quality": 0.9983511190148279, "primary_topic": 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"valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000004, "mass_delta": 4.440892098500626e-16, "missing_mass": 0.0, "excess_mass": 4.440892098500626e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10078, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10078, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:11.992317+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e3a9fc94-d896-45c7-b0f4-382ae9a431c5", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ef6531c5d95e9b458ddfce0f7649264bf2dfff66d15aeb8018d83549c69e9682", "source_metadata": {"uuid": "e3a9fc94-d896-45c7-b0f4-382ae9a431c5", "repo_name": "thethanh02/Bookstore", "relative_path": "shopping_fe/src/components/admin/OrderManagerPage.js", "category": "WEB", "algo_rel_score": 0.9417931437492371, "quality_score": 5.853264331817627, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 24, "repo_created_at": "2023-05-21T13:47:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "eba51ea798ad6dde13c995cbdbb9c590e8014a0b", "size_bytes": 7761, "file_timestamp": 1692197580, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c32d034c0384aab81431b975ee3a1d4d5bbff960"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 24, "repo_created_at": "2023-05-21T13:47:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "eba51ea798ad6dde13c995cbdbb9c590e8014a0b", "size_bytes": 7761, "file_timestamp": 1692197580, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "c32d034c0384aab81431b975ee3a1d4d5bbff960", "url": "https://github.com/thethanh02/Bookstore/blob/c32d034c0384aab81431b975ee3a1d4d5bbff960/shopping_fe/src/components/admin/OrderManagerPage.js"}, "gpt4o_tokens": 1440, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.568891190659242, "noise_severity": 0.05639964031620167, "missing_content_severity": 0.012352559996805791, "factual_density": 2.9321660321942695, "pretraining_utility": 0.4711595644498067, "math_density": 0.4265671851390698, "code_density": 3.8667912935079665, "reasoning_density": 0.2918803935117643, "education_level": 2.0660458161116995, "creative_writing_quality": -0.35379260168165616}, "score_applicability": {"education_level": 0.998392998845936, "creative_writing_quality": 0.001374067487643539}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/accardo/ddc-store/blob/1e76ca9e08e1d7e4379a91a3b749c8c4170af2ea/utils/util.js", "id": "javascript-0-29eb4292a502b5352905c8d5b72d86f5", "text": "/* 获取 值类型 */\nexport function getType(val){\n return Object.prototype.toString.call(val).slice(8,-1);\n}\n/**\n * Description: IOS 手机 时间格式转换\n * Author: yanlichen <>\n * Date: 2018/6/15\n */\nexport function strToDate(dateObj){\n\tdateObj = dateObj.replace(/T/g, ' ').replace(/\\.[\\d]{3}Z/, '').replace(/(-)/g, '/')\n\tdateObj = dateObj.slice(0, dateObj.indexOf(\".\"))\n\treturn new Date(dateObj)\n}\n\n/*\n * Description: 格式化时间 例：2018-07-23\n * Author: yanlichen <>\n * Date: 2018/7/25\n */\nexport function formatTime(data, objTime) {\n\treturn data.forEach((item) => {\n\t\tif (item[objTime] !== null) {\n\t\t\tlet time = strToDate(item[objTime])\n\t\t\treturn item[objTime] = `${time.getFullYear()}-${time.getMonth() + 1}-${time.getDate()}`;\n\t\t}\n\t})\n}\n\n/**\n * Description: 限制商品分类 订货-- 商品类型(1 - 现制现售品, 2 - 零售品, 3 - 课程, 4 - 原料, 5 - 耗材, 6 - 工具) --以逗号形式传参，如：1,2,3\n * Author: yanlichen <>\n * Date: 2018/5/22\n */\n\nexport function limitClass(num) {\n\tlet limitClass = '';\n\tswitch (parseInt(num)) {\n\t\tcase 0: // 0 订货\n\t\t\tlimitClass = '2,4,5,6';\n\t\t\tbreak;\n\t\tcase 1: // 1 盘点\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t\tcase 2: // 2 出库操作\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t\tcase 3: // 3 置换\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t\tcase 4: // 4 调拨\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t\tcase 5: // 5 库存查询\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t\tcase 6: // 6 课程消耗\n\t\t\tlimitClass = null;\n\t\t\tbreak;\n\t\tcase 7: // 7 退货\n\t\t\tlimitClass = '2,4,6';\n\t\t\tbreak;\n\t}\n\treturn limitClass;\n}\n/**\n * Description: attrValues 直接转 array 转 string\n * Author: yanlichen <>\n * Date: 2018/6/8\n */\nexport function attrValuesToString(data) {\n\treturn data.attrValues !== null ? data.attrValues.toString() : null;\n}\n\n/**\n * Description: 直接转 string 转 array\n * Author: yanlichen <>\n * Date: 2018/6/8\n */\nexport function attrValuesSplit(data) {\n\treturn data.attrValues != null ? data.attrValues.split(',') : null;\n}\n/**\n * Description: shopItemSkuVO 数据结构 attrValues string 转 array\n * Author: yanlichen <>\n * Date: 2018/6/8\n */\nexport function attrValuesSkuSplit(data) {\n\tdata.forEach((item) => {\n\t\tif (item.shopItemSkuVO !== null && item.shopItemSkuVO.attrValues !== null) {\n\t\t\titem.shopItemSkuVO.attrValues = item.shopItemSkuVO.attrValues.split(',');\n\t\t}\n\t})\n\treturn data;\n}\n/**\n * Description: shopItemSkuVO 数据结构 attrValues array 转 string\n * Author: yanlichen <>\n * Date: 2018/6/8\n */\nexport function attrValuesSkuToString(data) {\n\tdata.forEach((item) => {\n\t\tif (item.shopItemSkuVO !== null && item.shopItemSkuVO.attrValues !== null) {\n\t\t\titem.shopItemSkuVO.attrValues = item.shopItemSkuVO.attrValues.toString();\n\t\t}\n\t})\n\treturn data;\n}\n/**\n * Description: 数组拷贝转换\n * Author: yanlichen <>\n * Date: 2018/5/24\n */\nexport function ArrayDeepCopy(data) {\n\treturn JSON.parse(JSON.stringify(data))\n}\n\n/**\n * Description: 提示成功跳转\n * Author: yanlichen <>\n * Date: 2018/5/24\n */\nexport function showToast(data) {\n\tlet pages = data.pages[data.pages.length - (data.page + 1)]; // 当前页面选中的 this\n\twx.showToast({\n\t\ttitle: data.title,\n\t});\n\tsetTimeout(() => {\n\t\twx.navigateBack({\n\t\t\tdelta: data.page\n\t\t})\n\t\tif (pages == undefined || pages == null) {\n\t\t\treturn\n\t\t}\n\t\tpages.setData({\n\t\t\tlistData: [],\n\t\t\tpagetListData: [],\n\t\t\tcurrPage: 1\n\t\t})\n\t\tpages.onLoad(pages.options)\n\t}, 800);\n}\n/*\n * Description: 验证信息 node toast\n * Author: yanlichen <>\n * Date: 2018/7/23\n */\nexport function showToastNone(msg) {\n\twx.showToast({\n\t\ttitle: msg,\n\t\ticon: 'none'\n\t})\n}\n/**\n * Description: 总结页面缓存 二维数组处理成一维\n * Author: yanlichen <>\n * Date: 2018/6/3\n */\nexport function cacheDataDeal(data) {\n\treturn [].concat.apply([],data);\n}\n\n/**\n * Description: 设置商品总数\n * Author: yanlichen <>\n * Date: 2018/6/3\n */\nexport function setTotalNumber(data) {\n\tlet shopPieceN = 0;\n\tlet tempArray = cacheDataDeal(data).map((item) => {\n\t\treturn shopPieceN += Number(item.needNumber)\n\t})\n\treturn {\n\t\ttotal: tempArray.length,\n\t\tshopPieceN\n\t}\n}\n\nexport function digitLength(num){\n\tconst eSplit = num.toString().split(/[eE]/);\n\tconst len = (eSplit[0].split('.')[1] || '').length - (+(eSplit[1] || 0));\n\treturn len > 0 ? len : 0;\n}\n\n//精确加法\nexport function add(num1, num2){\n\tconst baseNum = Math.pow(10, Math.max(digitLength(num1), digitLength(num2)));\n\treturn (sub(num1, baseNum) + sub(num2, baseNum)) / baseNum;\n}\n\n//精确减法\nexport function reduce(num1, num2){\n\tconst baseNum = Math.pow(10, Math.max(digitLength(num1), digitLength(num2)));\n\treturn (sub(num1, baseNum) - sub(num2, baseNum)) / baseNum;\n}\n\n// 精确乘法\nexport function sub(num1, num2){\n\tconst num1Changed = Number(num1.toString().replace('.', ''));\n\tconst num2Changed = Number(num2.toString().replace('.', ''));\n\tconst baseNum = digitLength(num1) + digitLength(num2);\n\treturn num1Changed * num2Changed / Math.pow(10, baseNum);\n}\n\n//精确除法\nexport function divide(num1, num2){\n\tconst num1Changed = Number(num1.toString().replace('.', ''));\n\tconst num2Changed = Number(num2.toString().replace('.', ''));\n\treturn sub((num1Changed / num2Changed), Math.pow(10, digitLength(num2) - digitLength(num1)));\n}\n\nexport default {\n getType,\n\tlimitClass,\n\tArrayDeepCopy,\n\tshowToast,\n\tcacheDataDeal,\n\tsetTotalNumber,\n\tattrValuesToString,\n\tattrValuesSplit,\n\tattrValuesSkuSplit,\n\tattrValuesSkuToString,\n\tadd,\n\treduce,\n\tsub,\n\tdivide,\n\tshowToastNone,\n\tformatTime,\n}", "text_preview": "/* 获取 值类型 */ export function getType(val){ return Object.prototype.toString.call(val).slice(8,-1); } /** * Description: IOS 手机 时间格式转换 * Author: yanlichen <> * Date: 2018/6/15 */ export function strToDate(dateObj){ dateObj = dateObj.r", "input_sha256": "c5e089c245dd1dc7b0b39e35a2e8c07cd7e8e34f6811d1d3cac9554acc757b50", "text_sha256": "29eb4292a502b5352905c8d5b72d86f5e5a3759f1d409c6be4025f350db6606c", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8042, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, 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"missing_content_severity": 0.9800670173334165, "factual_density": 0.4787172225508757, "pretraining_utility": 0.4804474184018329, "math_density": 0.3898470030873139, "code_density": 0.8388001531301841, "reasoning_density": 0.5333061862712951, "education_level": 0.7571440878892071, "creative_writing_quality": 0.9970651672175339, "primary_topic": 0.9831971512930979, "secondary_topic": 0.9171817368286238}, "probability_margin": {"primary": 0.9913589618639339, "secondary": 0.9336342205062925, "tertiary": 0.9669633436335503, "education_level": 0.6452641000895949, "creative_writing_quality": 0.9961264585461463, "primary_topic": 0.9775200751665732, "secondary_topic": 0.881165733974362}, "raw_probabilities": {"primary": {"Academic Writing": 4.133316154806928e-05, "Content Listing": 5.0006451584395145e-05, "Creative Writing": 2.1294026671819663e-05, "Customer Support Page": 1.4335562051004023e-05, "Q&A Forum": 1.713147347277639e-05, "Discussion Thread": 9.741485572118503e-06, "Comment Section": 2.403946308519514e-05, "Missing Content": 5.01114794352355e-05, "Knowledge Article": 0.00017765858882644946, "Legal Notices": 4.360363422985663e-05, "News Article": 6.446996075667419e-06, "Organizational Announcement": 1.5028756177005961e-05, "Nonfiction Writing": 1.4189094335411169e-05, "Organizational About Page": 1.6045791931280755e-05, "Personal About Page": 2.0971672001352418e-05, "Personal Blog": 1.2825938186238695e-05, "Product Page": 1.2484801560876868e-05, "Spam / Ads": 1.7085267117467556e-05, "Structured Data": 0.0015284494267576525, "Technical Writing": 0.9943236201773686, "Transcript / Interview": 2.2406976343130043e-05, "Tutorial / How-To Guide": 0.0005869483322535123, "User Reviews": 9.583129979926647e-06, "Other / Unknown": 0.002964658313434782}, "secondary": {"Academic Writing": 0.00026557035117846735, "Content Listing": 0.00048710006581623844, "Creative Writing": 0.00022031089864071957, "Customer Support Page": 0.0001541731878172992, "Q&A Forum": 0.00011377790282429829, "Discussion Thread": 9.825799116570786e-05, "Comment Section": 0.0005425020468162576, "Missing Content": 0.0006532899255982485, "Knowledge Article": 0.0021951175407361047, "Legal Notices": 0.0008077619824405864, "News Article": 0.0001361525688665652, "Organizational Announcement": 0.00016717850150321052, "Nonfiction Writing": 0.00014285073364102914, "Organizational About Page": 0.0001720574216842184, "Personal About Page": 0.00019334192485901316, "Personal Blog": 0.0001679992822897306, "Product Page": 0.00010164902318675662, "Spam / Ads": 0.00017022675141338713, "Structured Data": 0.014319651143617128, "Technical Writing": 0.021453620845891086, "Transcript / Interview": 0.00017314884592562843, "Tutorial / How-To Guide": 0.002107580071937515, "User Reviews": 6.88396399672567e-05, "NONE": 0.9550878413521836}, "tertiary": {"Academic Writing": 0.0001286606382908641, "Content Listing": 0.0005024280255606382, "Creative Writing": 0.00015328172987858366, "Customer 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{"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/rushmore-ops/hls-proxy/blob/0b3b2e9ab8bdb9f87ddd99a9f84ef9958417b1b4/routes/jobs.js", "id": "javascript-0-421c767c5e2d69dbed9b0023f58b05b5", "text": "'use strict';\n\nvar express = require('express');\nvar router = express.Router();\n\nvar videoJobs = require('../services/videojob.js');\nvar utils = require('../utils.js');\n\n// List of current jobs\nvar jobs = {};\n\n// Return the list of jobs\nfunction listJobs(req, res) {\n let result = Object.keys(jobs).map((key, index) => {\n return {\"id\":jobs[key].id, \"streamUrl\": jobs[key].streamUrl, \"upcloseStreamUrl\": jobs[key].upcloseStreamUrl, \"status\": jobs[key].status, \"liveDelay\": jobs[key].liveDelay};\n });\n utils.responseOk(res, result);\n}\n\n// Start a new job\nfunction startJob(req, res) {\n let id = req.params.id;\n \n if (jobs[id] !== undefined) {\n console.log(\"I can not start a job that is already in progress!!!\");\n utils.responseError(res, 500, \"Stream is already in progress\"); \n return; \n }\n \n if(req.body === undefined || req.body.streamUrl === undefined) {\n console.log(\"Bad start request received\");\n utils.responseError(res, 500, \"Stream url not provided\");\n } else {\n console.log(\"New job. Id: \" + id + \", streamUrl: \" + req.body.streamUrl);\n let streamUrl = req.body.streamUrl;\n // If we are working with an Akamai stream, force a keyframe in segment boundaries\n if (streamUrl.indexOf(\"akamaihd.net/i/\") > 0) {\n if (streamUrl.indexOf(\"?\") > 0) {\n streamUrl = streamUrl + \"&set-akamai-hls-revision=4\";\n } else {\n streamUrl = streamUrl + \"?set-akamai-hls-revision=4\";\n }\n }\n let callbackUrl = req.body.callbackUrl;\n let job = videoJobs.newJob(id, streamUrl, callbackUrl, req.appConfig);\n jobs[id] = job;\n \n job.on(\"end\", function() {\n console.log(\"Job finished!!!\"); \n delete jobs[job.id];\n });\n \n job.on(\"errors\", function(err, desc) {\n console.log(\"Job with errors. Removing it from the list of pending jobs!!!. Job Id: \" + job.id + \". Err: \" + err + \", Desc: \" + desc); \n req.ravenClient.captureMessage(err, {extra: {\"err\": desc, \"jobId\": job.id, \"hlsProcessed\": job.processStarted}});\n delete jobs[job.id];\n });\n \n job.on(\"warning\", function(err) {\n console.log(\"Job with warning\"); \n req.ravenClient.captureMessage(\"Warning: \", {extra: {\"err\": err, \"jobId\": job.id, \"hlsProcessed\": job.processStarted}});\n });\n \n job.start();\n \n utils.responseOk(res);\n }\n}\n\n// Delete all the files of a job (m3u8 and ts segments) and stop it\n// in case it is still running\nfunction deleteJob(req, res) {\n let id = req.params.id;\n console.log(\"Deleting job.... Id: \" + id);\n \n let job = jobs[id];\n if (job !== undefined) {\n console.log(\"Stopped job Id: \" + id);\n job.stop(); \n } else {\n job = videoJobs.newJob(id, \"\", \"\", req.appConfig);\n }\n \n job.removeAllFiles();\n \n utils.responseOk(res);\n}\n\n// Stop an existent job\nfunction stopJob(req, res) {\n \n let id = req.params.id;\n console.log(\"Stopping job.... Id: \" + id);\n \n let job = jobs[id];\n if (job !== undefined) {\n console.log(\"Stopped job Id: \" + id);\n job.stop();\n utils.responseOk(res);\n } else {\n console.log(\"The job Id \" + id + \" doesn't exist\"); \n utils.responseError(res, 404, \"Job \" + id + \"doesn't exist\");\n }\n}\n\n// Mark a stream as deleted\nfunction markAsDeleted(req, res) {\n let id = req.params.id;\n console.log(\"Marking as deleted the job.... Id: \" + id);\n \n getStatusInternal(id, req, res)\n .then((status) => {\n if (status !== videoJobs.STATUS_DELETED && status !== videoJobs.STATUS_RUNNING) {\n let job = videoJobs.newJob(id, \"\", \"\", req.appConfig);\n job.markAsDeleted()\n .then (() => {\n utils.responseOk(res);\n }, (err) => {\n console.log(\"Job \" + id + \", stream couldn't be marked as deleted\");\n req.ravenClient.captureMessage(\"JobStatusError. Job Delete\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n });\n } else {\n console.log(\"Job \" + id + \", can not not change status to deleted due to current status is \" + status);\n //req.ravenClient.captureMessage(\"JobStatusError. Job Delete can not not change status to deleted due to current status is \" + status, {extra: {\"jobId\": id}});\n utils.responseError(res, 400, \"Can not not change status to deleted due to current status is \" + status);\n } \n }, (err) => {\n console.log(\"Error in markAsDeleted. \" + err);\n req.ravenClient.captureMessage(\"JobStatusError. Job marking as deleted.\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n }); \n}\n\n// Mark a stream as private\nfunction markAsPrivate(req, res) {\n let id = req.params.id;\n console.log(\"Marking as private the job.... Id: \" + id);\n \n getStatusInternal(id, req, res)\n .then((status) => {\n if (status !== videoJobs.STATUS_PRIVATE && status !== videoJobs.STATUS_RUNNING) {\n let job = videoJobs.newJob(id, \"\", \"\", req.appConfig);\n job.markAsPrivate()\n .then (() => {\n utils.responseOk(res);\n }, (err) => {\n console.log(\"Job \" + id + \", stream couldn't be marked as private\");\n req.ravenClient.captureMessage(\"JobStatusError. Stream couldn't be marked as private\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n });\n } else {\n console.log(\"Job \" + id + \", can not not change status to private due to current status is \" + status);\n //req.ravenClient.captureMessage(\"JobStatusError. Can not not change status to private due to current status is \" + status, {extra: {\"jobId\": id}});\n utils.responseError(res, 400, \"Can not not change status to private due to current status is \" + status);\n } \n }, (err) => {\n console.log(\"Error in markAsPrivate. \" + err);\n req.ravenClient.captureMessage(\"JobStatusError. Marking as private.\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n });\n}\n\n// Remove deleted/privated flags of a stream\nfunction markAsRestored(req, res) {\n let id = req.params.id;\n console.log(\"Marking as public the job.... Id: \" + id);\n \n getStatusInternal(id, req, res)\n .then((status) => {\n if (status === videoJobs.STATUS_PRIVATE || status === videoJobs.STATUS_DELETED) {\n let job = videoJobs.newJob(id, \"\", \"\", req.appConfig);\n job.markAsRestored()\n .then (() => {\n utils.responseOk(res);\n }, (err) => {\n console.log(\"JobStatusError. Job \" + id + \", stream couldn't be restored\");\n req.ravenClient.captureMessage(\"JobStatusError. Stream couldn't be restored\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n });\n \n } else {\n console.log(\"Job \" + id + \", can not not change status to restored due to current status is neither private nor deleted\");\n //req.ravenClient.captureMessage(\"JobStatusError. Can not not change status to restored due to current status is neither private nor deleted\", {extra: {\"jobId\": id}});\n utils.responseError(res, 400, \"Can not not change status to restored due to current status is neither private nor deleted\");\n } \n }, (err) => {\n console.log(\"Error in markAsRestoredin. \" + err);\n req.ravenClient.captureMessage(\"JobStatusError. Marking as restored.\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 400, err);\n });\n}\n\n// Return the status of a job\nfunction getStatus(req, res) {\n let id = req.params.id;\n console.log(\"Getting status of the job.... Id: \" + id);\n \n getStatusInternal(id, req, res)\n .then((status) => {\n utils.responseOk(res, status);\n }, (err) => {\n req.ravenClient.captureMessage(\"JobStatusError. While getting status.\", {extra: {\"err\": err, \"jobId\": id}});\n utils.responseError(res, 404, err);\n });\n}\n\nfunction getStatusInternal(id, req, res) {\n let job = jobs[id];\n \n return new Promise((resolve, reject) => {\n if (job !== undefined) {\n // If we have a reference to the job,then it is running\n resolve(videoJobs.STATUS_RUNNING);\n } else {\n job = videoJobs.newJob(id, \"\", \"\", req.appConfig); \n \n job.getStatus().\n then ((status) => {\n resolve(status);\n }, (err) => {\n reject(err);\n })\n }\n });\n}\n\n/** ROUTES */\n// Get the list of jobs\nrouter.get('/', listJobs);\n\n// Start a new job\n// curl -H \"Content-Type: application/json\" -X POST -d '{\"streamUrl\": \"http://tokbox001-lh.akamaihd.net/i/8c891e94f1d240af9e71c15a29137f2c_1@392088/master.m3u8\"}' localhost:3000/api/v1/jobs/1231\nrouter.post('/:id', startJob);\nrouter.post('/:id/start', startJob);\n\n// Delete all the files of a job (m3u8 and ts segments) and stop it\n// in case it is still 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5.208057200159245e-06}, "content_quality": {"-2": 0.020976812181933136, "-1": 0.21397704876474133, "0": 0.37693430848086584, "1": 0.32389875612012137, "2": 0.06421307445233841}, "noise_severity": {"0": 0.9549903327320113, "1": 0.03841467750770174, "2": 0.005246344075321016, "3": 0.0008627095391922243, "4": 0.00048593614577378396}, "missing_content_severity": {"0": 0.9714492617347426, "1": 0.015060196934803106, "2": 0.010794729188337168, "3": 0.0021406722927691797, "4": 0.0005551398493480054}, "factual_density": {"0": 0.006777482758125186, "1": 0.017897964929007262, "2": 0.1633041644822994, "3": 0.42547189017583964, "4": 0.3865484976547285}, "pretraining_utility": {"-2": 0.00758509942152145, "-1": 0.07513461031862873, "0": 0.39600409000096315, "1": 0.4647052836563142, "2": 0.056570916602572505}, "math_density": {"0": 0.9717682145302036, "1": 0.024931890434728446, "2": 0.0014579420780822037, "3": 0.000561346412986345, "4": 0.0012806065439993925}, "code_density": {"0": 0.0002769792797880269, "1": 0.0005157548439479338, "2": 0.014012719870435975, "3": 0.14692005762131644, "4": 0.8382744883845116}, "reasoning_density": {"0": 0.5445433792351705, "1": 0.23024918209983855, "2": 0.1743827932470121, "3": 0.03880128377541607, "4": 0.012023361642562742}, "education_level": {"none": 0.0010183467213410718, "0": 0.0015535568903793542, "1": 0.018695597408823247, "2": 0.7391770664845301, "3": 0.2260948914454993, "4": 0.01346054104942691}, "creative_writing_quality": {"not_applicable": 0.9984946775195876, "-2": 7.860151759064899e-05, "-1": 0.00037291415730764614, "0": 0.0008938377556085023, "1": 0.00012462793747954973, "2": 3.5341112426036145e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.491199065743368e-05, "Art & Design": 3.2117128350859066e-05, "Law & Crime": 2.0827227068018125e-05, "Education & Careers": 2.306007054695105e-05, "Entertainment & Media": 0.001592582584295123, "Fashion & Beauty": 1.621311571704586e-05, "Business, Economics & Finance": 0.00015414031088195254, "Food & Dining": 2.047770520017811e-05, "Games & Gaming": 6.668142437611659e-05, "Electronics & Hardware": 0.00010017129406023611, "Health, Healthcare & Wellness": 1.638531993017853e-05, "History & Geography": 2.2233479960179086e-05, "Home, Pets & Hobbies": 1.7722466875415502e-05, "Industry & Production": 3.482463224488777e-05, "Language, Literature & Philosophy": 2.399214083351924e-05, "Politics & Government": 1.1421230655448318e-05, "Religion & Spirituality": 1.904073159409806e-05, "Science, Mathematics & Engineering": 0.00027698791833491595, "Relationships & Community": 1.2725474456498669e-05, "Software Usage & Applications": 0.000894051639013444, "Programming & Software Engineering": 0.9960887787591451, "Sports & Fitness": 2.993240161049277e-05, "Vehicles & Transportation": 2.4948438193813716e-05, "Travel & Tourism": 2.2965075257758634e-05, "Other / Uncovered Subject": 0.0004148824189436894, "Unknown / Insufficient Content": 3.7925021796567264e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005209128446384876, "Art & Design": 0.00036147846038497535, "Law & Crime": 0.0003109916473612155, "Education & Careers": 0.00023191833772887642, "Entertainment & Media": 0.050862519267318086, "Fashion & Beauty": 0.00017028548937638948, "Business, Economics & Finance": 0.000642628353421779, "Food & Dining": 0.00019105976756661805, "Games & Gaming": 0.0004561154349515283, "Electronics & Hardware": 0.00035394195443054656, "Health, Healthcare & Wellness": 0.00017148518041073732, "History & Geography": 0.00016397363122024313, "Home, Pets & Hobbies": 0.00015838187553006867, "Industry & Production": 0.00016408563570346167, "Language, Literature & Philosophy": 0.0001266599501964999, "Politics & Government": 0.00014111664273532045, "Religion & Spirituality": 0.0002088678839668745, "Science, Mathematics & Engineering": 0.0006039315797810704, "Relationships & Community": 0.00010538988004513493, "Software Usage & Applications": 0.008143514803204214, "Programming & Software Engineering": 0.02005134613313388, "Sports & Fitness": 0.00020615287045612195, "Vehicles & Transportation": 0.00011230196280262736, "Travel & Tourism": 0.00020178903626150692, "Other / Uncovered Subject": 0.0009991225574445966, "NONE": 0.9143400288199292}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11134, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11134, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:31:16.337386+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "6b24b218-b151-47a6-95c9-2c5342613abb", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d197267b85889646f4bc764e719fc015cd1bfe1e78c6720e6dc95f3b60080f95", "source_metadata": {"uuid": "6b24b218-b151-47a6-95c9-2c5342613abb", "repo_name": "rushmore-ops/hls-proxy", "relative_path": "routes/jobs.js", "category": "WEB", "algo_rel_score": 0.923061728477478, "quality_score": 5.6699090003967285, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 90, "repo_created_at": "2016-03-22T14:08:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 4, "pull_requests": 0}, "file_meta": {"content_id": "0842bad5b378b9dc4b0024ed23e9f788c64c8fe0", "size_bytes": 9580, "file_timestamp": 1487894859, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0b3b2e9ab8bdb9f87ddd99a9f84ef9958417b1b4"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 90, "repo_created_at": "2016-03-22T14:08:38.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 4, "pull_requests": 0}, "file_meta": {"content_id": "0842bad5b378b9dc4b0024ed23e9f788c64c8fe0", "size_bytes": 9580, "file_timestamp": 1487894859, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0b3b2e9ab8bdb9f87ddd99a9f84ef9958417b1b4", "url": "https://github.com/rushmore-ops/hls-proxy/blob/0b3b2e9ab8bdb9f87ddd99a9f84ef9958417b1b4/routes/jobs.js"}, "gpt4o_tokens": 2389, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.1963942318961906, "noise_severity": 0.05343923885901558, "missing_content_severity": 0.045292231587177004, "factual_density": 3.167115955040039, "pretraining_utility": 0.48754230769978757, "math_density": 0.034654240005849456, "code_density": 3.8223993209868157, "reasoning_density": 0.743512066490362, "education_level": 2.231448957641944, "creative_writing_quality": -0.22241548539526335}, "score_applicability": {"education_level": 0.9989816532786588, "creative_writing_quality": 0.0015053224804123834}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/dcodeprodigy/petite-notas/blob/a2e584fadf77c3da87d772b387f9b9f28082af56/src/components/noteslist.jsx", "id": "javascript-0-81736dce7837d73f62e02c9414870081", "text": "import React, { useEffect, useState, useRef } from \"react\";\nimport { FastAverageColor } from \"fast-average-color\";\nimport chroma from \"chroma-js\";\nimport readingTime from \"../utilis/ReadingTimeCalc\";\nimport FilterDropdown from \"./filterdropdown\";\n\nconst fac = new FastAverageColor();\n\nconst NotesAsList = function ({ notes, displayNote, setDisplayNote }) {\n const [avThumbnailColors, setAvThumbnailColors] = useState({}); // Store colors by note ID\n const thumbnailElems = useRef({}); // Store refs by note ID\n const baseThumbnailDivStyle =\n \"flex flex-1 justify-center items-center rounded-[18px]\";\n const [clicked, setClicked] = useState({});\n const [isHovered, setIsHovered] = useState({});\n\n \n\n const setNote = async ({ e, id }) => {\n // Open or CLose the Note\n const element = e.target;\n console.log(id, element.className);\n\n setClicked((prevState) => {\n const newClicked = { ...prevState };\n Object.keys(newClicked).forEach((key) => {\n if (key !== id) newClicked[key] = false; // Set all others to false\n });\n newClicked[id] = !prevState[id]; // Toggle value of clicked one\n\n return newClicked;\n });\n\n /* \n Get the element and style it accordingly\n 1. Check current state of the displaye note div \n 2. Open if Closed\n 3. Close if Open\n */\n };\n\n return (\n <>\n
\n {notes.length === 0 ? (\n
\n No notes available\n
\n ) : (\n notes.map((note) => {\n const handleMouseEnter = () => {\n\n if (!clicked[note._id]) {\n setIsHovered((prevState) => ({\n ...prevState,\n [note._id] : true\n }))\n } else {\n setIsHovered((prevState) => ({\n ...prevState,\n [note._id] : false\n }))\n }\n \n };\n \n const handleMouseLeave = () => {\n if (!clicked[note._id]) {\n setIsHovered((prevState) => ({\n ...prevState,\n [note._id] : false\n }))\n } else {\n setIsHovered((prevState) => ({\n ...prevState,\n [note._id]: true\n }))\n }\n };\n\n return (\n setNote({ e, id: note._id })}\n style={{ \n backgroundColor: `${!isHovered[note._id] ? (!clicked[note._id] ? \"rgb(246 247 249)\" /* Normal Color when not cliked or hovered */ : avThumbnailColors[note._id][\"onClick\"]) : (clicked[note._id] ? avThumbnailColors[note._id][\"onClick\"] : avThumbnailColors[note._id][\"onHover\"] /* Hover color */) }`, // This code is ass but it works anyway. Goodluck refactoring later\n }}\n onMouseEnter={handleMouseEnter}\n onMouseLeave={handleMouseLeave}\n className={`transition-all cursor-pointer duration-500 flex gap-4 rounded-2xl py-6 px-5 mb-4`}\n >\n
\n (thumbnailElems.current[note.id] = el)} // Store ref with note ID\n onLoad={() => {\n let [onClick, onHover] = \"\";\n if (thumbnailElems.current[note.id]) {\n fac\n .getColorAsync(thumbnailElems.current[note.id])\n .then((color) => {\n onClick = chroma(\n chroma(color.hex).brighten().hex()\n )\n .brighten()\n .hex();\n onHover = chroma(onClick).brighten().hex();\n\n setAvThumbnailColors((prevColors) => ({\n ...prevColors,\n [note._id]: {onClick: onClick, onHover: onHover},\n }));\n })\n .catch((err) => console.error(err));\n }\n\n // setBackgroundColor(note.id, lightened);\n }}\n />\n
\n\n
\n

\n {note?.title}\n

\n

\n {note?.content}\n

\n\n
\n \n {readingTime(note?.content)} mins\n \n

{new Date(note?.createdAt).toLocaleString()}

\n
\n
\n \n );\n })\n )}\n
\n \n );\n};\n\nconst NotesList = function ({ notes, displayNote, setDisplayNote }) {\n return (\n <>\n
\n
\n \"Notes\"\n

All Notes

\n
\n\n
\n \n {notes.length} Note{`${notes.length > 1 ? \"s\" : \"\"}`}\n \n\n
\n \n
\n
\n\n {/* Here, we shall map the content in accordance with the number of notes existing. Let's build the UI components first */}\n \n
\n \n );\n};\n\nexport default NotesList;\n", "text_preview": "import React, { useEffect, useState, useRef } from \"react\"; import { FastAverageColor } from \"fast-average-color\"; import chroma from \"chroma-js\"; import readingTime from \"../utilis/ReadingTimeCalc\"; import FilterDropdown from \"./filterdrop", "input_sha256": "1a970e7effab931e223d3c6740dbcaf8b0505bd73bb6d8e10b5178ff7b394027", "text_sha256": "81736dce7837d73f62e02c94148700818c512f1ef75672192b8bdb2fa4d8a599", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8043, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, 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"valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10068, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10068, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:02.960207+00:00", "scoring_key_slot": "primary", "dataset_uid": "cc10a07c-47e1-486f-85ab-54c5f47c3018", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8e7a75b953723f725c645e0158de1b818a629f12751f3752f7d1150a1da2b7e1", "source_metadata": {"uuid": "cc10a07c-47e1-486f-85ab-54c5f47c3018", "repo_name": "dcodeprodigy/petite-notas", "relative_path": "src/components/noteslist.jsx", "category": "WEB", "algo_rel_score": 0.9363744854927063, "quality_score": 4.675646781921387, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-02-06T10:08:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5711018e21ea38f3919af8caa0742e9e99d1da1d", "size_bytes": 6629, "file_timestamp": 1743238853, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2e584fadf77c3da87d772b387f9b9f28082af56"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 9, "repo_created_at": "2025-02-06T10:08:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5711018e21ea38f3919af8caa0742e9e99d1da1d", "size_bytes": 6629, "file_timestamp": 1743238853, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2e584fadf77c3da87d772b387f9b9f28082af56", "url": "https://github.com/dcodeprodigy/petite-notas/blob/a2e584fadf77c3da87d772b387f9b9f28082af56/src/components/noteslist.jsx"}, "gpt4o_tokens": 1453, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2456986812121552, "noise_severity": 0.16376677861137906, "missing_content_severity": 0.05327528599958141, "factual_density": 2.779307468833903, "pretraining_utility": 0.43907098779381515, "math_density": 0.024284195778883258, "code_density": 3.8283005502348826, "reasoning_density": 0.2385954737198213, "education_level": 2.0838953745239754, "creative_writing_quality": -0.5621315697036344}, "score_applicability": {"education_level": 0.9986489615328508, "creative_writing_quality": 0.003999780823252808}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/rowanwins/bentley-ottmann-intersections/blob/617297dde6c8c07b58d581e1c9058d5f111cd9e0/src/Sweepline.js", "id": "javascript-0-7a5dd992dac849a9adf7f3254b8397f1", "text": "import SplayTree from 'splaytree'\nimport Segment from './Segment'\nimport {compareSegments} from './compareSegments'\nimport Event from './Event'\nimport Point from './Point'\n\n// import {debugTwoSegments, debugIntersectionEventAndSegments, debugSegmentBelowAbove, debugSplitSeg, debugPriorSegNewSeg, debugOutputSequence, debugReconstructionOnMultiHit, checkRewiring, debugMultilineSplit, debugP, debugP2, debugPlotLine, debugShowLineDirectionAfterSplit, debugShowLineDirectionPriorSplit} from './debug'\n\nexport default class SweepLine {\n constructor () {\n this.tree = new SplayTree(compareSegments)\n }\n\n addSegment (event) {\n const seg = new Segment(event)\n const node = this.tree.insert(seg)\n const nextNode = this.tree.next(node)\n const prevNode = this.tree.prev(node)\n if (nextNode !== null) {\n seg.segmentAbove = nextNode.key\n seg.segmentAbove.segmentBelow = seg\n }\n if (prevNode !== null) {\n seg.segmentBelow = prevNode.key\n seg.segmentBelow.segmentAbove = seg\n }\n return node.key\n }\n\n removeSegmentFromSweepline (seg) {\n const node = this.tree.find(seg)\n if (node === null) return\n const nextNode = this.tree.next(node)\n const prevNode = this.tree.prev(node)\n\n if (nextNode !== null) {\n const nextSeg = nextNode.key\n nextSeg.segmentBelow = seg.segmentBelow\n }\n if (prevNode !== null) {\n const prevSeg = prevNode.key\n prevSeg.segmentAbove = seg.segmentAbove\n }\n this.tree.remove(seg)\n }\n\n flipSegments (seg1, seg2) {\n const node1 = this.tree.find(seg1)\n const node2 = this.tree.find(seg2)\n\n if (node1 === null || node2 === null) {\n return\n }\n const tempAbove1 = seg1.segmentAbove\n const tempAbove2 = seg2.segmentAbove\n\n const tempBelow1 = seg1.segmentBelow\n const tempBelow2 = seg2.segmentBelow\n\n if (seg2.segmentAbove === seg1) {\n seg1.segmentAbove = seg2\n seg2.segmentAbove = tempAbove1\n seg1.segmentBelow = tempBelow2\n seg2.segmentBelow = seg1\n if (tempAbove1 !== null) tempAbove1.segmentBelow = seg2\n if (tempBelow2 !== null) tempBelow2.segmentAbove = seg1\n } else {\n seg1.segmentAbove = tempAbove2\n seg2.segmentAbove = seg1\n seg1.segmentBelow = seg2\n seg2.segmentBelow = tempBelow1\n if (tempAbove2 !== null) tempAbove2.segmentBelow = seg1\n if (tempBelow1 !== null) tempBelow1.segmentAbove = seg2\n }\n const tmp1 = node1.key\n const tmp2 = node2.key\n\n node2.key = tmp1\n node1.key = tmp2\n }\n\n testIntersect (seg1, seg2) {\n if (seg1 === null || seg2 === null) return false\n\n if (seg1.rightSweepEvent.isSamePoint(seg2.leftSweepEvent) ||\n seg1.rightSweepEvent.isSamePoint(seg2.rightSweepEvent) ||\n seg1.leftSweepEvent.isSamePoint(seg2.leftSweepEvent) ||\n seg1.leftSweepEvent.isSamePoint(seg2.rightSweepEvent)) return false\n\n const x1 = seg1.leftSweepEvent.p.x\n const y1 = seg1.leftSweepEvent.p.y\n const x2 = seg1.rightSweepEvent.p.x\n const y2 = seg1.rightSweepEvent.p.y\n const x3 = seg2.leftSweepEvent.p.x\n const y3 = seg2.leftSweepEvent.p.y\n const x4 = seg2.rightSweepEvent.p.x\n const y4 = seg2.rightSweepEvent.p.y\n\n\n const denom = ((y4 - y3) * (x2 - x1)) - ((x4 - x3) * (y2 - y1))\n const numeA = ((x4 - x3) * (y1 - y3)) - ((y4 - y3) * (x1 - x3))\n const numeB = ((x2 - x1) * (y1 - y3)) - ((y2 - y1) * (x1 - x3))\n\n if (denom === 0) {\n if (numeA === 0 && numeB === 0) return false\n return false\n }\n\n const uA = numeA / denom\n const uB = numeB / denom\n\n if (uA >= 0 && uA <= 1 && uB >= 0 && uB <= 1) {\n const x = x1 + (uA * (x2 - x1))\n const y = y1 + (uA * (y2 - y1))\n\n const p = new Point([x, y])\n const intersectionEvent = new Event(p)\n\n if (seg2.segmentAbove === seg1) {\n intersectionEvent.segment1 = seg1\n intersectionEvent.segment2 = seg2\n } else if (seg1.segmentAbove === seg2) {\n intersectionEvent.segment1 = seg2\n intersectionEvent.segment2 = seg1\n }\n\n const comp = compareSegments(seg1, seg2)\n if (comp > 0) {\n intersectionEvent.segment1 = seg1\n intersectionEvent.segment2 = seg2\n } else {\n intersectionEvent.segment1 = seg2\n intersectionEvent.segment2 = seg1\n }\n\n return intersectionEvent\n }\n return false\n }\n\n}\n", "text_preview": "import SplayTree from 'splaytree' import Segment from './Segment' import {compareSegments} from './compareSegments' import Event from './Event' import Point from './Point' // import {debugTwoSegments, debugIntersectionEventAndSegments, debu", "input_sha256": "8c72e47ec04ae8b364ec4cdd37407c3056ecb79df77be6de6b7d4d33324e939e", "text_sha256": "7a5dd992dac849a9adf7f3254b8397f1d7a1e7a69005f92dd4814b4130aa4e28", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8044, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9287569662517644, "noise_severity": 0.08616414011781107, "missing_content_severity": 0.05271769562740678, "factual_density": 3.482319328929993, "pretraining_utility": 0.5804628430410229, "math_density": 2.3943704530540177, "code_density": 3.834986637230153, "reasoning_density": 1.2045570992929941, "education_level": 2.524127768084782, "creative_writing_quality": -0.3982439095349851}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004900889023836119, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997145249493934, "secondary": 0.9921598104546236, "tertiary": 0.9972737816099307, "structured_records": 0.0005461182135358696, "procedural_steps": 0.0026681900441849833, "worked_solution": 0.005454469033613385, "contextual_dialogue": 5.533978402260746e-06, "content_quality": 0.5432536597173054, "noise_severity": 0.9281965499018241, "missing_content_severity": 0.956068586977161, "factual_density": 0.5685994407749944, "pretraining_utility": 0.46215148283969365, "math_density": 0.15481381984581744, "code_density": 0.8624888643584605, "reasoning_density": 0.0, "education_level": 0.45956287028984405, "creative_writing_quality": 0.9996658411172834, "primary_topic": 0.9356292573291967, "secondary_topic": 0.5061661648337217}, "probability_margin": {"primary": 0.995831548024507, "secondary": 0.9886891799912378, "tertiary": 0.9958814941664895, "education_level": 0.19704332460317503, "creative_writing_quality": 0.9996118727021795, "primary_topic": 0.8797680922393442, "secondary_topic": 0.18285903654542868}, "raw_probabilities": {"primary": {"Academic Writing": 6.404946873204124e-05, "Content Listing": 1.9598266956622343e-05, "Creative Writing": 1.7208162012040375e-05, "Customer Support Page": 6.466592534453318e-06, "Q&A Forum": 1.0070348065943347e-05, "Discussion Thread": 7.429375680823536e-06, "Comment Section": 1.064940931859187e-05, "Missing Content": 2.571286165645151e-05, "Knowledge Article": 0.0001123078256172427, "Legal Notices": 2.4706272578716855e-05, "News Article": 4.295888719572321e-06, "Organizational Announcement": 9.372883515737718e-06, "Nonfiction Writing": 1.2000660290077277e-05, "Organizational About Page": 1.0903720401186925e-05, "Personal About Page": 1.097075742514175e-05, "Personal Blog": 6.548795939699999e-06, "Product Page": 6.539178818255263e-06, "Spam / Ads": 8.259641764418829e-06, "Structured Data": 0.0005527281164992094, "Technical Writing": 0.9972641974316868, "Transcript / Interview": 1.3943404356972924e-05, "Tutorial / How-To Guide": 0.00036324719852565574, "User Reviews": 6.1443317247473135e-06, "Other / Unknown": 0.0014326494071798214}, "secondary": {"Academic Writing": 0.0003917494970481683, "Content Listing": 5.3962477139806373e-05, "Creative Writing": 5.681044026375332e-05, "Customer Support Page": 2.5306699064395514e-05, "Q&A Forum": 2.263363955697502e-05, "Discussion Thread": 1.861905155931179e-05, "Comment Section": 3.9006090655514094e-05, "Missing Content": 0.00021671652536544358, "Knowledge Article": 0.0002644384823165976, "Legal Notices": 9.454673457223046e-05, "News Article": 2.811222644924882e-05, "Organizational Announcement": 2.652870587058336e-05, "Nonfiction Writing": 2.567280947428021e-05, "Organizational About Page": 3.44144332688036e-05, "Personal About Page": 3.009900032031318e-05, "Personal Blog": 2.2659826653409053e-05, "Product Page": 1.7156993142444622e-05, "Spam / Ads": 2.722435604344406e-05, "Structured Data": 0.0018656663466238767, "Technical Writing": 0.003797305027776493, "Transcript / Interview": 3.2885961505269156e-05, "Tutorial / How-To Guide": 0.00040668951128022113, "User Reviews": 1.5310145035168846e-05, "NONE": 0.9924864850190144}, "tertiary": {"Academic Writing": 0.00013829514350880326, "Content Listing": 2.2846636304531998e-05, "Creative Writing": 2.5622670175046883e-05, "Customer Support Page": 1.008123995698794e-05, "Q&A Forum": 9.848755493559237e-06, "Discussion Thread": 7.693962726503519e-06, "Comment Section": 1.687118254786782e-05, "Missing Content": 9.643592705508677e-05, "Knowledge Article": 8.194911735472592e-05, "Legal Notices": 5.6244784830007026e-05, "News Article": 1.3819935526082987e-05, "Organizational Announcement": 1.2124115810052187e-05, "Nonfiction Writing": 1.3300201878125893e-05, "Organizational About Page": 2.2960731176638767e-05, "Personal About Page": 1.2999768654346206e-05, "Personal Blog": 1.1439261904675136e-05, "Product Page": 9.017481085088174e-06, "Spam / Ads": 1.2927716179074588e-05, "Structured Data": 0.0002712164913907638, "Technical Writing": 0.0015058798763608046, "Transcript / Interview": 1.3723036869262792e-05, "Tutorial / How-To Guide": 0.00023970314943334118, "User Reviews": 7.624770928417572e-06, "NONE": 0.9973873740428503}, "structured_records": {"false": 0.9994538817864641, "true": 0.0005461182135358696}, "procedural_steps": {"false": 0.997331809955815, "true": 0.0026681900441849833}, "worked_solution": {"false": 0.9945455309663866, "true": 0.005454469033613385}, "contextual_dialogue": {"false": 0.9999944660215977, "true": 5.533978402260746e-06}, "content_quality": {"-2": 0.00868959799147876, "-1": 0.026118203047783825, "0": 0.23136412097373063, "1": 0.4954017906915079, "2": 0.23842628729549892}, "noise_severity": {"0": 0.9193174251990899, "1": 0.07674334543310146, "2": 0.002825574556572687, "3": 0.0006849736733796958, "4": 0.00042868113785628524}, "missing_content_severity": {"0": 0.9556275490406061, "1": 0.03829113891853761, "2": 0.004281303954651157, "3": 0.0013360835452540809, "4": 0.0004639245409511535}, "factual_density": {"0": 0.0056507006730212896, "1": 0.009822826891763682, "2": 0.07347831800995111, "3": 0.31865275168272833, "4": 0.5923954027425355}, "pretraining_utility": {"-2": 0.008813488179587032, "-1": 0.0763054155450596, "0": 0.35342639314679203, "1": 0.44851417131186627, "2": 0.11294053181669515}, "math_density": {"0": 0.05661101206411552, "1": 0.18559416511923219, "2": 0.26890511513468957, "3": 0.2845927730624443, "4": 0.2042969346195184}, "code_density": {"0": 0.00027761314157559376, "1": 0.000565181003670772, "2": 0.017328333501723876, "3": 0.1275507001890849, "4": 0.8542781721639449}, "reasoning_density": {"0": 0.5775016688782949, "1": 0.04724745284698195, "2": 0.09988563987306258, "3": 0.14392258690675497, "4": 0.13144265149490555}, "education_level": {"none": 0.0010240611027191907, "0": 0.0013660862107751945, "1": 0.005677550868464764, "2": 0.5496357252415367, "3": 0.35259240063836167, "4": 0.08970417593814242}, "creative_writing_quality": {"not_applicable": 0.9997215342644028, "-2": 5.520685852112443e-05, "-1": 6.413789111070762e-05, "0": 0.00010966156222329704, "1": 3.526452254696041e-05, "2": 1.419490119511495e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 9.809349717712838e-05, "Art & Design": 0.0004152060920417377, "Law & Crime": 0.00012159300860177289, "Education & Careers": 9.245876408840217e-05, "Entertainment & Media": 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0.0006033151235976516, "Education & Careers": 0.00044098706451068705, "Entertainment & Media": 0.0004962289970933635, "Fashion & Beauty": 0.00034549227541989786, "Business, Economics & Finance": 0.0005086392909624866, "Food & Dining": 0.0002653262849932928, "Games & Gaming": 0.0008065099063757124, "Electronics & Hardware": 0.000528381900486099, "Health, Healthcare & Wellness": 0.000482021437012104, "History & Geography": 0.0005007901040263066, "Home, Pets & Hobbies": 0.00029350868675660015, "Industry & Production": 0.00031604608583863975, "Language, Literature & Philosophy": 0.00027055899001928, "Politics & Government": 0.00027926215028628446, "Religion & Spirituality": 0.0004239418792718552, "Science, Mathematics & Engineering": 0.3423007373331498, "Relationships & Community": 0.00019009751836879526, "Software Usage & Applications": 0.0006063254447825852, "Programming & Software Engineering": 0.12004214692122568, "Sports & Fitness": 0.0003790016619418221, "Vehicles & Transportation": 0.00026327078905821966, "Travel & Tourism": 0.0005951770082444078, "Other / Uncovered Subject": 0.0013337970848641889, "NONE": 0.5251597738785785}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 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null, "input_tokens": 9840, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:06.110166+00:00", "scoring_key_slot": "primary", "dataset_uid": "c1f3a8f1-f996-473b-9e0f-67d4b3c00014", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "1dad8c9e76459728c26ba0035b79401665f33f0c58d2602791d08082557ec85d", "source_metadata": {"uuid": "c1f3a8f1-f996-473b-9e0f-67d4b3c00014", "repo_name": "rowanwins/bentley-ottmann-intersections", "relative_path": "src/Sweepline.js", "category": "TOOL", "algo_rel_score": 0.9074229598045349, "quality_score": 4.089777946472168, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2019-08-08T13:25:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 2, "issues": 0, "pull_requests": 21}, "file_meta": {"content_id": "82bc8b2e16dd89512a850043133c31a59583abdc", "size_bytes": 4866, "file_timestamp": 1569492023, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "617297dde6c8c07b58d581e1c9058d5f111cd9e0"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 8, "repo_created_at": "2019-08-08T13:25:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 2, "issues": 0, "pull_requests": 21}, "file_meta": {"content_id": "82bc8b2e16dd89512a850043133c31a59583abdc", "size_bytes": 4866, "file_timestamp": 1569492023, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "617297dde6c8c07b58d581e1c9058d5f111cd9e0", "url": "https://github.com/rowanwins/bentley-ottmann-intersections/blob/617297dde6c8c07b58d581e1c9058d5f111cd9e0/src/Sweepline.js"}, "gpt4o_tokens": 1253, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9287569662517644, "noise_severity": 0.08616414011781107, "missing_content_severity": 0.05271769562740678, "factual_density": 3.482319328929993, "pretraining_utility": 0.5804628430410229, "math_density": 2.3943704530540177, "code_density": 3.834986637230153, "reasoning_density": 1.2045570992929941, "education_level": 2.524127768084782, "creative_writing_quality": -0.3982439095349851}, "score_applicability": {"education_level": 0.998975938897281, "creative_writing_quality": 0.00027846573559720444}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/nikhil03a/Hostel-Management-System/blob/2bc979dcf9491c44bf4d80114f8a6f44cf1c7f7f/client/src/pages/ViewAttendance.js", "id": "javascript-0-c3b3783a178f0dc81239df097473a0a8", "text": "import Navbar from '../components/Navbar';\nimport React, { useEffect, useState } from 'react'\nimport { useNavigate } from 'react-router-dom';\nimport SideBar from '../components/SideBar';\nimport { FaTachometerAlt, FaBed, FaUserCheck, FaFileInvoice, FaMinusCircle } from 'react-icons/fa'\nimport Transitions from '../components/Transitions';\nimport swal from 'sweetalert';\nconst ViewAttendance = () => {\n const navigate = useNavigate();\n const [isLoggedIn, setIsLoggedIn] = useState(false);\n const [isRendered, setIsRendered] = useState(false);\n const [currentDate, setCurrentDate] = useState(new Date());\n const [doj,setDoj] = useState(new Date());\n useEffect(()=>{\n const fetchDoj = async()=>{\n await fetch(\"http://localhost:8800/student/doj/\" + localStorage.getItem('id'), {\n method: 'POST',\n headers: {\n \"Content-Type\": \"application/json\"\n }\n })\n .then((response) => {\n return response.json()\n })\n .then(data => {\n setDoj(new Date(data.doj));\n })\n .catch(error => {\n window.alert(error);\n return;\n })\n }\n fetchDoj();\n },[])\n const checkUserToken = () => {\n const userToken = localStorage.getItem('user-token');\n if (userToken === 'studentstudent') {\n setIsLoggedIn(true);\n }\n else {\n setIsLoggedIn(false);\n navigate('/');\n }\n }\n useEffect(() => {\n checkUserToken();\n }, [isLoggedIn])\n const [data, setData] = useState({\n month: \"\",\n year: \"\"\n })\n const [arr, setArr] = useState({\n present: [],\n reduction: []\n })\n const handleChange = (e) => {\n setData({ ...data, [e.target.name]: e.target.value });\n }\n const handleSubmit = async () => {\n if(data.month === ''){\n swal(\"Select Month\",\"\",\"warning\")\n return;\n }else if(data.year === ''){\n swal(\"Select Year\",\"\",\"warning\")\n return;\n }\n await fetch(\"http://localhost:8800/student/attendance/\" + localStorage.getItem('id'), {\n method: 'POST',\n body: JSON.stringify({\n month: data.month,\n year: data.year\n }),\n headers: {\n \"Content-Type\": \"application/json\"\n }\n })\n .then((response) => {\n return response.json()\n })\n .then(data => {\n setData({ ...data, month: data.month, year: data.year })\n setArr({ ...arr, present: data.present, reduction: data.reduction })\n setIsRendered(true);\n })\n .catch(error => {\n window.alert(error);\n return;\n })\n\n }\n const getBoxColor = (date) => {\n if (arr.reduction.includes(date.getDate())) {\n return 'bg-yellow-500 text-white';\n }\n if (date > currentDate ) {\n return 'bg-gray-300 text-white';\n }\n if (arr.present.includes(date.getDate())) {\n return 'bg-green-600 text-white';\n }\n return 'bg-red-400 text-white';\n };\n const renderAttendanceSheet = () => {\n const daysInMonth = new Date(data.year, data.month, 0).getDate();\n const firstDayOfMonth = new Date(data.year, data.month - 1, 1);\n const firstDayOfWeek = firstDayOfMonth.getDay();\n const attendanceSheet = [];\n for (let i = 0; i < firstDayOfWeek; i++) {\n attendanceSheet.push(\n \n );\n }\n for (let day = 1; day <= daysInMonth; day++) {\n const date = new Date(data.year, data.month - 1, day);\n const doj2 = new Date(doj.getFullYear(),doj.getMonth(),doj.getDate())\n const boxColor = date < doj2 ? 'bg-white text-black' : getBoxColor(date);\n const formattedDate = date.toLocaleString('en-US', { day: 'numeric' });\n const formattedDay = date.toLocaleString('en-US', { weekday: 'short' });\n attendanceSheet.push(\n \n
{formattedDate}
\n
{formattedDay}
\n \n );\n }\n\n return attendanceSheet;\n };\n return (\n \n \n
\n
\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
\n View Attendance
\n \n \n \n
\n \n
\n
\n {isRendered &&
\n
Sun
\n
Mon
\n
Tue
\n
Wed
\n
Thu
\n
Fri
\n
Sat
\n {renderAttendanceSheet()}\n
}\n
\n\n
\n
\n \n )\n}\n\nexport default ViewAttendance\n", "text_preview": "import Navbar from '../components/Navbar'; import React, { useEffect, useState } from 'react' import { useNavigate } from 'react-router-dom'; import SideBar from '../components/SideBar'; import { FaTachometerAlt, FaBed, FaUserCheck, FaFileI", "input_sha256": "99fac6f972bc94fa59472e7e099cdd6ea0f55007eed4242425c3a89e6bb54822", "text_sha256": "c3b3783a178f0dc81239df097473a0a8d6d035ba13b7123643b183cab165544d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8041, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.42807414608000144, "noise_severity": 0.09367138615520439, "missing_content_severity": 0.03660390587858198, "factual_density": 2.8804696783723127, "pretraining_utility": 0.3143142169459756, "math_density": 0.1517411076750432, "code_density": 3.7577754192346613, "reasoning_density": 0.4554387253248224, "education_level": 2.0217330025531854, "creative_writing_quality": -0.5008917020598969}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.04440865899919333, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9945285570115775, "secondary": 0.8756889897609764, "tertiary": 0.8293495195009486, "structured_records": 0.21599167020783624, "procedural_steps": 0.0026058906169319864, "worked_solution": 0.004661862414683503, "contextual_dialogue": 7.335768593695788e-06, "content_quality": 0.3729762627619234, "noise_severity": 0.9219405115373297, "missing_content_severity": 0.9694967451011817, "factual_density": 0.06705806531026015, "pretraining_utility": 0.4854387947874106, "math_density": 0.873549076937464, "code_density": 0.7981461826955513, "reasoning_density": 0.6204677288959813, "education_level": 0.858003830065738, "creative_writing_quality": 0.9991696093182884, "primary_topic": 0.9498845631933783, 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-1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10540, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10540, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:06.731611+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e6c4c0be-e81e-469a-a5ae-e2dc2e9cf04c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "da620ded7eaf39afbc9ea8118924203cd737ed72c7f0360d4f4a3ecac8256dc8", "source_metadata": {"uuid": "e6c4c0be-e81e-469a-a5ae-e2dc2e9cf04c", "repo_name": "nikhil03a/Hostel-Management-System", "relative_path": "client/src/pages/ViewAttendance.js", "category": "WEB", "algo_rel_score": 0.954735279083252, "quality_score": 5.022704124450684, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 23, "repo_created_at": "2023-05-02T11:40:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "da4a438c9c6d5143a5d30698aca7a2677852d85d", "size_bytes": 7557, "file_timestamp": 1691314946, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2bc979dcf9491c44bf4d80114f8a6f44cf1c7f7f"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 23, "repo_created_at": "2023-05-02T11:40:53.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "da4a438c9c6d5143a5d30698aca7a2677852d85d", "size_bytes": 7557, "file_timestamp": 1691314946, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "2bc979dcf9491c44bf4d80114f8a6f44cf1c7f7f", "url": "https://github.com/nikhil03a/Hostel-Management-System/blob/2bc979dcf9491c44bf4d80114f8a6f44cf1c7f7f/client/src/pages/ViewAttendance.js"}, "gpt4o_tokens": 1867, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.42807414608000144, "noise_severity": 0.09367138615520439, "missing_content_severity": 0.03660390587858198, "factual_density": 2.8804696783723127, "pretraining_utility": 0.3143142169459756, "math_density": 0.1517411076750432, "code_density": 3.7577754192346613, "reasoning_density": 0.4554387253248224, "education_level": 2.0217330025531854, "creative_writing_quality": -0.5008917020598969}, "score_applicability": {"education_level": 0.9980542161329661, "creative_writing_quality": 0.0006919922347597688}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/phamvanhieu3012/WebsiteBanDongHo/blob/d81aff6a9536817c403d32186cc159efa0af2f16/frontend/src/pages/Admin/ProductReviews.jsx", "id": "javascript-0-433878d5f7a6e1eb09ec94d55e93e2f5", "text": "import ChevronLeftIcon from \"@mui/icons-material/ChevronLeft\";\nimport ChevronRightIcon from \"@mui/icons-material/ChevronRight\";\nimport DeleteIcon from \"@mui/icons-material/Delete\";\nimport MenuIcon from \"@mui/icons-material/Menu\";\nimport Star from \"@mui/icons-material/Star\";\nimport { Avatar, Button, Paper, TextField } from \"@mui/material\";\nimport MuiAppBar from \"@mui/material/AppBar\";\nimport Box from \"@mui/material/Box\";\nimport CssBaseline from \"@mui/material/CssBaseline\";\nimport Divider from \"@mui/material/Divider\";\nimport Drawer from \"@mui/material/Drawer\";\nimport IconButton from \"@mui/material/IconButton\";\nimport { styled, useTheme } from \"@mui/material/styles\";\nimport Toolbar from \"@mui/material/Toolbar\";\nimport Typography from \"@mui/material/Typography\";\nimport { makeStyles } from \"@mui/styles\";\nimport { DataGrid, GridToolbar } from \"@mui/x-data-grid\";\nimport React, { useEffect, useState } from \"react\";\nimport { useDispatch, useSelector } from \"react-redux\";\nimport { useHistory } from \"react-router-dom\";\nimport {\n clearErrors,\n deleteReviews,\n getAllProductReviews,\n getAllReviews,\n} from \"../../actions/productAction\";\nimport MetaData from \"../../components/Layout/MetaData\";\nimport { DELETE_REVIEW_RESET } from \"../../constants/productConstants\";\nimport \"./Admin.scss\";\nimport Sidebar from \"./components/Sidebar\";\nimport Snackbar from \"@mui/material/Snackbar\";\nimport MuiAlert from \"@mui/material/Alert\";\nimport Loader from \"../../components/Common/Loader\";\n\nconst drawerWidth = 240;\n\nconst useStyles = makeStyles({\n root: {\n fontSize: \"100%\",\n },\n});\n\nconst Alert = React.forwardRef(function Alert(props, ref) {\n return ;\n});\n\nconst Main = styled(\"main\", { shouldForwardProp: (prop) => prop !== \"open\" })(\n ({ theme, open }) => ({\n flexGrow: 1,\n padding: theme.spacing(3),\n transition: theme.transitions.create(\"margin\", {\n easing: theme.transitions.easing.sharp,\n duration: theme.transitions.duration.leavingScreen,\n }),\n marginLeft: `-${drawerWidth}px`,\n ...(open && {\n transition: theme.transitions.create(\"margin\", {\n easing: theme.transitions.easing.easeOut,\n duration: theme.transitions.duration.enteringScreen,\n }),\n marginLeft: 0,\n }),\n })\n);\n\nconst AppBar = styled(MuiAppBar, {\n shouldForwardProp: (prop) => prop !== \"open\",\n})(({ theme, open }) => ({\n transition: theme.transitions.create([\"margin\", \"width\"], {\n easing: theme.transitions.easing.sharp,\n duration: theme.transitions.duration.leavingScreen,\n }),\n ...(open && {\n width: `calc(100% - ${drawerWidth}px)`,\n marginLeft: `${drawerWidth}px`,\n transition: theme.transitions.create([\"margin\", \"width\"], {\n easing: theme.transitions.easing.easeOut,\n duration: theme.transitions.duration.enteringScreen,\n }),\n }),\n}));\n\nconst DrawerHeader = styled(\"div\")(({ theme }) => ({\n display: \"flex\",\n alignItems: \"center\",\n padding: theme.spacing(0, 1),\n // necessary for content to be below app bar\n ...theme.mixins.toolbar,\n justifyContent: \"flex-end\",\n}));\n\nexport default function ProductReviews() {\n const [openError, setOpenError] = useState(false);\n const [openSuccess, setOpenSuccess] = useState(false);\n const [errorAlert, setErrorAlert] = useState(\"\");\n const [successAlert, setSuccessAlert] = useState(\"\");\n\n const handleCloseError = (event, reason) => {\n if (reason === \"clickaway\") {\n return;\n }\n setOpenError(false);\n };\n const handleCloseSuccess = (event, reason) => {\n if (reason === \"clickaway\") {\n return;\n }\n setOpenSuccess(false);\n };\n\n const { user } = useSelector((state) => state.user);\n const theme = useTheme();\n const classes = useStyles();\n const [open, setOpen] = React.useState(true);\n const { error: deleteError, isDeleted } = useSelector(\n (state) => state.review\n );\n\n const { error, reviews, loading } = useSelector(\n (state) => state.productReviews\n );\n\n const {\n error: allReviewsError,\n products,\n loading: allReviewsLoading,\n } = useSelector((state) => state.allReviews);\n\n const [productId, setProductId] = React.useState(\"\");\n\n let history = useHistory();\n\n const dispatch = useDispatch();\n\n const handleDrawerOpen = () => {\n setOpen(true);\n };\n\n const handleDrawerClose = () => {\n setOpen(false);\n };\n\n const handleHistory = (his) => {\n history.push(`/admin/${his}`);\n };\n\n const deleteReviewHandler = (reviewId) => {\n dispatch(deleteReviews(reviewId, productId));\n };\n\n const productReviewsSubmitHandler = (e) => {\n e.preventDefault();\n dispatch(getAllReviews(productId));\n };\n\n React.useEffect(() => {\n if (productId.length === 24) {\n dispatch(getAllReviews(productId));\n }\n if (error) {\n setOpenError(true);\n setErrorAlert(error);\n dispatch(clearErrors());\n }\n\n if (deleteError) {\n setOpenError(true);\n setErrorAlert(deleteError);\n dispatch(clearErrors());\n }\n\n if (isDeleted) {\n // alert(\"Xóa bình luận thành công\");\n setOpenSuccess(true);\n setSuccessAlert(\"Xóa bình luận thành công\");\n history.push(\"/admin/reviews\");\n dispatch({ type: DELETE_REVIEW_RESET });\n }\n dispatch(getAllProductReviews());\n }, [dispatch, error, deleteError, history, isDeleted, productId]);\n\n const columns = [\n { field: \"id\", headerName: \"Review ID\", minWidth: 200, flex: 0.5 },\n\n {\n field: \"user\",\n headerName: \"User\",\n minWidth: 200,\n flex: 0.6,\n },\n\n {\n field: \"comment\",\n headerName: \"Bình luận\",\n minWidth: 350,\n flex: 1,\n },\n\n {\n field: \"rating\",\n headerName: \"Đánh giá\",\n type: \"number\",\n minWidth: 180,\n flex: 0.4,\n\n cellClassName: (params) => {\n return params.getValue(params.id, \"rating\") >= 3\n ? \"greenColor\"\n : \"redColor\";\n },\n },\n\n {\n field: \"actions\",\n flex: 0.3,\n headerName: \"Actions\",\n minWidth: 150,\n type: \"number\",\n sortable: false,\n renderCell: (params) => {\n return (\n \n \n deleteReviewHandler(params.getValue(params.id, \"id\"))\n }\n >\n \n \n \n );\n },\n },\n ];\n\n const rows = [];\n\n reviews &&\n reviews.forEach((item) => {\n rows.push({\n id: item._id,\n rating: item.rating,\n comment: item.comment,\n user: item.name,\n });\n });\n\n return (\n \n ;\n \n \n \n {errorAlert}\n \n \n \n \n {successAlert}\n \n \n \n \n
\n \n \n \n \n Đồng hồ PVH\n \n
\n \n Hi, {user.name}\n \n \n \n \n \n \n \n {theme.direction === \"ltr\" ? (\n \n ) : (\n \n )}\n \n \n \n \n {/* */}\n \n
\n \n

Tất cả đánh giá

\n \n \n {/* setProductId(e.target.value)}\n /> */}\n\n setProductId(e.target.value)}\n />\n\n \n Tìm kiếm\n \n \n\n {productId ? (\n <>\n {reviews && reviews.length > 0 ? (\n \n ) : (\n

\n Không tìm thấy đánh giá nào\n

\n )}\n \n ) : (\n \"\"\n )}\n
\n {allReviewsLoading ? (\n \n ) : (\n <>\n {products &&\n products.map((product) => {\n const rows = [];\n product.reviews.forEach((item) => {\n rows.push({\n id: item._id,\n rating: item.rating,\n comment: item.comment,\n user: item.name,\n });\n });\n return (\n <>\n \n \n Tên sản phẩm: {product.name}\n \n \n \n \n \n \n \n );\n })}\n \n )}\n
\n \n );\n}\n", "text_preview": "import ChevronLeftIcon from \"@mui/icons-material/ChevronLeft\"; import ChevronRightIcon from \"@mui/icons-material/ChevronRight\"; import DeleteIcon from \"@mui/icons-material/Delete\"; import MenuIcon from \"@mui/icons-material/Menu\"; import Sta", "input_sha256": "a646760586e94a12271763ea4c385531e07c517a5bbb220a0f2142cc19e4f4be", "text_sha256": "433878d5f7a6e1eb09ec94d55e93e2f5a8772fc15e7b143b08843287332f33ed", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8037, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6040526962847589, "noise_severity": 0.1875808415543918, "missing_content_severity": 0.11741366372164415, "factual_density": 2.8615801979125184, "pretraining_utility": 0.35074838725475, "math_density": 0.04637697098550034, "code_density": 3.83463263889254, "reasoning_density": 0.14830513601961648, "education_level": 2.175537355891133, "creative_writing_quality": -0.38552742309133475}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009568640430744145, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9944321831947732, "secondary": 0.8940009422993779, "tertiary": 0.8876087088766739, "structured_records": 0.15617828139911127, "procedural_steps": 0.0013132015576286264, "worked_solution": 0.001251509268548514, "contextual_dialogue": 9.776039587910096e-06, "content_quality": 0.4474887643065818, "noise_severity": 0.8436826320380069, "missing_content_severity": 0.9021552802319632, "factual_density": 0.3159250497589402, "pretraining_utility": 0.4794817320805359, "math_density": 0.9613525241787497, "code_density": 0.8621938657437833, "reasoning_density": 0.8764123866503196, "education_level": 0.7516806797056111, "creative_writing_quality": 0.9993381348525796, "primary_topic": 0.9705844928819862, "secondary_topic": 0.731019825042301}, "probability_margin": {"primary": 0.9930374660056971, "secondary": 0.865151810314882, "tertiary": 0.8597356999539663, "education_level": 0.6116251992295642, "creative_writing_quality": 0.9991404227449042, "primary_topic": 0.961812290048317, "secondary_topic": 0.6729900641816721}, "raw_probabilities": {"primary": {"Academic Writing": 4.182351771550742e-05, "Content Listing": 0.0007680647032839353, "Creative Writing": 3.003071034668474e-05, "Customer Support Page": 1.6683926696979508e-05, "Q&A Forum": 1.578613772142565e-05, "Discussion Thread": 1.5364450441037077e-05, "Comment Section": 0.00032026526020149644, "Missing Content": 0.00013465493824415885, "Knowledge Article": 6.497536380831038e-05, "Legal Notices": 5.8967031155182864e-05, "News Article": 9.380830063778547e-06, "Organizational Announcement": 1.8802257404702734e-05, "Nonfiction Writing": 1.880309122538479e-05, "Organizational About Page": 2.76148705838709e-05, "Personal About Page": 3.275768070102572e-05, "Personal Blog": 1.565434391004588e-05, "Product Page": 6.391986911661679e-05, "Spam / Ads": 1.7583690211575893e-05, "Structured Data": 0.0013603934686958303, "Technical Writing": 0.9946641755616576, "Transcript / Interview": 2.26188206211591e-05, "Tutorial / How-To Guide": 0.0001933963495774166, "User Reviews": 0.0004615735706556533, "Other / Unknown": 0.0016267095559606075}, "secondary": {"Academic Writing": 0.0005153077685375326, "Content Listing": 0.022987338955435328, "Creative Writing": 0.0005390299218430295, "Customer Support Page": 0.0003370506945619443, "Q&A Forum": 0.00022440214976170495, "Discussion Thread": 0.0002306001538924263, "Comment Section": 0.0011667132868832864, "Missing Content": 0.0014292539014926467, "Knowledge Article": 0.0005589152392512332, "Legal Notices": 0.0006315212329425022, "News Article": 0.00029061641201363187, "Organizational Announcement": 0.00031925868672450804, "Nonfiction Writing": 0.00032296332741832136, "Organizational About Page": 0.0004229986579179382, "Personal About Page": 0.0004092401946701614, "Personal Blog": 0.00029079634025163005, "Product Page": 0.0008503340851207832, "Spam / Ads": 0.00034742878697334803, "Structured Data": 0.03326575938868845, "Technical Writing": 0.020905473779563876, "Transcript / Interview": 0.00036466606740873575, "Tutorial / How-To Guide": 0.0009396967490336791, "User Reviews": 0.014233064516042965, "NONE": 0.8984175697035705}, "tertiary": {"Academic Writing": 0.0004621673139138051, "Content Listing": 0.03255597938617947, "Creative Writing": 0.0005236672193250849, "Customer Support Page": 0.0003693722449399151, "Q&A Forum": 0.00020956648745276593, "Discussion Thread": 0.0002495834397076287, "Comment Section": 0.0016661817572689484, "Missing Content": 0.0014102816693589615, "Knowledge Article": 0.0005963080150014102, "Legal Notices": 0.0006725097585563001, "News Article": 0.00026782472663321595, "Organizational Announcement": 0.0003274504643612807, "Nonfiction Writing": 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"dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "58a22b7b4b08b122aaf9234767219b19df7072a806672e36f623ae4020e09907", "source_metadata": {"uuid": "2b1db3c2-a845-4fdb-a8e6-f8f7f651f629", "repo_name": "phamvanhieu3012/WebsiteBanDongHo", "relative_path": "frontend/src/pages/Admin/ProductReviews.jsx", "category": "WEB", "algo_rel_score": 0.9374013543128967, "quality_score": 5.167046546936035, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 93, "repo_created_at": "2022-04-17T07:19:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e59ddc2329eb5d8bd165c902973a07f3ad38f549", "size_bytes": 12113, "file_timestamp": 1652794179, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d81aff6a9536817c403d32186cc159efa0af2f16"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 93, "repo_created_at": "2022-04-17T07:19:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e59ddc2329eb5d8bd165c902973a07f3ad38f549", "size_bytes": 12113, "file_timestamp": 1652794179, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d81aff6a9536817c403d32186cc159efa0af2f16", "url": "https://github.com/phamvanhieu3012/WebsiteBanDongHo/blob/d81aff6a9536817c403d32186cc159efa0af2f16/frontend/src/pages/Admin/ProductReviews.jsx"}, "gpt4o_tokens": 2701, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6040526962847589, "noise_severity": 0.1875808415543918, "missing_content_severity": 0.11741366372164415, "factual_density": 2.8615801979125184, "pretraining_utility": 0.35074838725475, "math_density": 0.04637697098550034, "code_density": 3.83463263889254, "reasoning_density": 0.14830513601961648, "education_level": 2.175537355891133, "creative_writing_quality": -0.38552742309133475}, "score_applicability": {"education_level": 0.9979401831688658, "creative_writing_quality": 0.0005515542895170772}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/yehorkarabanov/db_project/blob/4015691b14c66dbf4a0133392a8c754f345ea984/frontend/src/pages/Products/ProductCreate.jsx", "id": "javascript-0-3f1c6bf47447ea6daa0dbeeda8f3d893", "text": "import React, { useState, useEffect } from \"react\";\nimport axios from \"axios\";\nimport {\n Card,\n CardContent,\n CardHeader,\n CardTitle,\n} from \"@/components/ui/card\";\nimport { Input } from \"@/components/ui/input\";\nimport { Button } from \"@/components/ui/button\";\nimport { useNavigate } from \"react-router-dom\";\nimport {\n Package,\n ArrowLeft,\n Factory,\n Warehouse,\n Tag,\n Loader2,\n AlertCircle,\n Plus,\n} from \"lucide-react\";\nimport { Alert, AlertDescription } from \"@/components/ui/alert\";\nimport {\n Dialog,\n DialogContent,\n DialogHeader,\n DialogTitle,\n DialogTrigger,\n} from \"@/components/ui/dialog\";\n\nconst ProductCreation = () => {\n const navigate = useNavigate();\n const [loading, setLoading] = useState(true);\n const [error, setError] = useState(\"\");\n const [typeError, setTypeError] = useState(\"\");\n const [isTypeDialogOpen, setIsTypeDialogOpen] = useState(false);\n const [newType, setNewType] = useState({ name: \"\" });\n const [formData, setFormData] = useState({\n name: \"\",\n price: \"\",\n place_taken: \"\",\n warehouse_id: \"\",\n manufacturer_id: \"\",\n types: []\n });\n const [formOptions, setFormOptions] = useState({\n manufacturers: [],\n warehouses: [],\n types: []\n });\n\n useEffect(() => {\n fetchFormData();\n }, []);\n\n const fetchFormData = async () => {\n try {\n const response = await axios.get(\"http://localhost:8080/api/products/data/\");\n setFormOptions(response.data.result);\n setLoading(false);\n } catch (err) {\n setError(\"Failed to load form data. Please try again.\");\n setLoading(false);\n }\n };\n\n const handleInputChange = (e) => {\n const { name, value } = e.target;\n setFormData(prev => ({\n ...prev,\n [name]: value\n }));\n };\n\n const handleSelectChange = (name, value) => {\n setFormData(prev => ({\n ...prev,\n [name]: value\n }));\n };\n\n const handleTypesChange = (typeId) => {\n setFormData(prev => {\n const types = prev.types.includes(typeId)\n ? prev.types.filter(id => id !== typeId)\n : [...prev.types, typeId];\n return { ...prev, types };\n });\n };\n\n const handleNewTypeSubmit = async (e) => {\n e.preventDefault();\n e.stopPropagation(); // Prevent event from bubbling up to product form\n setTypeError(\"\");\n\n try {\n const response = await axios.post(\"http://localhost:8080/api/types/\", newType);\n setFormOptions(prev => ({\n ...prev,\n types: [...prev.types, response.data]\n }));\n setNewType({ name: \"\" });\n setIsTypeDialogOpen(false);\n } catch (err) {\n setTypeError(\"Failed to create new type. Please try again.\");\n }\n };\n\n const handleProductSubmit = async (e) => {\n e.preventDefault();\n setLoading(true);\n setError(\"\");\n\n try {\n await axios.post(\"http://localhost:8080/api/products/\", formData);\n navigate(\"/products\");\n } catch (err) {\n setError(\"Failed to create product. Please try again.\");\n setLoading(false);\n }\n };\n\n if (loading) {\n return (\n
\n \n

Loading form...

\n
\n );\n }\n\n return (\n
\n
\n
\n navigate(-1)}\n className=\"flex items-center\"\n >\n \n Back\n \n
\n\n \n \n \n \n Create New Product\n \n \n \n
\n {error && (\n \n \n {error}\n \n )}\n\n
\n
\n \n \n
\n\n
\n
\n \n \n
\n
\n \n \n
\n
\n\n
\n \n handleSelectChange(\"manufacturer_id\", e.target.value)}\n required\n >\n \n {formOptions.manufacturers.map((manufacturer) => (\n \n ))}\n \n
\n\n
\n \n handleSelectChange(\"warehouse_id\", e.target.value)}\n required\n >\n \n {formOptions.warehouses.map((warehouse) => (\n \n ))}\n \n
\n\n
\n
\n \n \n \n \n \n Add Type\n \n \n e.stopPropagation()}>\n \n Add New Product Type\n \n
\n {typeError && (\n \n \n {typeError}\n \n )}\n \n
\n \n setNewType({ name: e.target.value })}\n placeholder=\"Enter type name\"\n required\n />\n
\n \n Create Type\n \n \n
\n \n \n
\n
\n {formOptions.types.map((type) => (\n \n handleTypesChange(type.id.toString())}\n className=\"rounded border-gray-300\"\n />\n {type.name}\n \n ))}\n
\n
\n
\n\n \n {loading ? (\n <>\n \n Creating Product...\n \n ) : (\n \"Create Product\"\n )}\n \n \n \n \n
\n
\n );\n};\n\nexport default ProductCreation;", "text_preview": "import React, { useState, useEffect } from \"react\"; import axios from \"axios\"; import { Card, CardContent, CardHeader, CardTitle, } from \"@/components/ui/card\"; import { Input } from \"@/components/ui/input\"; import { Button } from \"@/compon", "input_sha256": "eeb5c1083cb652e99ed77ffe2a8064b898dd8b6de60d4230dc20c58d03b21386", "text_sha256": "3f1c6bf47447ea6daa0dbeeda8f3d8933e9ef972a9748417adb80bf07b9894c5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8045, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1103214983067249, "noise_severity": 0.10296319658373458, "missing_content_severity": 0.018851023491468912, "factual_density": 2.6034526747236524, "pretraining_utility": 0.6186558543228121, "math_density": 0.03237607442818129, "code_density": 3.835850143007957, "reasoning_density": 0.15552284227893548, "education_level": 2.1256162751726624, "creative_writing_quality": -0.19593167841679526}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010832304531260162, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9944696528190828, "secondary": 0.9728598955353277, "tertiary": 0.961556621241839, "structured_records": 0.02173841911567895, "procedural_steps": 0.007180531935621205, "worked_solution": 0.010747718388814052, "contextual_dialogue": 6.267233282162235e-06, "content_quality": 0.5841638337433204, "noise_severity": 0.9141973361802211, "missing_content_severity": 0.9842908137571093, "factual_density": 0.19798990279345918, "pretraining_utility": 0.5292091123509632, "math_density": 0.9730199379765156, "code_density": 0.8632084525066304, "reasoning_density": 0.8703976314342204, "education_level": 0.8035253910925508, "creative_writing_quality": 0.999089079213423, "primary_topic": 0.9851604880485826, "secondary_topic": 0.84771615087222}, "probability_margin": {"primary": 0.9914708027491762, "secondary": 0.960838742331241, "tertiary": 0.948043432308601, "education_level": 0.6999012046397937, "creative_writing_quality": 0.9987556136864978, "primary_topic": 0.9801059203058375, "secondary_topic": 0.791813073540101}, "raw_probabilities": {"primary": {"Academic Writing": 3.7998083335039164e-05, "Content Listing": 6.52886919508e-05, "Creative Writing": 3.113620622484646e-05, "Customer Support Page": 2.0087963154553037e-05, "Q&A Forum": 1.4352393685981653e-05, "Discussion Thread": 8.494804582605195e-06, "Comment Section": 2.182956955712301e-05, "Missing Content": 4.907593874975886e-05, "Knowledge Article": 8.281505859541952e-05, "Legal Notices": 4.7022945110148656e-05, "News Article": 7.77877895183373e-06, "Organizational Announcement": 1.4218792831848457e-05, "Nonfiction Writing": 1.654205567252901e-05, "Organizational About Page": 2.2898965675851752e-05, "Personal About Page": 2.2027086626470938e-05, "Personal Blog": 1.1760866450096754e-05, "Product Page": 0.0003287005289836515, "Spam / Ads": 1.4062060817959035e-05, "Structured Data": 0.0002737080767846247, "Technical Writing": 0.994700083951621, "Transcript / Interview": 2.0623726787178877e-05, "Tutorial / How-To Guide": 0.0009500726388287912, "User Reviews": 1.013961257695383e-05, "Other / Unknown": 0.0032292812024448054}, "secondary": {"Academic Writing": 0.00018792234630665025, "Content Listing": 0.0014493040999858345, "Creative Writing": 0.00024418517873937975, "Customer Support Page": 0.0001498038261760209, "Q&A Forum": 6.532534513260957e-05, "Discussion Thread": 5.159165870997387e-05, "Comment Section": 0.00013069555356974384, "Missing Content": 0.0003951055064291928, "Knowledge Article": 0.000381411848698821, "Legal Notices": 0.0002430006410425888, "News Article": 9.749115537657205e-05, "Organizational Announcement": 9.916046998514709e-05, "Nonfiction Writing": 8.586285502530852e-05, "Organizational About Page": 0.00011786293066774936, "Personal About Page": 0.00010463389576813199, "Personal Blog": 9.562907777281884e-05, "Product Page": 0.0012101879099633852, "Spam / Ads": 0.00010450529995871886, "Structured Data": 0.005424053184099538, "Technical Writing": 0.013151990890114781, "Transcript / Interview": 0.00011745903025111315, "Tutorial / How-To Guide": 0.0020477991976535345, "User Reviews": 5.4284877216514127e-05, "NONE": 0.9739907332213558}, "tertiary": {"Academic Writing": 0.00018138867903599499, "Content Listing": 0.004077366896862761, "Creative Writing": 0.00023536394569182107, "Customer Support Page": 0.00020642083473870838, "Q&A Forum": 6.176785374367544e-05, "Discussion Thread": 5.668677455921395e-05, "Comment Section": 0.0001721522644856492, "Missing Content": 0.0005695063726085765, "Knowledge Article": 0.0004691328904327171, "Legal Notices": 0.0005546355505774671, "News Article": 0.00011502841609342231, "Organizational Announcement": 0.00010956593842072107, "Nonfiction Writing": 0.00011791204483129808, "Organizational About Page": 0.00014969105698746534, "Personal About Page": 0.00011044681266998145, "Personal Blog": 9.94900955242275e-05, "Product Page": 0.003099413268136308, "Spam / Ads": 0.00013862622881419032, "Structured Data": 0.008621074489835177, "Technical Writing": 0.015114996381494708, "Transcript / Interview": 0.0001257016145584074, "Tutorial / How-To Guide": 0.002394930165613681, "User Reviews": 6.027273418808162e-05, "NONE": 0.9631584286900957}, "structured_records": {"false": 0.978261580884321, "true": 0.02173841911567895}, "procedural_steps": {"false": 0.9928194680643788, "true": 0.007180531935621205}, "worked_solution": {"false": 0.989252281611186, "true": 0.010747718388814052}, "contextual_dialogue": {"false": 0.9999937327667179, "true": 6.267233282162235e-06}, "content_quality": {"-2": 0.005019847318726479, "-1": 0.014504424609779071, "0": 0.15027255942490778, "1": 0.5255407197392165, "2": 0.30466244890737015}, "noise_severity": {"0": 0.9205455385558754, "1": 0.061379269370287315, "2": 0.014071174778723867, "3": 0.002574491524455043, "4": 0.0014295257706586056}, "missing_content_severity": {"0": 0.9906903251979978, "1": 0.0023578803877039385, "2": 0.0047720381024335765, "3": 0.0017699583485607778, "4": 0.000409797963303872}, "factual_density": {"0": 0.0665112501597336, "1": 0.12573201805655027, "2": 0.22848193436979686, "3": 0.2963424017281685, "4": 0.28293239568575074}, "pretraining_utility": {"-2": 0.008788699593533615, "-1": 0.057925761505312805, "0": 0.3309289836367896, "1": 0.5105540955135358, "2": 0.09180245975082815}, "math_density": {"0": 0.9734111609613672, "1": 0.023241655004394457, "2": 0.0018010777259090942, "3": 0.0006521612613488735, "4": 0.0008939450469805046}, "code_density": {"0": 0.0002761859370658064, "1": 0.000505804705326147, "2": 0.017042492633341292, "3": 0.12744271386111936, "4": 0.8547328028631475}, "reasoning_density": {"0": 0.9038003672000609, "1": 0.04568508718725669, "2": 0.04359999964347266, "3": 0.005020428072106276, "4": 0.001894117897103671}, "education_level": {"none": 0.0012637249311957843, "0": 0.0017551124531708102, "1": 0.018694098826459546, "2": 0.8362711592437924, "3": 0.13636995460399876, "4": 0.005645949941382759}, "creative_writing_quality": {"not_applicable": 0.9992408993445192, "-2": 6.611601746378994e-05, "-1": 0.00012365976833196855, "0": 0.0004852856580213924, "1": 6.091848558367591e-05, "2": 2.312072608010231e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.900732720701243e-05, "Art & Design": 0.001728513778731019, "Law & Crime": 5.2067974943075376e-05, "Education & Careers": 5.149231725495918e-05, "Entertainment & Media": 3.2900300024966066e-05, "Fashion & Beauty": 4.6616446990351825e-05, "Business, Economics & Finance": 0.005625318202414984, "Food & Dining": 6.537666770023728e-05, "Games & Gaming": 0.00010576122671257545, "Electronics & Hardware": 9.569617685379002e-05, "Health, Healthcare & Wellness": 3.856899807769099e-05, "History & Geography": 4.217837873824267e-05, "Home, Pets & Hobbies": 5.1491899847997086e-05, "Industry & Production": 0.002050103950677591, "Language, Literature & Philosophy": 4.4444897009174916e-05, "Politics & Government": 2.7060571014038223e-05, "Religion & Spirituality": 3.765160832862112e-05, "Science, Mathematics & Engineering": 7.488976945897087e-05, "Relationships & Community": 2.3967421910309806e-05, "Software Usage & Applications": 0.001119839803670432, "Programming & Software Engineering": 0.9857312385082525, "Sports & Fitness": 4.963629671571254e-05, "Vehicles & Transportation": 0.0013240165084925423, "Travel & Tourism": 6.363666513699758e-05, "Other / Uncovered Subject": 0.0013794904078750613, "Unknown / Insufficient Content": 8.903389596074461e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007132111989998297, "Art & Design": 0.03183952163630526, "Law & Crime": 0.000719150561756177, "Education & Careers": 0.0004079457693332003, "Entertainment & Media": 0.0004343496164510244, "Fashion & Beauty": 0.0003006489205915079, "Business, Economics & Finance": 0.0617601484524182, "Food & Dining": 0.00038038926529861567, "Games & Gaming": 0.000497022092167818, "Electronics & Hardware": 0.00045833868552803357, "Health, Healthcare & Wellness": 0.0003395570803784532, "History & Geography": 0.0002893146646033197, "Home, Pets & Hobbies": 0.00037081840047968406, "Industry & Production": 0.004617269308637221, "Language, Literature & Philosophy": 0.0002358208483482861, "Politics & Government": 0.00026920030914112273, "Religion & Spirituality": 0.00036577090785172665, "Science, Mathematics & Engineering": 0.0004311084228408491, "Relationships & Community": 0.00016434410445570932, "Software Usage & Applications": 0.009437279269230766, "Programming & Software Engineering": 0.022102514926359312, "Sports & Fitness": 0.00026652432915783475, "Vehicles & Transportation": 0.0036802374724818577, "Travel & Tourism": 0.0004109420747171262, "Other / Uncovered Subject": 0.005935349689947953, "NONE": 0.8535732219925192}}, "probabilities": {"primary": {"Academic Writing": 3.799808333503917e-05, "Content Listing": 6.528869195080002e-05, "Creative Writing": 3.1136206224846466e-05, "Customer Support Page": 2.008796315455304e-05, "Q&A Forum": 1.4352393685981654e-05, "Discussion Thread": 8.494804582605197e-06, "Comment Section": 2.1829569557123013e-05, "Missing Content": 4.907593874975887e-05, "Knowledge Article": 8.281505859541953e-05, "Legal Notices": 4.702294511014866e-05, "News Article": 7.778778951833732e-06, "Organizational Announcement": 1.4218792831848459e-05, "Nonfiction Writing": 1.6542055672529014e-05, "Organizational About Page": 2.2898965675851756e-05, "Personal About Page": 2.202708662647094e-05, "Personal Blog": 1.1760866450096755e-05, "Product Page": 0.00032870052898365155, "Spam / Ads": 1.4062060817959037e-05, "Structured Data": 0.0002737080767846248, "Technical Writing": 0.9947000839516211, "Transcript / Interview": 2.062372678717888e-05, "Tutorial / How-To Guide": 0.0009500726388287913, "User Reviews": 1.0139612576953832e-05, "Other / Unknown": 0.003229281202444806}, "secondary": {"Academic Writing": 0.00018792234630665027, "Content Listing": 0.0014493040999858347, "Creative Writing": 0.0002441851787393798, "Customer Support Page": 0.00014980382617602094, "Q&A Forum": 6.532534513260959e-05, "Discussion Thread": 5.1591658709973876e-05, "Comment Section": 0.00013069555356974387, "Missing Content": 0.00039510550642919283, "Knowledge Article": 0.00038141184869882103, "Legal Notices": 0.00024300064104258883, "News Article": 9.749115537657206e-05, "Organizational Announcement": 9.91604699851471e-05, "Nonfiction Writing": 8.586285502530853e-05, "Organizational About Page": 0.00011786293066774937, "Personal About Page": 0.000104633895768132, "Personal Blog": 9.562907777281885e-05, "Product Page": 0.0012101879099633854, "Spam / Ads": 0.00010450529995871888, "Structured Data": 0.005424053184099539, "Technical Writing": 0.013151990890114783, "Transcript / Interview": 0.00011745903025111317, "Tutorial / How-To Guide": 0.002047799197653535, "User Reviews": 5.428487721651413e-05, "NONE": 0.9739907332213559}, "tertiary": {"Academic Writing": 0.00018138867903599499, "Content Listing": 0.004077366896862761, "Creative Writing": 0.00023536394569182107, "Customer Support Page": 0.00020642083473870838, "Q&A Forum": 6.176785374367544e-05, "Discussion Thread": 5.668677455921395e-05, "Comment Section": 0.0001721522644856492, "Missing Content": 0.0005695063726085765, "Knowledge Article": 0.0004691328904327171, "Legal Notices": 0.0005546355505774671, "News Article": 0.00011502841609342231, "Organizational Announcement": 0.00010956593842072107, "Nonfiction Writing": 0.00011791204483129808, "Organizational About Page": 0.00014969105698746534, "Personal About Page": 0.00011044681266998145, "Personal Blog": 9.94900955242275e-05, "Product Page": 0.003099413268136308, "Spam / Ads": 0.00013862622881419032, "Structured Data": 0.008621074489835177, "Technical Writing": 0.015114996381494708, "Transcript / Interview": 0.0001257016145584074, "Tutorial / How-To Guide": 0.002394930165613681, "User Reviews": 6.027273418808162e-05, "NONE": 0.9631584286900957}, "structured_records": {"false": 0.978261580884321, "true": 0.02173841911567895}, "procedural_steps": {"false": 0.9928194680643788, "true": 0.007180531935621205}, "worked_solution": {"false": 0.989252281611186, "true": 0.010747718388814052}, "contextual_dialogue": {"false": 0.9999937327667179, "true": 6.267233282162235e-06}, "content_quality": {"-2": 0.00501984731872648, "-1": 0.014504424609779073, "0": 0.1502725594249078, "1": 0.5255407197392166, "2": 0.3046624489073702}, "noise_severity": {"0": 0.9205455385558752, "1": 0.0613792693702873, "2": 0.014071174778723864, "3": 0.0025744915244550427, "4": 0.0014295257706586054}, "missing_content_severity": {"0": 0.9906903251979978, "1": 0.0023578803877039385, "2": 0.0047720381024335765, "3": 0.0017699583485607778, "4": 0.000409797963303872}, "factual_density": {"0": 0.0665112501597336, "1": 0.12573201805655027, "2": 0.22848193436979686, "3": 0.2963424017281685, "4": 0.28293239568575074}, "pretraining_utility": {"-2": 0.008788699593533615, "-1": 0.057925761505312805, "0": 0.3309289836367896, "1": 0.5105540955135358, "2": 0.09180245975082815}, "math_density": {"0": 0.9734111609613672, "1": 0.023241655004394457, "2": 0.0018010777259090942, "3": 0.0006521612613488735, "4": 0.0008939450469805046}, "code_density": {"0": 0.0002761859370658064, "1": 0.000505804705326147, "2": 0.017042492633341292, "3": 0.12744271386111936, "4": 0.8547328028631475}, "reasoning_density": {"0": 0.9038003672000606, "1": 0.04568508718725668, "2": 0.04359999964347265, "3": 0.005020428072106275, "4": 0.0018941178971036705}, "education_level": {"none": 0.0012637249311957843, "0": 0.0017551124531708102, "1": 0.018694098826459546, "2": 0.8362711592437924, "3": 0.13636995460399876, "4": 0.005645949941382759}, "creative_writing_quality": {"not_applicable": 0.999240899344519, "-2": 6.611601746378992e-05, "-1": 0.00012365976833196853, "0": 0.00048528565802139227, "1": 6.09184855836759e-05, "2": 2.3120726080102305e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.900732720701245e-05, "Art & Design": 0.0017285137787310199, "Law & Crime": 5.2067974943075397e-05, "Education & Careers": 5.14923172549592e-05, "Entertainment & Media": 3.290030002496608e-05, "Fashion & Beauty": 4.6616446990351846e-05, "Business, Economics & Finance": 0.0056253182024149865, "Food & Dining": 6.537666770023731e-05, "Games & Gaming": 0.00010576122671257549, "Electronics & Hardware": 9.569617685379006e-05, "Health, Healthcare & Wellness": 3.856899807769101e-05, "History & Geography": 4.217837873824269e-05, "Home, Pets & Hobbies": 5.1491899847997106e-05, "Industry & Production": 0.002050103950677592, "Language, Literature & Philosophy": 4.4444897009174936e-05, "Politics & Government": 2.7060571014038237e-05, "Religion & Spirituality": 3.7651608328621135e-05, "Science, Mathematics & Engineering": 7.48897694589709e-05, "Relationships & Community": 2.3967421910309816e-05, "Software Usage & Applications": 0.0011198398036704324, "Programming & Software Engineering": 0.985731238508253, "Sports & Fitness": 4.963629671571256e-05, "Vehicles & Transportation": 0.001324016508492543, "Travel & Tourism": 6.36366651369976e-05, "Other / Uncovered Subject": 0.001379490407875062, "Unknown / Insufficient Content": 8.903389596074465e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007132111989998297, "Art & Design": 0.03183952163630526, "Law & Crime": 0.000719150561756177, "Education & Careers": 0.0004079457693332003, "Entertainment & Media": 0.0004343496164510244, "Fashion & Beauty": 0.0003006489205915079, "Business, Economics & Finance": 0.0617601484524182, "Food & Dining": 0.00038038926529861567, "Games & Gaming": 0.000497022092167818, "Electronics & Hardware": 0.00045833868552803357, "Health, Healthcare & Wellness": 0.0003395570803784532, "History & Geography": 0.0002893146646033197, "Home, Pets & Hobbies": 0.00037081840047968406, "Industry & Production": 0.004617269308637221, "Language, Literature & Philosophy": 0.0002358208483482861, "Politics & Government": 0.00026920030914112273, "Religion & Spirituality": 0.00036577090785172665, "Science, Mathematics & Engineering": 0.0004311084228408491, "Relationships & Community": 0.00016434410445570932, "Software Usage & Applications": 0.009437279269230766, "Programming & Software Engineering": 0.022102514926359312, "Sports & Fitness": 0.00026652432915783475, "Vehicles & Transportation": 0.0036802374724818577, "Travel & Tourism": 0.0004109420747171262, "Other / Uncovered Subject": 0.005935349689947953, "NONE": 0.8535732219925192}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999996, "mass_delta": -4.440892098500626e-16, "missing_mass": 4.440892098500626e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11011, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11011, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:06.994154+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "e4df422f-700b-4a72-a18f-702ae96b4c8c", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "efdbc21ac559c38294bb9fa9835bece84dac748ae7a633b08aae73a42f9ffc95", "source_metadata": {"uuid": "e4df422f-700b-4a72-a18f-702ae96b4c8c", "repo_name": "yehorkarabanov/db_project", "relative_path": "frontend/src/pages/Products/ProductCreate.jsx", "category": "WEB", "algo_rel_score": 0.9312154054641724, "quality_score": 7.374095439910889, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 31, "repo_created_at": "2024-12-27T21:13:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e0128de669b01094bdc5985e5d340583384efad0", "size_bytes": 15869, "file_timestamp": 1747665823, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4015691b14c66dbf4a0133392a8c754f345ea984"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 31, "repo_created_at": "2024-12-27T21:13:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e0128de669b01094bdc5985e5d340583384efad0", "size_bytes": 15869, "file_timestamp": 1747665823, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "4015691b14c66dbf4a0133392a8c754f345ea984", "url": "https://github.com/yehorkarabanov/db_project/blob/4015691b14c66dbf4a0133392a8c754f345ea984/frontend/src/pages/Products/ProductCreate.jsx"}, "gpt4o_tokens": 2281, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.1103214983067247, "noise_severity": 0.10296319658373458, "missing_content_severity": 0.018851023491468912, "factual_density": 2.6034526747236524, "pretraining_utility": 0.6186558543228121, "math_density": 0.03237607442818129, "code_density": 3.835850143007957, "reasoning_density": 0.1555228422789355, "education_level": 2.1256162751726624, "creative_writing_quality": -0.19593167841679526}, "score_applicability": {"education_level": 0.9987362750688042, "creative_writing_quality": 0.0007591006554809289}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/adwqd/springfull/blob/9d6e4f754e7f2b0453f9e31a210bba247ddb6a02/frontend/src/pages/MyPage.jsx", "id": "javascript-0-c6e0b2c35887e55b50e5bdddcefba32b", "text": "import React, { useEffect, useState, useContext } from \"react\";\nimport { useNavigate } from \"react-router-dom\";\nimport { FaArrowLeft, FaEdit, FaCog, FaBookmark, FaStar, FaFile, FaPortrait, FaBell } from \"react-icons/fa\";\nimport KakaoLoginButton from \"../components/KakaoLoginButton\";\nimport Image from \"../assets/Rules!.png\"; // ✅ 이미지 경로 확인\nimport axios from \"axios\";\nimport {MyContext} from \"../App\";\n\nconst REST_API_KEY = \"6d359ee5b5e0d0aa0ae57d4714f1b04c\"; // 🔹 카카오 REST API 키\nconst REDIRECT_URI = \"http://m-it.iptime.org:50580\"; // 🔹 로그아웃 리디렉트 URI\n\nconst MyPage = () => {\n const navigate = useNavigate();\n const [userInfo, setUserInfo] = useState(null);\n const [profileUrl, setProfileUrl] = useState({});\n const {apiURL} = useContext(MyContext);\n\n // ✅ 로그인 상태 확인 (localStorage + 카카오 SDK 세션 확인)\n useEffect(() => {\n const storedUserInfo = localStorage.getItem(\"userInfo\");\n if (storedUserInfo) {\n setUserInfo(JSON.parse(storedUserInfo));\n } else {\n navigate(\"/login\"); // ✅ 로그인 안 되어 있으면 로그인 페이지로 이동\n }\n }, [navigate]);\n useEffect(() => {\n const fetchImage = async () => {\n if (!userInfo || !userInfo.member_uuid) return;\n\n try {\n // 이미지 데이터를 비동기로 요청\n const profileResponse = await axios.get(\n `${apiURL}/profile/${userInfo.member_uuid}`,\n { responseType: 'blob' }\n );\n\n // 이미지 URL을 생성하고 상태 업데이트\n const imageUrl = URL.createObjectURL(profileResponse.data);\n setProfileUrl(imageUrl);\n\n } catch (error) {\n console.error('Error fetching image:', error);\n setProfileUrl(null);\n }\n };\n\n fetchImage();\n }, [userInfo, apiURL]);\n\n // ✅ 사용자 정보 요청 함수\n // const fetchUserInfo = async (accessToken) => {\n // try {\n // const response = await fetch(\"https://kapi.kakao.com/v2/user/me\", {\n // method: \"GET\",\n // headers: {\n // Authorization: `Bearer ${accessToken}`,\n // \"Content-Type\": \"application/json\",\n // },\n // });\n\n // const userData = await response.json();\n // console.log(\"✅ 카카오 사용자 정보:\", userData);\n\n // const name = userData.kakao_account?.profile?.name || \"사용자\";\n // const profileImage = userData.kakao_account?.profile?.profile_image_url || \"https://source.unsplash.com/100x100/?avatar\";\n\n // const userProfile = {\n // id: userData.id || \"Unknown\",\n // name,\n // profile_image: profileImage,\n // };\n\n // localStorage.setItem(\"userInfo\", JSON.stringify(userProfile)); // ✅ 사용자 정보 저장\n // setUserInfo(userProfile);\n // } catch (error) {\n // console.error(\"❌ 사용자 정보 요청 실패:\", error);\n // }\n // };\n\n // ✅ 로그아웃 처리\n const handleLogout = () => {\n if (window.Kakao && window.Kakao.Auth) {\n window.Kakao.Auth.logout(() => {\n console.log(\"✅ 카카오 로그아웃 완료!\");\n });\n }\n localStorage.removeItem(\"token\");\n localStorage.removeItem(\"refreshToken\");\n localStorage.removeItem(\"userInfo\"); // ✅ localStorage에서 로그인 정보 삭제\n setUserInfo(null);\n console.log(\"✅ 로컬 스토리지 초기화 완료!\");\n const logOut = () => {\n const logout = `https://kauth.kakao.com/oauth/logout?client_id=${REST_API_KEY}&logout_redirect_uri=${REDIRECT_URI}`;\n window.location.href = logout;\n }\n \n logOut();\n //alert(\"로그아웃되었습니다.\");\n navigate(\"/login\"); // ✅ 로그인 페이지로 이동\n };\n\n // ✅ 로그인되지 않은 경우 로그인 페이지로 리다이렉트\n if (!userInfo) return null;\n\n return (\n
\n {/* 🔙 뒤로가기 버튼 */}\n \n\n {/* 🏷 마이페이지 헤더 */}\n

마이페이지 🙍‍♂️

\n\n {/* 프로필 카드 */}\n
\n \n

{userInfo.name}

\n\n \n 로그아웃\n \n
\n\n {/* 📌 메뉴 목록 */}\n
\n navigate(\"/users/me/edit\")}\n >\n 프로필 관리 \n \n navigate(\"/users/me/posts\")}\n >\n 작성한 글 \n \n navigate(\"/users/me/ratings\")}\n >\n 별점 준 글 \n \n navigate(\"/users/me/bookmarks\")}\n >\n 북마크 \n \n {userInfo.state==0 &&\n
\n navigate(\"/admin/reports\")}\n >\n 신고 게시판 \n \n navigate(\"/admin/list\")}\n >\n 전체 게시판 \n \n
\n }\n \n
\n
\n );\n};\n\nexport default MyPage;\n", "text_preview": "import React, { useEffect, useState, useContext } from \"react\"; import { useNavigate } from \"react-router-dom\"; import { FaArrowLeft, FaEdit, FaCog, FaBookmark, FaStar, FaFile, FaPortrait, FaBell } from \"react-icons/fa\"; import KakaoLoginBu", "input_sha256": "8b99bd8c2623fb1c8cdaaaec3b4cb018dba77d7773f35c797dae572833dacd42", "text_sha256": "c6e0b2c35887e55b50e5bdddcefba32bfa80308d38dcc60969e87f92d20661b4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8046, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2096786764879675, "noise_severity": 0.3664431637508577, "missing_content_severity": 0.068822550262072, "factual_density": 2.821198230552222, "pretraining_utility": 0.2764368059339297, "math_density": 0.011946390380592246, "code_density": 3.742127349956102, "reasoning_density": 0.23202517842540538, "education_level": 2.022547041936141, "creative_writing_quality": -0.4091252221399497}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.022767290473961865, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9887775445655279, "secondary": 0.893400550746228, "tertiary": 0.8696433780777462, "structured_records": 0.019852148826785286, "procedural_steps": 0.004869621265248863, "worked_solution": 0.0018710179418192979, "contextual_dialogue": 8.161308935677914e-06, "content_quality": 0.36543656661668056, "noise_severity": 0.6946306968742852, "missing_content_severity": 0.9426478747816066, "factual_density": 0.3412363374265657, "pretraining_utility": 0.47599105642184125, "math_density": 0.9900446746828397, "code_density": 0.7851061249634181, "reasoning_density": 0.8066456846454955, "education_level": 0.8669552747842459, "creative_writing_quality": 0.9979560168458976, "primary_topic": 0.9899602649739843, "secondary_topic": 0.9086234735111925}, "probability_margin": {"primary": 0.9853351190131915, "secondary": 0.8316670808247273, "tertiary": 0.7961503443164121, "education_level": 0.8268507794483936, "creative_writing_quality": 0.9974143762444408, "primary_topic": 0.9872807605316094, "secondary_topic": 0.8800947550550946}, "raw_probabilities": {"primary": {"Academic Writing": 9.624213844365772e-05, "Content Listing": 0.0025063967553022462, "Creative Writing": 9.201820374966619e-05, "Customer Support Page": 3.375148147441628e-05, "Q&A Forum": 4.898411790977184e-05, "Discussion Thread": 2.9176203286791168e-05, "Comment Section": 5.885588205265266e-05, "Missing Content": 0.0002735897995518687, "Knowledge Article": 0.00016003372217889578, "Legal Notices": 0.00012610209574667055, "News Article": 2.1552981117701523e-05, "Organizational Announcement": 4.004475136343362e-05, "Nonfiction Writing": 4.35368692246971e-05, "Organizational About Page": 8.928181822368939e-05, 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{"content_quality": 0.2096786764879675, "noise_severity": 0.3664431637508577, "missing_content_severity": 0.06882255026207201, "factual_density": 2.821198230552222, "pretraining_utility": 0.2764368059339297, "math_density": 0.011946390380592246, "code_density": 3.742127349956102, "reasoning_density": 0.2320251784254054, "education_level": 2.022547041936141, "creative_writing_quality": -0.4091252221399497}, "score_applicability": {"education_level": 0.9976114963413706, "creative_writing_quality": 0.0017033192950851482}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/aultfarms/export-treatments/blob/1eef227b8324b35eafac4450507a1e37c92e80fc/aultfarms-lib/src/weights/module/sequences.js", "id": "javascript-0-01586ed3179eb6067c4cae8933156862", "text": "import moment from 'moment';\nimport _ from 'lodash';\nimport { set,increment } from 'cerebral/operators';\nimport { state,props } from 'cerebral/tags';\nimport { sequence } from 'cerebral';\n\nimport { tagStrToObj } from '../../util/tagHelpers';\nimport * as google from '../../google/module/sequences';\n\n//---------------------------------------------------------------------\n// clear the cache of weight records\nexport const clearCache = sequence('weights.clearCache', [\n set(state`weights.records`, []),\n]);\n\n//-------------------------------------------------------------------\n// set a row in the spreadsheet and update the cache from the response\n// need to have at least props.row, props.cols\nexport const putRow = sequence('weights.putRow', [\n ({props,state}) => ({\n key: props.key || state.get(`weights.sheet.key`),\n id: props.id || state.get(`weights.sheet.id`),\n worksheetName: props.worksheetName || state.get(`weights.sheet.worksheetName`),\n }),\n increment(props`row`), // move to Google's 1-index\n google.putRow, // save to google, then update our copy from resulting props.values\n increment(props`row`,-1), // move back to our zero-index \n ({state,props}) => {\n const record = weightRowToRecordMapper(props.values,props.row);\n if (!record) return; // header row\n return state.set(`weights.records.${props.row}`, weightRowToRecordMapper(props.values,props.row))\n },\n]);\n\n// need props.row, will fill in the rest from the state\nexport const saveRecordRow = sequence('weights.saveRecordRow', [\n ({props,state}) => ({ cols: weightRecordToRowMapper(state.get(`weights.records.${props.row}`)) }),\n putRow,\n]);\n\n\n//---------------------------------------------------------------------\n// fetch all weight records for a particular day:\nfunction weightRecordToRowMapper(record) {\n return [\n record.tag ? record.tag.color : '',\n record.tag ? record.tag.number : '',\n record.weight ? record.weight : '',\n record.adjWeight ? record.adjWeight : '',\n record.group ? record.group : '',\n record.inDate ? record.inDate : '',\n record.days ? record.days : '',\n record.lbsGain ? record.lbsGain : '',\n record.rog ? record.rog : '',\n record.out ? record.out : '',\n ];\n}\nfunction weightRowToRecordMapper(row,index) {\n if (index === 0) return; // row 0 is header\n return {\n tag: {\n color: row[0],\n number: +(row[1]),\n },\n weight: +(row[2]),\n adjWeight: +(row[3]),\n group: row[4],\n inDate: row[5],\n days: +(row[6]),\n lbsGain: +(row[7]),\n rog: +(row[8]),\n out: !!(row[9] && row[9] === 'TRUE'),\n row: index,\n };\n}\nfunction weightRowToRecordReducer(acc,row,index) {\n if (index > 0) // ignore header row\n acc.push(weightRowToRecordMapper(row,index));\n return acc;\n}\nexport const fetch = sequence('weights.fetch', [\n ({props}) => ({ \n path: `/Ault Farms Shared/LiveData/Weights/${props.date}_Weights`, \n createIfNotExist: true,\n worksheetName: 'weights',\n key: `${props.date}_Weights`\n }),\n // get everything from Google\n google.loadSheetRows,\n // keep track of sheet meta info\n ({props,state}) => state.set(`weights.sheet`, { id: props.id, key: props.key, worksheetName: props.worksheetName }),\n\n // check if we have at least a header row. If not, make one:\n ({props,state,path}) => {\n const sheet = state.get(`google.sheets.${props.key}`);\n if (sheet.rows && sheet.rows.length > 0) return path.haveHeader();\n return path.addHeader({id: sheet.id, worksheetName: sheet.worksheetName});\n },\n { \n haveHeader: [],\n addHeader: [\n ({props}) => ({id: props.id, worksheetName: props.worksheetName, key: props.key,\n row: -1, // putRow will increment expecting to move past header\n cols: [ 'color', 'number', 'weight', 'adj_wt', 'group', 'in_date', 'days', 'lbs_gain', 'rog', 'out' ],\n }),\n putRow,\n () => console.log('weights: Added header row to weights sheet'),\n ],\n },\n\n // convert the google sheets rows to records and store in state for weights\n ({props,state}) => state.set('weights.records', \n _.reduce(state.get(`google.sheets.${props.key}.rows`), weightRowToRecordReducer, [])\n ),\n]);\n\n\n", "text_preview": "import moment from 'moment'; import _ from 'lodash'; import { set,increment } from 'cerebral/operators'; import { state,props } from 'cerebral/tags'; import { sequence } from 'cerebral'; import { tagStrToObj } from '../../util/tagHelpers'; ", "input_sha256": "cb7aa68fc6fe1116d4ff3f1cbed8eeae738d7a37102ac3d96e9f611cd56f8f37", "text_sha256": "01586ed3179eb6067c4cae8933156862074cc33612f35ff03db9bdf1ab0b8eed", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8047, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 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0.0005845343146329063, "Home, Pets & Hobbies": 0.0021476568762365486, "Industry & Production": 0.091123821536781, "Language, Literature & Philosophy": 0.00040945264775547473, "Politics & Government": 0.00046064237139546396, "Religion & Spirituality": 0.0006367488652932016, "Science, Mathematics & Engineering": 0.0021920591787442994, "Relationships & Community": 0.0003561690655471092, "Software Usage & Applications": 0.03997530204851831, "Programming & Software Engineering": 0.018713136249536296, "Sports & Fitness": 0.0017568319420799095, "Vehicles & Transportation": 0.0005212770079814158, "Travel & Tourism": 0.0004445202289901867, "Other / Uncovered Subject": 0.006473883904316526, "NONE": 0.8070448403759082}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": 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"decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:08.839122+00:00", "scoring_key_slot": "primary", "dataset_uid": "b486b73f-4f55-474e-8f35-1cd0cea5fbaf", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d22210ea87e3aecdd6777a4ea7bac204b11f20b2f14f80e813916b623c39fc73", "source_metadata": {"uuid": "b486b73f-4f55-474e-8f35-1cd0cea5fbaf", "repo_name": "aultfarms/export-treatments", "relative_path": "aultfarms-lib/src/weights/module/sequences.js", "category": "WEB", "algo_rel_score": 0.9122068881988525, "quality_score": 6.077862739562988, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 31, "repo_created_at": "2018-05-17T02:45:56.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e8fe20463a64052b1b6013dbcfae8bf4f0e127e7", "size_bytes": 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"noise_severity": 0.017972181294754308, "missing_content_severity": 0.024239475973553432, "factual_density": 3.41760158963975, "pretraining_utility": 0.3555732695920948, "math_density": 0.3847531474472302, "code_density": 3.884176860146495, "reasoning_density": 0.8662667979157926, "education_level": 2.260756177666448, "creative_writing_quality": -0.21605991768950597}, "score_applicability": {"education_level": 0.9985653941023277, "creative_writing_quality": 0.00041552081644703157}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/PabloVergara039/evaluacion4-react/blob/c88190cbd9d6704cba381c3d5c0aea0640e0298f/src/ListaContactos.jsx", "id": "javascript-0-a7f332a958aad59216f5f62e4384311e", "text": "import React, { useState } from 'react';\n\nconst ListaContactos = ({ contactos, eliminarContacto, ordenarPorNombre, actualizarContactos }) => {\n const [editandoIndex, setEditandoIndex] = useState(null);\n const [editandoContacto, setEditandoContacto] = useState({});\n const [currentPage, setCurrentPage] = useState(1);\n const [criterioBusqueda, setCriterioBusqueda] = useState('');\n const contactsPerPage = 4;\n\n const editarContactoLocal = (index) => {\n setEditandoIndex(index);\n setEditandoContacto({ ...contactos[index] });\n };\n\n const handleChange = (e) => {\n const { name, value } = e.target;\n setEditandoContacto({\n ...editandoContacto,\n [name]: value,\n });\n };\n\n const actualizarContacto = () => {\n if (editandoIndex !== null) {\n const nuevosContactos = contactos.map((contacto, index) =>\n index === editandoIndex ? editandoContacto : contacto\n );\n actualizarContactos(nuevosContactos);\n cancelarEdicion();\n }\n };\n\n const cancelarEdicion = () => {\n setEditandoIndex(null);\n setEditandoContacto({});\n };\n\n const handlePageChange = (pageNumber) => {\n setCurrentPage(pageNumber);\n };\n\n const handleBusqueda = (e) => {\n setCriterioBusqueda(e.target.value);\n setCurrentPage(1);\n };\n\n const startIndex = (currentPage - 1) * contactsPerPage;\n\n const filteredContacts = contactos.filter((contacto) =>\n contacto.nombre.toLowerCase().includes(criterioBusqueda.toLowerCase()) ||\n contacto.apellido.toLowerCase().includes(criterioBusqueda.toLowerCase()) ||\n contacto.email.toLowerCase().includes(criterioBusqueda.toLowerCase()) ||\n contacto.telefono.includes(criterioBusqueda)\n );\n\n const selectedContacts = filteredContacts.slice(startIndex, startIndex + contactsPerPage);\n\n const totalPages = Math.ceil(filteredContacts.length / contactsPerPage);\n\n return (\n
\n

Lista de Contactos 📞📄

\n \n \n
\n {selectedContacts.map((contacto, index) => (\n
\n {editandoIndex === startIndex + index ? (\n
\n \n \n \n \n \n \n
\n ) : (\n
\n

\n Nombre: {contacto.nombre}\n

\n

\n Apellido: {contacto.apellido}\n

\n

\n Correo: {contacto.email}\n

\n

\n Teléfono: {contacto.telefono}\n

\n
\n )}\n
\n \n \n
\n
\n ))}\n
\n
\n
    \n {Array.from({ length: totalPages }, (_, i) => (\n
  • \n \n
    • \n ))}\n
\n
\n
\n );\n};\n\nexport default ListaContactos;\n", "text_preview": "import React, { useState } from 'react'; const ListaContactos = ({ contactos, eliminarContacto, ordenarPorNombre, actualizarContactos }) => { const [editandoIndex, setEditandoIndex] = useState(null); const [editandoContacto, setEditandoCont", "input_sha256": "6e9d3bde1c746225c63a370d7c6e366e33cbb4e33a44a93cf3006709e6fa431a", "text_sha256": "a7f332a958aad59216f5f62e4384311e6609abccc093a54c91e64c4b8c495146", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8049, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0009246471151414, "noise_severity": 0.03667395953191048, "missing_content_severity": 0.011614110289356522, "factual_density": 3.1622835071998403, "pretraining_utility": 0.6697426921274627, "math_density": 0.48093318827936693, "code_density": 3.744705875114122, "reasoning_density": 0.24646034996085306, "education_level": 1.907653237769955, "creative_writing_quality": -0.14636567378780677}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.016328705623301132, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9949082695068014, "secondary": 0.966022949111922, "tertiary": 0.9830615984579756, "structured_records": 0.06810491319769003, "procedural_steps": 0.0016420338375984411, "worked_solution": 0.00978735986340324, "contextual_dialogue": 5.0648448301699025e-06, "content_quality": 0.5873275253060679, "noise_severity": 0.9694383670567412, "missing_content_severity": 0.9903215747588696, "factual_density": 0.30190292266653396, "pretraining_utility": 0.5686946005518551, "math_density": 0.5992223431005275, "code_density": 0.7872548959284346, "reasoning_density": 0.7946163750326225, "education_level": 0.8313630246507344, "creative_writing_quality": 0.9990421888268006, "primary_topic": 0.9934300554752789, "secondary_topic": 0.9383320320892149}, "probability_margin": {"primary": 0.9922998174753728, "secondary": 0.9529523703942022, "tertiary": 0.9771112435314542, "education_level": 0.747659890639278, "creative_writing_quality": 0.9986587262370111, "primary_topic": 0.9919792300470461, "secondary_topic": 0.9183607668544358}, "raw_probabilities": {"primary": {"Academic Writing": 4.026696999354045e-05, "Content Listing": 0.0003403637392758743, "Creative Writing": 2.893538554072075e-05, "Customer Support Page": 9.555655408126438e-06, "Q&A Forum": 1.4647875955230474e-05, "Discussion Thread": 8.725634367606942e-06, "Comment Section": 2.025368405277475e-05, "Missing Content": 4.144869808389227e-05, "Knowledge Article": 9.368415529926e-05, "Legal Notices": 3.593567473388056e-05, "News Article": 6.4476321129211936e-06, "Organizational Announcement": 1.503071188747564e-05, "Nonfiction Writing": 1.4920108044471938e-05, "Organizational About Page": 1.7249328869499592e-05, "Personal About Page": 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"dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.0009246471151414, "noise_severity": 0.03667395953191048, "missing_content_severity": 0.011614110289356522, "factual_density": 3.1622835071998407, "pretraining_utility": 0.6697426921274627, "math_density": 0.48093318827936693, "code_density": 3.744705875114122, "reasoning_density": 0.24646034996085306, "education_level": 1.907653237769955, "creative_writing_quality": -0.14636567378780677}, "score_applicability": {"education_level": 0.9984589472707468, "creative_writing_quality": 0.0007981759776664651}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/SayaliZambre/Caremitra/blob/22fcdd9ff9d070a7783e569ca2763afd9e03c4b9/my-app/src/app/pages/Ouestion.jsx", "id": "javascript-0-b763e947189974e3b6bf2f76e0a034dc", "text": "'use client';\n\nimport React, { useState } from 'react';\nimport axios from 'axios';\nimport { useRouter, useSearchParams } from 'next/navigation';\n\nexport default function Question() {\n const router = useRouter();\n const searchParams = useSearchParams();\n\n const patientId = searchParams.get('patientId');\n const idParam = searchParams.get('id');\n const id = idParam ? JSON.parse(idParam) : null;\n\n console.log('patientId==', patientId);\n console.log('id:', id);\n\n const [chronic, setChronic] = useState('');\n const [serious, setSerious] = useState('');\n const [symptoms, setSymptoms] = useState('');\n const [medications, setMedications] = useState('');\n\n const question = async () => {\n try {\n const healthprofile = {\n anychronicdiseases: chronic,\n anyallergies: serious,\n anyundergoingtreatment: symptoms,\n anycurrentmedications: medications,\n };\n\n const response = await axios.post(\n ` https://ba86-152-59-6-114.ngrok-free.app/patient/addhealthprofile2/${id.patient_id}`,\n { healthprofile2: healthprofile }\n );\n\n console.log('response', response);\n router.push(`/tab?id=${JSON.stringify(id)}`);\n } catch (error) {\n console.log(error);\n }\n };\n\n return (\n
\n
\n

Health Profile Setup

\n\n
\n \n setChronic(e.target.value)}\n style={styles.input}\n rows={3}\n />\n
\n\n
\n \n setSerious(e.target.value)}\n style={styles.input}\n rows={3}\n />\n
\n\n
\n \n setSymptoms(e.target.value)}\n style={styles.input}\n rows={3}\n />\n
\n\n
\n \n setMedications(e.target.value)}\n style={styles.input}\n rows={3}\n />\n
\n\n \n
\n
\n );\n}\n\nconst styles: { [key: string]: React.CSSProperties } = {\n main: {\n marginTop: '20px',\n width: '100%',\n display: 'flex',\n justifyContent: 'center',\n },\n container: {\n width: '100%',\n maxWidth: '700px',\n padding: '20px',\n },\n title: {\n fontSize: '20px',\n color: '#00BFFF',\n fontWeight: 'bold',\n marginBottom: '10px',\n },\n block: {\n marginBottom: '20px',\n },\n label: {\n fontSize: '16px',\n display: 'block',\n marginBottom: '8px',\n color: 'black',\n },\n input: {\n width: '100%',\n padding: '10px',\n border: '1px solid black',\n borderRadius: '10px',\n resize: 'vertical',\n },\n button: {\n backgroundColor: '#005cb9',\n padding: '16px 60px',\n borderRadius: '40px',\n border: 'none',\n color: '#fff',\n fontWeight: 'bold',\n cursor: 'pointer',\n },\n buttonText: {\n color: '#fff',\n fontWeight: 'bold',\n },\n};\n", "text_preview": "'use client'; import React, { useState } from 'react'; import axios from 'axios'; import { useRouter, useSearchParams } from 'next/navigation'; export default function Question() { const router = useRouter(); const searchParams = useSearchP", "input_sha256": "4833cc0b27b9ac43ab4654da75e9a1e73f31996897f2d67df9341919879ca3dc", "text_sha256": "b763e947189974e3b6bf2f76e0a034dc07f8ec623b1ac2168a67907f9f0771f3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8050, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Health, Healthcare & Wellness", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5959676381028409, "noise_severity": 0.24337686221905533, "missing_content_severity": 0.006971230459420457, "factual_density": 2.5446767134355315, "pretraining_utility": 0.30299480942344953, "math_density": 0.01052678666517145, "code_density": 3.73988191602345, "reasoning_density": 0.11140986053033894, "education_level": 1.9242769482964976, "creative_writing_quality": -0.31922063976483844}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Health, Healthcare & Wellness"}, "review_reasons": ["secondary_topic probability 0.6602 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007568845305820832, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9869135540955483, "secondary": 0.8983284139524289, "tertiary": 0.9012274048958776, "structured_records": 0.07798609013123171, "procedural_steps": 0.016350002599584537, "worked_solution": 0.006218904992867135, "contextual_dialogue": 5.1691039931662107e-05, "content_quality": 0.4692240975301599, "noise_severity": 0.7971859481507871, "missing_content_severity": 0.9941906412838163, "factual_density": 0.23036333658261188, "pretraining_utility": 0.49681337941685766, "math_density": 0.9912276777790238, "code_density": 0.7832349300195416, "reasoning_density": 0.9071584495580509, "education_level": 0.8208512366599033, "creative_writing_quality": 0.9985292493548231, "primary_topic": 0.7858631197152809, "secondary_topic": 0.6465803386534659}, "probability_margin": {"primary": 0.9796157607339075, "secondary": 0.8596436554688276, "tertiary": 0.8654903628355517, "education_level": 0.7788444911415299, "creative_writing_quality": 0.9980827419751432, "primary_topic": 0.5974083818838491, "secondary_topic": 0.47235530054503294}, "raw_probabilities": {"primary": {"Academic Writing": 6.561440286355953e-05, "Content Listing": 0.00011443147608191169, "Creative Writing": 6.463310214556532e-05, "Customer Support Page": 0.0015857500839472564, "Q&A Forum": 7.05347290069816e-05, "Discussion Thread": 2.4947230028737737e-05, "Comment Section": 4.712310479377385e-05, "Missing Content": 0.0001797242386299223, "Knowledge Article": 0.00015397696620425157, "Legal Notices": 0.0001248234993888872, "News Article": 1.6186939857277153e-05, "Organizational Announcement": 3.210931025754758e-05, "Nonfiction Writing": 4.883354608040699e-05, "Organizational About Page": 7.293051690434116e-05, "Personal About Page": 9.329574333322357e-05, "Personal Blog": 2.6663113726533015e-05, "Product Page": 5.2989914814353654e-05, "Spam / Ads": 4.123508844743703e-05, "Structured Data": 0.0010996873231791764, "Technical Writing": 0.9874588226749004, "Transcript / Interview": 0.00011095532896618369, "Tutorial / How-To Guide": 0.0006513772944413098, "User Reviews": 2.0292431008277982e-05, "Other / Unknown": 0.007843061940992825}, "secondary": {"Academic Writing": 0.0004892136234322705, "Content Listing": 0.0010329359207228972, "Creative Writing": 0.0006502156923678593, "Customer Support Page": 0.003984132936578038, "Q&A Forum": 0.0005695947182011276, "Discussion Thread": 0.00016161395277829738, "Comment Section": 0.0003065632123240678, "Missing Content": 0.0009784561752847606, "Knowledge Article": 0.0014273532653259675, "Legal Notices": 0.000556957505494707, "News Article": 0.000273204628565721, "Organizational Announcement": 0.00026628895400263833, "Nonfiction Writing": 0.0004917043236020071, "Organizational About Page": 0.0004608431538178904, "Personal About Page": 0.00046308120167796936, "Personal Blog": 0.0003731551816670516, "Product Page": 0.0004304441527900451, "Spam / Ads": 0.00032887110682784236, "Structured Data": 0.03785189621833561, "Technical Writing": 0.04292107456891685, "Transcript / Interview": 0.0008880366986186296, "Tutorial / How-To Guide": 0.0023710626970415193, "User Reviews": 0.00015857007388171606, "NONE": 0.9025647300377444}, "tertiary": {"Academic Writing": 0.0004553233275368436, "Content Listing": 0.0013219721702489265, "Creative Writing": 0.0005785577332925963, "Customer Support Page": 0.004294183856114473, "Q&A Forum": 0.0006524843371027282, "Discussion Thread": 0.00016236121577497682, "Comment Section": 0.0003405253229475653, "Missing Content": 0.0012141503638571423, "Knowledge Article": 0.0026118297070219995, "Legal Notices": 0.0007984652370119243, "News Article": 0.00025271145764284997, "Organizational Announcement": 0.00025961831725591266, "Nonfiction 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{"0": 0.818732596190269, "1": 0.13309996518314132, "2": 0.03822480496862385, "3": 0.005943247533198116, "4": 0.003999386124768001}, "missing_content_severity": {"0": 0.9965634265251718, "1": 0.0009636540759219468, "2": 0.001645411175470183, "3": 0.0005932788611872463, "4": 0.00023422936224910167}, "factual_density": {"0": 0.03997984492952192, "1": 0.14840551289447537, "2": 0.2726930807551506, "3": 0.30480120665265337, "4": 0.23412035476819865}, "pretraining_utility": {"-2": 0.013202893935018247, "-1": 0.12400877976812412, "0": 0.45543774157425365, "1": 0.36129179238359793, "2": 0.04605879233900611}, "math_density": {"0": 0.9918789007027699, "1": 0.00687416826011178, "2": 0.0005260175364848967, "3": 0.00028307067044366045, "4": 0.0004378428301897235}, "code_density": {"0": 0.0003852824797266465, "1": 0.0007865239636237831, "2": 0.026780798612287687, "3": 0.2026557849421968, "4": 0.7693916100021652}, "reasoning_density": {"0": 0.9338772111083069, "1": 0.02551923089121997, "2": 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0.0008333045991172934, "Health, Healthcare & Wellness": 0.6601734025514095, "History & Geography": 0.0007431871016147603, "Home, Pets & Hobbies": 0.0005688027647396666, "Industry & Production": 0.0004959542017625817, "Language, Literature & Philosophy": 0.0004344602831175638, "Politics & Government": 0.0004688507621859428, "Religion & Spirituality": 0.0007569238625891109, "Science, Mathematics & Engineering": 0.0007736886435015167, "Relationships & Community": 0.0006304189845224042, "Software Usage & Applications": 0.005032748026813406, "Programming & Software Engineering": 0.18781810200637652, "Sports & Fitness": 0.0008239576893541901, "Vehicles & Transportation": 0.0003891187538453559, "Travel & Tourism": 0.0006200196323029104, "Other / Uncovered Subject": 0.0009852481491967303, "NONE": 0.11949277343720809}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9644, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9644, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:09.989954+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b80f1765-f09b-4a94-9787-abe93c4c1a9b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "41372ead0128992c01bda7bcfffa61393999792f7459428a65f6ef436cff2fbc", "source_metadata": {"uuid": "b80f1765-f09b-4a94-9787-abe93c4c1a9b", "repo_name": "SayaliZambre/Caremitra", "relative_path": "my-app/src/app/pages/Ouestion.jsx", "category": "WEB", "algo_rel_score": 0.9563142657279968, "quality_score": 7.074851989746094, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 7, "repo_created_at": "2025-07-04T20:57:42.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "77b4c7e8862a9e3d6981a0bc80f329050130a70f", "size_bytes": 4141, "file_timestamp": 1751745253, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "22fcdd9ff9d070a7783e569ca2763afd9e03c4b9"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 7, "repo_created_at": "2025-07-04T20:57:42.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "77b4c7e8862a9e3d6981a0bc80f329050130a70f", "size_bytes": 4141, "file_timestamp": 1751745253, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "22fcdd9ff9d070a7783e569ca2763afd9e03c4b9", "url": "https://github.com/SayaliZambre/Caremitra/blob/22fcdd9ff9d070a7783e569ca2763afd9e03c4b9/my-app/src/app/pages/Ouestion.jsx"}, "gpt4o_tokens": 1001, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.5959676381028409, "noise_severity": 0.24337686221905533, "missing_content_severity": 0.006971230459420457, "factual_density": 2.5446767134355315, "pretraining_utility": 0.30299480942344953, "math_density": 0.01052678666517145, "code_density": 3.73988191602345, "reasoning_density": 0.11140986053033894, "education_level": 1.9242769482964976, "creative_writing_quality": -0.31922063976483844}, "score_applicability": {"education_level": 0.995515971675148, "creative_writing_quality": 0.001225625537647379}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/danno77-glu/RT1b/blob/df0e4a41fdb4b54aa29690286893450469c450d0/src/components/Settings/AuditorSettings.jsx", "id": "javascript-0-78199a2946079e51f7a2fa4e025469ec", "text": "import React, { useState, useEffect } from 'react';\nimport { supabase } from '../../supabase';\nimport './styles.css';\n\nconst AuditorSettings = () => {\n const [auditors, setAuditors] = useState([]);\n const [newAuditor, setNewAuditor] = useState({ \n name: '', \n color: '#ffffff',\n email: '',\n phone: '' \n });\n const [loading, setLoading] = useState(true);\n const [editingAuditor, setEditingAuditor] = useState(null);\n const [error, setError] = useState(null);\n\n const fetchAuditors = async () => {\n try {\n setLoading(true);\n const { data, error } = await supabase\n .from('auditors')\n .select('*')\n .order('name');\n\n if (error) throw error;\n setAuditors(data || []);\n } catch (error) {\n console.error('Error fetching auditors:', error);\n setError(error.message);\n } finally {\n setLoading(false);\n }\n };\n\n useEffect(() => {\n fetchAuditors();\n }, []);\n\n const handleAddAuditor = async () => {\n if (!newAuditor.name.trim()) {\n setError(\"Auditor name is required\");\n return;\n }\n\n try {\n setError(null);\n console.log('Adding auditor:', newAuditor);\n const { data, error } = await supabase\n .from('auditors')\n .insert({\n name: newAuditor.name.trim(),\n color: newAuditor.color,\n email: newAuditor.email.trim(),\n phone: newAuditor.phone.trim()\n })\n .select()\n .single();\n\n if (error) {\n console.error('Error adding auditor to Supabase:', error);\n setError('Failed to add auditor: ' + error.message);\n return;\n }\n console.log('Auditor added successfully:', data);\n await fetchAuditors();\n setNewAuditor({ \n name: '', \n color: '#ffffff',\n email: '',\n phone: '' \n });\n } catch (error) {\n console.error('Error adding auditor:', error);\n setError('Failed to add auditor: ' + error.message);\n }\n };\n\n const handleRemoveAuditor = async (id) => {\n try {\n setError(null);\n console.log('Removing auditor with id:', id);\n const { error } = await supabase\n .from('auditors')\n .delete()\n .eq('id', id);\n\n if (error) {\n console.error('Error removing auditor from Supabase:', error);\n setError('Failed to remove auditor: ' + error.message);\n return;\n }\n console.log('Auditor removed successfully');\n await fetchAuditors();\n } catch (error) {\n console.error('Error removing auditor:', error);\n setError('Failed to remove auditor: ' + error.message);\n }\n };\n\n const handleUpdateAuditor = async () => {\n if (!editingAuditor || !editingAuditor.name.trim()) {\n setError(\"Auditor name is required\");\n return;\n }\n\n try {\n setError(null);\n const { error } = await supabase\n .from('auditors')\n .update({\n name: editingAuditor.name.trim(),\n color: editingAuditor.color,\n email: editingAuditor.email.trim(),\n phone: editingAuditor.phone.trim()\n })\n .eq('id', editingAuditor.id);\n\n if (error) {\n console.error('Error updating auditor in Supabase:', error);\n setError('Failed to update auditor: ' + error.message);\n return;\n }\n \n await fetchAuditors();\n setEditingAuditor(null);\n } catch (error) {\n console.error('Error updating auditor:', error);\n setError('Failed to update auditor: ' + error.message);\n }\n };\n\n const handleColorChange = async (e, id) => {\n if (editingAuditor && editingAuditor.id === id) {\n setEditingAuditor({\n ...editingAuditor,\n color: e.target.value\n });\n return;\n }\n\n try {\n setError(null);\n console.log('Updating color for auditor with id:', id, 'New color:', e.target.value);\n const { data, error } = await supabase\n .from('auditors')\n .update({ color: e.target.value })\n .eq('id', id)\n .select()\n .single();\n\n if (error) {\n console.error('Error updating auditor color in Supabase:', error);\n setError('Failed to update auditor color: ' + error.message);\n return;\n }\n console.log('Auditor color updated successfully:', data);\n await fetchAuditors();\n } catch (error) {\n console.error('Error updating auditor color:', error);\n setError('Failed to update auditor color: ' + error.message);\n }\n };\n\n const handleEditAuditor = (auditor) => {\n setEditingAuditor({...auditor});\n };\n\n const handleCancelEdit = () => {\n setEditingAuditor(null);\n setError(null);\n };\n\n const handleInputChange = (e, field) => {\n if (editingAuditor) {\n setEditingAuditor({\n ...editingAuditor,\n [field]: e.target.value\n });\n } else {\n setNewAuditor({\n ...newAuditor,\n [field]: e.target.value\n });\n }\n };\n\n if (loading) {\n return
Loading auditors...
;\n }\n\n return (\n
\n

Manage Auditors

\n \n {error &&
{error}
}\n \n
\n {auditors.map((auditor) => (\n
\n {editingAuditor && editingAuditor.id === auditor.id ? (\n
\n
\n \n handleInputChange(e, 'name')}\n required\n />\n
\n
\n \n handleInputChange(e, 'email')}\n placeholder=\"\"\n />\n
\n
\n \n handleInputChange(e, 'phone')}\n placeholder=\"(123) 456-7890\"\n />\n
\n
\n \n handleInputChange(e, 'color')}\n />\n
\n
\n \n Save\n \n \n Cancel\n \n
\n
\n ) : (\n <>\n
\n \n
\n {auditor.name}\n {auditor.email && {auditor.email}}\n {auditor.phone && {auditor.phone}}\n
\n
\n handleColorChange(e, auditor.id)}\n title=\"Change color\"\n className=\"color-picker\"\n />\n handleEditAuditor(auditor)}\n className=\"edit-auditor-btn\"\n title=\"Edit auditor\"\n >\n Edit\n \n handleRemoveAuditor(auditor.id)}\n className=\"remove-option\"\n title=\"Remove auditor\"\n >\n Remove\n \n
\n
\n \n )}\n
\n ))}\n
\n \n
\n

Add New Auditor

\n
\n
\n \n handleInputChange(e, 'name')}\n placeholder=\"\"\n required\n />\n
\n
\n \n handleInputChange(e, 'email')}\n placeholder=\"\"\n />\n
\n
\n \n handleInputChange(e, 'phone')}\n placeholder=\"(123) 456-7890\"\n />\n
\n
\n \n handleInputChange(e, 'color')}\n />\n
\n \n Add Auditor\n \n
\n
\n
\n );\n};\n\nexport default AuditorSettings;\n", "text_preview": "import React, { useState, useEffect } from 'react'; import { supabase } from '../../supabase'; import './styles.css'; const AuditorSettings = () => { const [auditors, setAuditors] = useState([]); const [newAuditor, setNewAuditor] = useState", "input_sha256": "5fb9839a7d09dfe476c3ad64face3713eb0c22b9456ef3ecdb38ed3682e57faf", "text_sha256": "78199a2946079e51f7a2fa4e025469ec4f0630c05d0b1645ecb23ba9a89ba161", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8048, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9385166491549368, "noise_severity": 0.07286760254335892, "missing_content_severity": 0.025064272600313013, "factual_density": 2.7752222643144884, "pretraining_utility": 0.5379638411780922, "math_density": 0.009050290883514214, "code_density": 3.729119616632553, "reasoning_density": 0.2609964863098401, "education_level": 2.1097209141568003, "creative_writing_quality": -0.16402336014822905}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & 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{"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11001, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:10.214919+00:00", "scoring_key_slot": "primary", "dataset_uid": "73d9a181-7613-494f-b68c-6f0ec3c3859e", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "5e4e06f0bd8d09ca88494d75cd0839b233fdb2dd51713623d9152915d2235d92", "source_metadata": {"uuid": "73d9a181-7613-494f-b68c-6f0ec3c3859e", "repo_name": "danno77-glu/RT1b", "relative_path": "src/components/Settings/AuditorSettings.jsx", "category": "WEB", "algo_rel_score": 0.9200325012207031, "quality_score": 7.350281715393066, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-03-17T03:34:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5183a6b3fbf1f91fe61a56fb556efe186c365bb1", "size_bytes": 10508, "file_timestamp": 1742182496, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "df0e4a41fdb4b54aa29690286893450469c450d0"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2025-03-17T03:34:50.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5183a6b3fbf1f91fe61a56fb556efe186c365bb1", "size_bytes": 10508, "file_timestamp": 1742182496, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "df0e4a41fdb4b54aa29690286893450469c450d0", "url": "https://github.com/danno77-glu/RT1b/blob/df0e4a41fdb4b54aa29690286893450469c450d0/src/components/Settings/AuditorSettings.jsx"}, "gpt4o_tokens": 2270, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9385166491549368, "noise_severity": 0.07286760254335893, "missing_content_severity": 0.025064272600313013, "factual_density": 2.7752222643144884, "pretraining_utility": 0.5379638411780922, "math_density": 0.009050290883514214, "code_density": 3.729119616632553, "reasoning_density": 0.2609964863098402, "education_level": 2.1097209141568003, "creative_writing_quality": -0.16402336014822905}, "score_applicability": {"education_level": 0.998657109340159, "creative_writing_quality": 0.0014047164780390145}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/HarshavardhanDanda/Strateos/blob/1067f1628b363715ae0479c4deae9739465f9c4b/app/client/main/components/properties.jsx", "id": "javascript-0-c813cf176f5a3d97006827b6db29ab87", "text": "import keycode from 'keycode';\nimport _ from 'lodash';\nimport PropTypes from 'prop-types';\nimport React from 'react';\nimport ReactDOM from 'react-dom';\n\nimport CharControllableInput from 'main/components/CharControllableInput';\nimport ajax from 'main/util/ajax';\n\nimport { Button, SearchField, DropDown } from '@transcriptic/amino';\nimport EditableProperty from './EditableProperty';\nimport './properties.scss';\n\nexport class AddProperty extends React.Component {\n constructor(props, context) {\n super(props, context);\n this.cancel = this.cancel.bind(this);\n this.onKeyDown = this.onKeyDown.bind(this);\n this.submit = this.submit.bind(this);\n }\n\n componentDidMount() {\n if (this.props.focusValue) {\n return this.charInput.inputNode.focus();\n } else {\n return this.key.focus();\n }\n }\n\n onKeyDown(e) {\n const char = keycode(e);\n if (char === 'enter') {\n this.submit();\n } else if (char === 'esc') {\n this.cancel();\n }\n }\n\n cancel() {\n return this.props.onCancel();\n }\n\n submit() {\n const name = this.key.value.trim();\n const value = this.charInput.inputNode.value.trim();\n\n if (name.length > 0 || !this.props.nameEditable) {\n return this.props.onCreate({\n key: name,\n value\n });\n } else {\n return this.cancel();\n }\n }\n\n render() {\n return (\n
\n
\n {\n this.key = node;\n }}\n defaultValue={this.props.name}\n onKeyDown={this.keyDown}\n disabled={!this.props.nameEditable}\n />\n
\n
\n {\n this.charInput = node;\n }}\n defaultValue={this.props.value}\n onKeyDown={this.onKeyDown}\n illegalChars={this.props.illegalChars}\n />\n
\n
\n \n \n
\n
\n );\n }\n}\n\nAddProperty.defaultProps = {\n nameEditable: true\n};\n\nAddProperty.propTypes = {\n focusValue: PropTypes.string,\n onCancel: PropTypes.func,\n nameEditable: PropTypes.bool,\n onCreate: PropTypes.func,\n name: PropTypes.string,\n value: PropTypes.string,\n illegalChars: PropTypes.array\n};\n\nclass SearchChoosingProperty extends React.Component {\n constructor(props, context) {\n super(props, context);\n this.cancel = this.cancel.bind(this);\n this.edit = this.edit.bind(this);\n this.remove = this.remove.bind(this);\n this.selectedResource = this.selectedResource.bind(this);\n this.state = {\n showingPopover: false\n };\n }\n\n edit() {\n this.setState({\n showingPopover: true\n });\n return typeof this.props.onEdit === 'function'\n ? this.props.onEdit()\n : undefined;\n }\n\n remove() {\n return this.props.onSave(undefined);\n }\n\n cancel() {\n this.setState({\n showingPopover: false\n });\n return typeof this.props.onCancel === 'function'\n ? this.props.onCancel()\n : undefined;\n }\n\n selectedResource(r) {\n this.setState({\n showingPopover: false,\n hovered: false\n });\n return this.props.onSave(r);\n }\n\n render() {\n return (\n \n this.setState({\n hovered: true\n })}\n onMouseLeave={() =>\n this.setState({\n hovered: false\n })}\n >\n \n
\n {this.props.name}\n
\n \n \n \n
\n \n \n \n \n
\n \n \n \n \n \n \n \n \n \n {' '}\n {this.props.value}\n \n \n \n \n {this.props.value}\n \n \n \n

\n {this.props.emptyText}\n

\n \n \n \n \n \n \n );\n }\n}\n\nSearchChoosingProperty.defaultProps = {\n emptyText: '(no value)',\n editable: true,\n deletable: true\n};\n\nSearchChoosingProperty.propTypes = {\n name: PropTypes.string,\n value: PropTypes.string.isRequired,\n url: PropTypes.string,\n engine: PropTypes.object.isRequired,\n onSave: PropTypes.func.isRequired,\n editable: PropTypes.bool,\n emptyText: PropTypes.string,\n onCancel: PropTypes.func,\n onEdit: PropTypes.func,\n deletable: PropTypes.bool,\n searchData: PropTypes.object\n};\n\nclass GenericSearcher extends React.Component {\n constructor(props, context) {\n super(props, context);\n this.keydown = this.keydown.bind(this);\n this.state = {\n results: undefined,\n q: ''\n };\n }\n\n componentDidMount() {\n window.addEventListener('keydown', this.keydown);\n ReactDOM.findDOMNode(this).style.marginTop = '-5px';\n ReactDOM.findDOMNode(this).style.opacity = '0';\n return setTimeout(() => {\n ReactDOM.findDOMNode(this).style.transition =\n '200ms margin-top, 100ms opacity';\n ReactDOM.findDOMNode(this).style.opacity = '1';\n ReactDOM.findDOMNode(this).style.marginTop = '0px';\n });\n }\n\n componentWillUnmount() {\n window.removeEventListener('keydown', this.keydown);\n }\n\n keydown(e) {\n if (e.which === 27) {\n e.preventDefault();\n e.stopPropagation();\n this.props.onCancel();\n }\n\n if (e.which === 40) {\n if (this.results != undefined) {\n this.results.cursorDown();\n }\n e.preventDefault();\n }\n if (e.which === 38) {\n if (this.results != undefined) {\n this.results.cursorUp();\n }\n e.preventDefault();\n }\n if (e.which === 13) {\n if (this.results != undefined) {\n this.results.select();\n }\n }\n }\n\n render() {\n const EngineEmpty = this.props.engine.empty;\n\n return (\n
\n {\n this.resource_search = node;\n }}\n onChange={e =>\n this.setState({\n q: e.target.value\n })}\n value={this.state.q}\n reset={() => {\n this.setState({\n q: ''\n });\n }}\n searchType={this.props.searchType}\n />\n \n \n
\n \n \n \n
\n \n \n {\n this.results = node;\n }}\n engine={this.props.engine}\n q={this.state.q}\n resultType={this.props.engine.resultType}\n onSelected={this.props.onSelected}\n searchData={this.props.searchData}\n />\n \n \n
\n );\n }\n}\n\nGenericSearcher.propTypes = {\n onCancel: PropTypes.func,\n onSelected: PropTypes.func,\n engine: PropTypes.object,\n searchType: PropTypes.string,\n searchData: PropTypes.object\n};\n\nclass SearchResults extends React.Component {\n\n static get propTypes() {\n return {\n resultType: PropTypes.node,\n engine: PropTypes.object,\n onSelected: PropTypes.func,\n q: PropTypes.string,\n searchData: PropTypes.object\n };\n }\n\n constructor(props, context) {\n super(props, context);\n\n this.cursorDown = this.cursorDown.bind(this);\n this.cursorUp = this.cursorUp.bind(this);\n this.select = this.select.bind(this);\n this.query = this.query.bind(this);\n\n this.state = {\n results: undefined,\n selected: -1\n };\n }\n\n componentDidMount() {\n this._mounted = true;\n this.request_queue = ajax.singly();\n\n this.query(this.props.q);\n }\n\n componentDidUpdate(prevProps) {\n if (this.props.q !== prevProps.q) {\n this.query(this.props.q);\n }\n }\n\n componentWillUnmount() {\n this._mounted = false;\n }\n\n cursorDown() {\n const resultsLength = (this.state.results && this.state.results.length) || 0;\n\n this.setState({\n selected: Math.min(resultsLength - 1, this.state.selected + 1)\n });\n }\n\n cursorUp() {\n return this.setState({\n selected: Math.max(0, this.state.selected - 1)\n });\n }\n\n select() {\n if (!(this.state.selected >= 0)) {\n return;\n }\n\n this.props.onSelected(this.state.results[this.state.selected]);\n }\n\n query(q) {\n return this.request_queue((done) => {\n if (!this._mounted) {\n done();\n return;\n }\n\n this.props.engine.query(q, (data) => {\n done();\n\n if (!this._mounted) {\n return;\n }\n\n this.setState({\n results: data.results,\n page: data.page,\n selected: -1\n });\n }, this.props.searchData);\n });\n }\n\n render() {\n const ResultType = this.props.resultType;\n\n if (this.state.results == undefined) {\n return
Loading...
;\n } else if (this.state.results.length === 0) {\n return
No results.
;\n } else {\n return (\n \n );\n }\n }\n}\n\nclass CustomPropertySet extends React.Component {\n constructor(props, context) {\n super(props, context);\n this.state = {\n adding: false\n };\n }\n\n renderCustomProperty() {\n return (\n
\n
\n { this.props.hasCustomProperties ?\n (_.map(this.props.properties.data, (property) => {\n return (\n \n this.setState({\n adding: false\n })}\n onSave={(value) => {\n const key = property.attributes.key;\n this.props.onChangeProperty(\n {\n key,\n value\n }\n );\n }\n }\n onDelete={() => this.props.onRemoveProperty(property.attributes.key)}\n slim\n displayColonSeparated\n />\n );\n\n })) :\n (_.map(this.props.properties, (v, k) => {\n return (\n {\n let object = {};\n if (typeof args === 'string') {\n object.key = k;\n object.value = args;\n } else {\n object = args;\n }\n this.props.onChangeProperty(object);\n }}\n onDelete={() => this.props.onRemoveProperty(k)}\n nameEditable={this.props.nameEditable}\n slim\n displayColonSeparated\n />\n );\n }))}\n { this.state.adding && !this.props.hasCustomProperties && (\n \n this.setState({\n adding: false\n })}\n onSave={(kv) => {\n this.setState({\n adding: false\n });\n return this.props.onAddProperty(kv);\n }}\n expandOnEdit={false}\n />\n )\n }\n { this.props.editable && !this.props.hasCustomProperties && (\n {\n this.props.onChangeProperty(\n {\n key,\n value\n }\n );\n }}\n canAddNewKeyValuePair\n nameEditable\n />\n )\n }\n
\n
\n );\n }\n\n render() {\n return (\n \n \n \n {this.renderCustomProperty()}\n \n \n \n {this.renderCustomProperty()}\n \n \n );\n }\n}\n\nCustomPropertySet.propTypes = {\n editable: PropTypes.bool,\n onChangeProperty: PropTypes.func.isRequired,\n onAddProperty: PropTypes.func.isRequired,\n onRemoveProperty: PropTypes.func.isRequired,\n properties: PropTypes.object.isRequired,\n orientation: PropTypes.oneOf(['vertical', 'horizontal']),\n customStyle: PropTypes.string,\n nameEditable: PropTypes.bool\n};\n\nCustomPropertySet.defaultProps = {\n orientation: 'vertical',\n editable: true,\n nameEditable: false\n};\n\nexport {\n CustomPropertySet,\n EditableProperty,\n GenericSearcher,\n SearchChoosingProperty\n};\n", "text_preview": "import keycode from 'keycode'; import _ from 'lodash'; import PropTypes from 'prop-types'; import React from 'react'; import ReactDOM from 'react-dom'; import CharControllableInput from 'main/components/CharControllableInput'; import ajax f", "input_sha256": "565fe6ec969f6177974b1ba086332de678ebadd34d4e87ee0eb7ebe8b1968e9f", "text_sha256": "c813cf176f5a3d97006827b6db29ab8788e8c988ba9a1eb703e3dc54978b2246", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8051, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2436516153219639, "noise_severity": 0.21957760680385457, "missing_content_severity": 0.7803733746663599, "factual_density": 3.252697195952906, "pretraining_utility": 0.36296275051356247, "math_density": 0.1234632085720267, "code_density": 3.8268545468296846, "reasoning_density": 0.19239789760928933, "education_level": 2.3725973327630006, "creative_writing_quality": -0.31347496373271855}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.013450335918619327, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964134611602612, "secondary": 0.9752937504133936, "tertiary": 0.989328883545866, "structured_records": 0.020220955674316685, "procedural_steps": 0.0010644960830557423, "worked_solution": 0.0005653678264875315, "contextual_dialogue": 4.718192551018238e-06, "content_quality": 0.1348870145531924, "noise_severity": 0.8170186609967878, "missing_content_severity": 0.3496888544446999, "factual_density": 0.3772476632940881, "pretraining_utility": 0.4713397823896167, "math_density": 0.8971139928566444, "code_density": 0.8557121223580704, "reasoning_density": 0.8396684186589256, "education_level": 0.5630697838964063, "creative_writing_quality": 0.9994218453643723, "primary_topic": 0.9954076870788393, "secondary_topic": 0.9542653553248234}, "probability_margin": {"primary": 0.9945244987065723, "secondary": 0.966172107723565, "tertiary": 0.9864325771192176, "education_level": 0.30732283694684226, "creative_writing_quality": 0.9992823679071715, "primary_topic": 0.994249731333076, "secondary_topic": 0.9338532053206847}, "raw_probabilities": {"primary": {"Academic Writing": 3.060005909946517e-05, "Content Listing": 2.7295457214729265e-05, "Creative Writing": 1.947224032478272e-05, "Customer Support Page": 7.93145936826579e-06, "Q&A Forum": 1.1816932642609656e-05, "Discussion Thread": 7.5908003746407306e-06, "Comment Section": 1.5155222536003916e-05, "Missing Content": 0.00021410346913579417, "Knowledge Article": 4.3293047175519466e-05, "Legal Notices": 5.6962314430612584e-05, "News Article": 4.730736943057915e-06, "Organizational Announcement": 9.825961444877739e-06, "Nonfiction Writing": 9.020107428645478e-06, "Organizational About Page": 1.165346579260057e-05, "Personal About Page": 1.2972366191079018e-05, "Personal Blog": 6.4210857393616164e-06, "Product Page": 7.96228580690073e-06, "Spam / Ads": 8.045911701669077e-06, "Structured Data": 0.0007114974790629636, "Technical Writing": 0.9965629002785836, "Transcript / Interview": 1.2879721246221341e-05, "Tutorial / How-To Guide": 0.00016257452055547028, "User Reviews": 6.893505189715952e-06, "Other / Unknown": 0.002038401572011313}, "secondary": {"Academic Writing": 0.00013478057769784419, "Content Listing": 0.00033541125788064325, "Creative Writing": 0.00014998545268688314, "Customer Support Page": 7.355878937740832e-05, "Q&A Forum": 5.14162856542534e-05, "Discussion Thread": 4.303618605780056e-05, "Comment Section": 0.00014926086532598336, "Missing Content": 0.010151069755937223, "Knowledge Article": 0.00019788453282742097, "Legal Notices": 0.0006500616469297044, "News Article": 7.45278358715699e-05, "Organizational Announcement": 6.575882466747518e-05, "Nonfiction Writing": 5.9980106168458375e-05, "Organizational About Page": 8.628778620288479e-05, "Personal About Page": 6.860995424487826e-05, "Personal Blog": 5.960850854480136e-05, "Product Page": 4.334027214997306e-05, "Spam / Ads": 7.85574946413135e-05, "Structured Data": 0.005675627241258919, "Technical Writing": 0.00506727659863252, "Transcript / 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"2023-07-28T10:53:31.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5c827065b5cee32e55bd23f117aa557dd37ff640", "size_bytes": 16150, "file_timestamp": 1690544030, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "1067f1628b363715ae0479c4deae9739465f9c4b", "url": "https://github.com/HarshavardhanDanda/Strateos/blob/1067f1628b363715ae0479c4deae9739465f9c4b/app/client/main/components/properties.jsx"}, "gpt4o_tokens": 3358, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2436516153219639, "noise_severity": 0.21957760680385457, "missing_content_severity": 0.7803733746663599, "factual_density": 3.252697195952906, "pretraining_utility": 0.36296275051356247, "math_density": 0.1234632085720267, "code_density": 3.8268545468296846, "reasoning_density": 0.19239789760928933, "education_level": 2.3725973327630006, "creative_writing_quality": -0.31347496373271855}, "score_applicability": {"education_level": 0.9987328829547083, "creative_writing_quality": 0.00048179552968955473}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/harshbansal7/amity-events-frontend/blob/56fe2f22b8b784ab0c2443d55dc0c68404c660d4/src/components/Admin/Attendance.jsx", "id": "javascript-0-044a3b9760047d9d68640633407b09e8", "text": "import { useState, useEffect } from \"react\";\nimport {\n getEvents,\n getEventParticipants,\n markAttendance,\n getCurrentUserId,\n} from \"../../services/api\";\nimport { format } from \"date-fns\";\nimport { Search, Filter, CheckCircle, XCircle, Save } from \"lucide-react\";\nimport Toast from \"../UI/Toast\";\n\nconst Attendance = () => {\n const [events, setEvents] = useState([]);\n const [selectedEvent, setSelectedEvent] = useState(null);\n const [participants, setParticipants] = useState([]);\n const [loading, setLoading] = useState(true);\n const [saving, setSaving] = useState(false);\n const [error, setError] = useState(\"\");\n const [success, setSuccess] = useState(\"\");\n const [searchTerm, setSearchTerm] = useState(\"\");\n const [filterStatus, setFilterStatus] = useState(\"all\"); // all, present, absent\n const [hasUnsavedChanges, setHasUnsavedChanges] = useState(false);\n const currentUserId = getCurrentUserId();\n const [apiError, setApiError] = useState(\"\");\n\n const fetchEvents = async () => {\n try {\n const data = await getEvents();\n const userEvents = data.filter(\n (event) => event.creator_id === currentUserId,\n );\n setEvents(userEvents);\n if (userEvents.length > 0) {\n setSelectedEvent(userEvents[0]);\n await fetchParticipants(userEvents[0]._id);\n } else {\n setLoading(false);\n }\n } catch (error) {\n setError(\"Failed to fetch events\");\n setLoading(false);\n }\n };\n\n const fetchParticipants = async (eventId) => {\n try {\n const event = events.find((e) => e._id === eventId);\n if (!event || event.creator_id !== currentUserId) {\n setApiError(\"Unauthorized to view these participants\");\n return;\n }\n\n setApiError(\"\");\n setLoading(true);\n const data = await getEventParticipants(eventId);\n setParticipants(data);\n } catch (error) {\n setApiError(\"Failed to fetch participants\");\n } finally {\n setLoading(false);\n }\n };\n\n useEffect(() => {\n fetchEvents();\n }, []);\n\n useEffect(() => {\n if (selectedEvent) {\n fetchParticipants(selectedEvent._id);\n }\n }, [selectedEvent]);\n\n const handleAttendanceToggle = (enrollmentNumber) => {\n setParticipants((prev) =>\n prev.map((p) => {\n if (p.enrollment_number === enrollmentNumber) {\n return { ...p, attendance: !p.attendance, modified: true };\n }\n return p;\n }),\n );\n setHasUnsavedChanges(true);\n };\n\n const handleSaveAttendance = async () => {\n try {\n setSaving(true);\n // Verify this is user's event before saving attendance\n if (!selectedEvent || selectedEvent.creator_id !== currentUserId) {\n setError(\"Unauthorized to mark attendance for this event\");\n return;\n }\n const modifiedAttendance = participants\n .filter((p) => p.modified)\n .map((p) => ({\n enrollment_number: p.enrollment_number,\n attendance: p.attendance,\n }));\n\n if (modifiedAttendance.length === 0) {\n setError(\"No attendance changes to save\");\n return;\n }\n\n await markAttendance(selectedEvent._id, modifiedAttendance);\n setSuccess(\"Attendance saved successfully\");\n setHasUnsavedChanges(false);\n\n // Clear modification flags\n setParticipants((prev) => prev.map((p) => ({ ...p, modified: false })));\n } catch (error) {\n setError(\"Failed to save attendance\");\n } finally {\n setSaving(false);\n }\n };\n\n const filteredParticipants = participants.filter((participant) => {\n const matchesSearch =\n participant.name.toLowerCase().includes(searchTerm.toLowerCase()) ||\n participant.enrollment_number\n .toLowerCase()\n .includes(searchTerm.toLowerCase());\n\n switch (filterStatus) {\n case \"present\":\n return matchesSearch && participant.attendance;\n case \"absent\":\n return matchesSearch && !participant.attendance;\n default:\n return matchesSearch;\n }\n });\n\n const stats = {\n total: participants.length,\n present: participants.filter((p) => p.attendance).length,\n absent: participants.filter((p) => !p.attendance).length,\n };\n\n return (\n
\n
\n
\n

\n Attendance Management\n

\n

Mark and track event attendance

\n
\n {hasUnsavedChanges && (\n \n \n {saving ? \"Saving...\" : \"Save Changes\"}\n \n )}\n
\n\n {/* Event Selection */}\n
\n \n {\n if (\n hasUnsavedChanges &&\n !window.confirm(\"You have unsaved changes. Continue anyway?\")\n ) {\n return;\n }\n const event = events.find((evt) => evt._id === e.target.value);\n setSelectedEvent(event);\n }}\n className=\"w-full border border-gray-300 rounded-lg px-3 py-2 focus:ring-2 \n focus:ring-indigo-500 focus:border-indigo-500\"\n >\n {events.map((event) => (\n \n ))}\n \n
\n\n {/* Attendance Stats */}\n
\n
\n
\n Total Participants\n
\n
{stats.total}
\n
\n
\n
Present
\n
{stats.present}
\n
\n
\n
Absent
\n
{stats.absent}
\n
\n
\n\n {/* Filters and Search */}\n
\n {/* Search and Filter Controls - Stack on mobile */}\n
\n
\n \n setSearchTerm(e.target.value)}\n className=\"pl-10 pr-4 py-2 w-full border border-gray-300 rounded-lg focus:ring-2 \n focus:ring-indigo-500 focus:border-indigo-500\"\n />\n
\n
\n \n setFilterStatus(e.target.value)}\n className=\"border border-gray-300 rounded-lg px-3 py-2 focus:ring-2 \n focus:ring-indigo-500 focus:border-indigo-500\"\n >\n \n \n \n \n
\n
\n\n {/* Table with horizontal scroll */}\n
\n
\n \n \n \n \n \n \n \n \n \n \n \n {filteredParticipants.map((participant) => (\n \n \n \n \n \n \n \n ))}\n \n
\n Name\n \n Enrollment\n \n Branch\n \n Year\n \n Attendance\n
\n
\n {participant.name}\n
\n 		\n {participant.enrollment_number}\n \n {participant.branch}\n \n {participant.year}\n \n \n handleAttendanceToggle(participant.enrollment_number)\n }\n className={`inline-flex items-center px-3 py-1 rounded-full text-sm font-medium \n ${\n participant.attendance\n ? \"bg-green-100 text-green-800 hover:bg-green-200\"\n : \"bg-red-100 text-red-800 hover:bg-red-200\"\n } transition-colors\n ${participant.modified ? \"ring-2 ring-indigo-500\" : \"\"}`}\n >\n {participant.attendance ? (\n <>\n \n Present\n \n ) : (\n <>\n \n Absent\n \n )}\n \n
\n
\n
\n
\n\n {apiError && (\n setApiError(\"\")}\n />\n )}\n {success && (\n setSuccess(\"\")}\n />\n )}\n
\n );\n};\n\nexport default Attendance;\n", "text_preview": "import { useState, useEffect } from \"react\"; import { getEvents, getEventParticipants, markAttendance, getCurrentUserId, } from \"../../services/api\"; import { format } from \"date-fns\"; import { Search, Filter, CheckCircle, XCircle, Save } f", "input_sha256": "1c04d44236cc136bc56bf9959ccd786e26d4ea1decb1508610781a8eda910a14", "text_sha256": "044a3b9760047d9d68640633407b09e8344a454b121db5f13195c467c003f30e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8052, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0724969792030572, "noise_severity": 0.06760204321058118, "missing_content_severity": 0.013807252307094404, "factual_density": 2.999051960645108, "pretraining_utility": 0.635032727184676, "math_density": 0.05082473657078202, "code_density": 3.7837415994385863, "reasoning_density": 0.6440216355906896, "education_level": 2.084840570672393, "creative_writing_quality": -0.131747565424388}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010989301996469636, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9965599797975319, "secondary": 0.9505386984912777, "tertiary": 0.9620057962267242, "structured_records": 0.196608593879437, "procedural_steps": 0.005309812104565884, "worked_solution": 0.004536450225919463, "contextual_dialogue": 4.343378413219567e-06, "content_quality": 0.5883645043253789, "noise_severity": 0.9436649639911824, "missing_content_severity": 0.9884939564107547, "factual_density": 0.38476849869537877, "pretraining_utility": 0.5336511248081786, "math_density": 0.9576460528576817, "code_density": 0.8197846661988221, "reasoning_density": 0.4633153036744253, "education_level": 0.8314630045590418, "creative_writing_quality": 0.998532580711601, "primary_topic": 0.9734791826460772, "secondary_topic": 0.8415838643035417}, "probability_margin": {"primary": 0.9949146781971303, "secondary": 0.9307491728823833, "tertiary": 0.9464553653039746, "education_level": 0.7510554398319397, "creative_writing_quality": 0.9979535856803785, "primary_topic": 0.9662747439327674, "secondary_topic": 0.8019134432629658}, "raw_probabilities": {"primary": {"Academic Writing": 2.9391432568672888e-05, "Content Listing": 3.6457486699844835e-05, "Creative Writing": 1.6789734414443127e-05, "Customer Support Page": 8.529210176472222e-06, "Q&A Forum": 1.0068418761569228e-05, "Discussion Thread": 6.8571786388784875e-06, "Comment Section": 1.1366961603165502e-05, "Missing Content": 2.4280850128733995e-05, "Knowledge Article": 4.9903335074529354e-05, "Legal Notices": 3.88216004793216e-05, "News Article": 4.537705016953045e-06, "Organizational Announcement": 1.1615937138170121e-05, "Nonfiction Writing": 1.0997695798995746e-05, "Organizational About Page": 1.248239117693498e-05, "Personal About Page": 1.2616696786061105e-05, "Personal Blog": 6.6802976351508305e-06, "Product Page": 9.638729277222383e-06, "Spam / Ads": 8.76540616788922e-06, "Structured Data": 0.0008771996366672358, "Technical Writing": 0.9967033139726347, "Transcript / Interview": 1.2907224479348145e-05, "Tutorial / How-To Guide": 0.0003018607915914854, "User Reviews": 6.2815315795953805e-06, "Other / Unknown": 0.0017886357755043966}, "secondary": {"Academic Writing": 0.00023128826430318577, "Content Listing": 0.001979921517839935, "Creative Writing": 0.00024327312253014774, "Customer Support Page": 0.00012184699586931153, "Q&A Forum": 7.39166615756624e-05, "Discussion Thread": 6.326365622353984e-05, "Comment Section": 0.00021892712489066294, "Missing Content": 0.0003809792878624228, "Knowledge Article": 0.00040157603518578046, "Legal Notices": 0.0002474398021003514, "News Article": 0.00012073032672451561, "Organizational Announcement": 0.00012396757025085126, "Nonfiction Writing": 0.00011620403065914433, "Organizational About Page": 0.00014295398366111857, "Personal About Page": 0.00011545431879815446, "Personal Blog": 9.891250776184683e-05, "Product Page": 0.00013063731315820468, "Spam / Ads": 0.0001111540190271281, "Structured Data": 0.019386881899878534, "Technical Writing": 0.021850413171757687, "Transcript / Interview": 0.00015263162473985392, "Tutorial / How-To Guide": 0.0010335011438622678, "User Reviews": 5.453956719858689e-05, "NONE": 0.9525995860541411}, "tertiary": {"Academic Writing": 0.000153028393477775, "Content Listing": 0.003442731940355974, "Creative Writing": 0.00017816952587355943, "Customer Support Page": 0.00010812553489440444, "Q&A Forum": 5.441629322826892e-05, "Discussion Thread": 5.2641151513089164e-05, "Comment Section": 0.00015415780542030947, "Missing Content": 0.0004077421321829533, "Knowledge Article": 0.00040870431635624623, "Legal Notices": 0.0004155114070447455, "News Article": 8.440161183334498e-05, "Organizational Announcement": 0.00010032794400017107, "Nonfiction Writing": 0.0001305727634492311, "Organizational About Page": 0.00015407726151465273, "Personal About Page": 0.00010132753408846441, "Personal Blog": 7.9280075314707e-05, "Product Page": 0.00011144509158626665, "Spam / Ads": 9.523517167959597e-05, "Structured Data": 0.017133522746636185, "Technical Writing": 0.011869889363510302, "Transcript / Interview": 0.00010087865419649786, "Tutorial / How-To Guide": 0.0010305128789168993, "User Reviews": 4.4412352315752426e-05, "NONE": 0.9635888880506107}, "structured_records": {"false": 0.803391406120563, "true": 0.196608593879437}, "procedural_steps": {"false": 0.9946901878954341, "true": 0.005309812104565884}, "worked_solution": {"false": 0.9954635497740806, "true": 0.004536450225919463}, "contextual_dialogue": {"false": 0.9999956566215867, "true": 4.343378413219567e-06}, "content_quality": {"-2": 0.003920528156382241, "-1": 0.015920868919476527, "0": 0.16712948549514434, "1": 0.5297993304226956, "2": 0.28322978700630125}, "noise_severity": {"0": 0.9448311652679122, "1": 0.045469471269914304, "2": 0.007697507961507939, "3": 0.0012698659850113756, "4": 0.0007319895156542188}, "missing_content_severity": {"0": 0.9927125461113706, "1": 0.002227101832416944, "2": 0.0037958524376570813, "3": 0.0010695528748576875, "4": 0.0001949467436975582}, "factual_density": {"0": 0.027110802439095697, "1": 0.054896759586229776, "2": 0.17848699397047213, "3": 0.3708405628988758, "4": 0.3686648811053267}, "pretraining_utility": {"-2": 0.007698643880234101, "-1": 0.05166774578334805, "0": 0.33586153831535653, "1": 0.5074463833136305, "2": 0.09732568870743086}, "math_density": {"0": 0.9594079326550102, "1": 0.0349776398724569, "2": 0.002750375328677646, "3": 0.0011098625344507064, "4": 0.0017541896094044242}, "code_density": {"0": 0.0003094265034769216, "1": 0.0005747370999512414, "2": 0.020644730032623557, "3": 0.17200702318240496, "4": 0.8064640831815433}, "reasoning_density": {"0": 0.6322481554193847, 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0.045763349336593574, "Entertainment & Media": 0.002267964053238888, "Fashion & Beauty": 0.00037212593926046444, "Business, Economics & Finance": 0.028592344137194678, "Food & Dining": 0.0004268207066743041, "Games & Gaming": 0.00045511695145296425, "Electronics & Hardware": 0.0004110149242344549, "Health, Healthcare & Wellness": 0.0004900353508259285, "History & Geography": 0.0003952463844502524, "Home, Pets & Hobbies": 0.0005525219461991214, "Industry & Production": 0.00023573897090680709, "Language, Literature & Philosophy": 0.00024177119041785837, "Politics & Government": 0.00044811229657449025, "Religion & Spirituality": 0.0004837815429833319, "Science, Mathematics & Engineering": 0.00045586497091829986, "Relationships & Community": 0.0017539111454704234, "Software Usage & Applications": 0.004187846643999103, "Programming & Software Engineering": 0.025106732041893794, "Sports & Fitness": 0.0003616143255557692, "Vehicles & Transportation": 0.00015307844787100465, "Travel & Tourism": 0.0007793833503053578, "Other / Uncovered Subject": 0.014549520184841057, "NONE": 0.8476767925995593}}, "probabilities": {"primary": {"Academic Writing": 2.9391432568672895e-05, "Content Listing": 3.645748669984484e-05, "Creative Writing": 1.678973441444313e-05, "Customer Support Page": 8.529210176472223e-06, "Q&A Forum": 1.006841876156923e-05, "Discussion Thread": 6.857178638878489e-06, "Comment Section": 1.1366961603165504e-05, "Missing Content": 2.4280850128734002e-05, "Knowledge Article": 4.990333507452937e-05, "Legal Notices": 3.882160047932161e-05, "News Article": 4.537705016953046e-06, "Organizational Announcement": 1.1615937138170125e-05, "Nonfiction Writing": 1.0997695798995748e-05, "Organizational About Page": 1.2482391176934984e-05, "Personal About Page": 1.2616696786061108e-05, "Personal Blog": 6.680297635150832e-06, "Product Page": 9.638729277222385e-06, "Spam / Ads": 8.765406167889222e-06, "Structured Data": 0.000877199636667236, "Technical Writing": 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"Structured Data": 0.019386881899878534, "Technical Writing": 0.021850413171757687, "Transcript / Interview": 0.00015263162473985392, "Tutorial / How-To Guide": 0.0010335011438622678, "User Reviews": 5.453956719858689e-05, "NONE": 0.9525995860541411}, "tertiary": {"Academic Writing": 0.000153028393477775, "Content Listing": 0.003442731940355974, "Creative Writing": 0.00017816952587355943, "Customer Support Page": 0.00010812553489440444, "Q&A Forum": 5.441629322826892e-05, "Discussion Thread": 5.2641151513089164e-05, "Comment Section": 0.00015415780542030947, "Missing Content": 0.0004077421321829533, "Knowledge Article": 0.00040870431635624623, "Legal Notices": 0.0004155114070447455, "News Article": 8.440161183334498e-05, "Organizational Announcement": 0.00010032794400017107, "Nonfiction Writing": 0.0001305727634492311, "Organizational About Page": 0.00015407726151465273, "Personal About Page": 0.00010132753408846441, "Personal Blog": 7.9280075314707e-05, "Product Page": 0.00011144509158626665, "Spam / Ads": 9.523517167959597e-05, "Structured Data": 0.017133522746636185, "Technical Writing": 0.011869889363510302, "Transcript / Interview": 0.00010087865419649786, "Tutorial / How-To Guide": 0.0010305128789168993, "User Reviews": 4.4412352315752426e-05, "NONE": 0.9635888880506107}, "structured_records": {"false": 0.803391406120563, "true": 0.196608593879437}, "procedural_steps": {"false": 0.9946901878954341, "true": 0.005309812104565884}, "worked_solution": {"false": 0.9954635497740806, "true": 0.004536450225919463}, "contextual_dialogue": {"false": 0.9999956566215867, "true": 4.343378413219567e-06}, "content_quality": {"-2": 0.003920528156382242, "-1": 0.01592086891947653, "0": 0.16712948549514436, "1": 0.5297993304226957, "2": 0.2832297870063013}, "noise_severity": {"0": 0.9448311652679122, "1": 0.045469471269914304, "2": 0.007697507961507939, "3": 0.0012698659850113756, "4": 0.0007319895156542188}, "missing_content_severity": {"0": 0.9927125461113707, "1": 0.0022271018324169444, "2": 0.0037958524376570817, "3": 0.0010695528748576877, "4": 0.00019494674369755822}, "factual_density": {"0": 0.027110802439095697, "1": 0.054896759586229776, "2": 0.17848699397047213, "3": 0.3708405628988758, "4": 0.3686648811053267}, "pretraining_utility": {"-2": 0.007698643880234101, "-1": 0.05166774578334805, "0": 0.33586153831535653, "1": 0.5074463833136305, "2": 0.09732568870743086}, "math_density": {"0": 0.9594079326550103, "1": 0.03497763987245691, "2": 0.0027503753286776465, "3": 0.0011098625344507067, "4": 0.0017541896094044245}, "code_density": {"0": 0.0003094265034769216, "1": 0.0005747370999512414, "2": 0.020644730032623557, "3": 0.17200702318240496, "4": 0.8064640831815433}, "reasoning_density": {"0": 0.6322481554193848, "1": 0.14358317047485972, "2": 0.18129023711066167, "3": 0.03365575708586903, "4": 0.009222679909224865}, "education_level": {"none": 0.0011538486452404202, "0": 0.0018958149416850197, "1": 0.025921431294084214, "2": 0.8595525037992016, "3": 0.10849706396726183, "4": 0.002979337352526951}, "creative_writing_quality": {"not_applicable": 0.9987771505930007, "-2": 7.142574742950555e-05, "-1": 0.00018938731726673514, "0": 0.0008235649126223959, "1": 0.00010581147948856204, "2": 3.2659950192179754e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.781418658366622e-05, "Art & Design": 0.0011120519705568206, "Law & Crime": 7.920944334532662e-05, "Education & Careers": 0.008224470149999323, "Entertainment & Media": 0.00010843459216189157, "Fashion & Beauty": 6.256944999851237e-05, "Business, Economics & Finance": 0.005834270051035591, "Food & Dining": 8.211882660093722e-05, "Games & Gaming": 7.300853842091511e-05, "Electronics & Hardware": 9.88087182881044e-05, "Health, Healthcare & Wellness": 7.418838965200665e-05, "History & Geography": 5.8144843586113396e-05, "Home, Pets & Hobbies": 8.886100101103602e-05, "Industry & Production": 5.0140147906409286e-05, "Language, Literature & Philosophy": 5.780743514658543e-05, "Politics & Government": 8.598653137327516e-05, "Religion & Spirituality": 7.25024113768313e-05, "Science, Mathematics & Engineering": 6.96095148928433e-05, "Relationships & Community": 0.00074873117763821, "Software Usage & Applications": 0.0008643020481421686, "Programming & Software Engineering": 0.9744992140827666, "Sports & Fitness": 9.408668835614846e-05, "Vehicles & Transportation": 3.199031055298796e-05, "Travel & Tourism": 0.0001343840762336954, "Other / Uncovered Subject": 0.0072673783427325165, "Unknown / Insufficient Content": 7.991707164141199e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000682850594344332, "Art & Design": 0.02207210614705791, "Law & Crime": 0.0010843558171763849, "Education & Careers": 0.04576334933659358, "Entertainment & Media": 0.0022679640532388885, "Fashion & Beauty": 0.0003721259392604645, "Business, Economics & Finance": 0.02859234413719468, "Food & Dining": 0.00042682070667430415, "Games & Gaming": 0.0004551169514529643, "Electronics & Hardware": 0.00041101492423445494, "Health, Healthcare & Wellness": 0.0004900353508259286, "History & Geography": 0.0003952463844502525, "Home, Pets & Hobbies": 0.0005525219461991215, "Industry & Production": 0.0002357389709068071, "Language, Literature & Philosophy": 0.0002417711904178584, "Politics & Government": 0.0004481122965744903, "Religion & Spirituality": 0.00048378154298333193, "Science, Mathematics & Engineering": 0.0004558649709182999, "Relationships & Community": 0.0017539111454704236, "Software Usage & Applications": 0.0041878466439991035, "Programming & Software Engineering": 0.025106732041893797, "Sports & Fitness": 0.00036161432555576926, "Vehicles & Transportation": 0.00015307844787100467, "Travel & Tourism": 0.0007793833503053579, "Other / Uncovered Subject": 0.014549520184841059, "NONE": 0.8476767925995594}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11489, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11489, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:32:11.809934+00:00", "scoring_key_slot": "primary", "dataset_uid": "ec926cb6-73d5-459e-977e-acda087075f6", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "52005f434efb48db795d2999dfad95b032c075f760f2b52b13ff4105400ef11f", "source_metadata": {"uuid": "ec926cb6-73d5-459e-977e-acda087075f6", "repo_name": "harshbansal7/amity-events-frontend", "relative_path": "src/components/Admin/Attendance.jsx", "category": "WEB", "algo_rel_score": 0.930065929889679, "quality_score": 7.387973308563232, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 80, "repo_created_at": "2024-12-05T17:23:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c36782a6eb4364fdab125dc215cafef11598c4b1", "size_bytes": 12176, "file_timestamp": 1742497940, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "56fe2f22b8b784ab0c2443d55dc0c68404c660d4"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 80, "repo_created_at": "2024-12-05T17:23:17.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "c36782a6eb4364fdab125dc215cafef11598c4b1", "size_bytes": 12176, "file_timestamp": 1742497940, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "56fe2f22b8b784ab0c2443d55dc0c68404c660d4", "url": "https://github.com/harshbansal7/amity-events-frontend/blob/56fe2f22b8b784ab0c2443d55dc0c68404c660d4/src/components/Admin/Attendance.jsx"}, "gpt4o_tokens": 2676, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.0724969792030572, "noise_severity": 0.06760204321058118, "missing_content_severity": 0.013807252307094404, "factual_density": 2.999051960645108, "pretraining_utility": 0.635032727184676, "math_density": 0.05082473657078202, "code_density": 3.7837415994385863, "reasoning_density": 0.6440216355906895, "education_level": 2.084840570672393, "creative_writing_quality": -0.131747565424388}, "score_applicability": {"education_level": 0.9988461513547596, "creative_writing_quality": 0.0012228494069993785}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/rawatjii/mvn-react-old/blob/b166bc927147add7b9a487d6c9cbd1e3f4b91b89/src/frontend/components/MicroPage/Amenities.jsx", "id": "javascript-0-d41df28722bc9db0866053b4064264ee", "text": "import React, { useEffect, useRef, useState } from \"react\";\nimport gsap from \"gsap\";\nimport { ScrollTrigger } from \"gsap/ScrollTrigger\";\nimport * as CONFIG from \"../../../config/config\";\nimport Watermark from \"../../../common/watermark/Index\";\nimport OtherProjects from \"../../components/homepage/OtheProjects\"; // Assuming this is used for displaying other projects\n\nimport mvnSchoolMobile from \"../../assets/images/other-projects/mvn-school.webp\";\nimport mvnUniversityMobile from \"../../assets/images/other-projects/mvn-university.webp\";\nimport mvnSportsAcademyMobile from \"../../assets/images/other-projects/mvn-sports-academy.webp\";\n\nimport mvnSchoolDesktop from \"../../assets/images/other-projects/mvn-school-desktop.webp\";\nimport mvnUniversityDesktop from \"../../assets/images/other-projects/mvn-university-desktop.webp\";\nimport mvnSportsAcademyDesktop from \"../../assets/images/other-projects/mvn-sports-academy-desktop.webp\";\n\nconst amenityData = [\n {\n name: \"MVN School\",\n Mobilethumbnail: mvnSchoolMobile,\n Desktophumbnail: mvnSchoolDesktop,\n },\n {\n name: \"MVN University\",\n Mobilethumbnail: mvnUniversityMobile,\n Desktophumbnail: mvnUniversityDesktop,\n },\n {\n name: \"MVN Sports Academy\",\n Mobilethumbnail: mvnSportsAcademyMobile,\n Desktophumbnail: mvnSportsAcademyDesktop,\n },\n];\n\ngsap.registerPlugin(ScrollTrigger);\n\nexport default function Amenities({ data }) {\n const [isMobile, setIsMobile] = useState(window.innerWidth <= 768);\n const sectionsRef = useRef([]);\n const triggers = useRef([]);\n\n // Debounced window resize listener to update isMobile\n useEffect(() => {\n const debounce = (func, delay) => {\n let timeout;\n return () => {\n clearTimeout(timeout);\n timeout = setTimeout(func, delay);\n };\n };\n\n const handleResize = debounce(() => {\n setIsMobile(window.innerWidth <= 768);\n }, 200);\n\n window.addEventListener(\"resize\", handleResize);\n return () => window.removeEventListener(\"resize\", handleResize);\n }, []);\n\n // Set up GSAP ScrollTrigger for desktop view\n useEffect(() => {\n if (!isMobile) {\n const getRatio = (el) => window.innerHeight / (window.innerHeight + el.offsetHeight);\n \n triggers.current = [];\n sectionsRef.current.forEach((section, i) => {\n const bg = section.querySelector(\".bg\");\n if (bg) {\n const imageUrl = `url(${CONFIG.IMAGE_URL}amenities/${data[i].imgSrc.desktop})`;\n bg.style.backgroundImage = imageUrl;\n \n const defaultBgPos = i === 0 ? \"50% 0\" : `50% ${-window.innerHeight * getRatio(section)}px`;\n \n const trigger = gsap.fromTo(\n bg,\n { backgroundPosition: defaultBgPos },\n {\n backgroundPosition: `50% ${window.innerHeight * (1 - getRatio(section))}px`,\n ease: \"none\",\n scrollTrigger: {\n trigger: section,\n start: i === 0 ? \"top top\" : \"top bottom\",\n end: \"bottom top\",\n scrub: 0.5, // Use a small scrub time to control the speed of animation\n invalidateOnRefresh: true,\n },\n }\n );\n \n triggers.current.push(trigger.scrollTrigger);\n }\n });\n\n ScrollTrigger.refresh();\n }\n \n // Cleanup ScrollTriggers\n return () => {\n triggers.current.forEach((trigger) => trigger.kill());\n };\n }, [isMobile, data]);\n\n // Refresh ScrollTrigger after images are loaded\n useEffect(() => {\n const images = document.querySelectorAll(\"img\");\n let loadedCount = 0;\n\n images.forEach((img) => {\n if (img.complete) {\n loadedCount++;\n } else {\n img.addEventListener(\"load\", () => {\n loadedCount++;\n if (loadedCount === images.length) {\n ScrollTrigger.refresh();\n }\n });\n }\n });\n }, []);\n\n const renderMobileView = () => (\n
\n
\n\n
\n

Amenities

\n
\n\n {/*
\n

Amenities

\n
*/}\n\n {data.map((single, index) => (\n
\n
\n \n \n \n
\n
\n {single.name}\n {Array.isArray(single.desc) ? (\n single.desc.map((desc, idx) => (\n

{desc}

\n ))\n ) : (\n

{single.desc}

\n )}\n
\n
\n ))}\n
\n
\n );\n\n const renderDesktopView = () => (\n
\n
\n

Amenities

\n
\n\n {data.map((amenity, i) => (\n (sectionsRef.current[i] = el)}\n >\n
\n \n
\n
\n {amenity.name}\n

{amenity.desc}

\n
\n \n ))}\n
\n );\n\n return (\n <>\n {isMobile ? renderMobileView() : renderDesktopView()}\n \n \n );\n}\n", "text_preview": "import React, { useEffect, useRef, useState } from \"react\"; import gsap from \"gsap\"; import { ScrollTrigger } from \"gsap/ScrollTrigger\"; import * as CONFIG from \"../../../config/config\"; import Watermark from \"../../../common/watermark/Inde", "input_sha256": "589bfae66ef01d265bb76e9076db6f19809d3c8fc2f081de28ba2f91b4615bf9", "text_sha256": "d41df28722bc9db0866053b4064264ee39d02de0c46bd4b56608bc569e1997b1", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8056, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6330355022123368, "noise_severity": 0.22444585373613968, "missing_content_severity": 0.08058769092148602, "factual_density": 2.3781797889976977, "pretraining_utility": 0.382742925900436, "math_density": 0.31336816483414953, "code_density": 3.885059356155781, "reasoning_density": 0.1855061205633537, "education_level": 2.0725426454572258, "creative_writing_quality": -0.2397698410424537}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", 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"gpt4o_tokens": 1523, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6330355022123368, "noise_severity": 0.22444585373613968, "missing_content_severity": 0.08058769092148602, "factual_density": 2.3781797889976977, "pretraining_utility": 0.382742925900436, "math_density": 0.31336816483414953, "code_density": 3.885059356155781, "reasoning_density": 0.1855061205633537, "education_level": 2.0725426454572258, "creative_writing_quality": -0.2397698410424537}, "score_applicability": {"education_level": 0.9979807184497299, "creative_writing_quality": 0.002019323765797882}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Xr7TSi/Ticketing-App-Back-End/blob/fbe7a23bdf7bc1d9709afb67d79f4b79ed241485/src/routers/ticket.router.js", "id": "javascript-0-f8659a58503027fd1575a025911d076c", "text": "const express = require(\"express\");\nconst router = express.Router();\nconst {\n insertTicket,\n getAllTickets,\n getAllOpenTickets,\n getAllClosedTickets,\n getTicketsByStatus,\n getTicketsByUserId,\n getTicketById,\n addClientMessage,\n updateStatusClosed,\n deleteTicket,\n} = require(\"../models/ticket/Ticket.model\");\nconst { userAuthorization } = require(\"../middleware/authorization.middleware\");\n\n// consider removing router.all. may not serve a purpose\nrouter.all(\"/\", (req, res, next) => {\n // res.json({ message: \"Ticket Route has been hit\" });\n // console.log(\"Ticket Route has been hit\");\n next();\n});\n\n// create new ticket\nrouter.post(\"/\", userAuthorization, async (req, res) => {\n try {\n const { subject, sender, message } = req.body;\n\n // req.userId is a property of the request header\n const userId = req.userId;\n\n const ticketObj = {\n clientId: userId,\n subject,\n conversations: [\n {\n sender,\n message,\n },\n ],\n };\n const result = await insertTicket(ticketObj);\n if (result._id) {\n res.json({ status: \"success\", message: \"Ticket has been created\" });\n }\n } catch (error) {\n console.log(\"Error at router.post / \" + error);\n }\n});\n\n// get all tickets \n// this is the V1/ticket/allTickets route\nrouter.get(\"/all-tickets\", userAuthorization, async (req, res) => {\n try {\n const result = await getAllTickets();\n\n return res.json({ status: \"success\", result });\n } catch (error) {\n console.log(\"Error at router.get / \" + error);\n }\n});\n\n// get all open tickets \n// this is the V1/ticket/all-open-tickets route\nrouter.get(\"/all-open-tickets\", userAuthorization, async (req, res) => {\n try {\n \n const result = await getAllOpenTickets();\n\n if(result.length) {\n return res.json({ status: \"success\", result });\n } else {\n return res.json({ status: \"failure\", message: \"No open tickets found\" });\n }\n \n \n } catch (error) {\n console.log(\"Error at router.get / \" + error);\n }\n});\n\n// get all closed tickets \n// this is the V1/ticket/all-closed-tickets route\nrouter.get(\"/all-closed-tickets\", userAuthorization, async (req, res) => {\n try {\n \n const result = await getAllClosedTickets();\n\n if(result.length) {\n return res.json({ status: \"success\", result });\n } else {\n return res.json({ status: \"failure\", message: \"No closed tickets found\" });\n }\n \n \n } catch (error) {\n console.log(\"Error at router.get / \" + error);\n }\n});\n\n// get tickets by status \n// this is the V1/ticket/tickets-by-status route\nrouter.get(\"/tickets-by-status\", userAuthorization, async (req, res) => {\n try {\n\n // req.status is a property of the request header\n const status = req.body.status;\n \n const result = await getTicketsByStatus(status);\n\n if(result.length) {\n return res.json({ status: \"success\", result });\n } else {\n return res.json({ status: \"failure\", message: \"No tickets found\" });\n }\n \n \n } catch (error) {\n console.log(\"Error at router.get / \" + error);\n }\n});\n\n// get all tickets for a specific user\n// this is the V1/ticket route\nrouter.get(\"/\", userAuthorization, async (req, res) => {\n try {\n // req.userId is a property of the request header\n const userId = req.userId;\n\n const result = await getTicketsByUserId(userId);\n\n return res.json({ status: \"success\", result });\n } catch (error) {\n console.log(\"Error at router.get / \" + error);\n }\n});\n\n// get ticket by ticket id (from request parameter) and user id (from request header)\n// this is the V1/ticket/:ticketId route\nrouter.get(\"/:_Id\", userAuthorization, async (req, res) => {\n try {\n // _id is an individual ticket id in mongodb\n // req.userId is a property of the request header\n const _id = req.params._Id;\n const clientId = req.userId;\n\n const result = await getTicketById(_id, clientId);\n\n return res.json({ status: \"success\", result });\n } catch (error) {\n console.log(\"Error at router.get /:_Id \" + error);\n }\n});\n\n// add a new message to a ticket\n// this is the V1/ticket/:ticketId route\nrouter.put(\"/:_Id\", userAuthorization, async (req, res) => {\n try {\n // _id is an individual ticket id in mongodb\n const _id = req.params._Id;\n const clientId = req.userId;\n const { message, sender } = req.body;\n\n // the clientId parameter ensure only the client can make this change\n const result = await addClientMessage(_id, message, sender, clientId);\n\n if (result._id) {\n return res.json({\n status: \"success\",\n message: \"Ticket has been updated.\",\n });\n }\n res.json({ status: \"error\", message: \"Ticket has not been updated.\" });\n } catch (error) {\n console.log(\"Error at router.get /:_Id \" + error);\n }\n});\n\n// update ticket status to \"closed\"\n// this is the V1/ticket/close-ticket/:ticketId route\nrouter.patch(\"/close-ticket/:_Id\", userAuthorization, async (req, res) => {\n try {\n // _id is an individual ticket id in mongodb\n\n const _id = req.params._Id;\n\n const clientId = req.userId;\n\n // the clientId parameter ensure only the client can make this change\n const result = await updateStatusClosed(_id, clientId);\n\n if (result._id) {\n return res.json({\n status: \"success\",\n message: \"Ticket status is now closed.\",\n });\n }\n res.json({\n status: \"error\",\n message: \"Ticket status has not been updated.\",\n });\n } catch (error) {\n console.log(\"Error at router.patch /close-ticket/:_Id \" + error);\n }\n});\n\n// delete a ticket by id\n// this is the V1/ticket/:ticketId route\nrouter.delete(\"/:_Id\", userAuthorization, async (req, res) => {\n try {\n // _id is an individual ticket id in mongodb\n const _id = req.params._Id;\n\n const clientId = req.userId;\n\n // the clientId parameter ensure only the client can make this change\n const result = await deleteTicket(_id, clientId);\n\n return res.json({\n status: \"success\",\n message: \"Ticket has been deleted.\",\n });\n \n } catch (error) {\n console.log(\"Error at router.delete /:_Id \" + error);\n }\n});\n\nmodule.exports = router;\n", "text_preview": "const express = require(\"express\"); const router = express.Router(); const { insertTicket, getAllTickets, getAllOpenTickets, getAllClosedTickets, getTicketsByStatus, getTicketsByUserId, getTicketById, addClientMessage, updateStatusClosed, d", "input_sha256": "c9c119c8a5dc2ab9cb3e74bdc8aeb55e5b1b2051d6ea32d5b431f5cf3b6df814", "text_sha256": "f8659a58503027fd1575a025911d076c117e23771c547e9cd40a2620d21f4988", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8057, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": 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0.00035095729670098946, "Electronics & Hardware": 0.00021212516267390478, "Health, Healthcare & Wellness": 0.0003100863865947322, "History & Geography": 9.722329481002871e-05, "Home, Pets & Hobbies": 9.677745922534873e-05, "Industry & Production": 8.423561210325442e-05, "Language, Literature & Philosophy": 9.22019500843626e-05, "Politics & Government": 0.0001283346297172489, "Religion & Spirituality": 0.0001390124290154826, "Science, Mathematics & Engineering": 0.00022950191304111918, "Relationships & Community": 0.00012057153043898157, "Software Usage & Applications": 0.006465742315532913, "Programming & Software Engineering": 0.01417898389116996, "Sports & Fitness": 9.923331632304747e-05, "Vehicles & Transportation": 6.135657064138462e-05, "Travel & Tourism": 0.00017937722340094194, "Other / Uncovered Subject": 0.002239730976491578, "NONE": 0.9702814367590546}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10180, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10180, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:02.706340+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "52c4a27a-8466-4e90-bbb9-ff929a1efbea", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "3ebb61eebdf995492f81f586683ee1ca59d56dc6d50f0d4a3371b5eb6fcf3a07", "source_metadata": {"uuid": "52c4a27a-8466-4e90-bbb9-ff929a1efbea", "repo_name": "Xr7TSi/Ticketing-App-Back-End", "relative_path": "src/routers/ticket.router.js", "category": "WEB", "algo_rel_score": 0.9336709976196289, "quality_score": 7.037458896636963, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2022-02-13T20:49:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fff9616687d1b514d7df3dd3400fbae498530fa4", "size_bytes": 6138, "file_timestamp": 1645047163, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "fbe7a23bdf7bc1d9709afb67d79f4b79ed241485"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 7, "repo_created_at": "2022-02-13T20:49:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fff9616687d1b514d7df3dd3400fbae498530fa4", "size_bytes": 6138, "file_timestamp": 1645047163, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "fbe7a23bdf7bc1d9709afb67d79f4b79ed241485", "url": "https://github.com/Xr7TSi/Ticketing-App-Back-End/blob/fbe7a23bdf7bc1d9709afb67d79f4b79ed241485/src/routers/ticket.router.js"}, "gpt4o_tokens": 1540, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.48925299249889626, "noise_severity": 0.09806475796892693, "missing_content_severity": 0.019822241743903936, "factual_density": 3.2295928876292024, "pretraining_utility": 0.5499066451807576, "math_density": 0.0037710295967153494, "code_density": 3.81726397349595, "reasoning_density": 0.3542189434607283, "education_level": 2.1177464168270945, "creative_writing_quality": -0.3813925937874289}, "score_applicability": {"education_level": 0.9990135838117193, "creative_writing_quality": 0.0006093881951578193}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/arifitantotito/Multi-Warehouse-E-Commerce/blob/839fabb3f8d706412a6a3485a03d1343284f5a09/projects/client/src/pages/login/login.jsx", "id": "javascript-0-90036dd60db1f4c88996c06406702944", "text": "import { useContext, useRef, useState, useEffect } from \"react\";\nimport { Link, useLocation, useNavigate } from \"react-router-dom\";\nimport toast, { Toaster } from \"react-hot-toast\";\nimport { userData } from \"../../data/userData\";\nimport { LoginAccount } from \"../../utils/login\";\nimport { AiOutlineEye, AiOutlineEyeInvisible } from \"react-icons/ai\";\nimport { Spinner } from \"flowbite-react\";\nimport Loading from \"../../components/loading/loading\";\n\nexport default function Login(props) {\n\n const [disable, setDisable] = useState(false)\n const [visiblePassword, setVisiblePassword] = useState(false)\n const [typePassword, setTypePassword] = useState('password')\n\n let navigate = useNavigate()\n\n const { user, setUser } = useContext(userData)\n\n let email = useRef()\n let password = useRef()\n // console.log(props.data.conditionPage)\n\n let confirmation = (data) => {\n if (data.id == undefined) {\n toast.error(data.response)\n setDisable(false)\n } else {\n \n localStorage.setItem('token', data.id)\n setDisable(false)\n toast.success(data.response, {\n style: {\n background: \"black\",\n color: 'white'\n }\n })\n\n setTimeout(() => {\n toast('Redirecting...', {\n duration: 2500\n })\n }, 2000)\n\n setTimeout(() => {\n if (props.data.conditionPage === true) {\n navigate('/')\n props.data.setConditionPage(false)\n setUser(data)\n } else {\n navigate(-1)\n setUser(data)\n }\n }, 3000)\n\n }\n\n }\n\n let changeVisiblePassword = () => {\n // console.log('masuk')\n if (typePassword === 'password') {\n setVisiblePassword(true)\n setTypePassword('')\n } else if (typePassword === 'text') {\n setVisiblePassword(false)\n setTypePassword('')\n }\n }\n\n\n return (\n user == null ?\n \n :\n user.id != null ?\n navigate('/my-account')\n :\n <>\n {/* Main */}\n
\n {/* Card */}\n
\n\n
\n

Login

\n
\n\n
\n
\n Email\n
\n \n
\n\n
\n
\n Password\n
\n
\n \n \n
\n
\n\n {\n !disable ?\n \n :\n \n }\n\n\n
\n Don't have an account?\n \n Register here\n \n
\n\n
\n \n Forgot Password\n \n
\n\n
\n
\n \n \n )\n}", "text_preview": "import { useContext, useRef, useState, useEffect } from \"react\"; import { Link, useLocation, useNavigate } from \"react-router-dom\"; import toast, { Toaster } from \"react-hot-toast\"; import { userData } from \"../../data/userData\"; import { L", "input_sha256": "faa044732eb8ff868d223b7e338e06d9c6a5a84a8e394ad1431468273365fc63", "text_sha256": "90036dd60db1f4c88996c0640670294454b6bf07a9d83b9b788c09201fce5cc0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8058, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.20018117097528432, "noise_severity": 0.1471761569694267, "missing_content_severity": 0.056960837526316234, "factual_density": 2.8007811632088204, "pretraining_utility": 0.2868558070515536, "math_density": 0.011237208816087047, "code_density": 3.849188606290466, "reasoning_density": 0.3082638066311029, "education_level": 2.0680728925569176, "creative_writing_quality": -0.47349461252072955}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", 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null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:03.291344+00:00", "scoring_key_slot": "primary", "dataset_uid": "fce9bee8-2a09-496b-9dca-84e0c2088b96", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "0d83f88e4e87961ed5f04c10d87ab89fdc6ad42bdf6bed067550f3354711c5db", "source_metadata": {"uuid": "fce9bee8-2a09-496b-9dca-84e0c2088b96", "repo_name": "arifitantotito/Multi-Warehouse-E-Commerce", "relative_path": "projects/client/src/pages/login/login.jsx", "category": "WEB", "algo_rel_score": 0.9359917640686035, "quality_score": 6.424115180969238, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2023-04-17T12:38:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4d066378b8ac658c1d8e5706962bb414d8412563", "size_bytes": 6058, "file_timestamp": 1681735279, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "839fabb3f8d706412a6a3485a03d1343284f5a09"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2023-04-17T12:38:58.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4d066378b8ac658c1d8e5706962bb414d8412563", "size_bytes": 6058, "file_timestamp": 1681735279, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "839fabb3f8d706412a6a3485a03d1343284f5a09", "url": "https://github.com/arifitantotito/Multi-Warehouse-E-Commerce/blob/839fabb3f8d706412a6a3485a03d1343284f5a09/projects/client/src/pages/login/login.jsx"}, "gpt4o_tokens": 1025, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.20018117097528432, "noise_severity": 0.1471761569694267, "missing_content_severity": 0.056960837526316234, "factual_density": 2.8007811632088204, "pretraining_utility": 0.2868558070515536, "math_density": 0.011237208816087047, "code_density": 3.849188606290466, "reasoning_density": 0.308263806631103, "education_level": 2.0680728925569176, "creative_writing_quality": -0.47349461252072955}, "score_applicability": {"education_level": 0.9977386765931794, "creative_writing_quality": 0.0007014692074891745}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/ProyectoFinal2020/TweetAnalyzer-Frontend/blob/dd35433069468316a62b5e0935372f0f043b8b41/src/components/dataUploader/TweetFetcher.jsx", "id": "javascript-0-f93095b6ff4ff9a183eb036d1122d3e1", "text": "import {\n Box,\n Button,\n Card,\n CardActions,\n CardContent,\n CardHeader,\n Grid,\n IconButton,\n Tooltip,\n Typography,\n} from \"@material-ui/core\";\nimport { AddCircle } from \"@material-ui/icons\";\nimport CloudDownloadIcon from \"@material-ui/icons/CloudDownload\";\nimport React, { useEffect, useState } from \"react\";\nimport { TextValidator, ValidatorForm } from \"react-material-ui-form-validator\";\nimport { get, post } from \"../../utils/api/api\";\nimport { downloadFile } from \"../../utils/fileDownloader/downloadFile.js\";\nimport { DatePicker } from \"../shared/datePicker/DatePicker\";\nimport { LanguageButtons } from \"./LanguageButtons\";\nimport { TagChips } from \"./TagChips\";\nimport \"./TweetFetcher.scss\";\n\nconst TweetFetcher = () => {\n const substractDays = (date, days) => {\n return new Date(date - days * 24 * 60 * 60 * 1000);\n };\n\n const [tag, setTag] = useState(\"\");\n const [tags, setTags] = useState([]);\n const [initialDate, setInitialDate] = useState(substractDays(new Date(), 1));\n const [endDate, setEndDate] = useState(new Date());\n const [language, setLanguage] = useState(\"en\");\n const [title, setTitle] = useState(\"\");\n const [tweetAmount, setTweetAmount] = useState(\"\");\n const [queryResult, setQueryResult] = useState(undefined);\n const [availableSpace, setAvailableSpace] = useState(0);\n const [loadingQuery, setLoadingQuery] = useState(false);\n const [topicsWithLanguage, setTopicsWithLanguage] = useState({});\n const [contentErrors, setContentErrors] = useState({\n endDateGreaterThanInitial: false,\n });\n\n useEffect(() => {\n get(\"/user/availableSpace\").then((response) => {\n setAvailableSpace(response.data);\n });\n }, [queryResult]);\n\n useEffect(() => {\n get(\"/user/info\").then((response) => {\n let aux = {};\n response.data.additionalInformation.forEach(\n (item) => (aux[item.topic] = item.language)\n );\n setTopicsWithLanguage(aux);\n });\n }, []);\n\n useEffect(() => {\n if (endDate < initialDate) {\n return setContentErrors({ endDateGreaterThanInitial: true });\n }\n return setContentErrors({ endDateGreaterThanInitial: false });\n }, [initialDate, endDate]);\n\n useEffect(() => {\n ValidatorForm.addValidationRule(\"titleMaxLength\", (value) => {\n return value.length < 30;\n });\n\n ValidatorForm.addValidationRule(\"dateCantBeMoreThan7DaysAgo\", (value) => {\n if (value < substractDays(new Date(), 7)) {\n return false;\n }\n return true;\n });\n\n ValidatorForm.addValidationRule(\"endDateNotGreaterThanToday\", (value) => {\n if (value > new Date()) {\n return false;\n }\n return true;\n });\n\n ValidatorForm.addValidationRule(\"tagsNotEmpty\", (value) => {\n if (tags && tags.length > 0) {\n return true;\n }\n if (document.activeElement === document.getElementById(\"tag-field\")) {\n return true;\n }\n return false;\n });\n\n ValidatorForm.addValidationRule(\n \"maxTweetsNotGreaterThanAvailableSpace\",\n (value) => {\n return availableSpace - value >= 0;\n }\n );\n\n return () => {\n ValidatorForm.removeValidationRule(\"titleMaxLength\");\n ValidatorForm.removeValidationRule(\"dateCantBeMoreThan7DaysAgo\");\n ValidatorForm.removeValidationRule(\"endDateNotGreaterThanToday\");\n ValidatorForm.removeValidationRule(\"tagsNotEmpty\");\n ValidatorForm.removeValidationRule(\n \"maxTweetsNotGreaterThanAvailableSpace\"\n );\n };\n }, [tags, endDate, initialDate, availableSpace]);\n\n const handleAddTag = (e) => {\n if (tag && tag !== \"\") {\n setTags([...tags, tag]);\n setTag(\"\");\n }\n };\n\n const pad = (n) => {\n return n < 10 ? \"0\" + n : n;\n };\n\n const getDateAsString = (date) => {\n return (\n date.getFullYear() +\n \"-\" +\n pad(date.getMonth() + 1) +\n \"-\" +\n pad(date.getDate())\n );\n };\n\n const createPayload = () => {\n return {\n topic_title: title,\n since: getDateAsString(initialDate),\n until: getDateAsString(endDate),\n tags: tags,\n maxAmount: parseInt(tweetAmount),\n language: language,\n };\n };\n\n const handleSubmit = (e) => {\n e.preventDefault();\n const payload = createPayload();\n setQueryResult(undefined);\n setLoadingQuery(true);\n post(\"/tweetRetrieval\", payload)\n .then((response) => {\n setQueryResult(response.data);\n setLoadingQuery(false);\n post(\"/emotionAnalyzer\", {\n topicTitle: title,\n });\n })\n .catch((error) => setLoadingQuery(false));\n };\n\n const handleEnterPress = (e) => {\n if (e.key === \"Enter\") {\n e.preventDefault();\n document.getElementById(\"add-tag-btn\").click();\n return false;\n }\n };\n\n return (\n <>\n \n Búsqueda de tweets\n \n\n \n \n \n \n Espacio disponible: {availableSpace}\n \n \n }\n classes={{ action: \"card-action\" }}\n />\n \n \n \n setTitle(e.target.value)}\n validators={[\"required\", \"titleMaxLength\"]}\n errorMessages={[\n \"El título es requerido\",\n \"No debe superar los 30 caracteres\",\n ]}\n />\n \n \n setTweetAmount(e.target.value)}\n type=\"number\"\n variant=\"outlined\"\n validators={[\n \"required\",\n \"minNumber:0\",\n \"maxTweetsNotGreaterThanAvailableSpace\",\n ]}\n errorMessages={[\n \"La cantidad máxima es requerida\",\n \"Debe ser un entero positivo\",\n \"No debe superar el espacio disponible\",\n ]}\n />\n \n \n setTag(e.target.value)}\n onKeyPress={handleEnterPress}\n />\n \n \n \n \n \n \n \n \n {tags && tags.length > 0 ? (\n \n \n \n ) : null}\n \n setInitialDate(date)}\n isValid={!contentErrors.endDateGreaterThanInitial}\n />\n \n {contentErrors.endDateGreaterThanInitial\n ? \"La fecha inicial debe ser anterior a la fecha final\"\n : null}\n \n \n \n setEndDate(date)}\n isValid={!contentErrors.endDateGreaterThanInitial}\n />\n \n {contentErrors.endDateGreaterThanInitial\n ? \"La fecha final debe ser posterior a la fecha inicial\"\n : null}\n \n \n \n \n \n \n \n \n downloadFile(queryResult, title + \"-tweets\")}\n disabled={!queryResult || loadingQuery}\n startIcon={}\n >\n Descargar\n \n \n Buscar\n \n \n \n \n \n );\n};\n\nexport default TweetFetcher;\n", "text_preview": "import { Box, Button, Card, CardActions, CardContent, CardHeader, Grid, IconButton, Tooltip, Typography, } from \"@material-ui/core\"; import { AddCircle } from \"@material-ui/icons\"; import CloudDownloadIcon from \"@material-ui/icons/CloudDown", "input_sha256": "8682fa210694731b9c4b4dc01cec2ff9db84a43f103d3b5482b2fd219b3d0331", "text_sha256": "f93095b6ff4ff9a183eb036d1122d3e1eb9d339b282c08a001df1beed1618f58", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8054, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.794616163735846, "noise_severity": 0.06754617428835338, "missing_content_severity": 0.03577878465622682, "factual_density": 2.864523881247153, "pretraining_utility": 0.36571653514409985, "math_density": 0.4823741860301175, "code_density": 3.8785719279191895, "reasoning_density": 0.368748989542404, "education_level": 2.14955105038065, "creative_writing_quality": -0.2848488545263661}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.011471535250353937, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953853128062308, "secondary": 0.9706820910185291, "tertiary": 0.9826523886710723, "structured_records": 0.00594016808951856, "procedural_steps": 0.004207952456705423, "worked_solution": 0.004675010319984821, "contextual_dialogue": 7.086636441579776e-06, "content_quality": 0.5244731242681981, "noise_severity": 0.9437115214263722, "missing_content_severity": 0.970184346119811, "factual_density": 0.3138249364708392, "pretraining_utility": 0.4723750781924072, "math_density": 0.5980215116415688, "code_density": 0.8988099399326585, "reasoning_density": 0.6927091753813299, "education_level": 0.7850787068775743, "creative_writing_quality": 0.9990458973193184, "primary_topic": 0.9860824797764614, "secondary_topic": 0.8353325895411878}, "probability_margin": {"primary": 0.9926391401922113, "secondary": 0.9541250964528066, "tertiary": 0.9746505463338754, "education_level": 0.6670266772685814, "creative_writing_quality": 0.9987717257289609, "primary_topic": 0.9818504861195451, "secondary_topic": 0.8055874778224096}, "raw_probabilities": {"primary": {"Academic Writing": 3.6421068163567146e-05, "Content Listing": 3.833285117647647e-05, "Creative Writing": 2.4120793456671283e-05, "Customer Support Page": 1.835820128912842e-05, "Q&A Forum": 1.4366781374776396e-05, "Discussion Thread": 9.866641953806963e-06, "Comment Section": 1.5696859763596048e-05, "Missing Content": 5.898246877051864e-05, "Knowledge Article": 9.517885374692567e-05, "Legal Notices": 5.790638589198823e-05, "News Article": 6.703566237694766e-06, "Organizational Announcement": 1.5347813745866616e-05, "Nonfiction Writing": 1.4441778311720511e-05, "Organizational About Page": 1.7559190550836275e-05, "Personal About Page": 1.642457689086003e-05, "Personal Blog": 8.925924150460003e-06, "Product Page": 1.600629993288958e-05, "Spam / Ads": 1.3121428754909193e-05, "Structured Data": 0.0004927758664579371, "Technical Writing": 0.9955775914393045, "Transcript / Interview": 1.7503340385691375e-05, "Tutorial / How-To Guide": 0.00048687324406314987, "User Reviews": 9.04337853294647e-06, "Other / Unknown": 0.0029384512470931328}, "secondary": {"Academic Writing": 0.0001977729665786772, "Content Listing": 0.0004596298595563761, "Creative Writing": 0.00019910876825018358, "Customer Support Page": 0.00014518130616474127, "Q&A Forum": 6.574331466635383e-05, "Discussion Thread": 4.9398231080468424e-05, "Comment Section": 0.00013933384736689332, "Missing Content": 0.0003456052440350549, "Knowledge Article": 0.00043310509960365967, "Legal Notices": 0.0003342928197539864, "News Article": 9.190317843934035e-05, "Organizational Announcement": 0.00010031835912932858, "Nonfiction Writing": 8.785903287937606e-05, "Organizational About Page": 0.000109368301117105, "Personal About Page": 9.148719214142885e-05, "Personal Blog": 8.762289848786746e-05, "Product Page": 9.237750979838264e-05, "Spam / Ads": 9.421822551431622e-05, "Structured Data": 0.005372590243661741, "Technical Writing": 0.017778574106617134, "Transcript / Interview": 0.00011564970983200371, "Tutorial / How-To Guide": 0.001658097721229279, "User Reviews": 4.709150467260463e-05, "NONE": 0.9719036705594237}, "tertiary": {"Academic Writing": 0.0001046357164358529, "Content Listing": 0.0005891130803688902, "Creative Writing": 0.00015098145122519022, "Customer Support Page": 0.0001367935911298917, "Q&A Forum": 3.910847945524508e-05, "Discussion Thread": 3.238907783181353e-05, "Comment Section": 0.00010173628084705087, "Missing Content": 0.0004104120922925023, "Knowledge Article": 0.00036217030289829974, "Legal Notices": 0.0003521682172002501, "News Article": 6.46226331870395e-05, "Organizational Announcement": 6.67531864275703e-05, "Nonfiction Writing": 7.456983126534242e-05, "Organizational About Page": 8.710359418052857e-05, "Personal About Page": 6.107032851330001e-05, "Personal Blog": 5.58337672060102e-05, "Product Page": 7.631683951935347e-05, "Spam / Ads": 6.578944750677086e-05, "Structured Data": 0.003943553582320484, "Technical Writing": 0.008724659475902272, "Transcript / Interview": 7.657663727372814e-05, "Tutorial / How-To Guide": 0.001015291080702823, "User Reviews": 3.31454965321759e-05, "NONE": 0.9833752058097777}, "structured_records": {"false": 0.9940598319104814, "true": 0.00594016808951856}, "procedural_steps": {"false": 0.9957920475432945, "true": 0.004207952456705423}, "worked_solution": {"false": 0.9953249896800151, "true": 0.004675010319984821}, "contextual_dialogue": {"false": 0.9999929133635584, "true": 7.086636441579776e-06}, "content_quality": {"-2": 0.008310624897214859, "-1": 0.04946879575164334, "0": 0.26413857737622687, "1": 0.49545779466791073, "2": 0.1826242073070041}, "noise_severity": {"0": 0.9484716990653209, "1": 0.03990359602139162, "2": 0.00844348741126084, "3": 0.0019692665636666675, "4": 0.0012119509383600217}, "missing_content_severity": {"0": 0.9821665034117905, "1": 0.0046201617643501685, "2": 0.009034422949094912, "3": 0.0036258705053701474, "4": 0.0005530413693940931}, "factual_density": {"0": 0.0493367034343779, "1": 0.07218384706141596, "2": 0.1870652930679543, "3": 0.34744717769517874, "4": 0.3439669787410731}, "pretraining_utility": {"-2": 0.010483394226288582, "-1": 0.11274989705992866, "0": 0.42814323528351567, "1": 0.39781372620392835, "2": 0.05080974722633863}, "math_density": {"0": 0.5961946310567159, "1": 0.3557848173029581, "2": 0.02996058446552657, "3": 0.005571668903091285, "4": 0.012488298271708094}, "code_density": {"0": 0.00025112257138196735, "1": 0.000490830835738359, "2": 0.010883842898442544, "3": 0.09718340349118178, "4": 0.8911908002032555}, "reasoning_density": {"0": 0.7846947736079868, "1": 0.09326024201462814, "2": 0.09709616557628155, "3": 0.018498858829201218, "4": 0.006449959971902267}, "education_level": {"none": 0.0014329071179954845, "0": 0.0016137533095183178, "1": 0.01522410832817524, "2": 0.8208989223979786, "3": 0.15387224512939712, "4": 0.006958063716935326}, "creative_writing_quality": {"not_applicable": 0.9992049144327653, "-2": 8.382431377998132e-05, "-1": 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2.5742380741655602e-05, "Travel & Tourism": 7.887523143152861e-05, "Other / Uncovered Subject": 0.004767282896283112, "Unknown / Insufficient Content": 0.00011924169818465016}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013467623270334067, "Art & Design": 0.02146391259532905, "Law & Crime": 0.0008418448863315329, "Education & Careers": 0.0004341015534106383, "Entertainment & Media": 0.02398330212009671, "Fashion & Beauty": 0.00039465601712843665, "Business, Economics & Finance": 0.0017456986098508206, "Food & Dining": 0.0003066377469348076, "Games & Gaming": 0.0008233523394967796, "Electronics & Hardware": 0.0005442234259850291, "Health, Healthcare & Wellness": 0.000689528118887935, "History & Geography": 0.00038744515225302723, "Home, Pets & Hobbies": 0.0003964520046491439, "Industry & Production": 0.00024028767934241077, "Language, Literature & Philosophy": 0.0006719608616863658, "Politics & Government": 0.0008885046557863445, "Religion & Spirituality": 0.0004944702527529401, "Science, Mathematics & Engineering": 0.004173678959836705, "Relationships & Community": 0.003617473416931807, "Software Usage & Applications": 0.030612804206747793, "Programming & Software Engineering": 0.0271114334546251, "Sports & Fitness": 0.00032381054055420313, "Vehicles & Transportation": 0.00016465750749297567, "Travel & Tourism": 0.0005985764254429274, "Other / Uncovered Subject": 0.036078473659501786, "NONE": 0.8416659514819114}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11234, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11234, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:06.467530+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "b489cf5f-e63d-44f4-8ad9-46e09be820c0", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "d9ed327cb83b57b29bbbb7027639ff8522e126c93f183e8a7a6327d90c200d8c", "source_metadata": {"uuid": "b489cf5f-e63d-44f4-8ad9-46e09be820c0", "repo_name": "ProyectoFinal2020/TweetAnalyzer-Frontend", "relative_path": "src/components/dataUploader/TweetFetcher.jsx", "category": "WEB", "algo_rel_score": 0.9673731327056885, "quality_score": 5.720052242279053, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 66, "repo_created_at": "2020-09-22T23:01:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a58bb79fe456bad4c6974fe1e5df47926eac9593", "size_bytes": 11926, "file_timestamp": 1616018728, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dd35433069468316a62b5e0935372f0f043b8b41"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 66, "repo_created_at": "2020-09-22T23:01:48.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a58bb79fe456bad4c6974fe1e5df47926eac9593", "size_bytes": 11926, "file_timestamp": 1616018728, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "dd35433069468316a62b5e0935372f0f043b8b41", "url": "https://github.com/ProyectoFinal2020/TweetAnalyzer-Frontend/blob/dd35433069468316a62b5e0935372f0f043b8b41/src/components/dataUploader/TweetFetcher.jsx"}, "gpt4o_tokens": 2478, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.794616163735846, "noise_severity": 0.06754617428835338, "missing_content_severity": 0.03577878465622682, "factual_density": 2.864523881247153, "pretraining_utility": 0.36571653514409985, "math_density": 0.4823741860301175, "code_density": 3.87857192791919, "reasoning_density": 0.368748989542404, "education_level": 2.14955105038065, "creative_writing_quality": -0.2848488545263661}, "score_applicability": {"education_level": 0.9985670928820046, "creative_writing_quality": 0.0007950855672348052}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/pillirajesh/nxtTrendzSpecificProductDetails/blob/cc259ab5867e9df403e5a8fe17726d0e9a54d94d/src/components/ProductItemDetails/index.js", "id": "javascript-0-012fd873246f588baa1cb9e2a7dc1fec", "text": "// Write your code here\nimport {Component} from 'react'\nimport {BsPlusSquare, BsDashSquare} from 'react-icons/bs'\nimport Loader from 'react-loader-spinner'\nimport Cookies from 'js-cookie'\nimport SimilarProductItem from '../SimilarProductItem'\nimport Header from '../Header'\n\nimport './index.css'\n\nconst status = {\n inProgress: 'IN_PROGRESS',\n success: 'SUCCESS',\n failure: 'FAILURE',\n}\nclass ProductItemDetails extends Component {\n state = {\n productDetails: {},\n count: 1,\n similarProducts: [],\n isLoading: true,\n isTrue: status.success,\n }\n\n componentDidMount() {\n this.getProductDetails()\n }\n\n decreaseCount = () => {\n const {count} = this.state\n if (count > 1) {\n this.setState(prevState => ({count: prevState.count - 1}))\n }\n }\n\n increaseCount = () => {\n this.setState(prevState => ({count: prevState.count + 1}))\n }\n\n goBackToProducts = () => {\n console.log(this.props)\n const {history} = this.props\n history.replace('/products')\n }\n\n getBadCredential = () => (\n
\n \n

Product Not Found

\n \n Continue Shopping\n \n
\n )\n\n getProductDetails = async () => {\n const {match} = this.props\n const {params} = match\n const {id} = params\n\n const apiUrl = `https://apis.ccbp.in/products/${id}\n `\n const jwtToken = Cookies.get('jwt_token')\n const options = {\n method: 'GET',\n headers: {\n Authorization: `Bearer ${jwtToken}`,\n },\n }\n const response = await fetch(apiUrl, options)\n if (response.ok) {\n const fetchedData = await response.json()\n const updatedData = {\n id: fetchedData.id,\n availability: fetchedData.availability,\n brand: fetchedData.brand,\n description: fetchedData.description,\n imageUrl: fetchedData.image_url,\n price: fetchedData.price,\n rating: fetchedData.rating,\n title: fetchedData.title,\n totalReviews: fetchedData.total_reviews,\n }\n const similarProduct = fetchedData.similar_products.map(eachProduct => ({\n id: eachProduct.id,\n availability: eachProduct.availability,\n brand: eachProduct.brand,\n description: eachProduct.description,\n imageUrl: eachProduct.image_url,\n price: eachProduct.price,\n rating: eachProduct.rating,\n totalReviews: eachProduct.total_reviews,\n style: eachProduct.style,\n title: eachProduct.title,\n }))\n console.log(fetchedData)\n\n this.setState({\n productDetails: updatedData,\n similarProducts: similarProduct,\n isLoading: false,\n isTrue: status.success,\n })\n } else {\n this.setState({isTrue: status.failure, productDetails: {}})\n }\n }\n\n getSuccessDetails = () => {\n const {productDetails, similarProducts, count, isLoading} = this.state\n\n const {\n availability,\n brand,\n description,\n imageUrl,\n price,\n rating,\n title,\n totalReviews,\n } = productDetails\n\n return (\n
\n
\n
\n {isLoading ? (\n
\n \n
\n ) : (\n <>\n
\n \"product\"\n
\n

{title}

\n

{`Rs ${price}/-`}

\n
\n
\n

{rating}

\n \n
\n

{totalReviews} Reviews

\n
\n

{description}

\n

{`Available: ${availability}`}

\n

{`Brand: ${brand}`}

\n
\n
\n \n \n \n

{count}

\n \n \n \n
\n \n
\n
\n
\n

Similar Products

\n
    \n {similarProducts.map(eachSimilar => (\n \n ))}\n
\n
\n \n )}\n
\n
\n )\n }\n\n render() {\n const {isTrue, productDetails} = this.state\n\n switch (isTrue) {\n case status.success:\n if (productDetails.length !== 0) {\n return this.getSuccessDetails()\n }\n return this.getBadCredential()\n default:\n return this.getBadCredential()\n }\n }\n}\n\nexport default ProductItemDetails\n", "text_preview": "// Write your code here import {Component} from 'react' import {BsPlusSquare, BsDashSquare} from 'react-icons/bs' import Loader from 'react-loader-spinner' import Cookies from 'js-cookie' import SimilarProductItem from '../SimilarProductIte", "input_sha256": "c7319eaa8bdf15ef969cd8523e4eb257b2709e1e5be22a8361cf0f076ff9d5cd", "text_sha256": "012fd873246f588baa1cb9e2a7dc1fec39e3a0579df3ad7baa881f6e02a005b3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8055, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.39227471268811925, "noise_severity": 0.09151396496919427, "missing_content_severity": 0.049067754603540056, "factual_density": 3.1071117348786674, "pretraining_utility": 0.5524672910986729, "math_density": 0.28220715201374275, "code_density": 3.8758917422788257, "reasoning_density": 0.5707892640484541, 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"reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:06.536235+00:00", "scoring_key_slot": "primary", "dataset_uid": "0c52b556-1de7-4c24-99a4-fc45ae177bd2", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9b64910a43fe14e43a7106ea3af6fb3486d62ddaa3377c9112279086af9927e2", "source_metadata": {"uuid": "0c52b556-1de7-4c24-99a4-fc45ae177bd2", "repo_name": "pillirajesh/nxtTrendzSpecificProductDetails", "relative_path": "src/components/ProductItemDetails/index.js", "category": "WEB", "algo_rel_score": 0.9528644680976868, "quality_score": 6.218785285949707, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-12-15T09:55:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f33b1f0d422f4fa5da657f938af131833b589558", "size_bytes": 6370, "file_timestamp": 1671098297, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cc259ab5867e9df403e5a8fe17726d0e9a54d94d"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 1, "repo_created_at": "2022-12-15T09:55:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "f33b1f0d422f4fa5da657f938af131833b589558", "size_bytes": 6370, "file_timestamp": 1671098297, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "cc259ab5867e9df403e5a8fe17726d0e9a54d94d", "url": "https://github.com/pillirajesh/nxtTrendzSpecificProductDetails/blob/cc259ab5867e9df403e5a8fe17726d0e9a54d94d/src/components/ProductItemDetails/index.js"}, "gpt4o_tokens": 1372, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.39227471268811925, "noise_severity": 0.09151396496919427, "missing_content_severity": 0.049067754603540056, "factual_density": 3.1071117348786674, "pretraining_utility": 0.5524672910986729, "math_density": 0.28220715201374275, "code_density": 3.8758917422788257, "reasoning_density": 0.5707892640484541, "education_level": 2.0632703335653413, "creative_writing_quality": -0.24919779648718157}, "score_applicability": {"education_level": 0.9986424865202779, "creative_writing_quality": 0.001897408897588751}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Charlychen93/Chick-Dict-Projiect/blob/503a6c21289447bca21c52015dd3052ec7fbe657/ChickDict_Project/src/pages/Admin/index.js", "id": "javascript-0-1146151468cd4301a80dc5659dc7a650", "text": "import React, { Component } from 'react';\nimport { withRouter } from 'react-router-dom'\nimport Nav from '../../components/Nav'\nimport Modal from '../tokenModal'\nimport style from './admin.module.less'\nimport { Layout, Breadcrumb, Button, Dropdown, Menu, Icon, Avatar } from 'antd';\nimport { connect } from 'react-redux'\nimport userManage from '../../api/userManage'\nconst { Header, Content, Footer, Sider } = Layout;\n\n\nclass Admin extends Component {\n state = {\n collapsed: false,\n user: '',\n show: false,\n administrator: '',\n page: '1',\n pageSize: '100',\n imgSrc: '',\n name: '',\n locationList: ''\n };\n\n onCollapse = collapsed => {\n this.setState({ collapsed });\n };\n toLogin = () => {\n this.props.history.push('/login')\n localStorage.removeItem('token')\n }\n changeUl = () => {\n this.setState({ visiable: true })\n }\n componentDidMount() {\n // 获取头像和_id\n if (localStorage.getItem('user')) {\n let { page, pageSize } = this.state\n userManage.userQuery({ page, pageSize }).then((res) => {\n // console.log(res)\n let { _id } = JSON.parse(localStorage.getItem('user'))\n res.list.forEach((item) => {\n let id = item._id\n if (id === _id) {\n this.setState({ imgSrc: item.img, name: item.user })\n }\n })\n })\n }else{\n this.props.history.push('/login')\n }\n\n // console.log(window.location.hash)\n //获取用户名\n if (localStorage.getItem('user')) {\n let result = JSON.parse(localStorage.getItem('user'))\n this.setState({ user: result.user })\n // console.log(result.leavel)\n if (result.leavel === 'root') {\n this.setState({ administrator: 1 })\n }\n }\n let token = localStorage.getItem('token')\n if (token) {\n this.setState({ show: true })\n }\n // console.log(this.props)\n }\n exitLogin = () => {\n localStorage.setItem('user', '')\n this.setState({ user: '' })\n }\n\n\n render() {\n const menu = (\n \n \n {/* */}\n \n {this.state.name}\n \n \n \n \n )\n let { tokenModal } = this.props\n // console.log(tokenModal)\n let { show, administrator } = this.state\n return (\n \n {tokenModal ? : ''}\n \n
\n
\n \n \n \n
\n
\n
\n
\n
\n\n {!show || \n \n {administrator === 1 ? '超级管理员' : '会员'}\n \n {/* \n {administrator === 1 ? '超级管理员' : '普通管理员'}\n */}\n }\n\n
\n {/* */}\n
\n \n {/*cy修改*/}\n \n \n \n {\n this.props.location.pathname.split('/')[1]\n }\n \n \n {\n this.props.location.pathname.split('/')[2]\n }\n {this.props.location.pathname.split('/')[3] ?\n \n \n {\n this.props.location.pathname.split('/')[3]\n } : null\n }\n\n\n \n
\n {/* {this.setState({locationList:this.props.location.pathname.split('/')})} */}\n {this.props.children}\n
\n \n
Ant Design ©2018 Created by Ant UED
\n \n \n );\n }\n}\n\nexport default connect(state => state)(withRouter(Admin));", "text_preview": "import React, { Component } from 'react'; import { withRouter } from 'react-router-dom' import Nav from '../../components/Nav' import Modal from '../tokenModal' import style from './admin.module.less' import { Layout, Breadcrumb, Button, Dr", "input_sha256": "05633a9ee310b5dc6da6cc530656fa40f722ef9fae9e1b8de00c73de24212703", "text_sha256": "1146151468cd4301a80dc5659dc7a65040d6e1d4129da7f11cabc29079811d1e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8059, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.43483721688283905, "noise_severity": 0.23882639390871166, "missing_content_severity": 0.049235949352338645, "factual_density": 3.0494289994136294, "pretraining_utility": 0.2507043944075309, "math_density": 0.008228452921171315, "code_density": 3.9221393961242126, "reasoning_density": 0.2516134546315094, "education_level": 2.2197396413380766, "creative_writing_quality": -0.4133788787582525}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010266835773322248, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953468268765998, "secondary": 0.9665110249834452, "tertiary": 0.9573551934186121, "structured_records": 0.01193176278759153, "procedural_steps": 0.0008681670812439613, "worked_solution": 0.0018064668785431985, "contextual_dialogue": 9.803029762929049e-06, "content_quality": 0.3784296757431814, "noise_severity": 0.8009780050760736, "missing_content_severity": 0.9589700422063845, "factual_density": 0.2078574995113578, "pretraining_utility": 0.5061737013402675, "math_density": 0.9931429558990239, "code_density": 0.9351161634368441, "reasoning_density": 0.7903221211404088, "education_level": 0.7072156147823905, "creative_writing_quality": 0.9990368979090557, "primary_topic": 0.9873269221553314, "secondary_topic": 0.9044874302739335}, "probability_margin": {"primary": 0.9928835874173157, "secondary": 0.9591650538114803, "tertiary": 0.9427725980179962, "education_level": 0.5363552026553617, "creative_writing_quality": 0.9988056964988062, "primary_topic": 0.9840976169649901, "secondary_topic": 0.8579993276716709}, "raw_probabilities": {"primary": {"Academic Writing": 4.9613877781742875e-05, "Content Listing": 9.853150223900995e-05, "Creative Writing": 3.1949485916514845e-05, "Customer Support Page": 1.1477752008480759e-05, "Q&A Forum": 1.7836671238632575e-05, "Discussion Thread": 1.1939749406521595e-05, "Comment Section": 2.0575669863341576e-05, "Missing Content": 0.00015573772886936653, "Knowledge Article": 9.052923808553368e-05, "Legal Notices": 8.103272251075403e-05, "News Article": 8.349197121611659e-06, "Organizational Announcement": 1.587217841980619e-05, "Nonfiction Writing": 1.72117279306509e-05, "Organizational About Page": 2.2674437106552288e-05, "Personal About Page": 3.262414065528644e-05, "Personal Blog": 1.1931622118365937e-05, "Product Page": 1.0571405968057229e-05, "Spam / Ads": 1.8602507643873385e-05, "Structured Data": 0.000591205376763788, "Technical Writing": 0.9955407090900749, "Transcript / Interview": 1.96164193286057e-05, "Tutorial / How-To Guide": 0.0004734345207515555, "User Reviews": 1.0851305437920261e-05, "Other / Unknown": 0.0026571216727591085}, "secondary": {"Academic Writing": 0.00025433026873511005, "Content Listing": 0.00874134513098798, "Creative Writing": 0.00029935437664984667, "Customer Support Page": 9.795720503672977e-05, "Q&A Forum": 7.536756985821412e-05, "Discussion Thread": 7.288463390747073e-05, "Comment Section": 0.0010302478732251246, "Missing Content": 0.0013669793331154711, "Knowledge Article": 0.0003454141303878301, "Legal Notices": 0.0060926265681848935, "News Article": 0.00013983330222708249, "Organizational Announcement": 0.00014711844149792384, "Nonfiction Writing": 0.00012549006885981075, "Organizational About Page": 0.0001971882888267737, "Personal About Page": 0.00019488093935938203, "Personal Blog": 0.00011713666106588091, "Product Page": 7.709929014313359e-05, "Spam / Ads": 0.00048960927584187, "Structured Data": 0.004101528678065621, "Technical Writing": 0.007036163904076855, "Transcript / Interview": 0.00013713901303461534, "Tutorial / How-To Guide": 0.0008877773274839551, "User Reviews": 6.612877696002342e-05, "NONE": 0.9679063989424683}, "tertiary": {"Academic Writing": 0.00021319785990017574, "Content Listing": 0.00814183322744786, "Creative Writing": 0.0003171710650864144, "Customer Support Page": 0.0001281185727975225, "Q&A Forum": 8.81710723548218e-05, "Discussion Thread": 8.101761189517452e-05, "Comment Section": 0.0011591532774467362, "Missing Content": 0.001847654247022178, "Knowledge Article": 0.00037126842569512705, "Legal Notices": 0.016359462341507144, "News Article": 0.0001578472439509261, "Organizational Announcement": 0.00020721745201168285, "Nonfiction Writing": 0.00018709328209738508, "Organizational About Page": 0.0003619089272220765, "Personal About Page": 0.0002349195670456737, "Personal Blog": 0.00013610436726436997, "Product Page": 9.201609307074155e-05, "Spam / Ads": 0.0015927889121392046, "Structured Data": 0.0026308891232320335, "Technical Writing": 0.005587513134675174, "Transcript / Interview": 0.00015124726591543458, "Tutorial / How-To Guide": 0.0007506751490204604, "User Reviews": 7.067142169842535e-05, "NONE": 0.9591320603595033}, "structured_records": {"false": 0.9880682372124084, "true": 0.01193176278759153}, "procedural_steps": {"false": 0.9991318329187561, "true": 0.0008681670812439613}, "worked_solution": {"false": 0.9981935331214568, "true": 0.0018064668785431985}, "contextual_dialogue": {"false": 0.999990196970237, "true": 9.803029762929049e-06}, "content_quality": {"-2": 0.018429040851412035, "-1": 0.11253501186806575, "0": 0.375166439822304, "1": 0.4035087044627077, "2": 0.09036080299551064}, "noise_severity": {"0": 0.8057751734357326, "1": 0.15757132704666596, "2": 0.030968539394627456, "3": 0.0034218524191056733, "4": 0.0022631077038684413}, "missing_content_severity": {"0": 0.971633958879786, "1": 0.013767793194147499, "2": 0.009465074006412593, "3": 0.003994687533250029, "4": 0.001138486386403968}, "factual_density": {"0": 0.03446118881752056, "1": 0.057139121243582086, "2": 0.15040032947186818, "3": 0.34050822264180575, "4": 0.4174911378252234}, "pretraining_utility": {"-2": 0.013153479936173067, "-1": 0.14463662211972794, "0": 0.4603996125640686, "1": 0.3419725943604558, "2": 0.03983769101957458}, "math_density": {"0": 0.9938081572838056, "1": 0.005171690083034587, "2": 0.0004063977347385794, "3": 0.00021105222502570763, "4": 0.00040270267339561174}, "code_density": {"0": 0.00017531514889628338, "1": 0.000298620829761783, "2": 0.007324595803066971, "3": 0.061614289184782615, "4": 0.9305871790334923}, "reasoning_density": 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0.00013177320150271356, "Health, Healthcare & Wellness": 8.217862503434068e-05, "History & Geography": 4.536123585788868e-05, "Home, Pets & Hobbies": 6.737791888835048e-05, "Industry & Production": 5.16569445441901e-05, "Language, Literature & Philosophy": 0.0005546946408050792, "Politics & Government": 4.510310618791718e-05, "Religion & Spirituality": 6.494822407000879e-05, "Science, Mathematics & Engineering": 0.00012131276050214786, "Relationships & Community": 6.736879507026152e-05, "Software Usage & Applications": 0.0024072179109732488, "Programming & Software Engineering": 0.9878143482262801, "Sports & Fitness": 7.113205134662714e-05, "Vehicles & Transportation": 2.9703691493670202e-05, "Travel & Tourism": 8.233300525731804e-05, "Other / Uncovered Subject": 0.0009625846113018842, "Unknown / Insufficient Content": 0.0023534761487864733}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005996088960501575, "Art & Design": 0.05016166297634212, "Law & Crime": 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"is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 6}, "file_meta": {"content_id": "10a0da8fa5ab582dd19d129d442063c06f8dc4cc", "size_bytes": 5277, "file_timestamp": 1585826743, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "503a6c21289447bca21c52015dd3052ec7fbe657"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2020-04-02T11:09:13.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 6}, "file_meta": {"content_id": "10a0da8fa5ab582dd19d129d442063c06f8dc4cc", "size_bytes": 5277, "file_timestamp": 1585826743, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "503a6c21289447bca21c52015dd3052ec7fbe657", "url": "https://github.com/Charlychen93/Chick-Dict-Projiect/blob/503a6c21289447bca21c52015dd3052ec7fbe657/ChickDict_Project/src/pages/Admin/index.js"}, "gpt4o_tokens": 1231, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.4348372168828391, "noise_severity": 0.23882639390871166, "missing_content_severity": 0.049235949352338645, "factual_density": 3.0494289994136294, "pretraining_utility": 0.2507043944075309, "math_density": 0.008228452921171315, "code_density": 3.9221393961242126, "reasoning_density": 0.2516134546315094, "education_level": 2.2197396413380766, "creative_writing_quality": -0.4133788787582525}, "score_applicability": {"education_level": 0.998410498340125, "creative_writing_quality": 0.0008025850757870102}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/adobecom/mas/blob/8114cf92e40cac5d8483bf841ff30b92e0faa802/studio/src/aem/aem-tag-picker-field.js", "id": "javascript-0-ab4936cbac1d60b15d4e9a318d1d3432", "text": "import { LitElement, html, css, nothing } from 'lit';\nimport { repeat } from 'lit/directives/repeat.js';\nimport { AEM } from './aem.js';\nimport { EVENT_OST_OFFER_SELECT } from '../constants.js';\nimport { VARIANTS } from '../editors/variant-picker.js';\n\nconst AEM_TAG_PATTERN = /^[a-zA-Z][a-zA-Z0-9]*:/;\nconst namespaces = {};\nconst SELECTION_CHECKBOX = 'checkbox';\n\n/**\n * Converts from attribute (tag format) to property (path format).\n * e.g. \"mas:product/photoshop\" --> \"/content/cq:tags/mas/product/photoshop\"\n */\nexport function fromAttribute(value) {\n if (!value) return [];\n const tags = value.split(',');\n return tags\n .map((tag) => tag.trim())\n .map((tag) => {\n if (AEM_TAG_PATTERN.test(tag) === false) return false;\n const [namespace, path] = tag.split(':');\n if (!namespace || !path) return '';\n return path ? `/content/cq:tags/${namespace}/${path}` : '';\n })\n .filter(Boolean);\n}\n\n/**\n * Converts from property (path format) to attribute (tag format).\n * e.g. \"/content/cq:tags/mas/product/photoshop\" --> \"mas:product/photoshop\"\n */\nexport function toAttribute(value) {\n if (!value || value.length === 0) return '';\n return value\n .map((path) => {\n const match = path.match(/\\/content\\/cq:tags\\/([^/]+)\\/(.+)$/);\n return match ? `${match[1]}:${match[2]}` : '';\n })\n .filter(Boolean)\n .join(',');\n}\n\nclass AemTagPickerField extends LitElement {\n static properties = {\n baseUrl: { type: String, attribute: 'base-url' },\n label: { type: String },\n bucket: { type: String },\n // Controls whether popover is open in 'checkbox' mode\n open: { type: Boolean, state: true },\n // The actual selected tag paths (e.g., [\"/content/cq:tags/namespace/top/foo\"])\n value: {\n type: Array,\n converter: { fromAttribute, toAttribute },\n reflect: true,\n },\n namespace: { type: String },\n top: { type: String },\n multiple: { type: Boolean }, // Whether multiple selection is allowed\n hierarchicalTags: { type: Object, state: true },\n selected: { type: String },\n ready: { type: Boolean, state: true },\n selection: { type: String }, // 'checkbox' | default-hierarchy\n flatTags: { type: Array, state: true },\n\n // Temporary selections in 'checkbox' mode (before Apply)\n tempValue: { type: Array, state: true },\n\n searchQuery: { type: String, state: true },\n };\n\n static styles = css`\n :host {\n display: flex;\n align-items: center;\n flex-direction: column;\n }\n\n :host([selection='checkbox']) {\n max-width: 248px;\n max-height: 326px;\n }\n\n sp-tags {\n width: 100%;\n position: relative;\n }\n\n sp-dialog {\n min-height: 340px;\n max-height: 50vh;\n overflow-y: auto;\n }\n\n sp-popover {\n margin-top: var(--margin-picker-top, 0px);\n }\n\n sp-checkbox {\n align-items: center;\n }\n\n #content {\n padding: 8px;\n }\n\n #footer {\n padding: 8px;\n height: 40px;\n align-items: center;\n display: flex;\n gap: 8px;\n justify-content: end;\n }\n\n #footer span {\n flex: 1;\n }\n\n sp-action-button {\n display: flex;\n flex-direction: row-reverse;\n }\n\n sp-popover.checkbox-popover {\n min-width: 248px;\n border-radius: 10px;\n }\n\n .checkbox-list {\n display: flex;\n flex-direction: column;\n gap: 2px;\n max-height: 246px;\n overflow-y: auto;\n padding-inline-start: 4px;\n }\n\n sp-checkbox {\n height: 40px;\n }\n `;\n\n #aem;\n\n constructor() {\n super();\n this.baseUrl = document.querySelector('meta[name=\"aem-base-url\"]')?.content;\n this.bucket = null;\n this.top = null;\n this.multiple = false;\n this.hierarchicalTags = new Map();\n this.flatTags = [];\n this.value = [];\n this.tempValue = [];\n this.#aem = null;\n this.ready = false;\n this.selection = ''; // e.g., 'checkbox' | ''\n this.searchQuery = '';\n }\n\n _onOstSelect = ({ detail: { offer } }) => {\n if (!offer) return;\n const extractedOffer = {\n offer_type: offer.offer_type,\n planType: offer.planType,\n customer_segment: offer.customer_segment,\n product_code: offer.product_code,\n market_segments:\n Array.isArray(offer.market_segments) && offer.market_segments.length > 0\n ? offer.market_segments[0]\n : offer.market_segments,\n };\n\n const convertCamelToSnake = (str) => {\n if (typeof str !== 'string') return '';\n return str.replace(/([a-z0-9])([A-Z])/g, '$1_$2').toLowerCase();\n };\n\n const categoriesToUpdate = new Set(['offer_type', 'plan_type', 'customer_segment', 'market_segments', 'product_code']);\n\n const existingTags = this.value.filter((tagPath) => {\n for (const category of categoriesToUpdate) {\n if (tagPath.includes(`/content/cq:tags/mas/${category}/`)) {\n return false; // Exclude this tagPath if it contains any of the categories\n }\n }\n return true;\n });\n\n const newTagPaths = Object.entries(extractedOffer)\n .filter(([_, value]) => value != null) // Filter out null/undefined values\n .map(([key, value]) => {\n const formattedKey = convertCamelToSnake(key);\n const formattedValue = String(value).toLowerCase();\n return `/content/cq:tags/mas/${formattedKey}/${formattedValue}`;\n });\n\n this.value = [...existingTags, ...newTagPaths].filter(Boolean);\n this.#notifyChange();\n };\n\n connectedCallback() {\n super.connectedCallback();\n this.multiple = this.multiple ?? this.selection === SELECTION_CHECKBOX;\n this.#aem = new AEM(this.bucket, this.baseUrl);\n this.loadTags();\n if (!this.top) {\n document.addEventListener(EVENT_OST_OFFER_SELECT, this._onOstSelect);\n }\n }\n\n disconnectedCallback() {\n super.disconnectedCallback();\n document.removeEventListener(EVENT_OST_OFFER_SELECT, this._onOstSelect);\n }\n\n get #tagsRoot() {\n if (this.top) return `${this.namespace}/${this.top}/`;\n return `${this.namespace}/`;\n }\n\n // Returns the cached data for this namespace (if loaded)\n get #data() {\n return namespaces[this.namespace];\n }\n\n get allTags() {\n return namespaces[this.namespace];\n }\n\n get selectedTags() {\n return this.value.map((path) => this.#data.get(path));\n }\n\n clear() {\n this.value = [];\n this.tempValue = [];\n }\n\n addVariantTags() {\n if (this.top !== 'variant' || this.flatTags.length) return;\n VARIANTS.forEach((variant) => {\n if (variant.value === 'all') return;\n const tagPath = `/content/cq:tags/mas/variant/${variant.value}`;\n this.flatTags.push(tagPath);\n this.#data.set(tagPath, {\n name: variant.value,\n title: variant.label,\n path: tagPath,\n });\n });\n }\n\n async loadTags() {\n if (!this.#data) {\n // Not loaded yet, create a placeholder Promise\n let resolveNamespace;\n namespaces[this.namespace] = new Promise((resolve) => {\n resolveNamespace = resolve;\n });\n // Fetch from AEM\n const rawTags = await this.#aem.tags.list(this.namespace);\n if (!rawTags) return;\n // Store as a Map keyed by tag path\n namespaces[this.namespace] = new Map(rawTags.hits.map((tag) => [tag.path, tag]));\n resolveNamespace();\n } else if (this.#data instanceof Promise) {\n // If still loading, wait\n await this.#data;\n }\n\n const allTags = [...this.#data.values()].filter((tag) => tag.path.startsWith(this.#tagsRoot));\n\n if (this.selection === SELECTION_CHECKBOX) {\n this.flatTags = allTags\n .filter((tag) => tag.title)\n .sort((a, b) =>\n a.title.localeCompare(b.title, undefined, {\n sensitivity: 'base',\n }),\n )\n .map((tag) => tag.path);\n this.addVariantTags();\n } else {\n // Otherwise build a hierarchical structure\n this.hierarchicalTags = this.buildHierarchy(allTags);\n }\n\n this.ready = true;\n }\n\n buildHierarchy(tags) {\n const root = new Map();\n tags.forEach((tag) => {\n const path = tag.path.replace(this.#tagsRoot, '');\n const parts = path.split('/');\n let currentLevel = root;\n\n parts.forEach((part, index) => {\n if (!currentLevel.has(part)) {\n currentLevel.set(part, {\n __info__: index === parts.length - 1 ? tag : null,\n __children__: new Map(),\n });\n }\n currentLevel = currentLevel.get(part).__children__;\n });\n });\n return root;\n }\n\n // For hierarchical or single-click modes\n async toggleTag(path) {\n await this.#data; // ensure data is loaded first\n const currentValue = [...(this.value || [])];\n const index = currentValue.indexOf(path);\n\n if (!this.multiple) {\n // single select\n this.value = [path];\n await this.#notifyChange();\n return;\n }\n // multi select\n if (index === -1) {\n currentValue.push(path);\n } else {\n currentValue.splice(index, 1);\n }\n this.value = currentValue;\n await this.#notifyChange();\n }\n\n // sp-sidenav \"change\" event handler\n async #handleChange(event) {\n const path = event.target.value;\n this.selected = path;\n this.toggleTag(path);\n }\n\n // sp-tag \"delete\" event\n #deleteTag(event) {\n const pathToDelete = event.target.dataset.path;\n this.toggleTag(pathToDelete);\n }\n\n // Convert a path to a tag's friendly title\n #resolveTagTitle(path) {\n const tag = this.#data.get(path);\n return tag ? tag.title : '';\n }\n\n // Recursively render for hierarchical tags\n renderSidenavItems(node, parentPath = '') {\n return [...node.entries()].map(([key, item]) => {\n const hasChildren = item.__children__.size > 0;\n const info = item.__info__;\n const label = info ? this.#resolveTagTitle(info.path) : key;\n const value = info ? info.path : `${parentPath}/${key}`;\n return html`\n \n ${hasChildren ? this.renderSidenavItems(item.__children__, value) : nothing}\n ${hasChildren\n ? html``\n : html``}\n \n `;\n });\n }\n\n // In hierarchical mode, only keep tags that start under #tagsRoot\n get tagsInHierarchy() {\n return this.value.filter((path) => path.startsWith(this.#tagsRoot));\n }\n\n // Renders the chosen tags for hierarchical or checkbox mode\n get tags() {\n if (!this.ready) return nothing;\n\n // hierarchical: display sp-tags with sp-tag for each selection\n if (this.tagsInHierarchy.length === 0) return nothing;\n return repeat(\n this.tagsInHierarchy,\n (path) => path,\n (path) => html`\n \n ${this.#resolveTagTitle(path)}\n \n \n `,\n );\n }\n\n // Keep the internal state & notify on changes\n updated(changedProperties) {\n if (changedProperties.has('value')) {\n this.tempValue = [...this.value];\n }\n this.#updateMargin();\n }\n\n async #notifyChange() {\n await this.updateComplete;\n this.dispatchEvent(\n new CustomEvent('change', {\n bubbles: true,\n composed: true,\n }),\n );\n }\n\n get overlayTrigger() {\n return this.shadowRoot.querySelector('overlay-trigger');\n }\n\n get popoverElement() {\n return this.shadowRoot.querySelector('sp-popover');\n }\n\n get selectedText() {\n const count = this.tempValue.length;\n if (count < 2) return `${count} tag selected`;\n return `${count} tags selected`;\n }\n\n async #updateMargin() {\n await this.updateComplete;\n if (!this.popoverElement || !/bottom/.test(this.popoverElement.placement)) return;\n const margin = this.shadowRoot.querySelector('sp-tag:last-child')?.offsetTop ?? 0;\n this.style.setProperty('--margin-picker-top', `${margin}px`);\n }\n\n get triggerLabel() {\n if (this.label) return this.label;\n return this.multiple ? 'Select tags' : 'Select a tag';\n }\n\n #handleCheckboxToggle(event) {\n event.stopPropagation();\n const checkboxes = this.shadowRoot.querySelectorAll('sp-checkbox');\n this.tempValue = [...checkboxes]\n .filter((checkbox) => checkbox.checked)\n .map((checkbox) => checkbox.getAttribute('value'));\n }\n\n resetSelection() {\n this.tempValue = [];\n this.shadowRoot.querySelectorAll('sp-checkbox').forEach((checkbox) => {\n checkbox.checked = this.tempValue.includes(checkbox.value);\n });\n }\n\n async applySelection() {\n this.value = [...this.tempValue];\n this.tempValue = [];\n this.overlayTrigger.open = false;\n this.#notifyChange();\n }\n\n #handleCheckoxMenuClose() {\n this.tempValue = [...this.value];\n }\n\n get checkboxMenu() {\n if (!this.ready) return nothing;\n\n let filteredTags = this.flatTags;\n if (this.flatTags.length > 7) {\n filteredTags = this.flatTags.filter((path) =>\n this.#resolveTagTitle(path).toLowerCase().includes(this.searchQuery.toLowerCase()),\n );\n }\n\n return html`\n
\n ${this.flatTags.length > 7\n ? html` (this.searchQuery = e.target.value)} placeholder=\"Search\"> `\n : nothing}\n
\n ${repeat(\n filteredTags,\n (path) => path, // Unique key for each item\n (path) => {\n const checked = this.tempValue.includes(path);\n return html`\n \n ${this.#resolveTagTitle(path)}\n \n `;\n },\n )}\n
\n
\n ${this.selectedText} \n \n Reset\n \n Apply \n
\n
\n `;\n }\n\n /**\n * - Clicking the action button toggles the popover.\n * - The list of sp-checkbox is scrollable if too large.\n * - The footer shows # selected, plus Reset/Apply.\n */\n renderCheckboxMode() {\n const selectCount = this.value.length > 0 ? html`(${this.value.length})` : '';\n return html`\n \n \n ${this.triggerLabel} ${selectCount}\n \n \n\n ${this.checkboxMenu} \n \n `;\n }\n\n render() {\n if (this.selection === 'checkbox') {\n return this.renderCheckboxMode();\n }\n if (!this.ready) return nothing;\n return html`\n \n \n \n ${this.triggerLabel}\n \n \n \n \n \n ${this.renderSidenavItems(this.hierarchicalTags)}\n \n \n \n \n ${this.tags}\n \n `;\n }\n}\n\ncustomElements.define('aem-tag-picker-field', AemTagPickerField);\n", "text_preview": "import { LitElement, html, css, nothing } from 'lit'; import { repeat } from 'lit/directives/repeat.js'; import { AEM } from './aem.js'; import { EVENT_OST_OFFER_SELECT } from '../constants.js'; import { VARIANTS } from '../editors/variant-", "input_sha256": "22f51b691033cedb377664f8bb7cdbac83e08d2dc0220c96f18caf1c90055a06", "text_sha256": "ab4936cbac1d60b15d4e9a318d1d3432e9756a79e04e021999445be0ee29ba4d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8053, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9229832634724072, "noise_severity": 0.018341084700905983, "missing_content_severity": 0.019628689640461532, "factual_density": 3.4230627566435414, "pretraining_utility": 0.6310504898100001, "math_density": 0.038287691165206694, "code_density": 3.9287454120823533, "reasoning_density": 0.6788054292574945, "education_level": 2.5758649584861, "creative_writing_quality": -0.1701305621249225}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008853962151697536, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9986601971189683, "secondary": 0.9596226048858473, "tertiary": 0.9688615219137056, "structured_records": 0.025992680969475674, "procedural_steps": 0.0029627414524388603, "worked_solution": 0.00748132804843671, "contextual_dialogue": 4.4948000588294855e-06, "content_quality": 0.5278847498291191, "noise_severity": 0.984715762749245, "missing_content_severity": 0.9836427586329487, "factual_density": 0.5192189638696179, "pretraining_utility": 0.5272645071387152, "math_density": 0.9680935906956611, "code_density": 0.9406211767352944, "reasoning_density": 0.4343288089520878, "education_level": 0.38364793005770886, "creative_writing_quality": 0.9994448139566138, "primary_topic": 0.9932123864022696, "secondary_topic": 0.8876532175702727}, "probability_margin": {"primary": 0.9980374580482883, "secondary": 0.9386280629340429, "tertiary": 0.9515542155706725, "education_level": 0.02619337594665805, "creative_writing_quality": 0.9993050375576834, "primary_topic": 0.9901830334449286, "secondary_topic": 0.8255972914655465}, "raw_probabilities": {"primary": {"Academic Writing": 2.3133365382236965e-05, "Content Listing": 1.175664359066949e-05, "Creative Writing": 7.111267470455437e-06, "Customer Support Page": 4.900732272599508e-06, "Q&A Forum": 4.570490365208226e-06, "Discussion Thread": 3.059619328788449e-06, "Comment Section": 5.7735597271224905e-06, "Missing Content": 9.971574433994454e-06, "Knowledge Article": 3.404034665181876e-05, "Legal Notices": 2.1908173460881107e-05, "News Article": 2.099467129443344e-06, "Organizational Announcement": 5.757957300784441e-06, "Nonfiction Writing": 3.938677167026109e-06, "Organizational About Page": 5.638142879994238e-06, "Personal About Page": 4.490873128062786e-06, "Personal Blog": 3.543014836751074e-06, "Product Page": 4.674463231393798e-06, "Spam / Ads": 4.228123329754693e-06, "Structured Data": 0.0003657794475748315, "Technical Writing": 0.9987160222390112, "Transcript / Interview": 5.463706203146306e-06, "Tutorial / How-To Guide": 7.017930133697515e-05, "User Reviews": 3.394623463756461e-06, "Other / Unknown": 0.0006785641907230315}, "secondary": {"Academic Writing": 0.0002645901439773919, "Content Listing": 0.000295142501882091, "Creative Writing": 0.00019329612846074866, "Customer Support Page": 0.0001175739167086668, "Q&A Forum": 6.085761642884794e-05, "Discussion Thread": 4.6111992903247806e-05, "Comment Section": 0.0003440225074473075, "Missing Content": 0.00025376089629970797, "Knowledge Article": 0.0008140120158474949, "Legal Notices": 0.0005186122990594553, "News Article": 9.090365558923766e-05, "Organizational Announcement": 0.0001062483599979636, "Nonfiction Writing": 8.269680285572986e-05, "Organizational About Page": 0.00010787090246804595, "Personal About Page": 8.477957689244222e-05, "Personal Blog": 6.860900909544711e-05, "Product Page": 6.983987429712947e-05, "Spam / Ads": 9.431971605535755e-05, "Structured Data": 0.011456852471168903, "Technical Writing": 0.022676933414894126, "Transcript / Interview": 0.00010661528682114734, "Tutorial / How-To Guide": 0.0007964099382037957, "User Reviews": 4.49446237088601e-05, "NONE": 0.961304996348937}, "tertiary": {"Academic Writing": 0.00017364438362326456, "Content Listing": 0.0003197391412447654, "Creative Writing": 0.00013111585224543366, "Customer Support Page": 9.155526800938662e-05, "Q&A Forum": 3.925459764551059e-05, "Discussion Thread": 3.747977830046211e-05, "Comment Section": 0.000632353101264463, "Missing Content": 0.000265000011097448, "Knowledge Article": 0.0013313288318134307, "Legal Notices": 0.001090548021822546, "News Article": 5.8807372010788156e-05, "Organizational Announcement": 7.613709354405985e-05, "Nonfiction Writing": 6.446790931820596e-05, "Organizational About Page": 8.908725012807352e-05, "Personal About Page": 5.4297104812025296e-05, "Personal Blog": 5.0926383648051716e-05, "Product Page": 7.612326259724953e-05, "Spam / Ads": 6.596214560249262e-05, "Structured Data": 0.005707954888982207, "Technical Writing": 0.018604742929961903, "Transcript / Interview": 6.991304798580265e-05, "Tutorial / How-To Guide": 0.0007790072326485837, "User Reviews": 3.1595891059368386e-05, "NONE": 0.9701589585006345}, "structured_records": {"false": 0.9740073190305243, "true": 0.025992680969475674}, "procedural_steps": {"false": 0.9970372585475611, "true": 0.0029627414524388603}, "worked_solution": {"false": 0.9925186719515633, "true": 0.00748132804843671}, "contextual_dialogue": {"false": 0.9999955051999412, "true": 4.4948000588294855e-06}, "content_quality": {"-2": 0.005315472307248589, "-1": 0.038125321830271, "0": 0.22958045778403713, "1": 0.4822179662397113, "2": 0.24476078183873207}, "noise_severity": {"0": 0.9849119857395723, "1": 0.012812382483623748, "2": 0.0015543509541697232, "3": 0.0004651229815941921, "4": 0.00025615784104005354}, "missing_content_severity": {"0": 0.9898539605098827, "1": 0.0031269548203656716, "2": 0.004835465901476982, "3": 0.0019036720559555426, "4": 0.00027994671231881607}, "factual_density": {"0": 0.0039364411566114475, "1": 0.008266075554462064, "2": 0.08771572555006701, "3": 0.36096180096649244, "4": 0.539119956772367}, "pretraining_utility": {"-2": 0.006744218000910139, "-1": 0.057005888962490196, "0": 0.33387161888406003, "1": 0.5032117335307689, "2": 0.09916654062177085}, "math_density": {"0": 0.9696518924653893, "1": 0.02610515659376596, "2": 0.002035084166686338, "3": 0.0007191008585659731, "4": 0.0014887659155925343}, "code_density": {"0": 0.00014540969657461121, "1": 0.00023025088728969982, "2": 0.005552987860728198, "3": 0.05887622074802269, "4": 0.9351951308073847}, "reasoning_density": {"0": 0.680614736603587, "1": 0.10263307485766883, "2": 0.1149885438023707, "3": 0.06085931215040984, "4": 0.040904332585963696}, "education_level": {"none": 0.000917698797374357, "0": 0.0010661672162468555, "1": 0.003943457461020139, "2": 0.46017989910143264, "3": 0.4863732750480907, "4": 0.04751950237583539}, "creative_writing_quality": {"not_applicable": 0.9995373449638448, "-2": 5.3973310952816034e-05, "-1": 8.597547145651222e-05, "0": 0.00023230740616139083, "1": 6.558736317787667e-05, "2": 2.4811484406626648e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001059392092691806, "Art & Design": 0.0032904150187921645, "Law & Crime": 3.4292353291103325e-05, "Education & Careers": 3.665098204114246e-05, "Entertainment & Media": 6.978701115823378e-05, "Fashion & Beauty": 3.167535773094335e-05, "Business, Economics & Finance": 0.0002729361653104599, "Food & Dining": 2.6872081834610777e-05, "Games & Gaming": 6.823326019552902e-05, "Electronics & Hardware": 7.918375221768051e-05, "Health, Healthcare & Wellness": 2.2055414005161403e-05, "History & Geography": 2.66181066581163e-05, "Home, Pets & Hobbies": 2.075303918392625e-05, "Industry & Production": 2.698435791831303e-05, "Language, Literature & Philosophy": 3.9839070513876845e-05, "Politics & Government": 1.5927294163206348e-05, "Religion & Spirituality": 2.4715807797069456e-05, "Science, Mathematics & Engineering": 7.740048872077362e-05, "Relationships & Community": 1.4902575712854982e-05, "Software Usage & Applications": 0.0018603508491836518, "Programming & Software Engineering": 0.9934734484637208, "Sports & Fitness": 3.790286584409122e-05, "Vehicles & Transportation": 1.553701696314382e-05, "Travel & Tourism": 3.598106485939124e-05, "Other / Uncovered Subject": 0.00022061315447552404, "Unknown / Insufficient Content": 7.098523843904742e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007918895690518531, "Art & Design": 0.06637695619817725, "Law & Crime": 0.0005014898366474803, "Education & Careers": 0.00029487280778514466, "Entertainment & Media": 0.0009865437166530187, "Fashion & Beauty": 0.00030322101597011347, "Business, Economics & Finance": 0.0017404649142124062, "Food & Dining": 0.0001828191999542345, "Games & Gaming": 0.0003309600997026614, "Electronics & Hardware": 0.00035952901164175183, "Health, Healthcare & Wellness": 0.0001828001096287059, "History & Geography": 0.00017976450547848666, "Home, Pets & Hobbies": 0.0001570171698322658, "Industry & Production": 0.000137538268751353, "Language, Literature & Philosophy": 0.00016561385102762473, "Politics & Government": 0.00016516960519122055, "Religion & Spirituality": 0.00019239439300032074, "Science, Mathematics & Engineering": 0.00038244773978187807, "Relationships & Community": 0.00012331273055775789, "Software Usage & Applications": 0.020629600883005084, "Programming & Software Engineering": 0.012812826865882043, "Sports & Fitness": 0.00013758589051115623, "Vehicles & Transportation": 8.729698525638682e-05, "Travel & Tourism": 0.00024915629650259125, "Other / Uncovered Subject": 0.0005544806720734304, "NONE": 0.8919742476637238}}, "probabilities": {"primary": {"Academic Writing": 2.3133365382236965e-05, "Content Listing": 1.175664359066949e-05, "Creative Writing": 7.111267470455437e-06, "Customer Support Page": 4.900732272599508e-06, "Q&A Forum": 4.570490365208226e-06, "Discussion Thread": 3.059619328788449e-06, "Comment Section": 5.7735597271224905e-06, "Missing Content": 9.971574433994454e-06, "Knowledge Article": 3.404034665181876e-05, "Legal Notices": 2.1908173460881107e-05, "News Article": 2.099467129443344e-06, "Organizational Announcement": 5.757957300784441e-06, "Nonfiction Writing": 3.938677167026109e-06, "Organizational About Page": 5.638142879994238e-06, "Personal About Page": 4.490873128062786e-06, "Personal Blog": 3.543014836751074e-06, "Product Page": 4.674463231393798e-06, "Spam / Ads": 4.228123329754693e-06, "Structured Data": 0.0003657794475748315, "Technical Writing": 0.9987160222390112, "Transcript / Interview": 5.463706203146306e-06, "Tutorial / How-To Guide": 7.017930133697515e-05, "User Reviews": 3.394623463756461e-06, "Other / Unknown": 0.0006785641907230315}, "secondary": {"Academic Writing": 0.0002645901439773919, "Content Listing": 0.000295142501882091, "Creative Writing": 0.00019329612846074866, "Customer Support Page": 0.0001175739167086668, "Q&A Forum": 6.085761642884794e-05, "Discussion Thread": 4.6111992903247806e-05, "Comment Section": 0.0003440225074473075, "Missing Content": 0.00025376089629970797, "Knowledge Article": 0.0008140120158474949, "Legal Notices": 0.0005186122990594553, "News Article": 9.090365558923766e-05, "Organizational Announcement": 0.0001062483599979636, "Nonfiction Writing": 8.269680285572986e-05, "Organizational About Page": 0.00010787090246804595, "Personal About Page": 8.477957689244222e-05, "Personal Blog": 6.860900909544711e-05, "Product Page": 6.983987429712947e-05, "Spam / Ads": 9.431971605535755e-05, "Structured Data": 0.011456852471168903, "Technical Writing": 0.022676933414894126, "Transcript / Interview": 0.00010661528682114734, "Tutorial / How-To Guide": 0.0007964099382037957, "User Reviews": 4.49446237088601e-05, "NONE": 0.961304996348937}, "tertiary": {"Academic Writing": 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0.9701589585006345}, "structured_records": {"false": 0.9740073190305243, "true": 0.025992680969475674}, "procedural_steps": {"false": 0.9970372585475611, "true": 0.0029627414524388603}, "worked_solution": {"false": 0.9925186719515633, "true": 0.00748132804843671}, "contextual_dialogue": {"false": 0.9999955051999412, "true": 4.4948000588294855e-06}, "content_quality": {"-2": 0.005315472307248589, "-1": 0.038125321830271, "0": 0.22958045778403713, "1": 0.4822179662397113, "2": 0.24476078183873207}, "noise_severity": {"0": 0.9849119857395723, "1": 0.012812382483623748, "2": 0.0015543509541697232, "3": 0.0004651229815941921, "4": 0.00025615784104005354}, "missing_content_severity": {"0": 0.989853960509883, "1": 0.0031269548203656725, "2": 0.004835465901476983, "3": 0.001903672055955543, "4": 0.0002799467123188161}, "factual_density": {"0": 0.0039364411566114475, "1": 0.008266075554462064, "2": 0.08771572555006701, "3": 0.36096180096649244, "4": 0.539119956772367}, "pretraining_utility": {"-2": 0.006744218000910139, "-1": 0.057005888962490196, "0": 0.33387161888406003, "1": 0.5032117335307689, "2": 0.09916654062177085}, "math_density": {"0": 0.9696518924653893, "1": 0.02610515659376596, "2": 0.002035084166686338, "3": 0.0007191008585659731, "4": 0.0014887659155925343}, "code_density": {"0": 0.00014540969657461124, "1": 0.00023025088728969984, "2": 0.0055529878607281985, "3": 0.0588762207480227, "4": 0.9351951308073848}, "reasoning_density": {"0": 0.680614736603587, "1": 0.10263307485766883, "2": 0.1149885438023707, "3": 0.06085931215040984, "4": 0.040904332585963696}, "education_level": {"none": 0.000917698797374357, "0": 0.0010661672162468555, "1": 0.003943457461020139, "2": 0.46017989910143264, "3": 0.4863732750480907, "4": 0.04751950237583539}, "creative_writing_quality": {"not_applicable": 0.9995373449638448, "-2": 5.3973310952816034e-05, "-1": 8.597547145651222e-05, "0": 0.00023230740616139083, "1": 6.558736317787667e-05, "2": 2.4811484406626648e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001059392092691806, "Art & Design": 0.0032904150187921645, "Law & Crime": 3.4292353291103325e-05, "Education & Careers": 3.665098204114246e-05, "Entertainment & Media": 6.978701115823378e-05, "Fashion & Beauty": 3.167535773094335e-05, "Business, Economics & Finance": 0.0002729361653104599, "Food & Dining": 2.6872081834610777e-05, "Games & Gaming": 6.823326019552902e-05, "Electronics & Hardware": 7.918375221768051e-05, "Health, Healthcare & Wellness": 2.2055414005161403e-05, "History & Geography": 2.66181066581163e-05, "Home, Pets & Hobbies": 2.075303918392625e-05, "Industry & Production": 2.698435791831303e-05, "Language, Literature & Philosophy": 3.9839070513876845e-05, "Politics & Government": 1.5927294163206348e-05, "Religion & Spirituality": 2.4715807797069456e-05, "Science, Mathematics & Engineering": 7.740048872077362e-05, "Relationships & Community": 1.4902575712854982e-05, "Software Usage & Applications": 0.0018603508491836518, "Programming & Software Engineering": 0.9934734484637208, "Sports & Fitness": 3.790286584409122e-05, "Vehicles & Transportation": 1.553701696314382e-05, "Travel & Tourism": 3.598106485939124e-05, "Other / Uncovered Subject": 0.00022061315447552404, "Unknown / Insufficient Content": 7.098523843904742e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007918895690518531, "Art & Design": 0.06637695619817725, "Law & Crime": 0.0005014898366474803, "Education & Careers": 0.00029487280778514466, "Entertainment & Media": 0.0009865437166530187, "Fashion & Beauty": 0.00030322101597011347, "Business, Economics & Finance": 0.0017404649142124062, "Food & Dining": 0.0001828191999542345, "Games & Gaming": 0.0003309600997026614, "Electronics & Hardware": 0.00035952901164175183, "Health, Healthcare & Wellness": 0.0001828001096287059, "History & Geography": 0.00017976450547848666, "Home, Pets & Hobbies": 0.0001570171698322658, "Industry & Production": 0.000137538268751353, "Language, Literature & Philosophy": 0.00016561385102762473, "Politics & Government": 0.00016516960519122055, "Religion & Spirituality": 0.00019239439300032074, "Science, Mathematics & Engineering": 0.00038244773978187807, "Relationships & Community": 0.00012331273055775789, "Software Usage & Applications": 0.020629600883005084, "Programming & Software Engineering": 0.012812826865882043, "Sports & Fitness": 0.00013758589051115623, "Vehicles & Transportation": 8.729698525638682e-05, "Travel & Tourism": 0.00024915629650259125, "Other / Uncovered Subject": 0.0005544806720734304, "NONE": 0.8919742476637238}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12800, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12800, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:06.775532+00:00", "scoring_key_slot": "primary", "dataset_uid": "9d4f2d0d-e79a-4d74-90e6-8a1552688af3", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6d3043f72f39c05a5fa0f7e5845c6ddc55ce916712a785e1f463469a11352643", "source_metadata": {"uuid": "9d4f2d0d-e79a-4d74-90e6-8a1552688af3", "repo_name": "adobecom/mas", "relative_path": "studio/src/aem/aem-tag-picker-field.js", "category": "WEB", "algo_rel_score": 0.9281671643257141, "quality_score": 7.660120964050293, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 251, "repo_created_at": "2024-06-17T13:22:54.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 2, "forks": 11, "issues": 0, "pull_requests": 6}, "file_meta": {"content_id": "88271f08766e465cc5f6c3e477d6324c8f5884c1", "size_bytes": 18080, "file_timestamp": 1752676972, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "8114cf92e40cac5d8483bf841ff30b92e0faa802"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 251, "repo_created_at": "2024-06-17T13:22:54.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 2, "forks": 11, "issues": 0, "pull_requests": 6}, "file_meta": {"content_id": "88271f08766e465cc5f6c3e477d6324c8f5884c1", "size_bytes": 18080, "file_timestamp": 1752676972, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "8114cf92e40cac5d8483bf841ff30b92e0faa802", "url": "https://github.com/adobecom/mas/blob/8114cf92e40cac5d8483bf841ff30b92e0faa802/studio/src/aem/aem-tag-picker-field.js"}, "gpt4o_tokens": 3871, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9229832634724072, "noise_severity": 0.018341084700905983, "missing_content_severity": 0.019628689640461532, "factual_density": 3.4230627566435414, "pretraining_utility": 0.6310504898100001, "math_density": 0.038287691165206694, "code_density": 3.9287454120823533, "reasoning_density": 0.6788054292574945, "education_level": 2.5758649584861, "creative_writing_quality": -0.1701305621249225}, "score_applicability": {"education_level": 0.9990823012026258, "creative_writing_quality": 0.0004626550361552224}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/rahulSailesh-shah/llm-platform-frontend/blob/5e529e0e6444b871d3451036b28abacccc6e17e1/content/platform/src/modules/private/project-detail/components/chat-box/index.jsx", "id": "javascript-0-e4da2b4027473651a0039388e5516c92", "text": "import React, { useState, useMemo, useRef, useEffect } from \"react\";\nimport { InputBox, MessageBox } from \"..\";\nimport { useDispatch, useSelector } from \"react-redux\";\nimport { WebSocketHandlers } from \"../../../../../services/web-sockets\";\nimport { Loader } from \"../../../../../components\";\nimport {\n createChatRequest,\n getMessagesRequest,\n recieveMessageRequest,\n sendMessageRequest,\n clearMessages,\n} from \"../../../../../redux/slicers/chat\";\nimport {\n CHAT_OPTIONS,\n MESSAGE_TIMEOUT,\n MESSAGE_TIMEOUT_ERROR,\n MESSAGE_TYPE_JSON,\n} from \"../../../../../constants\";\n// import { navigateToLogin } from \"../../../../../utils\";\nimport { Helmet } from \"react-helmet\";\nimport { Images } from \"../../../../../theme\";\nimport SplashScreen from \"../splash-screen\";\nimport { CustomDispatch } from \"../../../../../helpers\";\nimport { useCallback } from \"react\";\nimport { useLayoutEffect } from \"react\";\nimport clsx from \"clsx\";\nimport \"./styles.scss\";\nimport { useSearchParams } from \"react-router-dom\";\nimport {\n deleteUploadMediaRequest,\n generatePromptUploadURLRequest,\n getMediaStatusRequest,\n uploadMediaRequest,\n} from \"../../../../../redux/slicers/general\";\nimport { convertObjectToFormData, generateGuid } from \"../../../../../utils\";\n\nconst ChatBox = () => {\n // STATES\n const [sessionId, setSessionId] = useState(null);\n const [reversedScrolling, setReversedScrolling] = useState(false);\n const [initLoad, setinitLoad] = useState(true);\n const [scrollToKey, setScrollToKey] = useState();\n const [moreMessagessData, setmoreMessagesData] = useState(null);\n const [selectedChatOption, setselectedChatOption] = useState(CHAT_OPTIONS[0]);\n const [isLoading, setLoading] = useState(false);\n const [showToast, setShowToast] = useState(false);\n const [selectedQuery, setSelectedQuery] = useState(null);\n const [selectedFile, setSelectedFile] = useState([]);\n const [fileLoading, setFileLoading] = useState({});\n\n // CUSTOM DISPATCH\n const [createChat, chatLoader] = CustomDispatch(createChatRequest);\n const [getMessages, messgesLoader] = CustomDispatch(getMessagesRequest);\n const [uploadMedia] = CustomDispatch(uploadMediaRequest);\n const [generateUrl] = CustomDispatch(generatePromptUploadURLRequest);\n const [getMediaStatus] = CustomDispatch(getMediaStatusRequest);\n const [deleteMedia] = CustomDispatch(deleteUploadMediaRequest);\n\n // REDUX DATA\n const selectedProject = useSelector((state) => state.project.selectedProject);\n const { messages } = useSelector((state) => state.chat);\n\n // CONST VALS\n const [searchParams, setSearchParams] = useSearchParams();\n const dispatch = useDispatch();\n const messageBox = useRef(null);\n const messageHistory = useRef(null);\n const toastTimerRef = useRef(null);\n const messageWrapper = useRef(null);\n const itemRefs = useRef({});\n const selectedChat = searchParams.get(\"chat\");\n const { sendJsonMessage, lastMessage, getWebSocket } = WebSocketHandlers();\n\n // HELPERS\n const getUploadedMediaStatus = () => {\n const payload = {\n resource: \"data\",\n method: \"list_assets\",\n details: {\n session_id: selectedChat ?? sessionId,\n project_id: selectedProject.id,\n db_type: \"opensearch\",\n },\n };\n if (selectedQuery) payload.details[\"query_id\"] = selectedQuery;\n getMediaStatus({\n payload,\n success: ({ data }) => {\n Object.keys(fileLoading).forEach((key) => {\n const fileStatus = data?.files?.find(\n (x) => x.file_name === key\n )?.search_status;\n if (fileStatus === \"searchable\" || fileStatus === \"not_searchable\") {\n setFileLoading(({ [key]: _, ...rest }) => rest);\n }\n if (fileStatus === \"not_searchable\") {\n setSelectedFile((prevFiles) =>\n prevFiles.filter((x) => x.name !== key)\n );\n }\n });\n },\n });\n };\n\n const uploadMediaHelper = (data, files) => {\n data.forEach((filedata, index) => {\n const object = {\n ...filedata,\n file: files[index],\n };\n delete object.name;\n delete object.url;\n const payload = convertObjectToFormData(object);\n uploadMedia({\n url: filedata.url,\n payload,\n });\n });\n };\n\n const generateUrlHelper = (files) => {\n const payload = {\n resource: \"data\",\n method: \"chat_upload\",\n details: {\n db_type: selectedProject.database,\n project_id: selectedProject.id,\n session_id: selectedChat ?? sessionId,\n files: files.map((item) => {\n return { file_name: item.name };\n }),\n },\n };\n if (selectedQuery) payload.details[\"query_id\"] = selectedQuery;\n generateUrl({\n payload,\n promptMedia: true,\n success: (data) => {\n setSelectedQuery(data.queryId);\n uploadMediaHelper(data.files, files);\n setSelectedFile((prevFiles) =>\n prevFiles.filter((x) => data.files.some((y) => y.name === x.name))\n );\n setFileLoading((prevLoading) => {\n const temp = { ...prevLoading };\n Object.keys(prevLoading).forEach((key) => {\n if (!data.files.some((x) => x.name === key)) {\n delete temp[key];\n }\n });\n return temp;\n });\n },\n error: () => {\n setSelectedFile([]);\n setFileLoading({});\n },\n });\n };\n\n const stopResponseHelper = (data, closeConnection = true) => {\n setLoading(false);\n if (closeConnection) getWebSocket().close();\n if (messageHistory.current) messageHistory.current[\"isLoading\"] = false;\n dispatch(recieveMessageRequest(data));\n messageHistory.current = null;\n };\n\n const generateMessagePayloadHelper = ({\n message = \"\",\n isError = false,\n isLoading = false,\n }) => {\n const payload = {\n isCompleted: false,\n isLoading: isError ? false : isLoading,\n isPrompt: true,\n model: selectedProject.modelLabel,\n message,\n };\n if (isError) payload[\"isError\"] = true;\n return payload;\n };\n\n const sendImageMessageHelper = (message, images, session, queryId) => {\n const uploadedimages = images.filter((x) => x.type === \"image\");\n const uploadeddocs = images.filter((x) => x.type !== \"image\");\n const payload = {\n action: \"queryV2\",\n session_id: session ?? selectedChat ?? sessionId,\n query_id: queryId ?? selectedQuery,\n project_id: selectedProject.id,\n query: message,\n chat_upload: {\n images: uploadedimages.map((x) => x.name),\n docs: uploadeddocs.map((x) => x.name),\n },\n };\n if (uploadedimages.length >= 1) {\n payload[\"request_type\"] = \"vision\";\n payload[\"endpoint\"] = \"vision\";\n }\n if (MESSAGE_TYPE_JSON) {\n payload[\"response_format\"] = { type: \"json\" };\n }\n sendJsonMessage(payload);\n setSelectedQuery(null);\n };\n\n const sendMessageHelper = (message, session) => {\n startToastTimer();\n\n // create message payload\n const messagePayload = {\n action: \"queryV2\",\n session_id: session ?? selectedChat,\n project_id: selectedProject.id,\n query: message,\n };\n\n if (MESSAGE_TYPE_JSON) {\n messagePayload[\"response_format\"] = { type: \"json\" };\n messagePayload.search_params = {\n ...messagePayload.search_params,\n output_fields: [\"source_name\", \"page_number\", \"tags\", \"url\"],\n };\n }\n\n // send message to server\n sendJsonMessage(messagePayload);\n };\n\n const createChatHelper = (message, images, queryId) => {\n const name = message.split(\"\\n\")[0];\n const chatName = `${name.slice(0, 30)}${name.length > 30 ? \"...\" : \"\"}`;\n const payload = {\n method: \"create\",\n details: {\n app_id: selectedProject.id,\n session_name: chatName,\n },\n };\n if (images.length >= 1) payload.details[\"session_id\"] = sessionId;\n createChat({\n payload,\n success: (res) => {\n images.length >= 1\n ? sendImageMessageHelper(message, images, res?.id, queryId)\n : sendMessageHelper(message, res?.id);\n setSearchParams({ chat: res?.id });\n },\n });\n };\n\n const getMessagesHelper = () => {\n const payload = {\n method: \"describe\",\n details: {\n session_id: selectedChat,\n app_id: selectedProject.id,\n },\n item_limit: 30,\n };\n if (moreMessagessData) payload[\"next_page\"] = moreMessagessData;\n getMessages({\n payload,\n otherkeys: generateMessagePayloadHelper({}),\n success: (data) => {\n const isReversed = data.messages?.messages?.length >= 8;\n setReversedScrolling(isReversed);\n setScrollToKey(messages?.[0]?.id);\n setmoreMessagesData(data?.nextPageData);\n if (!isReversed) scrollToBottom();\n },\n });\n };\n\n // HANDLERS\n const scrollToBottom = () => {\n messageBox.current?.scrollIntoView({ behavior: \"smooth\" });\n };\n\n const handleScroll = useCallback(\n (e) => {\n const { scrollTop, scrollHeight, clientHeight } = e.target;\n const scrolledToTop = Math.abs(scrollTop) + clientHeight === scrollHeight;\n setinitLoad(false);\n if (!scrolledToTop) return;\n if (!moreMessagessData || messgesLoader) return;\n getMessagesHelper();\n },\n [getMessagesHelper, messgesLoader]\n );\n\n const setSelectedChatOptionHandler = (item) => {\n setselectedChatOption(item);\n };\n\n const selectFileHandler = (files) => {\n const newFileLoadingState = {};\n const promises = Array.from(files).map((file) => {\n const fileName = file.name;\n newFileLoadingState[fileName] = true;\n\n return new Promise((resolve, reject) => {\n const fileType = file.type;\n const reader = new FileReader();\n setFileLoading((prevLoading) => ({\n ...prevLoading,\n ...newFileLoadingState,\n }));\n\n reader.onload = (e) => {\n resolve({ type: \"image\", data: e.target.result, name: fileName });\n };\n reader.onerror = (e) => reject(e);\n\n if (fileType.includes(\"image\")) {\n reader.readAsDataURL(file);\n } else {\n resolve({ type: \"doc\", data: file, name: fileName });\n }\n });\n });\n\n Promise.all(promises)\n .then((results) => {\n setSelectedFile((prevFiles) => [...prevFiles, ...results]);\n generateUrlHelper(files);\n })\n .catch((error) => {\n console.error(\"Error reading files:\", error);\n });\n };\n\n const removeSelectedFileHandler = (file) => {\n const payload = {\n resource: \"data\",\n method: \"delete_assets\",\n details: {\n session_id: selectedChat ?? sessionId,\n query_id: selectedQuery,\n project_id: selectedProject.id,\n db_type: \"opensearch\",\n files: [{ file_name: file }],\n },\n };\n setSelectedFile((prevFiles) => prevFiles.filter((x) => x.name !== file));\n setFileLoading(({ [file]: _, ...rest }) => rest);\n deleteMedia({ payload });\n };\n\n const sendMessageHandler = (message, images, queryId) => {\n const promptImages = images || selectedFile;\n setSelectedFile([]);\n\n // save message to reducer\n const usermessage = {\n message,\n images: promptImages,\n };\n\n if (queryId || selectedQuery) {\n usermessage[\"queryId\"] = queryId || selectedQuery;\n }\n\n dispatch(sendMessageRequest(usermessage));\n\n // save message to history\n const payload = generateMessagePayloadHelper({\n isLoading: true,\n images: promptImages,\n });\n messageHistory.current = payload;\n\n // scroll to bottom\n scrollToBottom();\n\n setLoading(true);\n if (!selectedChat) {\n createChatHelper(message, promptImages, queryId);\n return;\n }\n // send image message\n if (promptImages.length >= 1) {\n sendImageMessageHelper(message, promptImages, null, queryId);\n return;\n }\n // else send text message\n sendMessageHelper(message);\n };\n\n const startToastTimer = () => {\n clearToastTimer();\n toastTimerRef.current = setTimeout(() => {\n setShowToast(true);\n }, MESSAGE_TIMEOUT * 1000);\n };\n\n const clearToastTimer = () => {\n if (toastTimerRef.current) {\n clearTimeout(toastTimerRef.current);\n toastTimerRef.current = null;\n }\n };\n\n const stopResponseHandler = () => {\n stopResponseHelper(messageHistory.current);\n };\n\n // MANIPULATORS\n const manipulateJsonMessage = (lastMessage) => {\n const data = JSON.parse(lastMessage);\n const message = data?.response;\n if (\"message\" in data) return;\n\n // need to check if error is in data\n if (\"error\" in data) {\n stopResponseHelper(\n generateMessagePayloadHelper({ message: data.error, isError: true })\n );\n return;\n }\n if (message.includes(``)) {\n setLoading(false);\n messageHistory.current[\"isCompleted\"] = true;\n messageHistory.current[\"sources\"] = data?.metadata?.sources ?? [];\n messageHistory.current[\"id\"] = data?.metadata?.query_id;\n dispatch(recieveMessageRequest(messageHistory.current));\n messageHistory.current = null;\n return;\n }\n if (\"message\" in data) return;\n const updatedmessage = messageHistory.current.message.concat(message);\n messageHistory.current = generateMessagePayloadHelper({\n message: updatedmessage,\n isLoading: false,\n });\n };\n const manipulateMessage = (lastMessage) => {\n if (lastMessage.includes(``)) {\n setLoading(false);\n messageHistory.current[\"isCompleted\"] = true;\n dispatch(recieveMessageRequest(messageHistory.current));\n messageHistory.current = null;\n return;\n }\n if (lastMessage.includes(`message\":`)) return;\n const message = messageHistory.current.message.concat(lastMessage);\n messageHistory.current = generateMessagePayloadHelper({\n message,\n isLoading: false,\n });\n };\n\n // HOOKS\n useMemo(() => {\n if (selectedChat) return;\n setSessionId(generateGuid());\n }, [selectedChat]);\n\n useLayoutEffect(() => {\n if (!scrollToKey || messageWrapper.current.scrollTop > 0 || initLoad)\n return;\n itemRefs.current?.[scrollToKey]?.scrollIntoView();\n }, [scrollToKey]);\n\n useEffect(() => {\n if (!showToast) return;\n stopResponseHelper(\n generateMessagePayloadHelper({\n message: MESSAGE_TIMEOUT_ERROR,\n isError: true,\n })\n );\n setShowToast(false);\n }, [showToast]);\n\n useMemo(() => {\n if (!selectedChat) {\n dispatch(clearMessages());\n getWebSocket()?.close();\n return;\n }\n if (!lastMessage) return;\n clearToastTimer();\n // if (lastMessage.includes(`message\":`)) {\n // // navigateToLogin()\n // return;\n // }\n\n // manipulate message based on type\n if (MESSAGE_TYPE_JSON) return manipulateJsonMessage(lastMessage.data);\n\n manipulateMessage(lastMessage.data);\n }, [lastMessage]);\n\n useEffect(() => {\n setinitLoad(true);\n setmoreMessagesData(null);\n if (isLoading) return;\n if (selectedChat) {\n getMessagesHelper();\n return;\n }\n dispatch(clearMessages());\n }, [selectedChat]);\n\n useEffect(() => {\n let interval;\n const handleMediaChange = () => {\n const pendingMedia = Object.keys(fileLoading).length;\n if (pendingMedia > 0) {\n interval = setInterval(() => {\n getUploadedMediaStatus(true);\n }, 2000); // 2 seconds\n }\n };\n handleMediaChange();\n return () => interval && clearInterval(interval);\n }, [fileLoading, selectedQuery]);\n\n // CUSTOM COMPONENT\n const PromptThumb = () => (\n \"\"\n );\n\n return (\n
= 1 && \"selected\")}>\n {selectedProject?.webInterface && (\n \n {selectedProject?.webTitle}\n \n )}\n \n {messgesLoader && !moreMessagessData ? (\n \n ) : (\n <>\n {reversedScrolling && (\n
\n )}\n {messages.length >= 1 ? (\n
\n {moreMessagessData && messgesLoader && (\n \n )}\n {messages.map((message, index) => (\n (itemRefs.current[message.id] = ref)}\n isLoading={isLoading || chatLoader}\n previousMessage={messages[index - 1]}\n reGenerateMessage={sendMessageHandler}\n promptThumb={}\n />\n ))}\n {messageHistory.current && (\n }\n />\n )}\n
\n ) : (\n \n )}\n {!reversedScrolling && (\n
\n )}\n \n )}\n
\n
\n \n
\n
\n );\n};\n\nexport default ChatBox;\n", "text_preview": "import React, { useState, useMemo, useRef, useEffect } from \"react\"; import { InputBox, MessageBox } from \"..\"; import { useDispatch, useSelector } from \"react-redux\"; import { WebSocketHandlers } from \"../../../../../services/web-sockets\";", "input_sha256": "67ea993a714318c72c4dd082bf3484c9084adde96f2aaa5af21e62b33f4368d5", "text_sha256": "e4da2b4027473651a0039388e5516c92a351347049174dd85a6b60cd8fe6ed18", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8061, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5835018899180845, "noise_severity": 0.055735241897927654, "missing_content_severity": 0.0777612474890881, "factual_density": 3.2231603582856727, "pretraining_utility": 0.5127893445795909, "math_density": 0.07526147702185039, "code_density": 3.890458621923288, "reasoning_density": 0.4626139484669159, "education_level": 2.5143337566883224, "creative_writing_quality": -0.33962229369246816}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0068787694849505865, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9961507261448166, "secondary": 0.9754817568767109, "tertiary": 0.9826596032957897, "structured_records": 0.022463753348505006, "procedural_steps": 0.007658666876838505, "worked_solution": 0.0022037264318686333, "contextual_dialogue": 2.4794944015442003e-05, "content_quality": 0.42736340149985774, "noise_severity": 0.9535539650850603, "missing_content_severity": 0.9351989604257599, "factual_density": 0.3526336319047271, "pretraining_utility": 0.4535466907095549, "math_density": 0.9372821024817913, "code_density": 0.9087155182694067, "reasoning_density": 0.61448837627757, "education_level": 0.4059097612100619, "creative_writing_quality": 0.9992609526106261, "primary_topic": 0.9936733701125144, "secondary_topic": 0.8828449969726128}, "probability_margin": {"primary": 0.9939813103016532, "secondary": 0.9653263861639773, "tertiary": 0.9764885926115228, "education_level": 0.04655100529146111, "creative_writing_quality": 0.9990785666859776, "primary_topic": 0.9919155103081698, "secondary_topic": 0.8487837260740453}, "raw_probabilities": {"primary": {"Academic Writing": 4.492563090999424e-05, "Content Listing": 3.15191395737591e-05, "Creative Writing": 2.839505005284874e-05, "Customer Support Page": 1.1470908739625687e-05, "Q&A Forum": 1.6666056520212504e-05, "Discussion Thread": 1.1057481205822293e-05, "Comment Section": 2.014736035936568e-05, "Missing Content": 6.635048843384259e-05, "Knowledge Article": 9.005092427913414e-05, "Legal Notices": 7.402591915834146e-05, "News Article": 6.438899762679221e-06, "Organizational Announcement": 1.5393604181017675e-05, "Nonfiction Writing": 1.4042386626565512e-05, "Organizational About Page": 1.643310461598848e-05, "Personal About Page": 1.7591796036693968e-05, "Personal Blog": 9.713857130429265e-06, "Product Page": 1.197756303298121e-05, "Spam / Ads": 1.4535131381427317e-05, "Structured Data": 0.0005625269311665757, "Technical Writing": 0.9963111125554492, "Transcript / Interview": 3.662026361630058e-05, "Tutorial / How-To Guide": 0.0002492696399604542, "User Reviews": 9.933054010639041e-06, "Other / Unknown": 0.0023298022537960976}, "secondary": {"Academic Writing": 0.00012500164503000385, "Content Listing": 0.00021973334987165138, "Creative Writing": 0.00013920638653541843, "Customer Support Page": 6.121405011978827e-05, "Q&A Forum": 3.926294224952614e-05, "Discussion Thread": 4.008637174051363e-05, "Comment Section": 0.00010492618348472144, "Missing Content": 0.00037400972227908037, "Knowledge Article": 0.00024684489175531984, "Legal Notices": 0.00026291566859693456, "News Article": 6.313885486110502e-05, "Organizational Announcement": 7.032362085039152e-05, "Nonfiction Writing": 5.602898174414249e-05, "Organizational About Page": 7.021693305721174e-05, "Personal About Page": 6.493351374470805e-05, "Personal Blog": 5.527049239335149e-05, "Product Page": 4.154847087430101e-05, "Spam / Ads": 6.127838756776794e-05, "Structured Data": 0.011176964176204009, "Technical Writing": 0.009596483212678605, "Transcript / Interview": 0.0001611978894096085, "Tutorial / How-To Guide": 0.0004343817618891264, "User Reviews": 3.1682152881453495e-05, "NONE": 0.9765033503401813}, "tertiary": {"Academic Writing": 0.00010181755791206212, "Content Listing": 0.00034122195151555716, "Creative Writing": 0.00012300495697771947, "Customer Support Page": 5.835031115047812e-05, "Q&A Forum": 3.6514030040067245e-05, "Discussion Thread": 3.6024086158696625e-05, "Comment Section": 0.0001581188759975644, "Missing Content": 0.000806942835981674, "Knowledge Article": 0.0003558132903526862, "Legal Notices": 0.0005823493149122725, "News Article": 5.684047506236268e-05, "Organizational Announcement": 6.323280269275422e-05, "Nonfiction Writing": 6.329099217618087e-05, "Organizational About Page": 7.583284668248388e-05, "Personal About Page": 5.8622222807771286e-05, "Personal Blog": 4.819930218672411e-05, "Product Page": 4.3544918336124716e-05, "Spam / Ads": 8.11214750659613e-05, "Structured Data": 0.006893527213609054, "Technical Writing": 0.005871706472553275, "Transcript / Interview": 0.00023156811809713513, "Tutorial / How-To Guide": 0.0004983673006176164, "User Reviews": 3.1868823981904714e-05, "NONE": 0.9833821198251318}, "structured_records": {"false": 0.977536246651495, "true": 0.022463753348505006}, "procedural_steps": {"false": 0.9923413331231615, "true": 0.007658666876838505}, "worked_solution": {"false": 0.9977962735681314, "true": 0.0022037264318686333}, "contextual_dialogue": {"false": 0.9999752050559846, "true": 2.4794944015442003e-05}, "content_quality": {"-2": 0.010738060891953746, "-1": 0.10197320166861092, "0": 0.31567042812796003, "1": 0.4362854052523478, "2": 0.13533290405912754}, "noise_severity": {"0": 0.9523100801696089, "1": 0.04203520298751613, "2": 0.003986500395826223, "3": 0.0009458276694353068, "4": 0.0007223887776132857}, "missing_content_severity": {"0": 0.9492363454093324, "1": 0.03148543683848481, "2": 0.0125254867805721, "3": 0.005786086796983312, "4": 0.0009666441746272875}, "factual_density": {"0": 0.021050514654123657, "1": 0.029675538981178612, "2": 0.1309453576653174, "3": 0.34172025082366225, "4": 0.476608337875718}, "pretraining_utility": {"-2": 0.010059179393554062, "-1": 0.08883038371030899, "0": 0.3636390076831914, "1": 0.4532047713488832, "2": 0.0842666578640625}, "math_density": {"0": 0.933846326245335, "1": 0.061126114022924415, "2": 0.0026537871325497324, "3": 0.0006673016629371713, "4": 0.0017064709362537506}, "code_density": {"0": 0.0002616209886280932, "1": 0.00042378397502420846, "2": 0.008376535693294171, "3": 0.09047047081053868, "4": 0.9004675885325149}, "reasoning_density": {"0": 0.7497923807332293, "1": 0.10382041125295301, "2": 0.09815402263113325, "3": 0.030447249579041855, "4": 0.017785935803642707}, "education_level": {"none": 0.0010462518439322526, "0": 0.0011208021063348157, "1": 0.0038017520353706992, "2": 0.5049248010083849, "3": 0.45837379571692377, "4": 0.0307325972890536}, "creative_writing_quality": {"not_applicable": 0.9993841271755217, "-2": 8.64390940174513e-05, "-1": 0.00014262043169949312, "0": 0.00030556048954415664, "1": 5.6171139971883207e-05, "2": 2.508166924518834e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.482990523798757e-05, "Art & Design": 0.00045613374261721664, "Law & Crime": 3.6611671605464776e-05, "Education & Careers": 4.265960795057344e-05, "Entertainment & Media": 3.28912765228673e-05, "Fashion & Beauty": 2.672629429234273e-05, "Business, Economics & Finance": 8.856878192450943e-05, "Food & Dining": 2.722540645026777e-05, "Games & Gaming": 0.00011362526461798212, "Electronics & Hardware": 7.350576031184622e-05, "Health, Healthcare & Wellness": 2.829534094578846e-05, "History & Geography": 5.324709119551787e-05, "Home, Pets & Hobbies": 2.3033497186819887e-05, "Industry & Production": 2.7933144924889958e-05, "Language, Literature & Philosophy": 5.920119300177917e-05, "Politics & Government": 1.9506885672813012e-05, "Religion & Spirituality": 3.1410557049497816e-05, "Science, Mathematics & Engineering": 0.001413298589491782, "Relationships & Community": 0.0008668859102611889, "Software Usage & Applications": 0.0020011917230940953, "Programming & Software Engineering": 0.9939167020312639, "Sports & Fitness": 4.5627382051390845e-05, "Vehicles & Transportation": 1.9647959246885198e-05, "Travel & Tourism": 4.946105188194819e-05, "Other / Uncovered Subject": 0.0003303350676156555, "Unknown / Insufficient Content": 0.0001614448635851026}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000877801569656174, "Art & Design": 0.012565422297741274, "Law & Crime": 0.0005822986487708676, "Education & Careers": 0.0004073056830429473, "Entertainment & Media": 0.0005388133119712584, "Fashion & Beauty": 0.0002664588718601837, "Business, Economics & Finance": 0.0009259344455135528, "Food & Dining": 0.00023311656499475303, "Games & Gaming": 0.0005672693218614764, "Electronics & Hardware": 0.0004618314826124144, "Health, Healthcare & Wellness": 0.00036556234908213776, "History & Geography": 0.0003544182528856322, "Home, Pets & Hobbies": 0.000235062358146074, "Industry & Production": 0.00020831001971238064, "Language, Literature & Philosophy": 0.0003698000600420972, "Politics & Government": 0.0002652057374677853, "Religion & Spirituality": 0.0003044871628102068, "Science, Mathematics & Engineering": 0.023892875308405226, "Relationships & Community": 0.0017319573303854227, "Software Usage & Applications": 0.027455562953715037, "Programming & Software Engineering": 0.038567232553466925, "Sports & Fitness": 0.00021509893523643248, "Vehicles & Transportation": 0.0001160648404983409, "Travel & Tourism": 0.00033316445707115577, "Other / Uncovered Subject": 0.0008079868555380331, "NONE": 0.8873509586275122}}, "probabilities": {"primary": {"Academic Writing": 4.492563090999424e-05, "Content Listing": 3.15191395737591e-05, "Creative Writing": 2.839505005284874e-05, "Customer Support Page": 1.1470908739625687e-05, "Q&A Forum": 1.6666056520212504e-05, "Discussion Thread": 1.1057481205822293e-05, "Comment Section": 2.014736035936568e-05, "Missing Content": 6.635048843384259e-05, "Knowledge Article": 9.005092427913414e-05, "Legal Notices": 7.402591915834146e-05, "News Article": 6.438899762679221e-06, "Organizational Announcement": 1.5393604181017675e-05, "Nonfiction Writing": 1.4042386626565512e-05, "Organizational About Page": 1.643310461598848e-05, "Personal About Page": 1.7591796036693968e-05, "Personal Blog": 9.713857130429265e-06, "Product Page": 1.197756303298121e-05, "Spam / Ads": 1.4535131381427317e-05, "Structured Data": 0.0005625269311665757, "Technical Writing": 0.9963111125554492, 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"Politics & Government": 1.950688567281301e-05, "Religion & Spirituality": 3.141055704949781e-05, "Science, Mathematics & Engineering": 0.0014132985894917817, "Relationships & Community": 0.0008668859102611887, "Software Usage & Applications": 0.002001191723094095, "Programming & Software Engineering": 0.9939167020312637, "Sports & Fitness": 4.562738205139084e-05, "Vehicles & Transportation": 1.9647959246885195e-05, "Travel & Tourism": 4.946105188194818e-05, "Other / Uncovered Subject": 0.00033033506761565544, "Unknown / Insufficient Content": 0.00016144486358510258}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000877801569656174, "Art & Design": 0.012565422297741274, "Law & Crime": 0.0005822986487708676, "Education & Careers": 0.0004073056830429473, "Entertainment & Media": 0.0005388133119712584, "Fashion & Beauty": 0.0002664588718601837, "Business, Economics & Finance": 0.0009259344455135528, "Food & Dining": 0.00023311656499475303, "Games & Gaming": 0.0005672693218614764, "Electronics & Hardware": 0.0004618314826124144, "Health, Healthcare & Wellness": 0.00036556234908213776, "History & Geography": 0.0003544182528856322, "Home, Pets & Hobbies": 0.000235062358146074, "Industry & Production": 0.00020831001971238064, "Language, Literature & Philosophy": 0.0003698000600420972, "Politics & Government": 0.0002652057374677853, "Religion & Spirituality": 0.0003044871628102068, "Science, Mathematics & Engineering": 0.023892875308405226, "Relationships & Community": 0.0017319573303854227, "Software Usage & Applications": 0.027455562953715037, "Programming & Software Engineering": 0.038567232553466925, "Sports & Fitness": 0.00021509893523643248, "Vehicles & Transportation": 0.0001160648404983409, "Travel & Tourism": 0.00033316445707115577, "Other / Uncovered Subject": 0.0008079868555380331, "NONE": 0.8873509586275122}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, 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"reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:08.305108+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "1a818012-c141-43a3-86f8-e06b01aff8bc", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6a5376e10ea99fa05d610af59daedb64c652f6802504739f81a721e246fb4c8a", "source_metadata": {"uuid": "1a818012-c141-43a3-86f8-e06b01aff8bc", "repo_name": "rahulSailesh-shah/llm-platform-frontend", "relative_path": "content/platform/src/modules/private/project-detail/components/chat-box/index.jsx", "category": "WEB", "algo_rel_score": 0.942769467830658, "quality_score": 6.51988410949707, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2025-06-07T15:21:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6c93a65bf16a71fff5a31c473ed0bc1c5dd1cd97", "size_bytes": 18309, "file_timestamp": 1749309677, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5e529e0e6444b871d3451036b28abacccc6e17e1"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2025-06-07T15:21:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "6c93a65bf16a71fff5a31c473ed0bc1c5dd1cd97", "size_bytes": 18309, "file_timestamp": 1749309677, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "5e529e0e6444b871d3451036b28abacccc6e17e1", "url": "https://github.com/rahulSailesh-shah/llm-platform-frontend/blob/5e529e0e6444b871d3451036b28abacccc6e17e1/content/platform/src/modules/private/project-detail/components/chat-box/index.jsx"}, "gpt4o_tokens": 4228, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.5835018899180845, "noise_severity": 0.055735241897927654, "missing_content_severity": 0.0777612474890881, "factual_density": 3.2231603582856727, "pretraining_utility": 0.5127893445795909, "math_density": 0.07526147702185039, "code_density": 3.890458621923288, "reasoning_density": 0.4626139484669159, "education_level": 2.5143337566883224, "creative_writing_quality": -0.33962229369246816}, "score_applicability": {"education_level": 0.9989537481560677, "creative_writing_quality": 0.0006158728244781727}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/QuocToan27402/web_blog/blob/504a9335f69da27d772cdfe7d8098bb4eea7fdde/frontend/components/Blog/CreateBlog.js", "id": "javascript-0-c1aa77832834ab518b04d58416cca102", "text": "import React, { useState, useEffect } from \"react\";\nimport controls from \"../../utils/RTEControl\";\nimport dynamic from \"next/dynamic\";\nconst RichTextEditor = dynamic(() => import(\"@mantine/rte\"), {\n\tssr: false,\n\tloading: () => null,\n});\nimport axios from \"axios\";\nimport { parseCookies } from \"nookies\";\nimport {baseUrl} from \"../../utils/baseUrl\";\nimport LoadingSpinner from \"../../utils/LoadingSpinner\";\nimport toast from \"react-hot-toast\";\nimport { useRouter } from \"next/router\";\n\nconst INITIAL_VALUE = {\n\ttitle: \"\",\n\tcontent: \"\",\n author: \"\",\n catId: \"\",\n};\n\nconst CreateBlog = ({ user }) => {\n\tconst { auth_token } = parseCookies();\n\tconst [blog, setBlog] = useState(INITIAL_VALUE);\n\tconst [disabled, setDisabled] = React.useState(false);\n\tconst [loading, setLoading] = React.useState(false);\n\tconst [categories, setCategories] = useState([]);\n\tconst router = useRouter();\n\n\t// useEffect(() => {\n\t// \tconst isBlog = Object.values(blog).every((el) => Boolean(el));\n\t// \tisBlog ? setDisabled(false) : setDisabled(true);\n\t// }, [blog]);\n\n\tuseEffect(() => {\n\t\tconst fetchData = async () => {\n\t\t\tconst response = await axios.get(`${baseUrl}/api/category/`);\n\t\t\tsetCategories(response.data);\n\t\t};\n\n\t\tfetchData();\n\t}, []);\n\n\tconst handleChange = (e) => {\n\t\tconst { name, value } = e.target;\n\t\tsetBlog((prevState) => ({ ...prevState, [name]: value }));\n\t};\n\n\tconst handleSubmit = async (e) => {\n\t\te.preventDefault();\n\t\ttry {\n\t\t\tsetLoading(true);\n\t\t\t\n\t\t\tconst options = {\n\t\t\t\theaders: {\n\t\t\t\t Authorization: `Bearer ${auth_token}`,\n\t\t\t\t},\n\t\t\t\twithCredentials: true,\n\t\t\t};\n console.log(user);\n blog.author = user._id;\n\t\t\tconst payload = { ...blog };\n console.log(payload);\n\t\t\tconst url = `${baseUrl}/api/posts/posts`;\n\t\t\tconst response = await axios.post(url, payload);\n\t\t\tsetLoading(false);\n\n\t\t\ttoast.success(\"Tạo bài viết thành công\", {\n\t\t\t\tstyle: {\n\t\t\t\t\tborder: \"1px solid #4BB543\",\n\t\t\t\t\tpadding: \"16px\",\n\t\t\t\t\tcolor: \"#4BB543\",\n\t\t\t\t},\n\t\t\t\ticonTheme: {\n\t\t\t\t\tprimary: \"#4BB543\",\n\t\t\t\t\tsecondary: \"#FFFAEE\",\n\t\t\t\t},\n\t\t\t});\n router.push(\"/\");\n\t\t} catch (err) {\n\t\t\tconsole.log(err);\n\t\t\t\n\t\t\ttoast.error(err.data, {\n\t\t\t\tstyle: {\n\t\t\t\t\tborder: \"1px solid #ff0033\",\n\t\t\t\t\tpadding: \"16px\",\n\t\t\t\t\tcolor: \"#ff0033\",\n\t\t\t\t},\n\t\t\t\ticonTheme: {\n\t\t\t\t\tprimary: \"#ff0033\",\n\t\t\t\t\tsecondary: \"#FFFAEE\",\n\t\t\t\t},\n\t\t\t});\n\t\t} finally {\n\t\t\tsetLoading(false);\n\t\t}\n\t};\n\n\treturn (\n\t\t
\n\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t{categories.length > 0 &&\n\t\t\t\t\t\t\t\tcategories.map((cat) => (\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t))}\n\t\t\t\t\t\t\n\t\t\t\t\t
\n\t\t\t\t
\n\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tsetBlog((prevState) => ({\n\t\t\t\t\t\t\t\t\t...prevState,\n\t\t\t\t\t\t\t\t\tcontent: e,\n\t\t\t\t\t\t\t\t}))\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t/>\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\n\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\tCreate Blog \n\t\t\t\t\t\t{/* {loading ? : \"\"} */}\n\t\t\t\t\t\n\t\t\t\t
\n\t\t\t
\n\t\t
\n\t);\n};\n\nexport default CreateBlog;\n", "text_preview": "import React, { useState, useEffect } from \"react\"; import controls from \"../../utils/RTEControl\"; import dynamic from \"next/dynamic\"; const RichTextEditor = dynamic(() => import(\"@mantine/rte\"), { ssr: false, loading: () => null, }); impor", "input_sha256": "d9303a76653732e664df373a0000b7993a2997bc04638d4cee8adee79a6c153e", "text_sha256": "c1aa77832834ab518b04d58416cca102afb55469c9487742f2be910647cbb64a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8060, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, 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"excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9737, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9737, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:08.556627+00:00", "scoring_key_slot": "primary", "dataset_uid": "f2d91b32-812c-4cb4-bd61-f803289975a4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a7640e09604596bfa10db6842fb7a33597b599528c3f29a45df15570a0b26236", "source_metadata": {"uuid": "f2d91b32-812c-4cb4-bd61-f803289975a4", "repo_name": "QuocToan27402/web_blog", "relative_path": "frontend/components/Blog/CreateBlog.js", "category": "WEB", "algo_rel_score": 0.9109174609184265, "quality_score": 6.315986633300781, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-04-17T05:36:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0ce5b608c3b49fee59a493f189cde7f95256d954", "size_bytes": 4067, "file_timestamp": 1713408523, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "504a9335f69da27d772cdfe7d8098bb4eea7fdde"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2024-04-17T05:36:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0ce5b608c3b49fee59a493f189cde7f95256d954", "size_bytes": 4067, "file_timestamp": 1713408523, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "504a9335f69da27d772cdfe7d8098bb4eea7fdde", "url": "https://github.com/QuocToan27402/web_blog/blob/504a9335f69da27d772cdfe7d8098bb4eea7fdde/frontend/components/Blog/CreateBlog.js"}, "gpt4o_tokens": 1102, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7001397130965062, "noise_severity": 0.13653275988026267, "missing_content_severity": 0.05518952657014813, "factual_density": 3.0495274765892035, "pretraining_utility": 0.4346321572414175, "math_density": 0.009203033911509687, "code_density": 3.8696346162503956, "reasoning_density": 0.19174228756108092, "education_level": 2.182362002122476, "creative_writing_quality": -0.2921042525057432}, "score_applicability": {"education_level": 0.9986800596432889, "creative_writing_quality": 0.0006030642205377702}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/sunkarivasu/city-super-market2/blob/88b5f865b59c2ffcb81df281047cece763799813/client/src/components/AdminAddProduct.component.js", "id": "javascript-0-c5dd7ed6793fb91105872ff7b6ba1c0f", "text": "import axios from \"axios\";\nimport React, { useEffect } from \"react\";\nimport { useState} from \"react\";\nimport \"../css/style.css\";\nimport { useNavigate} from \"react-router-dom\";\nimport { cssTransition, toast } from \"react-toastify\";\nimport 'react-toastify/dist/ReactToastify.css';\nimport {storage} from \"../firebase\";\nimport {ref,uploadBytes,getDownloadURL} from \"firebase/storage\";\n\n\n\nfunction AdminAddProduct(props)\n{\n \n var navigate = useNavigate()\n var intialState = \n {\n category:\"\",\n subCategory:\"\",\n brand:\"\",\n description:\"\",\n image:\"\",\n quantity:\"\",\n discount:\"\",\n price:\"\",\n categoryErr:\"init\",\n subCategoryErr:\"init\",\n priceErr:\"init\",\n discountErr:\"init\",\n descriptionErr:\"init\",\n brandErr:\"init\",\n quantityErr:\"init\",\n imageErr:\"init\"\n }\n\n var [form,setForm] = useState(intialState)\n var [categoryList,setCategoryList] = useState([]);\n var [canBeSubmitted,setCanBeSubmitted] = useState(false);\n var [checkForm,setCheckForm] = useState(false)\n var [addSuccess,setAddSuccess] = useState(false);\n var [addFailure,setAddFailure] = useState(false);\n var [add,setAdd] = useState(false);\n var [categoryChanged,setCategoryChanged] = useState(false);\n var [subCategoryList,setSubCategoryList] = useState([]);\n // var [productImage,setProductImage] = useState(null);\n // var [imageChanged,setImageChanged] = useState();\n\n if(addSuccess)\n {\n toast.success(\"Product added successfully\",{position:toast.POSITION.BOTTOM_CENTER});\n props.handleShowAllProducts();\n // setTimeout(() =>\n // {\n // setForm(intialState);\n // setAddSuccess(false);\n // setAddFailure(false);\n // setAdd(false);\n // },5000)\n }\n\n if(checkForm)\n {\n validate()\n setCheckForm(false)\n }\n\n\n function validate()\n {\n if (form.categoryErr==\"\" && form.subCategoryErr==\"\" && form.brandErr==\"\" && form.priceErr==\"\" && form.discountErr==\"\" && form.descriptionErr==\"\" && form.quantityErr==\"\" && form.imageErr==\"\")\n {\n // console.log(form,\"can be submitted\");\n setCanBeSubmitted(true);\n }\n else\n {\n // console.log(form);\n setCanBeSubmitted(false);\n }\n }\n\n if(add)\n {\n addProduct()\n setAdd(false)\n }\n \n\n function addProduct()\n {\n var newProduct = {\n category:form.category,\n subCategory:form.subCategory,\n price:form.price,\n discount:form.discount,\n description:form.description,\n brand:form.brand,\n quantity:form.quantity,\n image:form.image\n }\n axios.post(\"/products/add\",newProduct)\n .then(() => {console.log(\"Product added by admin\")\n setAddSuccess(true)\n setAddFailure(false)\n })\n .catch((err) => {console.log(\"Error:\"+err)\n setAddFailure(true)\n setAddSuccess(false)});\n }\n\n \n useEffect(() =>{\n\n axios.get(\"/categories/\")\n .then((res) => {setCategoryList(res.data)})\n .catch((err) => {console.log(\"Error while fetching categories:\"+err)});\n \n if(categoryChanged)\n {\n getSubCategories();\n setCategoryChanged(false)\n }\n \n\n function getSubCategories()\n {\n if(form.category==\"none\")\n {\n setSubCategoryList([])\n setForm(intialState)\n }\n else\n {\n axios.get(\"/categories/getSubCategories/\"+form.category)\n .then((res) => {setSubCategoryList(res.data);\n // console.log(form.category+\" \"+subCategoryList)\n })\n .catch((err) => {console.log(\"Error while fetching sub categoryList:\"+err)}) \n } \n } \n\n\n return () =>{\n\n }\n },[categoryChanged]);\n\n\n\n function handleChangeCategory(event)\n {\n setForm({...form,[event.target.id]:event.target.value})\n validateForm(event.target.id,event.target.value);\n setCategoryChanged(true)\n setCheckForm(true)\n }\n\n\n function validateForm(field,value)\n {\n switch(field)\n {\n case 'category':\n if(value==\"None\" || value==\"\")\n {\n setForm({...form,categoryErr:\"Select Category\",category:\"none\"})\n }\n else\n setForm({...form,categoryErr:\"\",[field]:value})\n break\n case 'subCategory':\n if(value==\"None\" || value==\"\")\n setForm({...form,subCategoryErr:\"Select Sub Category\",[field]:value})\n else\n setForm({...form,subCategoryErr:\"\",[field]:value})\n break\n case 'brand':\n if(value.length<3)\n setForm({...form,brandErr:\"Product Name should contain atleast 3 characters\",brand:value})\n else if (!value.match(/^[a-zA-Z ]{3,}$/gm))\n {\n setForm({...form,brandErr:\"Product Name should only contain characters\",brand:value})\n }\n else\n setForm({...form,brandErr:\"\",brand:value})\n break\n case 'price':\n if(value.length==0)\n setForm({...form,priceErr:\"Enter Price\",price:value})\n else\n setForm({...form,priceErr:\"\",price:value}) \n break\n case \"discount\":\n if(value.length==0)\n setForm({...form,discountErr:\"Enter Discount\",discount:value})\n else if(value<0 || value>100)\n setForm({...form,discountErr:\"Discount sholud be in between 0 and 100\",discount:value})\n else\n setForm({...form,discountErr:\"\",discount:value})\n break\n case \"quantity\":\n if(value.length==0)\n setForm({...form,quantityErr:\"Enter Quantity\",quantity:value})\n else\n setForm({...form,quantityErr:\"\",quantity:value}) \n break\n case \"description\":\n if(value.length==0)\n setForm({...form,descriptionErr:\"Enter Description\",description:value})\n else\n setForm({...form,descriptionErr:\"\",description:value}) \n }\n}\n\n function handleChangeForm(event)\n {\n if(event.target.id==\"discount\" || event.target.id==\"quantity\" || event.target.id==\"price\")\n {\n var l = event.target.value.length\n var lc = event.target.value.charAt(l-1)\n if(lc=='0' || lc=='1' || lc=='2' || lc=='3' || lc=='4' || lc=='5' || lc=='6' || lc=='7' || lc=='8' ||lc=='9')\n {\n setForm({...form,[event.target.id]:event.target.value});\n validateForm(event.target.id,event.target.value)\n }\n else\n {\n setForm({...form,[event.target.id]:event.target.value.slice(0,l-1)})\n validateForm(event.target.id,event.target.value.slice(0,l-1))\n }\n \n }\n else\n {\n setForm({...form,[event.target.id]:event.target.value});\n validateForm(event.target.id,event.target.value)\n }\n setCheckForm(true)\n }\n\n\n function createCategoryOption(props)\n {\n return (\n \n );\n }\n\n function createSubCategoryOption(props)\n {\n return(\n \n )\n }\n\n \n function handleChangeImage(event)\n {\n console.log(event.target.files[0]);\n if(event.target.files[0])\n {\n //setProductImage(event.target.files[0])\n UploadFile(event.target.files[0])\n }\n else\n {\n setForm({...form,image:\"\",imageErr:\"Please upload image\"})\n setCheckForm(true);\n }\n }\n\n function UploadFile(image)\n {\n console.log(image)\n const imageRef = ref(storage,form.category+form.subCategory+form.brand);\n const childRef = ref(storage,form.category+form.subCategory+form.brand+\"/image\");\n\n uploadBytes(childRef,image).then(()=>\n {\n getDownloadURL(childRef).then((url) =>\n {\n setForm({...form,image:url,imageErr:\"\"})\n setCheckForm(true);\n console.log(url);\n })\n .catch((err) =>{\n console.log(\"Error occured while uploading image\"+err);\n })\n })\n .catch((err)=>{console.log(\"Error occured while downloading the url\"+err)}) \n\n }\n\n\n\n\n return(
\n
\n

Add Product

\n
\n \n \n { form.categoryErr.length>0 && form.categoryErr!=\"init\" &&

{form.categoryErr}

}\n
\n
\n \n \n { form.subCategoryErr.length>0 && form.subCategoryErr!=\"init\" &&

{form.subCategoryErr}

}\n
\n
\n \n \n { form.brandErr.length>0 && form.brandErr!=\"init\" &&

{form.brandErr}

}\n
\n
\n \n {form.image && }\n \n { form.imageErr.length>0 && form.imageErr!=\"init\" &&

{form.imageErr}

}\n
\n
\n \n \n { form.quantityErr.length>0 && form.quantityErr!=\"init\" &&

{form.quantityErr}

}\n
\n
\n \n \n { form.priceErr.length>0 && form.priceErr!=\"init\" &&

{form.priceErr}

}\n
\n
\n \n \n { form.discountErr.length>0 && form.discountErr!=\"init\" &&

{form.discountErr}

}\n
\n \n
\n \n \n { form.descriptionErr.length>0 && form.descriptionErr!=\"init\" &&

{form.descriptionErr}

}\n
\n { canBeSubmitted?:}\n {addSuccess &&

Product added Successfully

}\n {addFailure &&

Please Try Again

}\n
\n
\n )\n};\n\nexport default AdminAddProduct;\n\n// https://github.com/sunkarivasu/city-super-market.git", "text_preview": "import axios from \"axios\"; import React, { useEffect } from \"react\"; import { useState} from \"react\"; import \"../css/style.css\"; import { useNavigate} from \"react-router-dom\"; import { cssTransition, toast } from \"react-toastify\"; import 'r", "input_sha256": "09d370af4df403b11fe80c49d20a51e5e7182c3117cd9e6f21fceea27175a806", "text_sha256": "c5dd7ed6793fb91105872ff7b6ba1c0fc388df8138bb35378fd585ad47ea521f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8062, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2849610236496881, "noise_severity": 0.2215028432197334, "missing_content_severity": 0.030604569478227227, "factual_density": 3.1156644499924493, "pretraining_utility": 0.4152961677034155, "math_density": 0.16729449648175304, "code_density": 3.795929856210714, "reasoning_density": 0.7069766276408981, "education_level": 2.101069658370175, "creative_writing_quality": -0.6327977148002226}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0057531632937217695, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9950349184210234, "secondary": 0.9498052596942291, "tertiary": 0.9550355611945588, "structured_records": 0.044433254129188984, "procedural_steps": 0.007029446821858112, "worked_solution": 0.007059084308577005, "contextual_dialogue": 7.484088168428717e-06, "content_quality": 0.4435474804221615, "noise_severity": 0.8154142973168887, "missing_content_severity": 0.9744961921014773, "factual_density": 0.263053708327041, "pretraining_utility": 0.4234689731540494, "math_density": 0.8605879195985392, "code_density": 0.8299415468422617, "reasoning_density": 0.4108528102992517, "education_level": 0.7929375827645152, "creative_writing_quality": 0.9989742641924491, "primary_topic": 0.9811509323734697, "secondary_topic": 0.8509195179193577}, "probability_margin": {"primary": 0.9928435090529637, "secondary": 0.9304089397352012, "tertiary": 0.939506344972463, "education_level": 0.7004573939907135, "creative_writing_quality": 0.9988237917628886, "primary_topic": 0.9695914641866875, "secondary_topic": 0.7734783828301349}, "raw_probabilities": {"primary": {"Academic Writing": 4.690698116624347e-05, "Content Listing": 5.2359443768583076e-05, "Creative Writing": 3.048073236531744e-05, "Customer Support Page": 1.4255586839864492e-05, "Q&A Forum": 2.355604308359108e-05, "Discussion Thread": 1.309238279494304e-05, "Comment Section": 2.7122433970989785e-05, "Missing Content": 4.2866659739386934e-05, "Knowledge Article": 8.089158887120627e-05, "Legal Notices": 0.00010599931680848191, "News Article": 7.870014127990357e-06, "Organizational Announcement": 1.6929275355136116e-05, "Nonfiction Writing": 1.768948074986874e-05, "Organizational About Page": 2.1738127747972466e-05, "Personal About Page": 2.2645633107825016e-05, "Personal Blog": 1.459596580185984e-05, "Product Page": 7.134938084657369e-05, "Spam / Ads": 4.139504846256438e-05, "Structured Data": 0.0007252379020477607, "Technical Writing": 0.9952417968201475, "Transcript / Interview": 2.1966301695421784e-05, "Tutorial / How-To Guide": 0.0009481057173509903, "User Reviews": 1.2861395966134277e-05, "Other / Unknown": 0.0023982877671837773}, "secondary": {"Academic Writing": 0.00033401985564745053, "Content Listing": 0.0005315884856777387, "Creative Writing": 0.0003217787153106524, "Customer Support Page": 0.00018293616379055637, "Q&A Forum": 0.00012529627646701194, "Discussion Thread": 0.00010613595139109874, "Comment Section": 0.0008456245009235731, "Missing Content": 0.0005712703361725767, "Knowledge Article": 0.0004227225415712521, "Legal Notices": 0.0013150430870412018, "News Article": 0.00016781371587882448, 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"https://github.com/sunkarivasu/city-super-market2/blob/88b5f865b59c2ffcb81df281047cece763799813/client/src/components/AdminAddProduct.component.js"}, "gpt4o_tokens": 2833, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2849610236496881, "noise_severity": 0.2215028432197334, "missing_content_severity": 0.030604569478227227, "factual_density": 3.1156644499924493, "pretraining_utility": 0.4152961677034155, "math_density": 0.16729449648175304, "code_density": 3.795929856210714, "reasoning_density": 0.7069766276408981, "education_level": 2.101069658370175, "creative_writing_quality": -0.6327977148002226}, "score_applicability": {"education_level": 0.9982859891824204, "creative_writing_quality": 0.0008547798396257494}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Napman22/Musi/blob/9303a88f3dccd144a4fd62f6075e0cc2e7727190/server/lib/firebase.js", "id": "javascript-0-6b3111a8a7854aed143a663521d5922d", "text": "const express = require(\"express\");\nconst cors = require(\"cors\");\n\nconst app = express();\n\nconst admin = require(\"firebase-admin\");\nconst credentials = require(\"./creds.json\");\n\nadmin.initializeApp({\n credential: admin.credential.cert(credentials),\n});\n\napp.use(cors());\napp.use(express.json());\napp.use(express.urlencoded({ extended: true }));\n\nconst db = admin.firestore();\n\n// ----------------- CRUD Routes -----------------\n\n// Create user\napp.post(\"/create\", async (req, res) => {\n try {\n console.log(req.body);\n const userJson = {\n email: req.body.email,\n firstName: req.body.firstName,\n lastName: req.body.lastName,\n artists: req.body.artists,\n genres: req.body.genres,\n songs: req.body.songs,\n };\n const response = await db.collection(\"users\").add(userJson);\n res.send(response);\n } catch (error) {\n res.send(error);\n }\n});\n\n// Get all users\napp.get(\"/read/all\", async (req, res) => {\n try {\n const usersRef = db.collection(\"users\");\n const response = await usersRef.get();\n let responseArr = [];\n response.forEach((doc) => {\n responseArr.push(doc.data());\n });\n res.send(responseArr);\n } catch (error) {\n res.send(error);\n }\n});\n\n// Get single user by doc ID\napp.get(\"/read/:id\", async (req, res) => {\n try {\n const userRef = db.collection(\"users\").doc(req.params.id);\n const response = await userRef.get();\n res.send(response.data());\n } catch (error) {\n res.send(error);\n }\n});\n\n// Update user\napp.post(\"/update\", async (req, res) => {\n try {\n const id = req.body.id;\n const userRef = db.collection(\"users\").doc(id);\n\n await userRef.update({\n firstName: \"hello\",\n });\n\n const updatedDoc = await userRef.get();\n res.send(updatedDoc.data());\n } catch (error) {\n res.send(error);\n }\n});\n\n// Delete user\napp.delete(\"/delete/:id\", async (req, res) => {\n try {\n const response = await db.collection(\"users\").doc(req.params.id).delete();\n res.send(response);\n } catch (error) {\n res.send(error);\n }\n});\n\n\nasync function getAllUsers() {\n try {\n const snapshot = await db.collection(\"users\").get();\n const users = snapshot.docs.map((doc) => ({\n id: doc.id,\n ...doc.data(),\n }));\n return users;\n } catch (error) {\n console.error(\"Error fetching users:\", error);\n return [];\n }\n}\n\nasync function authMiddleware(req, res, next) {\n try {\n const authHeader = req.headers.authorization || \"\";\n const token = authHeader.split(\" \")[1];\n if (!token) {\n return res.status(401).json({ error: \"Missing auth token\" });\n }\n\n const decodedToken = await admin.auth().verifyIdToken(token);\n console.log(\"Decoded token:\", decodedToken);\n req.user = decodedToken;\n next();\n } catch (error) {\n console.error(\"Auth middleware error:\", error);\n res.status(401).json({ error: \"Invalid or expired token\" });\n }\n}\n\n\napp.use(authMiddleware);\n\n(async () => {\n const users = await getAllUsers();\n module.exports = { users };\n console.log(\"All users:\", users);\n})();\n\nmodule.exports = { getAllUsers };\n\nconst { computeAllSimilarities } = require(\"./userMatches\");\n\napp.get(\"/matches\", async (req, res) => {\n try {\n const userDocs = await getAllUsers();\n\n const userMap = {};\n userDocs.forEach(({ id, genres, artists, songs }) => {\n userMap[id] = { genres, artists, songs };\n });\n\n const allSimilarities = computeAllSimilarities(userMap);\n\n const matchResults = {};\n Object.keys(userMap).forEach((userId) => {\n const sortedMatches = Object.entries(allSimilarities[userId])\n .filter(([otherId]) => otherId !== userId)\n .sort((a, b) => b[1] - a[1]) // descending by similarity\n .slice(0, 3) // top 3 matches\n .map(([matchedUserId, score]) => ({\n id: matchedUserId,\n score: score || 0,\n genres: userMap[matchedUserId]?.genres || [],\n artists: userMap[matchedUserId]?.artists || [],\n songs: userMap[matchedUserId]?.songs || [],\n }));\n\n matchResults[userId] = sortedMatches;\n });\n\n res.json(matchResults);\n } catch (err) {\n console.error(\"Error computing matches:\", err);\n res.status(500).json({ error: \"Failed to compute matches\" });\n }\n});\n\napp.post(\"/send-invite\", authMiddleware, async (req, res) => {\n try {\n const senderId = req.user.uid; \n const { receiverId, message } = req.body;\n\n if (!receiverId) {\n return res.status(400).json({ error: \"Receiver ID is required\" });\n }\n\n await db.collection(\"invites\").add({\n senderId,\n receiverId,\n message: message || \"Hi! I'd like to connect!\",\n timestamp: admin.firestore.FieldValue.serverTimestamp(),\n status: \"pending\", // You can add a default status if you want\n });\n\n res.status(200).json({ message: \"Invite sent successfully\" });\n } catch (error) {\n console.error(\"Error sending invite:\", error);\n res.status(500).json({ error: \"Failed to send invite\" });\n }\n});\n\n// Fetch pending invites\napp.get(\"/get-invites\", authMiddleware, async (req, res) => {\n try {\n const userId = req.user.uid;\n\n const invitesSnapshot = await db\n .collection(\"invites\")\n .where(\"receiverId\", \"==\", userId)\n .where(\"status\", \"==\", \"pending\")\n .get();\n\n const invites = invitesSnapshot.docs.map((doc) => ({\n id: doc.id,\n ...doc.data(),\n }));\n\n res.status(200).json(invites);\n } catch (error) {\n console.error(\"Error fetching invite:\", error);\n res.status(500).json({ error: \"Failed to fetch invites\" });\n }\n});\n\n// Accept invite\napp.post(\"/accept-invite\", authMiddleware, async (req, res) => {\n try {\n const { inviteId } = req.body;\n await db.collection(\"invites\").doc(inviteId).update({\n status: \"accepted\",\n acceptedAt: admin.firestore.FieldValue.serverTimestamp(),\n });\n res.status(200).json({ message: \"Invite accepted successfully!\" });\n } catch (error) {\n console.error(\"Error accepting invite:\", error);\n res.status(500).json({ error: \"Failed to accept invite\" });\n }\n});\n\nconst PORT = process.env.PORT || 8080;\napp.listen(PORT, () => {\n console.log(`Server is running on PORT ${PORT}.`);\n});\n", "text_preview": "const express = require(\"express\"); const cors = require(\"cors\"); const app = express(); const admin = require(\"firebase-admin\"); const credentials = require(\"./creds.json\"); admin.initializeApp({ credential: admin.credential.cert(credentia", "input_sha256": "92d3a86db705fce7231e5a585f957bceaf17750b7d62a59624979108f22f0a3e", "text_sha256": "6b3111a8a7854aed143a663521d5922d097fd1d6b458e851ba5950f374a5f699", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8063, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.14877371656981492, "noise_severity": 0.08858481958593056, "missing_content_severity": 0.05325751743061409, "factual_density": 3.2863854847883336, "pretraining_utility": 0.558357208679663, "math_density": 0.10017700990052872, "code_density": 3.76393468644209, "reasoning_density": 0.48890834961351065, "education_level": 2.227497241635575, "creative_writing_quality": -0.24601975172114732}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005274861163239941, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964682447722969, "secondary": 0.9827677099184917, "tertiary": 0.9882719128714378, "structured_records": 0.028447470120473984, "procedural_steps": 0.010984973864649037, "worked_solution": 0.006642509478185392, "contextual_dialogue": 8.766086180726997e-06, "content_quality": 0.41371999160980344, "noise_severity": 0.9261793170117245, "missing_content_severity": 0.9556187354744883, "factual_density": 0.40532123732361114, "pretraining_utility": 0.5053109932129436, "math_density": 0.9165191584162261, "code_density": 0.8032789053684084, "reasoning_density": 0.5925763753220745, "education_level": 0.6969862702341495, "creative_writing_quality": 0.9993008127850853, "primary_topic": 0.992525003828185, "secondary_topic": 0.8919912123980296}, "probability_margin": {"primary": 0.995180022687096, "secondary": 0.9755567559934691, "tertiary": 0.9850094320233499, "education_level": 0.5192483332428213, "creative_writing_quality": 0.9991104661811206, "primary_topic": 0.9894971759924444, "secondary_topic": 0.8347943193980782}, "raw_probabilities": {"primary": {"Academic Writing": 2.5509468382655844e-05, "Content Listing": 2.134554917919535e-05, "Creative Writing": 1.3724379603895861e-05, "Customer Support Page": 7.889437470508622e-06, "Q&A Forum": 1.0768876065774237e-05, "Discussion Thread": 6.985842381612906e-06, "Comment Section": 1.1560194873013255e-05, "Missing Content": 2.156515968772396e-05, "Knowledge Article": 7.925302923567562e-05, "Legal Notices": 2.1759993809533697e-05, "News Article": 4.3482441491379e-06, "Organizational Announcement": 9.414501158885438e-06, "Nonfiction Writing": 1.0046645889922758e-05, "Organizational About Page": 1.1329283491670703e-05, "Personal About Page": 1.5043856558996339e-05, "Personal Blog": 8.00214243936922e-06, "Product Page": 7.436617450191593e-06, "Spam / Ads": 9.407034119524405e-06, "Structured Data": 0.0006321604197144684, "Technical Writing": 0.9966154012401178, "Transcript / Interview": 1.3209020575542682e-05, "Tutorial / How-To Guide": 0.0010025279193634955, "User Reviews": 5.932591259658238e-06, "Other / Unknown": 0.0014353785530217473}, "secondary": {"Academic Writing": 0.00010912948980308593, "Content Listing": 0.00013119426675806787, "Creative Writing": 9.390035599020481e-05, "Customer Support Page": 5.701827958116501e-05, "Q&A Forum": 4.1087001238074836e-05, "Discussion Thread": 3.561969165602923e-05, "Comment Section": 0.0006055956767412403, "Missing Content": 0.0001969363420418659, "Knowledge Article": 0.0004768876399171679, "Legal Notices": 0.00011519865603017035, "News Article": 5.010285498620937e-05, "Organizational Announcement": 4.7917114501930556e-05, "Nonfiction Writing": 5.418069074729651e-05, "Organizational About Page": 6.968940879238906e-05, "Personal About Page": 5.401090893735403e-05, "Personal Blog": 4.684263933421149e-05, "Product Page": 3.359109500807978e-05, "Spam / Ads": 6.21269992382746e-05, "Structured Data": 0.00369740016522582, "Technical Writing": 0.007928966011752038, "Transcript / Interview": 5.854995035457413e-05, "Tutorial / How-To Guide": 0.0025237028802006342, "User Reviews": 2.4629875943001832e-05, "NONE": 0.9834857220052212}, "tertiary": {"Academic Writing": 6.337723780326624e-05, "Content Listing": 0.0001740796053223816, "Creative Writing": 7.760988169416768e-05, "Customer Support Page": 4.1133771559193225e-05, "Q&A Forum": 2.6315145269424715e-05, "Discussion Thread": 2.4850714634939395e-05, "Comment Section": 0.0003928567789392715, "Missing Content": 0.00028969155197891805, "Knowledge Article": 0.0004736098483129356, "Legal Notices": 0.0001413601389866909, "News Article": 3.6474402847569075e-05, "Organizational Announcement": 3.229900164299762e-05, "Nonfiction Writing": 4.155171936730921e-05, "Organizational About Page": 5.883577705587103e-05, "Personal About Page": 3.8814878498889795e-05, "Personal Blog": 3.2121650789467895e-05, "Product Page": 2.8204459390501835e-05, "Spam / Ads": 5.130534111055338e-05, "Structured Data": 0.0031177689266102863, "Technical Writing": 0.003751151145111288, "Transcript / Interview": 4.155909060726451e-05, "Tutorial / How-To Guide": 0.002287729277316083, "User Reviews": 1.6716486689555126e-05, "NONE": 0.9887605831684612}, "structured_records": {"false": 0.971552529879526, "true": 0.028447470120473984}, "procedural_steps": {"false": 0.989015026135351, "true": 0.010984973864649037}, "worked_solution": {"false": 0.9933574905218147, "true": 0.006642509478185392}, "contextual_dialogue": {"false": 0.9999912339138193, "true": 8.766086180726997e-06}, "content_quality": {"-2": 0.033573834013428755, "-1": 0.2102334787223529, "0": 0.38761856313349974, "1": 0.31099338494241174, "2": 0.057580739188306786}, "noise_severity": {"0": 0.9214608132644753, "1": 0.07092476446814669, "2": 0.005834445640548615, "3": 0.0011287426706309803, "4": 0.0006512339561984266}, "missing_content_severity": {"0": 0.9691426090224575, "1": 0.010974986985491863, "2": 0.01768362441061657, "3": 0.0018798367018469633, "4": 0.0003189428795870499}, "factual_density": {"0": 0.007145680012271793, "1": 0.018284411076239248, "2": 0.12760310509028167, "3": 0.37497235175329835, "4": 0.47199445206790885}, "pretraining_utility": {"-2": 0.007402889665878742, "-1": 0.06316752752583527, "0": 0.36909107568309557, "1": 0.4843464987131251, "2": 0.07599200841206528}, "math_density": {"0": 0.9211552592822833, "1": 0.06586845070445123, "2": 0.007688587342086711, "3": 0.002219426172811563, "4": 0.003068276498367345}, "code_density": {"0": 0.00041474699555823504, "1": 0.0008093392894085396, "2": 0.028938963454696126, "3": 0.1741003807980591, "4": 0.7957365694622779}, "reasoning_density": {"0": 0.7469918898346227, "1": 0.08151166498865424, "2": 0.12231298344902869, "3": 0.03396312918397738, "4": 0.015220332543716682}, "education_level": {"none": 0.0009667368194820085, "0": 0.0015490441072594197, "1": 0.013791898637896923, "2": 0.7474885585284579, "3": 0.22824022528563662, "4": 0.00796353662126723}, "creative_writing_quality": {"not_applicable": 0.9994173439875711, "-2": 5.80951928769519e-05, "-1": 0.00013413954462469327, "0": 0.00030687780645043296, "1": 6.0101894091771044e-05, "2": 2.344157438510989e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.8785336038372306e-05, "Art & Design": 4.295021540124575e-05, "Law & Crime": 3.538396874242723e-05, "Education & Careers": 4.214045609201887e-05, "Entertainment & Media": 0.0033153276885027443, "Fashion & Beauty": 4.538469829717605e-05, "Business, Economics & Finance": 7.292089730298635e-05, "Food & Dining": 3.2230208185873835e-05, "Games & Gaming": 0.0002129800235495622, "Electronics & Hardware": 5.0957700724654496e-05, "Health, Healthcare & Wellness": 2.0458476207818865e-05, "History & Geography": 2.507087508146084e-05, "Home, Pets & Hobbies": 4.580111085269737e-05, "Industry & Production": 2.0743692986006833e-05, "Language, Literature & Philosophy": 2.8387208319664786e-05, "Politics & Government": 1.8031037201477146e-05, "Religion & Spirituality": 2.5141195570911467e-05, "Science, Mathematics & Engineering": 0.0001064985934664075, "Relationships & Community": 0.0017899935510400275, "Software Usage & Applications": 0.0008326370898367681, "Programming & Software Engineering": 0.9928125036809471, "Sports & Fitness": 3.9810952488960215e-05, "Vehicles & Transportation": 1.5523689584702048e-05, "Travel & Tourism": 3.275175018058745e-05, "Other / Uncovered Subject": 0.0002653462465461797, "Unknown / Insufficient Content": 4.223965685220126e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006497415178070611, "Art & Design": 0.0006361883083310675, "Law & Crime": 0.0006574035999515007, "Education & Careers": 0.0003920952857800473, "Entertainment & Media": 0.06135107713848864, "Fashion & Beauty": 0.0003125310948176166, "Business, Economics & Finance": 0.000810268344189273, "Food & Dining": 0.000251311824244689, "Games & Gaming": 0.0006831282215496957, "Electronics & Hardware": 0.00036046938304496574, "Health, Healthcare & Wellness": 0.00023963858597277226, "History & Geography": 0.00019889949974878377, "Home, Pets & Hobbies": 0.00035269247174114256, "Industry & Production": 0.0001494128961087654, "Language, Literature & Philosophy": 0.00017232485006408467, "Politics & Government": 0.00020202843570369874, "Religion & Spirituality": 0.00026382540509653697, "Science, Mathematics & Engineering": 0.0007664212375037238, "Relationships & Community": 0.006001512704518164, "Software Usage & Applications": 0.014163894626134927, "Programming & Software Engineering": 0.013822731637505253, "Sports & Fitness": 0.0002242680514824497, "Vehicles & Transportation": 9.972041896942365e-05, "Travel & Tourism": 0.00029850948726067606, "Other / Uncovered Subject": 0.0007945084374182569, "NONE": 0.8961453965365669}}, "probabilities": {"primary": {"Academic Writing": 2.5509468382655844e-05, "Content Listing": 2.134554917919535e-05, "Creative Writing": 1.3724379603895861e-05, "Customer Support Page": 7.889437470508622e-06, "Q&A Forum": 1.0768876065774237e-05, "Discussion Thread": 6.985842381612906e-06, "Comment Section": 1.1560194873013255e-05, "Missing Content": 2.156515968772396e-05, "Knowledge Article": 7.925302923567562e-05, "Legal Notices": 2.1759993809533697e-05, "News Article": 4.3482441491379e-06, "Organizational Announcement": 9.414501158885438e-06, "Nonfiction Writing": 1.0046645889922758e-05, "Organizational About Page": 1.1329283491670703e-05, "Personal About Page": 1.5043856558996339e-05, "Personal Blog": 8.00214243936922e-06, "Product Page": 7.436617450191593e-06, "Spam / Ads": 9.407034119524405e-06, "Structured Data": 0.0006321604197144684, "Technical Writing": 0.9966154012401178, "Transcript / Interview": 1.3209020575542682e-05, "Tutorial / How-To Guide": 0.0010025279193634955, "User Reviews": 5.932591259658238e-06, "Other / Unknown": 0.0014353785530217473}, "secondary": {"Academic Writing": 0.00010912948980308593, "Content Listing": 0.00013119426675806787, "Creative Writing": 9.390035599020481e-05, "Customer Support Page": 5.701827958116501e-05, "Q&A Forum": 4.1087001238074836e-05, "Discussion Thread": 3.561969165602923e-05, 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"Discussion Thread": 2.4850714634939395e-05, "Comment Section": 0.0003928567789392715, "Missing Content": 0.00028969155197891805, "Knowledge Article": 0.0004736098483129356, "Legal Notices": 0.0001413601389866909, "News Article": 3.6474402847569075e-05, "Organizational Announcement": 3.229900164299762e-05, "Nonfiction Writing": 4.155171936730921e-05, "Organizational About Page": 5.883577705587103e-05, "Personal About Page": 3.8814878498889795e-05, "Personal Blog": 3.2121650789467895e-05, "Product Page": 2.8204459390501835e-05, "Spam / Ads": 5.130534111055338e-05, "Structured Data": 0.0031177689266102863, "Technical Writing": 0.003751151145111288, "Transcript / Interview": 4.155909060726451e-05, "Tutorial / How-To Guide": 0.002287729277316083, "User Reviews": 1.6716486689555126e-05, "NONE": 0.9887605831684612}, "structured_records": {"false": 0.971552529879526, "true": 0.028447470120473984}, "procedural_steps": {"false": 0.989015026135351, "true": 0.010984973864649037}, "worked_solution": {"false": 0.9933574905218147, "true": 0.006642509478185392}, "contextual_dialogue": {"false": 0.9999912339138193, "true": 8.766086180726997e-06}, "content_quality": {"-2": 0.03357383401342876, "-1": 0.21023347872235293, "0": 0.3876185631334998, "1": 0.3109933849424118, "2": 0.05758073918830679}, "noise_severity": {"0": 0.9214608132644753, "1": 0.07092476446814669, "2": 0.005834445640548615, "3": 0.0011287426706309803, "4": 0.0006512339561984266}, "missing_content_severity": {"0": 0.9691426090224575, "1": 0.010974986985491863, "2": 0.01768362441061657, "3": 0.0018798367018469633, "4": 0.0003189428795870499}, "factual_density": {"0": 0.0071456800122717936, "1": 0.01828441107623925, "2": 0.1276031050902817, "3": 0.3749723517532984, "4": 0.4719944520679089}, "pretraining_utility": {"-2": 0.007402889665878743, "-1": 0.06316752752583528, "0": 0.3690910756830956, "1": 0.48434649871312513, "2": 0.0759920084120653}, "math_density": {"0": 0.9211552592822833, "1": 0.06586845070445123, "2": 0.007688587342086711, "3": 0.002219426172811563, "4": 0.003068276498367345}, "code_density": {"0": 0.0004147469955582351, "1": 0.0008093392894085397, "2": 0.02893896345469613, "3": 0.17410038079805912, "4": 0.795736569462278}, "reasoning_density": {"0": 0.746991889834623, "1": 0.08151166498865427, "2": 0.12231298344902873, "3": 0.03396312918397739, "4": 0.015220332543716688}, "education_level": {"none": 0.0009667368194820085, "0": 0.0015490441072594197, "1": 0.013791898637896923, "2": 0.7474885585284579, "3": 0.22824022528563662, "4": 0.00796353662126723}, "creative_writing_quality": {"not_applicable": 0.9994173439875711, "-2": 5.80951928769519e-05, "-1": 0.00013413954462469327, "0": 0.00030687780645043296, "1": 6.0101894091771044e-05, "2": 2.344157438510989e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.8785336038372306e-05, "Art & Design": 4.295021540124575e-05, "Law & Crime": 3.538396874242723e-05, "Education & Careers": 4.214045609201887e-05, "Entertainment & Media": 0.0033153276885027443, "Fashion & Beauty": 4.538469829717605e-05, "Business, Economics & Finance": 7.292089730298635e-05, "Food & Dining": 3.2230208185873835e-05, "Games & Gaming": 0.0002129800235495622, "Electronics & Hardware": 5.0957700724654496e-05, "Health, Healthcare & Wellness": 2.0458476207818865e-05, "History & Geography": 2.507087508146084e-05, "Home, Pets & Hobbies": 4.580111085269737e-05, "Industry & Production": 2.0743692986006833e-05, "Language, Literature & Philosophy": 2.8387208319664786e-05, "Politics & Government": 1.8031037201477146e-05, "Religion & Spirituality": 2.5141195570911467e-05, "Science, Mathematics & Engineering": 0.0001064985934664075, "Relationships & Community": 0.0017899935510400275, "Software Usage & Applications": 0.0008326370898367681, "Programming & Software Engineering": 0.9928125036809471, "Sports & Fitness": 3.9810952488960215e-05, "Vehicles & Transportation": 1.5523689584702048e-05, "Travel & Tourism": 3.275175018058745e-05, "Other / Uncovered Subject": 0.0002653462465461797, "Unknown / Insufficient Content": 4.223965685220126e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006497415178070611, "Art & Design": 0.0006361883083310675, "Law & Crime": 0.0006574035999515007, "Education & Careers": 0.0003920952857800473, "Entertainment & Media": 0.06135107713848864, "Fashion & Beauty": 0.0003125310948176166, "Business, Economics & Finance": 0.000810268344189273, "Food & Dining": 0.000251311824244689, "Games & Gaming": 0.0006831282215496957, "Electronics & Hardware": 0.00036046938304496574, "Health, Healthcare & Wellness": 0.00023963858597277226, "History & Geography": 0.00019889949974878377, "Home, Pets & Hobbies": 0.00035269247174114256, "Industry & Production": 0.0001494128961087654, "Language, Literature & Philosophy": 0.00017232485006408467, "Politics & Government": 0.00020202843570369874, "Religion & Spirituality": 0.00026382540509653697, "Science, Mathematics & Engineering": 0.0007664212375037238, "Relationships & Community": 0.006001512704518164, "Software Usage & Applications": 0.014163894626134927, "Programming & Software Engineering": 0.013822731637505253, "Sports & Fitness": 0.0002242680514824497, "Vehicles & Transportation": 9.972041896942365e-05, "Travel & Tourism": 0.00029850948726067606, "Other / Uncovered Subject": 0.0007945084374182569, "NONE": 0.8961453965365669}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10227, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10227, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:09.785334+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "307fd859-4a14-4394-a80b-d6c6e2a38b45", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9bde4659295bb85a708b446268c6a44b68afd5395b734a173da43d21e702e807", "source_metadata": {"uuid": "307fd859-4a14-4394-a80b-d6c6e2a38b45", "repo_name": "Napman22/Musi", "relative_path": "server/lib/firebase.js", "category": "WEB", "algo_rel_score": 0.9410789012908936, "quality_score": 7.111545562744141, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2025-01-03T08:05:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fbe83f873271b7cb286cf3c26659df649ad9aa25", "size_bytes": 6183, "file_timestamp": 1736149514, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9303a88f3dccd144a4fd62f6075e0cc2e7727190"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 6, "repo_created_at": "2025-01-03T08:05:20.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "fbe83f873271b7cb286cf3c26659df649ad9aa25", "size_bytes": 6183, "file_timestamp": 1736149514, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "9303a88f3dccd144a4fd62f6075e0cc2e7727190", "url": "https://github.com/Napman22/Musi/blob/9303a88f3dccd144a4fd62f6075e0cc2e7727190/server/lib/firebase.js"}, "gpt4o_tokens": 1531, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.14877371656981492, "noise_severity": 0.08858481958593056, "missing_content_severity": 0.05325751743061409, "factual_density": 3.2863854847883336, "pretraining_utility": 0.558357208679663, "math_density": 0.10017700990052872, "code_density": 3.76393468644209, "reasoning_density": 0.48890834961351065, "education_level": 2.227497241635575, "creative_writing_quality": -0.24601975172114732}, "score_applicability": {"education_level": 0.999033263180518, "creative_writing_quality": 0.0005826560124289591}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/prateekkushwaha2/MicroBoto/blob/33395af846606d713f039a680b4363167663bfa8/client/src/Userprofile.jsx", "id": "javascript-0-8998cd7ca2998575bebd1ef7f5e31fc3", "text": "\nimport MicroBotoLogo from './assets/logo.png'\nimport React from 'react';\nimport './index.css'\nimport {useNavigate, useLocation, Link } from 'react-router-dom';\nimport { useState, useEffect, setState} from 'react';\nimport { ToastContainer, toast } from 'react-toastify';\nimport 'react-toastify/dist/ReactToastify.css';\n\n\n\n\nfunction Userprofile(){\n const navigate = useNavigate();\n\n const location = useLocation();\n const userData = location.state?.userData || null;\n const [additionalData, setAdditionalData] = useState(null);\n useEffect(() => {\n document.body.classList.remove('modal-open');\n if (userData) {\n console.log(userData.id);\n fetchDataFromServer(userData.id);\n }\n }, [userData]);\n \n const fetchDataFromServer = async (id) => {\n try {\n const response = await fetch('https://micro-boto-lt5j.vercel.app/loginValueCheck', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n id: id,\n }),\n });\n \n if (response.ok) {\n const serverData = await response.json();\n \n // Compare serverData with userData or perform any necessary logic\n console.log('Server Data:', serverData);\n \n // Set the additionalData state\n setAdditionalData(serverData[0]);\n\n const isAuthorized = compareUserData(userData, serverData[0]);\n if (isAuthorized){\n console.log('authorized')\n }else if(!isAuthorized){\n navigate('/');\n }\n\n\n } else {\n console.error('Failed to fetch additional data:', response.status);\n navigate('/');\n }\n } catch (error) {\n console.error('Error fetching data from the server:', error);\n }\n };\n\n //paymnet intregation\n\n const openRazorpay = async () => {\n const response = await fetch('https://micro-boto-lt5j.vercel.app/create-order', { method: 'POST' });\n const order = await response.json();\n \n const options = {\n key: '', // Replace with your Razorpay test key\n amount: order.amount,\n currency: order.currency,\n name: 'MicroBoto',\n description: 'Product/Service Description',\n order_id: order.id,\n handler: function (response) {\n console.log(response);\n \n // Check if the payment was successful\n if (response.razorpay_payment_id) {\n handlePaymentSuccess(response);\n } else {\n handlePaymentFailure(response);\n }\n },\n prefill: {\n name: \"\",\n email: \"87979080909\", \n },\n theme: {\n color: '#F37254',\n },\n };\n \n const rzp = new window.Razorpay(options);\n rzp.open();\n };\n \n const handlePaymentSuccess = async (response,) => {\n // Perform actions when payment is successful\n console.log('Payment successful!');\n \n try {\n \n // Make an API request to update isjava value\n \n const userEmail = \"\";\n const updateResponse = await fetch('https://micro-boto-lt5j.vercel.app/update-isjava', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n }, \n body: JSON.stringify({ email: userEmail }),\n });\n \n if (updateResponse.ok) {\n toast.success('Course purchased successfully. Happy learning!', {\n position: toast.POSITION.TOP_CENTER,\n });\n } else {\n console.error('Failed to update isjava:', updateResponse.statusText);\n alert('Failed to update isjava. Please try again later.');\n }\n } catch (error) {\n console.error('Error updating isjava:', error);\n alert('Error updating isjava. Please try again later.');\n }\n };\n \n const handlePaymentFailure = (response) => {\n // Perform actions when payment fails\n console.error('Payment failed:', response.error.description);\n alert(`Payment failed: ${response.error.description}`); // You can replace this with your desired action\n };\n \n //paymnet intregation\n\n\n \n const handleBot = () => {\n // You can use additionalData for comparison or other logic\n\n \n // Compare userData with additionalData\n const isAuthorized = compareUserData(userData, additionalData);\n \n if (isAuthorized) {\n // Navigate to /Bot with userData and additionalData\n console.log('User Data:', userData);\n console.log('Additional Data:', additionalData);\n navigate('/Bot', { state: { userData, additionalData } });\n } else {\n // Unauthorized login, redirect or show a message\n navigate('/Unauthorized');\n }\n };\n \n\n const compareUserData = (userData, serverData) => {\n // Implement your comparison logic here\n // For example, compare user IDs, names, or any other relevant data\n return userData.id === serverData.id && userData.name === serverData.name && userData.email === serverData.email;\n };\n \n\n\n if (!userData) {\n // Handle the case when userData is not available\n return (\n
\n

No user data available

\n
\n );\n }\n\n\n\n \n\n return (\n <>\n
\n \n
\n
\n
\n \n
\n
\n {/* header ends here */}\n
\n
\n
\n
\n

MicroBoto

\n

The Future Vision

\n

\n At MicroBoto, we embark on a mission to revolutionize education in\n India, propelling it into a future shaped by innovation and\n technological advancement. Our commitment lies in reshaping the\n learning landscape, with a primary focus on providing cutting-edge\n online education in technology.\n

\n \n
\n
\n
\n
\n
\n
\n

Our Batches

\n

\n

\n
\n {/* Course Box 1 */}\n
\n
\n
\n
Java Course
\n
    \n
  • Learn Java programming language
    • \n
  • Comprehensive course content
    • \n
  • Hands-on coding exercises
    • \n
\n

\n \n \n \n
\n
\n
\n\n {/* Course Box 2 */}\n
\n
\n
\n
Web Development Course
\n
    \n
  • HTML, CSS, JavaScript, and more
    • \n
  • Build dynamic web applications
    • \n
  • Practical projects and assignments
    • \n
\n

\n \n \n \n
\n
\n
\n\n {/* Course Box 3 */}\n
\n
\n
\n
Blockchain Course
\n
    \n
  • Understanding blockchain technology
    • \n
  • Smart contracts and decentralized apps
    • \n
  • Real-world blockchain applications
    • \n
\n

\n \n \n \n \n \n
\n
\n
\n
\n
\n
\n \n\n \n
\n \n \n )\n}\n\nexport default Userprofile\n", "text_preview": "import MicroBotoLogo from './assets/logo.png' import React from 'react'; import './index.css' import {useNavigate, useLocation, Link } from 'react-router-dom'; import { useState, useEffect, setState} from 'react'; import { ToastContainer, t", "input_sha256": "4fb800591f23e2833ce1a339b4ef50addd135038f3e5618b4bbc616c77aa3cfb", "text_sha256": "8998cd7ca2998575bebd1ef7f5e31fc3250cba45df1c72dfa86947c6bcef0c60", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8064, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Product Page", 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{"github_metadata": {"branch": "main", "commit_count": 97, "repo_created_at": "2024-03-06T06:02:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5640660e2d77fe8d3225298afd83bd436853113d", "size_bytes": 12923, "file_timestamp": 1739592695, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "33395af846606d713f039a680b4363167663bfa8"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 97, "repo_created_at": "2024-03-06T06:02:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "5640660e2d77fe8d3225298afd83bd436853113d", "size_bytes": 12923, "file_timestamp": 1739592695, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "33395af846606d713f039a680b4363167663bfa8", "url": "https://github.com/prateekkushwaha2/MicroBoto/blob/33395af846606d713f039a680b4363167663bfa8/client/src/Userprofile.jsx"}, "gpt4o_tokens": 2430, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": -0.2996132796154592, "noise_severity": 0.858757697133893, "missing_content_severity": 0.3421981943144104, "factual_density": 2.5328091907832317, "pretraining_utility": 0.13037798328300465, "math_density": 0.012348326593961538, "code_density": 3.8252547237552994, "reasoning_density": 0.5990159258184632, "education_level": 2.0420022109056433, "creative_writing_quality": -0.2966273900022318}, "score_applicability": {"education_level": 0.990529549400029, "creative_writing_quality": 0.04932811770630354}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/TUTHAITU/Shopii/blob/7c5b6c57a9d786d4e9022e7b72608e55aa1e6e90/front-end/src/pages/Address/Address.jsx", "id": "javascript-0-5db29e4fb01588321300954aa385e4ec", "text": "import React, { useEffect, useState } from 'react';\nimport { useDispatch, useSelector } from 'react-redux';\nimport { fetchAddresses, addAddress, updateAddress, deleteAddress, setDefaultAddress } from '../../features/address/addressSlice';\nimport { motion } from 'framer-motion';\nimport { \n Container, \n Typography, \n Box, \n Paper, \n Grid, \n Button, \n TextField, \n Divider, \n Chip,\n IconButton,\n FormControlLabel,\n Checkbox,\n Dialog,\n DialogTitle,\n DialogContent,\n DialogActions,\n CircularProgress,\n Alert\n} from \"@mui/material\";\nimport AddIcon from '@mui/icons-material/Add';\nimport EditIcon from '@mui/icons-material/Edit';\nimport DeleteIcon from '@mui/icons-material/Delete';\nimport HomeIcon from '@mui/icons-material/Home';\nimport CloseIcon from '@mui/icons-material/Close';\nimport LocationOnIcon from '@mui/icons-material/LocationOn';\n\nconst Address = () => {\n const dispatch = useDispatch();\n const { addresses, loading, error } = useSelector((state) => state.address);\n const [showForm, setShowForm] = useState(false);\n const [currentAddress, setCurrentAddress] = useState(null);\n const [phoneError, setPhoneError] = useState('');\n const [deleteConfirmOpen, setDeleteConfirmOpen] = useState(false);\n const [addressToDelete, setAddressToDelete] = useState(null);\n\n useEffect(() => {\n dispatch(fetchAddresses());\n }, [dispatch]);\n\n const handleAddAddress = (address) => {\n dispatch(addAddress(address));\n setShowForm(false);\n };\n\n const handleUpdateAddress = (address) => {\n dispatch(updateAddress({ id: address._id, addressData: address }));\n setCurrentAddress(null);\n };\n\n const confirmDeleteAddress = (id) => {\n setAddressToDelete(id);\n setDeleteConfirmOpen(true);\n };\n\n const handleDeleteAddress = () => {\n dispatch(deleteAddress(addressToDelete));\n setDeleteConfirmOpen(false);\n setAddressToDelete(null);\n };\n\n const handleSetDefault = (id) => {\n dispatch(setDefaultAddress(id));\n };\n\n return (\n \n \n \n \n \n My Addresses\n \n }\n onClick={() => setShowForm(true)}\n sx={{ \n backgroundColor: '#0F52BA',\n '&:hover': {\n backgroundColor: '#0A3C8A',\n }\n }}\n >\n Add New Address\n \n \n\n {error && (\n \n {error}\n \n )}\n\n {loading ? (\n \n \n \n ) : addresses.length === 0 ? (\n \n \n You don't have any saved addresses yet\n \n \n Add your first address to make checkout easier\n \n }\n onClick={() => setShowForm(true)}\n sx={{ \n backgroundColor: '#0F52BA',\n '&:hover': {\n backgroundColor: '#0A3C8A',\n }\n }}\n >\n Add New Address\n \n \n ) : (\n \n {addresses.map((address) => (\n \n \n \n {address.isDefault && (\n \n )}\n \n \n {address.fullName}\n \n \n \n {address.phone}\n \n \n \n {address.street}, {address.city}, {address.state}, {address.country}\n \n \n \n \n \n }\n size=\"small\"\n onClick={() => setCurrentAddress(address)}\n sx={{ \n borderColor: '#0F52BA',\n color: '#0F52BA',\n '&:hover': {\n borderColor: '#0A3C8A',\n backgroundColor: 'rgba(15, 82, 186, 0.04)',\n }\n }}\n >\n Edit\n \n \n }\n size=\"small\"\n onClick={() => confirmDeleteAddress(address._id)}\n sx={{ \n borderColor: '#d32f2f',\n color: '#d32f2f',\n '&:hover': {\n borderColor: '#b71c1c',\n backgroundColor: 'rgba(211, 47, 47, 0.04)',\n }\n }}\n >\n Delete\n \n \n {!address.isDefault && (\n }\n size=\"small\"\n onClick={() => handleSetDefault(address._id)}\n sx={{ \n borderColor: '#4caf50',\n color: '#4caf50',\n '&:hover': {\n borderColor: '#388e3c',\n backgroundColor: 'rgba(76, 175, 80, 0.04)',\n }\n }}\n >\n Set as Default\n \n )}\n \n \n \n \n ))}\n \n )}\n\n {/* Address Form Dialog */}\n {\n setShowForm(false);\n setCurrentAddress(null);\n setPhoneError('');\n }}\n maxWidth=\"sm\"\n fullWidth\n >\n \n \n \n {currentAddress ? 'Edit Address' : 'Add New Address'}\n \n {\n setShowForm(false);\n setCurrentAddress(null);\n setPhoneError('');\n }}\n >\n \n \n \n \n \n \n \n \n \n\n {/* Delete Confirmation Dialog */}\n setDeleteConfirmOpen(false)}\n >\n \n Confirm Deletion\n \n \n \n Are you sure you want to delete this address? This action cannot be undone.\n \n \n \n \n \n \n \n \n \n );\n};\n\nconst AddressForm = ({ address, onSubmit, phoneError, setPhoneError }) => {\n const [formData, setFormData] = useState(address || {\n fullName: '',\n phone: '',\n street: '',\n city: '',\n state: '',\n country: '',\n isDefault: false,\n });\n\n useEffect(() => {\n if (address) {\n setFormData(address);\n }\n }, [address]);\n\n const handleChange = (e) => {\n const { name, value } = e.target;\n setFormData({ ...formData, [name]: value });\n };\n\n const handleCheckboxChange = (e) => {\n const { name, checked } = e.target;\n setFormData({ ...formData, [name]: checked });\n };\n\n const validatePhoneNumber = (phone) => {\n const regex = /^0\\d{9}$/;\n return regex.test(phone);\n };\n\n const handleSubmit = () => {\n if (!validatePhoneNumber(formData.phone)) {\n setPhoneError('Invalid phone number. Must start with 0 and contain exactly 10 digits.');\n return;\n }\n setPhoneError('');\n onSubmit(formData);\n };\n\n return (\n \n {phoneError && (\n \n \n {phoneError}\n \n \n )}\n \n \n \"\n name=\"fullName\"\n value={formData.fullName}\n onChange={handleChange}\n required\n />\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n }\n label=\"Set as default address\"\n />\n \n \n \n \n \n {address ? 'Update Address' : 'Save Address'}\n \n \n \n \n );\n};\n\nexport default Address;", "text_preview": "import React, { useEffect, useState } from 'react'; import { useDispatch, useSelector } from 'react-redux'; import { fetchAddresses, addAddress, updateAddress, deleteAddress, setDefaultAddress } from '../../features/address/addressSlice'; i", "input_sha256": "72517e9b18cb1a2910fa676dc511108ab81f800c4d168016c0c170b5851b6109", "text_sha256": "5db29e4fb01588321300954aa385e4ec651fe612af078452b14770f4e0419696", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8065, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7496225994614871, "noise_severity": 0.11433409260338132, "missing_content_severity": 0.0951027328953503, "factual_density": 2.6778162055601094, "pretraining_utility": 0.567359433712294, "math_density": 0.051134953081935136, "code_density": 3.8393151459813444, "reasoning_density": 0.23877328944919707, "education_level": 2.1507914960760233, "creative_writing_quality": -0.1277202946023119}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": 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0.0004079968921060042, "Electronics & Hardware": 0.0003963805240925517, "Health, Healthcare & Wellness": 0.0002769390422411386, "History & Geography": 0.0003032948602310149, "Home, Pets & Hobbies": 0.0010531850405732924, "Industry & Production": 0.0001981961210332361, "Language, Literature & Philosophy": 0.0002110975175970998, "Politics & Government": 0.00018991584989177382, "Religion & Spirituality": 0.000257142193141224, "Science, Mathematics & Engineering": 0.00047586624425942483, "Relationships & Community": 0.0002176311231457651, "Software Usage & Applications": 0.022316765088899147, "Programming & Software Engineering": 0.021240388465131488, "Sports & Fitness": 0.00022622072329805197, "Vehicles & Transportation": 0.0008256758476469757, "Travel & Tourism": 0.0008407997678857519, "Other / Uncovered Subject": 0.002306615259824034, "NONE": 0.873179021928894}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": 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"scoring_key_slot": "primary", "dataset_uid": "3802431f-ac8b-4691-9d12-6c5d8386aed4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "790e32a56a9c84a29772bc2db572c6ac76f2c05dcca5c0a42b5d9e2ffbc74b8a", "source_metadata": {"uuid": "3802431f-ac8b-4691-9d12-6c5d8386aed4", "repo_name": "TUTHAITU/Shopii", "relative_path": "front-end/src/pages/Address/Address.jsx", "category": "WEB", "algo_rel_score": 0.9313594102859497, "quality_score": 7.470314025878906, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 8, "repo_created_at": "2025-06-01T18:22:26.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0a4c8d9160888d6f3e58540d7f91766badd23ce7", "size_bytes": 15051, "file_timestamp": 1753473364, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7c5b6c57a9d786d4e9022e7b72608e55aa1e6e90"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 8, "repo_created_at": "2025-06-01T18:22:26.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "0a4c8d9160888d6f3e58540d7f91766badd23ce7", "size_bytes": 15051, "file_timestamp": 1753473364, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "7c5b6c57a9d786d4e9022e7b72608e55aa1e6e90", "url": "https://github.com/TUTHAITU/Shopii/blob/7c5b6c57a9d786d4e9022e7b72608e55aa1e6e90/front-end/src/pages/Address/Address.jsx"}, "gpt4o_tokens": 3060, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7496225994614871, "noise_severity": 0.11433409260338132, "missing_content_severity": 0.09510273289535032, "factual_density": 2.6778162055601094, "pretraining_utility": 0.567359433712294, "math_density": 0.051134953081935136, "code_density": 3.8393151459813444, "reasoning_density": 0.23877328944919707, "education_level": 2.1507914960760233, "creative_writing_quality": -0.1277202946023119}, "score_applicability": {"education_level": 0.9984597054949844, "creative_writing_quality": 0.0013286875069543921}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/swetha-aggidevara/Superset-dashboards/blob/b6771d3c657bf5c50b407241b7a4d5900f97ce60/superset/assets/src/welcome/Welcome.jsx", "id": "javascript-0-cd9742551c1b166a66d636f9bf5c2ef9", "text": "/**\n * Licensed to the Apache Software Foundation (ASF) under one\n * or more contributor license agreements. See the NOTICE file\n * distributed with this work for additional information\n * regarding copyright ownership. The ASF licenses this file\n * to you under the Apache License, Version 2.0 (the\n * \"License\"); you may not use this file except in compliance\n * with the License. You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n * KIND, either express or implied. See the License for the\n * specific language governing permissions and limitations\n * under the License.\n */\nimport React from 'react';\nimport PropTypes from 'prop-types';\nimport { Panel, Row, Col, Tabs, Tab, FormControl } from 'react-bootstrap';\nimport { t } from '@superset-ui/translation';\nimport RecentActivity from '../profile/components/RecentActivity';\nimport Favorites from '../profile/components/Favorites';\nimport DashboardTable from './DashboardTable';\nimport moment from 'moment'\nimport { APIURLS } from 'src/explore/constants';\nimport { SupersetClient } from '@superset-ui/connection';\nimport _ from \"lodash\";\nconst propTypes = {\n user: PropTypes.object.isRequired,\n};\n\nexport default class Welcome extends React.PureComponent {\n constructor(props) {\n super(props);\n this.state = {\n canRender: false,\n search: '',\n };\n this.onSearchChange = this.onSearchChange.bind(this);\n }\n\n componentDidMount() {\n let tz = moment().format('Z');\n let url = APIURLS.url;\n let url2 = APIURLS.url2;\n let url3 = APIURLS.url3;\n fetch(url, { method: 'POST', body: JSON.stringify({ tz }) })\n .then((response) => response.json())\n .then((data) => {\n fetch(url2, { method: 'GET' })\n .then((response) => response.json())\n .then((data) => { })\n });\n\n localStorage.removeItem('dashData')\n fetch(url3, { method: \"GET\" })\n .then((res) => res.json())\n .then((data) => {\n console.log(\"data\", data);\n localStorage.setItem('dashData', JSON.stringify(data));\n }).catch((err) => {\n })\n SupersetClient.get({\n endpoint: '/dashboardasync/api/read?_oc_DashboardModelViewAsync=changed_on&_od_DashboardModelViewAsync=desc',\n })\n .then(({ json }) => {\n console.log(\"res\", json.result);\n\n if (\n json.result && json.result.length === 1) {\n window.location.href = json.result[0]['url'];\n return null;\n }\n else {\n this.setState({ canRender: true })\n }\n\n })\n .catch(() => {\n this.setState({ canRender: true })\n console.log(\"error\");\n });\n // Get PDA user roles and set in localstorage\n fetch(APIURLS.url5, { method: 'GET' })\n .then(response => response.json())\n .then((data) => {\n let pdaUserRoles = _.map(_.get(data, 'userRoles'), user => {\n return _.get(user, 'roles');\n });\n pdaUserRoles = _.flattenDeep(pdaUserRoles) || [];\n localStorage.setItem('pda_user_program_roles', pdaUserRoles);\n localStorage.setItem('pda_program_names', _.get(data, 'programNames') || [])\n }\n )\n .catch(err => {\n console.log('eerr', err);\n })\n }\n\n onSearchChange(event) {\n this.setState({ search: event.target.value });\n }\n render() {\n if (!this.state.canRender) {\n return null;\n }\n\n return (\n
\n \n \n \n \n

{t('Dashboards')}

\n \n \n \n \n
\n \n \n \n \n \n \n

{t('Recently Viewed')}

\n \n
\n \n \n \n \n \n \n

{t('Favorites')}

\n \n
\n \n \n \n \n
\n );\n }\n}\n\nWelcome.propTypes = propTypes;\n", "text_preview": "/** * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. See the NOTICE file * distributed with this work for additional information * regarding copyright ownership. 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"education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9801, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9801, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:12.743584+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "9aa720b1-faba-46df-ad29-e94a41da933d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2b15c68a72b9633ed3d7259a923c96ba28259003e5a534f66fc323e545019e9d", "source_metadata": {"uuid": "9aa720b1-faba-46df-ad29-e94a41da933d", "repo_name": "swetha-aggidevara/Superset-dashboards", "relative_path": "superset/assets/src/welcome/Welcome.jsx", "category": "WEB", "algo_rel_score": 0.9156805276870728, "quality_score": 6.644327163696289, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 30, "repo_created_at": "2021-11-08T13:01:44.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e7f1a2ef06116d738f6378a12935a5361207fbea", "size_bytes": 5000, "file_timestamp": 1636378601, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b6771d3c657bf5c50b407241b7a4d5900f97ce60"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 30, "repo_created_at": "2021-11-08T13:01:44.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e7f1a2ef06116d738f6378a12935a5361207fbea", "size_bytes": 5000, "file_timestamp": 1636378601, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b6771d3c657bf5c50b407241b7a4d5900f97ce60", "url": "https://github.com/swetha-aggidevara/Superset-dashboards/blob/b6771d3c657bf5c50b407241b7a4d5900f97ce60/superset/assets/src/welcome/Welcome.jsx"}, "gpt4o_tokens": 1174, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7764036058540277, "noise_severity": 0.6045050703937197, "missing_content_severity": 0.04677923047760833, "factual_density": 2.9215652896806077, "pretraining_utility": 0.3019333299129192, "math_density": 0.01493301109758957, "code_density": 3.6270244309175186, "reasoning_density": 0.22016756944212712, "education_level": 2.3329993486186544, "creative_writing_quality": -0.2671989288854132}, "score_applicability": {"education_level": 0.9984664593182156, "creative_writing_quality": 0.000486164810908389}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/timothychangke/govTech-CFT-assignment/blob/bdd6cff77f0bb1c812375637e16ff5486fcc7af6/client/src/components/Calculator.jsx", "id": "javascript-0-414e125368f15f9ed0285d40cc44587b", "text": "import { useState, useEffect } from 'react'\nimport { Container, Box, Card, CardContent, CardHeader } from '@mui/material'\nimport { toast } from 'react-hot-toast'\n\nimport isNumber from '../utils/helper/isNumber'\nimport TextInputs from './Input/TextInputs'\nimport Buttons from './Button/Buttons'\nimport Result from './Output/Result'\nimport styles from './Styling/calculator.module.css'\n\nconst MAX_SAFE_NUMBER = Number.MAX_SAFE_INTEGER\nconst MIN_SAFE_NUMBER = Number.MIN_SAFE_INTEGER\n\n/**\n * Calculator component to perform basic arithmetic operations.\n * Supports input validation, result display via API call, reseting and local storage persistence.\n */\nconst Calculator = () => {\n // State to hold the result of the calculation\n const [result, setResult] = useState(null)\n\n // State to manage input values and error messages\n const [inputState, setInputState] = useState({\n input1: '',\n input2: '',\n error1: '',\n error2: '',\n })\n // Load saved state from localStorage when component is first mounted\n useEffect(() => {\n const savedInputs = localStorage.getItem('calculatorState')\n if (savedInputs) {\n setInputState(JSON.parse(savedInputs))\n }\n }, [])\n\n // Save the current state to localStorage whenever inputs change, only if input is non-null\n useEffect(() => {\n if (\n inputState.input1 !== '' ||\n inputState.input2 !== '' ||\n inputState.error1 !== '' ||\n inputState.error2 !== ''\n ) {\n localStorage.setItem('calculatorState', JSON.stringify(inputState))\n }\n }, [inputState])\n\n // Reset inputs and result, and clears the saved state stored in local storage\n const handleReset = () => {\n const defaultState = {\n input1: '',\n input2: '',\n error1: '',\n error2: '',\n }\n setInputState(defaultState)\n setResult(null)\n window.localStorage.setItem('calculatorState', JSON.stringify(defaultState))\n }\n\n // Handles changes to input fields by validating and updating the state.\n const handleInputChange = (name, event) => {\n const inputToErrMap = { input1: 'error1', input2: 'error2' }\n const inputValue = event.target.value\n const processedValue = inputValue === '' ? '0' : inputValue\n setInputState((prevInputs) => {\n const numericValue = Number(processedValue)\n let errorMessage = ''\n\n // Validate the input value\n if (!isNumber(processedValue)) {\n errorMessage = 'Please enter a valid number.'\n } else if (numericValue > MAX_SAFE_NUMBER) {\n errorMessage = `Value exceeds maximum limit.`\n } else if (numericValue < MIN_SAFE_NUMBER) {\n errorMessage = `Value is below minimum limit.`\n }\n\n // Update state with the new value and any validation errors\n const newInputs = {\n ...prevInputs,\n [name]: inputValue,\n [inputToErrMap[name]]: errorMessage,\n }\n return newInputs\n })\n }\n\n // Handles API requests\n const handleSubmit = async (operation) => {\n // Check for input errors before proceeding\n if (!inputState.error1 && !inputState.error2) {\n const num1 = parseFloat(inputState.input1) || 0\n const num2 = parseFloat(inputState.input2) || 0\n let result\n\n // Check for Integer Overflow or Underflow errors\n switch (operation) {\n case 'addition':\n result = num1 + num2\n console.log(result)\n break\n case 'subtraction':\n result = num1 - num2\n break\n default:\n toast.error('Invalid operation')\n return\n }\n if (result > MAX_SAFE_NUMBER) {\n toast.error('Operation exceeds maximum limit')\n return\n } else if (result < MIN_SAFE_NUMBER) {\n toast.error('Operation is below minimum limit')\n } else {\n //// Send the calculation request to the backend\n try {\n const response = await fetch('http://localhost:5000/api/calculate', {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n num1,\n num2,\n operation,\n }),\n })\n\n if (!response.ok) {\n throw new Error('Network response was not ok')\n }\n const data = await response.json()\n setResult(data.result)\n } catch (error) {\n console.error('Error making POST request:', error)\n setResult('An error occurred')\n }\n }\n }\n }\n\n return (\n \n \n \"\n className={styles.cardHeader}\n classes={{\n title: styles.cardTitle,\n }}\n />\n \n \n \n handleSubmit('addition')}\n handleSubtraction={() => handleSubmit('subtraction')}\n error1={inputState.error1}\n error2={inputState.error2}\n handleReset={handleReset}\n />\n {result !== null && }\n \n \n \n \n )\n}\n\nexport default Calculator\n", "text_preview": "import { useState, useEffect } from 'react' import { Container, Box, Card, CardContent, CardHeader } from '@mui/material' import { toast } from 'react-hot-toast' import isNumber from '../utils/helper/isNumber' import TextInputs from './Inpu", "input_sha256": "a875561c349574286cfd8392497d17ecfc36e06fe59f822337215180e4291ef2", "text_sha256": "414e125368f15f9ed0285d40cc44587bac9ba7f1d340cf80b71b9d0509c18966", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8066, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6569500489598572, "noise_severity": 0.0671417756259194, "missing_content_severity": 0.0367411564112217, "factual_density": 3.1903724033475163, "pretraining_utility": 0.601194011851631, "math_density": 1.3026735381566714, "code_density": 3.732790086913121, "reasoning_density": 0.6725439403867002, "education_level": 2.08089051965876, "creative_writing_quality": -0.22793702304238944}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009601363608592095, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9952453173753275, "secondary": 0.9615911647244253, "tertiary": 0.9716099789246953, "structured_records": 0.006537251601296955, "procedural_steps": 0.006821951418291265, "worked_solution": 0.01282479969815637, "contextual_dialogue": 6.745796359263241e-06, "content_quality": 0.49353003609366153, "noise_severity": 0.9440485203117338, "missing_content_severity": 0.9693823696573153, "factual_density": 0.32531033612293014, "pretraining_utility": 0.521633359443783, "math_density": 0.43015311851241267, "code_density": 0.777325072427601, "reasoning_density": 0.43954671634441644, "education_level": 0.8390617574750028, "creative_writing_quality": 0.9989706151978144, "primary_topic": 0.9827446452028578, "secondary_topic": 0.839029153472926}, "probability_margin": {"primary": 0.9932871265124056, "secondary": 0.9473988621312933, "tertiary": 0.9607644364454809, "education_level": 0.7650895352423089, "creative_writing_quality": 0.998646357220523, "primary_topic": 0.9730937578073329, "secondary_topic": 0.7602228894443337}, "raw_probabilities": {"primary": {"Academic Writing": 6.623996657794548e-05, "Content Listing": 3.443097560812793e-05, "Creative Writing": 4.055521366744917e-05, "Customer Support Page": 1.5778193302665958e-05, "Q&A Forum": 2.0723327214874867e-05, "Discussion Thread": 1.2881526230746498e-05, "Comment Section": 2.8843689681106897e-05, "Missing Content": 5.2770401708641304e-05, "Knowledge Article": 0.0001295683665757068, "Legal Notices": 7.470571848831008e-05, "News Article": 9.1679565938984e-06, "Organizational Announcement": 2.400361651752587e-05, "Nonfiction Writing": 2.1116737343244708e-05, "Organizational About Page": 2.8058789087314263e-05, "Personal About Page": 3.7827761337060665e-05, "Personal Blog": 1.4203685616790034e-05, "Product Page": 1.6379678644102845e-05, "Spam / Ads": 1.9187808473509268e-05, "Structured Data": 0.0005934918561447178, "Technical Writing": 0.9954434291513555, "Transcript / Interview": 2.3529626193802704e-05, "Tutorial / How-To Guide": 0.0011236126579999034, "User Reviews": 1.3190656686982831e-05, "Other / Unknown": 0.00215630263894991}, "secondary": {"Academic Writing": 0.0002819071541662934, "Content Listing": 0.00018984460370316964, "Creative Writing": 0.0002453422393651402, "Customer Support Page": 0.00012957684210662335, "Q&A Forum": 9.131056939700396e-05, "Discussion Thread": 7.69911477450381e-05, "Comment Section": 0.0002493434612807006, "Missing Content": 0.0004188320144776023, "Knowledge Article": 0.0010571453237130618, "Legal Notices": 0.0004026606458751534, "News Article": 0.00012469051742218556, "Organizational Announcement": 0.00018298035088604915, "Nonfiction Writing": 0.000147080591889192, "Organizational About Page": 0.00018467754345608694, "Personal About Page": 0.00016861538210594156, "Personal Blog": 0.00011353314494631169, "Product Page": 7.884641065761619e-05, "Spam / Ads": 0.0001254013235077607, "Structured Data": 0.015792670729614173, "Technical Writing": 0.012721635389286692, "Transcript / Interview": 0.0001570486772583187, "Tutorial / How-To Guide": 0.0037997810474905158, "User Reviews": 6.85520287419029e-05, "NONE": 0.9631915328609075}, "tertiary": {"Academic Writing": 0.00020690237659334857, "Content Listing": 0.00017067591207942337, "Creative Writing": 0.00021869956558433275, "Customer Support Page": 0.0001272038193976579, "Q&A Forum": 6.428783508736039e-05, "Discussion Thread": 5.54312859267327e-05, "Comment Section": 0.00027634379968176294, "Missing Content": 0.0007415241416805888, "Knowledge Article": 0.0011755749878127693, "Legal Notices": 0.0007065797406435941, "News Article": 9.963197619103435e-05, "Organizational Announcement": 0.00023892196094873623, "Nonfiction Writing": 0.00018995720430428083, "Organizational About Page": 0.0002669382478774154, "Personal About Page": 0.00017331390867226634, "Personal Blog": 9.375133364834267e-05, "Product Page": 7.106374387902197e-05, "Spam / Ads": 0.00010791448177553803, "Structured Data": 0.012028460024018719, "Technical Writing": 0.00697761030508293, "Transcript / Interview": 0.00012150908548138834, "Tutorial / How-To Guide": 0.003041073751044397, "User Reviews": 5.373404308872507e-05, "NONE": 0.9727928964694996}, "structured_records": {"false": 0.9934627483987031, "true": 0.006537251601296955}, "procedural_steps": {"false": 0.9931780485817088, "true": 0.006821951418291265}, "worked_solution": {"false": 0.9871752003018436, "true": 0.01282479969815637}, "contextual_dialogue": {"false": 0.9999932542036407, "true": 6.745796359263241e-06}, "content_quality": {"-2": 0.009054107150156917, "-1": 0.06656261135207857, "0": 0.3151194097092465, "1": 0.4769068689647863, "2": 0.1323570028237316}, "noise_severity": {"0": 0.9408865224008516, "1": 0.05359228168450437, "2": 0.0037898581747829903, "3": 0.0009555733675939212, "4": 0.0007757643722668164}, "missing_content_severity": {"0": 0.9801558892886164, "1": 0.00720612146299778, "2": 0.008847482636106291, "3": 0.003321956773106287, "4": 0.00046854983917311806}, "factual_density": {"0": 0.012990124914066944, "1": 0.03492479096250825, "2": 0.14726339685103967, "3": 0.35836593040661197, "4": 0.44645575686577316}, "pretraining_utility": {"-2": 0.008283938548973824, "-1": 0.054681949436560684, "0": 0.3522072638878719, "1": 0.49720985786704786, "2": 0.08761699025954575}, "math_density": {"0": 0.19057135981421663, "1": 0.4886332740957482, "2": 0.21331340407347288, "3": 0.04251439215227212, "4": 0.06496756986429036}, "code_density": {"0": 0.00036589201974603807, "1": 0.0007209051046630317, "2": 0.028546564719019705, "3": 0.20649050025586613, "4": 0.7638761379007051}, "reasoning_density": {"0": 0.6537098553614302, "1": 0.12252870060328937, "2": 0.14431737672633682, "3": 0.05639578290503717, "4": 0.023048284403906414}, "education_level": {"none": 0.0011391423042616817, "0": 0.001666402668472611, "1": 0.025897624142195076, "2": 0.8658847978958356, "3": 0.10079526265352663, "4": 0.004616770335708397}, "creative_writing_quality": {"not_applicable": 0.999142179331512, "-2": 7.544502447384409e-05, "-1": 0.00018118681397032808, "0": 0.0004958221109889336, "1": 7.418566467068893e-05, "2": 3.118105438399413e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.276414858018328e-05, "Art & Design": 0.00035112854146240023, "Law & Crime": 4.151393352803295e-05, "Education & Careers": 0.0013272669207138937, "Entertainment & Media": 3.582766255521216e-05, "Fashion & Beauty": 2.598737900319481e-05, "Business, Economics & Finance": 0.0002354945484083372, "Food & Dining": 4.451886099427303e-05, "Games & Gaming": 0.00011450701313623427, "Electronics & Hardware": 6.922797352513843e-05, "Health, Healthcare & Wellness": 3.155544591416903e-05, "History & Geography": 4.7385109689165114e-05, "Home, Pets & Hobbies": 3.830248158959892e-05, "Industry & Production": 2.68816277614556e-05, "Language, Literature & Philosophy": 3.200936169011115e-05, "Politics & Government": 0.002365175106330568, "Religion & Spirituality": 3.572734956034893e-05, "Science, Mathematics & Engineering": 0.010314554887722755, "Relationships & Community": 1.8731614231907725e-05, "Software Usage & Applications": 0.0008782651830539032, "Programming & Software Engineering": 0.9834083126950556, "Sports & Fitness": 4.2850786843532026e-05, "Vehicles & Transportation": 2.108436417915231e-05, "Travel & Tourism": 4.6542887169375956e-05, "Other / Uncovered Subject": 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Transportation": 0.00011966598230779035, "Travel & Tourism": 0.00032682888657897124, "Other / Uncovered Subject": 0.0007353222431935724, "NONE": 0.8452203398778135}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": 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"client/src/components/Calculator.jsx", "category": "WEB", "algo_rel_score": 0.9773802161216736, "quality_score": 7.536067485809326, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 29, "repo_created_at": "2024-08-30T13:28:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9fcd4fa4e045e78f47cf8399d19c4dbac566a49c", "size_bytes": 5593, "file_timestamp": 1725158883, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bdd6cff77f0bb1c812375637e16ff5486fcc7af6"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 29, "repo_created_at": "2024-08-30T13:28:21.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 2, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9fcd4fa4e045e78f47cf8399d19c4dbac566a49c", "size_bytes": 5593, "file_timestamp": 1725158883, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "bdd6cff77f0bb1c812375637e16ff5486fcc7af6", "url": "https://github.com/timothychangke/govTech-CFT-assignment/blob/bdd6cff77f0bb1c812375637e16ff5486fcc7af6/client/src/components/Calculator.jsx"}, "gpt4o_tokens": 1231, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6569500489598572, "noise_severity": 0.0671417756259194, "missing_content_severity": 0.0367411564112217, "factual_density": 3.1903724033475163, "pretraining_utility": 0.601194011851631, "math_density": 1.3026735381566714, "code_density": 3.732790086913121, "reasoning_density": 0.6725439403867002, "education_level": 2.08089051965876, "creative_writing_quality": -0.22793702304238944}, "score_applicability": {"education_level": 0.9988608576957383, "creative_writing_quality": 0.000857820668487789}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/skillfull-florist/skilvul-florist/blob/b929384e7e16b4d9f0cbcff16c0499604b867933/src/contexts/KeranjangContext.js", "id": "javascript-0-f2c173b849ef043f31d967c32ffcbb68", "text": "import { useEffect, useReducer, createContext } from 'react';\nimport {\n AUTHENTICATED,\n CURRENT_USER,\n KERANJANG,\n ADD_KERANJANG,\n ADD_NEW_PRODUCT,\n DELETE_PRODUCT,\n INCREASE_PRODUCT,\n DECREASE_PRODUCT,\n SET_PRODUCT_COUNT,\n DELETE_KERANJANG,\n DELETE_KERANJANG_LOCAL,\n} from './ContextConsts';\nimport * as Helper from './../helpers/KeranjangHelper';\n\nexport const KeranjangContext = createContext();\n\n/* id: '', // id keranjang\n idUser: '', // id user pemilik keranjang\n data: [\n // data produk di keranjang\n {\n idProduk: '', // id produk\n nama: '', // nama produk\n gambar: '', // url gambar\n jumlah: '', // jumlah produk\n sisa: '', // sisa produk\n harga: '', // harga produk\n tipe: '', // tipe produk, buket / tanamanhias\n },\n ], */\nexport const keranjangInitialState = {\n id: '',\n idUser: '',\n data: [],\n};\n\nexport const keranjangReducer = (state, action) => {\n switch (action.type) {\n case ADD_KERANJANG:\n return {\n ...action.payload,\n };\n case ADD_NEW_PRODUCT:\n const productFound = state.data.filter(\n (item) =>\n item.idProduk === action.payload.idProduk &&\n item.tipe === action.payload.tipe,\n );\n\n if (productFound.length === 0) {\n const user = JSON.parse(localStorage.getItem(CURRENT_USER));\n const isAuthenticated = JSON.parse(localStorage.getItem(AUTHENTICATED));\n\n const newKeranjang = {\n ...state,\n data: [\n ...state.data,\n {\n ...action.payload,\n jumlah: 1,\n },\n ],\n };\n\n if (isAuthenticated) {\n newKeranjang.idUser = user.id;\n }\n\n localStorage.setItem(KERANJANG, JSON.stringify(newKeranjang));\n return { ...newKeranjang };\n }\n\n return {\n ...state,\n };\n case INCREASE_PRODUCT:\n return {\n ...(() => {\n const newProductData = state.data.map((item) => {\n if (\n item.idProduk === action.payload.idProduk &&\n item.tipe === action.payload.tipe\n ) {\n return {\n ...item,\n jumlah: item.jumlah + 1,\n };\n }\n return item;\n });\n\n const newKeranjang = {\n ...state,\n data: newProductData,\n };\n\n Helper.putKeranjangById(newKeranjang.id, newKeranjang)\n .then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n console.error(err);\n });\n\n return newKeranjang;\n })(),\n };\n case DECREASE_PRODUCT:\n return {\n ...(() => {\n const newProductData = state.data.map((item) => {\n if (\n item.idProduk === action.payload.idProduk &&\n item.tipe === action.payload.tipe\n ) {\n if (item.jumlah > 1) {\n return {\n ...item,\n jumlah: item.jumlah - 1,\n };\n }\n }\n return item;\n });\n const newKeranjang = {\n ...state,\n data: newProductData,\n };\n\n Helper.putKeranjangById(newKeranjang.id, newKeranjang)\n .then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n console.error(err);\n });\n\n return newKeranjang;\n })(),\n };\n case SET_PRODUCT_COUNT:\n return {\n ...(() => {\n const newProductData = state.data.map((item) => {\n if (\n item.idProduk === action.payload.idProduk &&\n item.tipe === action.payload.tipe\n ) {\n if (action.payload.jumlah > 0 && item.jumlah !== action.payload.jumlah) {\n return {\n ...item,\n jumlah: action.payload.jumlah,\n };\n }\n }\n return item;\n });\n const newKeranjang = {\n ...state,\n data: newProductData,\n };\n\n Helper.putKeranjangById(newKeranjang.id, newKeranjang)\n .then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n console.error(err);\n });\n\n return newKeranjang;\n })(),\n };\n case DELETE_PRODUCT:\n return {\n ...(() => {\n const filteredProductData = state.data.filter(\n (item) => item.idProduk !== action.payload.idProduk,\n );\n\n if (filteredProductData.length > 0) {\n const newKeranjang = {\n ...state,\n data: filteredProductData,\n };\n\n Helper.putKeranjangById(newKeranjang.id, newKeranjang)\n .then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n console.error(err);\n });\n\n localStorage.setItem(KERANJANG, JSON.stringify(newKeranjang));\n return newKeranjang;\n }\n return { ...state, data: [] };\n })(),\n };\n case DELETE_KERANJANG:\n return {\n ...(() => {\n Helper.deleteKeranjangById(state.id)\n .then(() => {\n localStorage.removeItem(KERANJANG);\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n console.error(err);\n });\n return { ...state, data: [] };\n })(),\n };\n case DELETE_KERANJANG_LOCAL:\n return {\n ...(() => {\n localStorage.removeItem(KERANJANG);\n return { ...state, data: [] };\n })(),\n };\n default:\n return state;\n }\n};\n\nexport const validateKeranjangAPIByUserId = (id, dispatch, keranjang = null) => {\n Helper.getKeranjangByUserId(id)\n .then((data) => {\n const keranjangFromApi = data;\n localStorage.setItem(KERANJANG, JSON.stringify(keranjangFromApi));\n dispatch({\n type: ADD_KERANJANG,\n payload: keranjangFromApi,\n });\n })\n .catch((err) => {\n // keranjang tidak ditemukan\n const keranjangLocalToApi = JSON.parse(localStorage.getItem(KERANJANG));\n\n if (keranjangLocalToApi !== null) {\n keranjangLocalToApi.idUser = id;\n Helper.postKeranjang(keranjangLocalToApi).then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n dispatch({\n type: ADD_KERANJANG,\n payload: data,\n });\n });\n return;\n }\n if (keranjangLocalToApi === null && keranjang !== null) {\n Helper.postKeranjang(keranjang).then((data) => {\n localStorage.setItem(KERANJANG, JSON.stringify(data));\n dispatch({\n type: ADD_KERANJANG,\n payload: data,\n });\n });\n return;\n }\n localStorage.removeItem(KERANJANG);\n });\n};\n\nconst KeranjangContextProvider = (props) => {\n const [keranjang, dispatch] = useReducer(keranjangReducer, keranjangInitialState);\n\n useEffect(() => {\n const isAuthenticated = JSON.parse(localStorage.getItem(AUTHENTICATED));\n if (isAuthenticated) {\n const user = JSON.parse(localStorage.getItem(CURRENT_USER));\n validateKeranjangAPIByUserId(user.id, dispatch);\n } else {\n const keranjangLocal = JSON.parse(localStorage.getItem(KERANJANG));\n if (keranjangLocal !== null) {\n dispatch({\n type: ADD_KERANJANG,\n payload: keranjangLocal,\n });\n }\n }\n }, []);\n\n return (\n \n {props.children}\n \n );\n};\n\nexport default KeranjangContextProvider;\n", "text_preview": "import { useEffect, useReducer, createContext } from 'react'; import { AUTHENTICATED, CURRENT_USER, KERANJANG, ADD_KERANJANG, ADD_NEW_PRODUCT, DELETE_PRODUCT, INCREASE_PRODUCT, DECREASE_PRODUCT, SET_PRODUCT_COUNT, DELETE_KERANJANG, DELETE_K", "input_sha256": "140eada4e68becfb1e5d06b733cd99ea6802da901f6ff9a57796ae0a5be858ed", "text_sha256": "f2c173b849ef043f31d967c32ffcbb685959aed9f2206440b41591d053dc98df", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8069, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7048354723062894, "noise_severity": 0.0593042590069461, "missing_content_severity": 0.06933294774151434, "factual_density": 3.263649311342773, "pretraining_utility": 0.5125790070832129, "math_density": 0.34796067167666367, "code_density": 3.805599751330159, "reasoning_density": 0.8509510377972103, "education_level": 2.0666833209631266, "creative_writing_quality": -0.23248641617212307}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008997294285010615, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.996173728693339, "secondary": 0.9560809610835909, "tertiary": 0.9654694420766455, 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"dataset_uid": "4d92478a-6d0f-47dd-aa12-05a24fc03259", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9e2f5f0c96bb54e80ee6ff69a4ab5fabab8bc0c743f0191194fb117bd47153e8", "source_metadata": {"uuid": "4d92478a-6d0f-47dd-aa12-05a24fc03259", "repo_name": "skillfull-florist/skilvul-florist", "relative_path": "src/contexts/KeranjangContext.js", "category": "WEB", "algo_rel_score": 0.9132751226425171, "quality_score": 6.513588905334473, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 100, "repo_created_at": "2022-07-11T04:28:04.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "bca3d6a397c80891431957e0207ebd9c5a8c3e82", "size_bytes": 8194, "file_timestamp": 1664045897, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b929384e7e16b4d9f0cbcff16c0499604b867933"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 100, "repo_created_at": "2022-07-11T04:28:04.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "bca3d6a397c80891431957e0207ebd9c5a8c3e82", "size_bytes": 8194, "file_timestamp": 1664045897, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b929384e7e16b4d9f0cbcff16c0499604b867933", "url": "https://github.com/skillfull-florist/skilvul-florist/blob/b929384e7e16b4d9f0cbcff16c0499604b867933/src/contexts/KeranjangContext.js"}, "gpt4o_tokens": 1820, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7048354723062895, "noise_severity": 0.0593042590069461, "missing_content_severity": 0.06933294774151434, "factual_density": 3.263649311342773, "pretraining_utility": 0.5125790070832129, "math_density": 0.34796067167666367, "code_density": 3.805599751330159, "reasoning_density": 0.8509510377972103, "education_level": 2.0666833209631266, "creative_writing_quality": -0.23248641617212307}, "score_applicability": {"education_level": 0.9987396620527391, "creative_writing_quality": 0.0010837180301698271}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/beyond-sharepoint/ntlm-remote-auth/blob/da66eb12b9f4c795919caad05dacbd5ad71999f4/lib/index.js", "id": "javascript-0-c0b98e659a566670ac6451e5d58667fb", "text": "\"use strict\";\n\nconst _ = require('lodash');\nconst request = require('request');\nconst url = require('url');\nconst ntlm = require('./ntlm');\nconst http = require('http');\nconst https = require('https');\nconst Promise = require(\"bluebird\");\nconst moment = require(\"moment\");\n\nconst ContextInfoServicePath = \"/_api/contextinfo\";\n\nmodule.exports = (function () {\n /**\n * Gets a keepaliveAgent for the given url.\n */\n let getKeepaliveAgent = function (tenantDomain) {\n // is https?\n let isHttps = false;\n let reqUrl = url.parse(tenantDomain);\n if (reqUrl.protocol == 'https:')\n isHttps = true;\n\n // set keepaliveAgent (http or https):\n let keepaliveAgent;\n\n if (isHttps) {\n keepaliveAgent = new https.Agent({ keepAlive: true });\n } else {\n keepaliveAgent = new http.Agent({ keepAlive: true });\n }\n\n return keepaliveAgent;\n };\n\n /**\n * Returns a promise that sends a type1 message for the given credentials and http options.\n */\n let sendType1Message = function (credentials, options, callback) {\n\n let type1msg = ntlm.createType1Message(credentials);\n _.set(options, 'headers.Connection', 'keep-alive');\n _.set(options, \"headers.Authorization\", type1msg);\n\n // send type1 message to server:\n return this.type1Request(options, callback);\n };\n\n /**\n * Returns a promise that performs the actual request, using the type1response and closing the keep-alive connection.\n */\n let sendType3Message = function (credentials, type1Response, options, callback) {\n // catch redirect here:\n if (type1Response.headers.location) {\n options.url = type1Response.headers.location;\n return this.type3Request(options, callback);\n }\n\n let wwwAuthenticateHeader = type1Response.headers['www-authenticate'];\n if (!wwwAuthenticateHeader)\n throw new Error('www-authenticate not found on response of second request');\n\n // parse type2 message from server:\n let type2msg = ntlm.parseType2Message(wwwAuthenticateHeader);\n\n if (!type2msg)\n throw new Error('Could not parse type2 message.');\n\n // create type3 message:\n let type3msg = ntlm.createType3Message(type2msg, credentials);\n\n // build type3 request from options.\n _.set(options, \"headers.Connection\", \"Close\");\n _.set(options, \"headers.Authorization\", type3msg);\n\n // send type3 message to server:\n return this.type3Request(options, callback);\n }\n\n /**\n * Obtain a SharePoint context info object using the specified credentials\n * \n * @param credentials {object} An object that contains the ntlm credentials to authenticate with\n * \n * @returns {object} The ContextInfo object returned by the SharePoint's ContextInfo service endpoint.\n */\n let obtainContextInfo = function* (credentials) {\n\n let options = {\n url: ContextInfoServicePath,\n method: \"POST\",\n headers: {\n \"Accept\": \"application/json;odata=verbose\",\n \"Content-Type\": \"application/json;odata=verbose\"\n },\n json: true,\n };\n\n //Authenticate while calling the ContextInfo service.\n let type1Response = yield this.sendType1MessageAsync.call(this, credentials, options);\n let type3Response = yield this.sendType3MessageAsync.call(this, credentials, type1Response, options);\n\n if (type3Response.statusCode !== 200) {\n throw new Error(type3Response.body || `${type3Response.statusCode} UNAUTHORIZED`);\n }\n\n let contextInfo = _.get(type3Response.body, \"d.GetContextWebInformation\");\n\n if (!contextInfo)\n throw Error(\"Unexpected Response from contextinfo service.\");\n\n let expiresDateString = contextInfo.FormDigestValue.split(\",\")[1];\n contextInfo.expires = moment(new Date(expiresDateString)).add(1800, \"seconds\").toDate();\n\n return contextInfo;\n };\n\n /**\n * Given a contextInfo object, returns or renews it.\n * \n * @param credentials {object} An object that contains the ntlm credentials to authenticate with\n * @param force {bool} Indicates if the context should be renewed even if it is not expired.\n * \n * @returns {object} A current context info object.\n */\n let ensureContextInfo = function* (credentials, force) {\n if (!force && this.contextInfo && moment(this.contextInfo.expires).isAfter(moment())) {\n return this.contextInfo;\n }\n\n return this.contextInfo = yield this.obtainContextInfoAsync.call(this, credentials);\n };\n\n /**\n * Performs the authentication flow to the specified tenant domain with the specified credentials.\n * Returns an object that contains the contextInfo and a function that can be used to perform authenticated requests.\n * \n * @param tenantDomain {string} The tenant SharePoint domain to request a access token\n * @param workstation {string} The client supplied workstation\n * @param domain {string} The domain that the user is a member of\n * @param username {string} The username to authenticate with\n * @param password {string} The secret associated with the username\n */\n let authenticate = function* (tenantDomain, workstation, domain, username, password) {\n\n let credentials = {};\n if (_.isObject(tenantDomain)) {\n credentials.username = tenantDomain.username;\n credentials.password = ;\n credentials.domain = tenantDomain.domain;\n credentials.workstation = tenantDomain.workstation;\n\n tenantDomain = tenantDomain.tenantDomain;\n } else {\n credentials.workstation = workstation;\n credentials.domain = domain;\n credentials.username = username;\n credentials.password = ;\n }\n\n if (!credentials.domain)\n credentials.domain = \"\";\n\n if (!credentials.workstation)\n credentials.workstation = \"\";\n\n if (!credentials.username)\n throw Error(\"A username must be specified.\");\n\n if (!credentials.password)\n throw Error(\"A password must be specified.\");\n\n if (!tenantDomain)\n throw Error(\"A Tenant Domain must be specified.\");\n\n let self = {\n contextInfo: null,\n ensureContextInfoAsync: Promise.coroutine(ensureContextInfo),\n obtainContextInfoAsync: Promise.coroutine(obtainContextInfo),\n sendType1MessageAsync: Promise.promisify(sendType1Message),\n sendType3MessageAsync: Promise.promisify(sendType3Message)\n };\n \n let keepaliveAgent = getKeepaliveAgent(tenantDomain);\n\n //Setup request default objects\n self.type1Request = request.defaults({\n baseUrl: tenantDomain,\n agent: keepaliveAgent,\n followRedirect: false\n });\n\n self.type3Request = request.defaults({\n baseUrl: tenantDomain,\n method: \"GET\",\n headers: {\n \"Accept\": \"application/json;odata=verbose\",\n \"Content-Type\": \"application/json;odata=verbose\"\n },\n agent: keepaliveAgent,\n followRedirect: false,\n json: true\n });\n\n yield self.ensureContextInfoAsync.call(self, credentials);\n\n let fnMethod = function (options, callback) {\n return self.ensureContextInfoAsync.call(self, credentials)\n .then(function (contextInfo) {\n\n _.set(options, \"baseUrl\", contextInfo.SiteFullUrl);\n _.set(options, \"headers.X-RequestDigest\", contextInfo.FormDigestValue);\n\n return self.sendType1MessageAsync.call(self, credentials, options);\n })\n .then(function (type1Response) {\n return self.sendType3MessageAsync.call(self, credentials, type1Response, options);\n })\n .then(function (result) {\n if (_.isFunction(callback))\n callback(null, result);\n return result;\n });\n };\n\n let defaultSPRequest = fnMethod;\n\n //Provide shortcuts for common operations.\n ['put', 'patch', 'post', 'head', 'delete', 'get', 'options'].forEach(function (method) {\n defaultSPRequest[method] = function (options, callback) {\n options.method = method;\n return fnMethod(options, callback);\n };\n });\n\n Promise.promisifyAll(defaultSPRequest);\n\n return {\n contextInfo: self.contextInfo,\n ensureContextInfo: ensureContextInfo.bind(self, credentials),\n ensureContextInfoAsync: self.ensureContextInfoAsync.bind(self, credentials),\n ntlm: ntlm,\n request: defaultSPRequest,\n requestAsync: Promise.promisify(defaultSPRequest),\n sendType1Message: sendType1Message.bind(self, credentials),\n sendType1MessageAsync: self.sendType1MessageAsync.bind(self, credentials),\n sendType3Message: sendType3Message.bind(self, credentials),\n sendType3MessageAsync: self.sendType3MessageAsync.bind(self, credentials),\n type1Request: self.type1Request,\n type3Request: self.type3Request\n }\n };\n\n let authenticateWrapper = Promise.coroutine(authenticate);\n return Object.assign(authenticateWrapper, {\n authenticate: Promise.coroutine(authenticate)\n });\n\n})();", "text_preview": "\"use strict\"; const _ = require('lodash'); const request = require('request'); const url = require('url'); const ntlm = require('./ntlm'); const http = require('http'); const https = require('https'); const Promise = require(\"bluebird\"); co", "input_sha256": "08ce7aa7fcaa3b2fcf45b8bb2e19e3c69a761bbf0fb070d196bd1da7178860b7", "text_sha256": "c0b98e659a566670ac6451e5d58667fb0be3c515dd1e8de5a59122bacc99a144", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8070, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7160472914624085, "noise_severity": 0.1739545228849415, "missing_content_severity": 0.3852887861233423, "factual_density": 3.252908286896942, "pretraining_utility": 0.5239389029885584, "math_density": 0.13011422031122352, "code_density": 3.8522579432380146, "reasoning_density": 0.8125669868351312, "education_level": 2.7259691577134837, "creative_writing_quality": -0.28371481097725665}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.014250204544044465, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997033466372869, "secondary": 0.9370162211047524, "tertiary": 0.9518859997594076, "structured_records": 0.006122418582715994, "procedural_steps": 0.0616453229088073, "worked_solution": 0.003561517173143089, "contextual_dialogue": 1.1866541450230998e-05, "content_quality": 0.4268459056796726, "noise_severity": 0.8550378975958821, "missing_content_severity": 0.6789260115638814, "factual_density": 0.37742357241411817, "pretraining_utility": 0.47345360069647024, "math_density": 0.8915714830739804, "code_density": 0.8768816193650123, "reasoning_density": 0.32286084430405715, "education_level": 0.42969296661465634, 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"https://github.com/beyond-sharepoint/ntlm-remote-auth/blob/da66eb12b9f4c795919caad05dacbd5ad71999f4/lib/index.js"}, "gpt4o_tokens": 1934, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7160472914624085, "noise_severity": 0.1739545228849415, "missing_content_severity": 0.3852887861233423, "factual_density": 3.252908286896942, "pretraining_utility": 0.5239389029885584, "math_density": 0.13011422031122355, "code_density": 3.8522579432380146, "reasoning_density": 0.8125669868351312, "education_level": 2.7259691577134837, "creative_writing_quality": -0.28371481097725665}, "score_applicability": {"education_level": 0.998923523484722, "creative_writing_quality": 0.0005995804949151104}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/geosolutions-it/MapStore2/blob/e75d26e98431cd4ba9f854a268c5d1035893cc4e/web/client/components/map/openlayers/Feature.jsx", "id": "javascript-0-7f288c2315aa0fd8b1d44cc9a1eaedc6", "text": "/*\n * Copyright 2017, GeoSolutions Sas.\n * All rights reserved.\n *\n * This source code is licensed under the BSD-style license found in the\n * LICENSE file in the root directory of this source tree.\n */\nimport React from 'react';\nimport PropTypes from 'prop-types';\n\nimport axios from 'axios';\nimport {isEqual, isUndefined} from 'lodash';\nimport find from 'lodash/find';\nimport castArray from 'lodash/castArray';\n\nimport { parseStyles } from './VectorStyle';\nimport { transformPolygonToCircle } from '../../../utils/openlayers/DrawSupportUtils';\nimport { createStylesAsync } from '../../../utils/VectorStyleUtils';\nimport GeoJSON from 'ol/format/GeoJSON';\n\nexport default class Feature extends React.Component {\n static propTypes = {\n type: PropTypes.string,\n layerStyle: PropTypes.object,\n style: PropTypes.object,\n properties: PropTypes.object,\n crs: PropTypes.string,\n container: PropTypes.object, // TODO it must be a ol.layer.vector (maybe pass the source is more correct here?)\n features: PropTypes.array,\n geometry: PropTypes.object, // TODO check for geojson format for geometry\n msId: PropTypes.oneOfType([PropTypes.string, PropTypes.number]),\n featuresCrs: PropTypes.string\n };\n\n static defaultProps = {\n featuresCrs: \"EPSG:4326\"\n };\n\n componentDidMount() {\n this.addFeatures(this.props);\n }\n shouldComponentUpdate(nextProps) {\n return !isEqual(nextProps.properties, this.props.properties)\n || !isEqual(nextProps.geometry, this.props.geometry)\n || !isEqual(nextProps.features, this.props.features)\n || !isEqual(nextProps.crs, this.props.crs)\n || !isEqual(nextProps.style, this.props.style);\n }\n\n\n UNSAFE_componentWillUpdate(nextProps) {\n this.removeFromContainer();\n this.addFeatures(nextProps);\n }\n\n componentWillUnmount() {\n this.removeFromContainer();\n }\n\n render() {\n return null;\n }\n\n addFeatures = (props) => {\n const format = new GeoJSON();\n let ftGeometry = null;\n let canRender = false;\n\n if (props.type === \"FeatureCollection\") {\n // show/hide feature\n const showHideFeature = !isUndefined(props.properties.visibility) ? props.properties.visibility : true;\n ftGeometry = { features: showHideFeature ? props.features : [] };\n canRender = !!(props.features);\n } else {\n // if type is geometryCollection or a simple geometry, the data will be in geometry prop\n ftGeometry = { geometry: props.geometry };\n canRender = !!(props.geometry && (props.geometry.geometries || props.geometry.coordinates));\n }\n\n if (props.container && canRender) {\n this._feature = format.readFeatures({\n type: props.type,\n properties: props.properties,\n id: props.msId,\n ...ftGeometry\n }, {\n // reproject features from featureCrs\n dataProjection: props.featuresCrs\n });\n this._feature.map(f => {\n let newF = f;\n if (f.getProperties().isCircle) {\n newF = transformPolygonToCircle(f, props.crs || 'EPSG:3857', props.featuresCrs);\n newF.setGeometry(newF.getGeometry().transform(props.crs || 'EPSG:3857', props.featuresCrs));\n }\n return newF;\n }).forEach(\n (f) => f.getGeometry().transform(props.featuresCrs, props.crs || 'EPSG:3857'));\n\n if (props.style && (props.style !== props.layerStyle)) {\n this._feature.forEach((f) => {\n\n let promises = [];\n let geoJSONFeature = {};\n if (props.type === \"FeatureCollection\") {\n geoJSONFeature = find(props.features, (ft) => ft.properties.id === f.getProperties().id);\n promises = createStylesAsync(castArray(geoJSONFeature.style));\n } else {\n // TODO Check if this works, it should be a normal geojson Feature\n promises = createStylesAsync(castArray(props.style));\n geoJSONFeature = {\n type: props.type,\n geometry: props.geometry,\n properties: props.properties,\n style: props.style\n };\n }\n axios.all(promises).then((styles) => {\n f.setStyle(() => parseStyles({ ...geoJSONFeature, style: styles }));\n });\n });\n }\n props.container.getSource().addFeatures(this._feature);\n }\n };\n\n removeFromContainer = () => {\n if (this._feature) {\n if (Array.isArray(this._feature)) {\n const layersSource = this.props.container.getSource();\n this._feature.map((feature) => {\n let featureId = feature.getId();\n if (featureId === undefined || !layersSource.getFeatureById(featureId)) {\n layersSource.removeFeature(feature);\n } else {\n layersSource.removeFeature(layersSource.getFeatureById(featureId));\n }\n });\n } else {\n this.props.container.getSource().removeFeature(this._feature);\n }\n }\n };\n}\n\n", "text_preview": "/* * Copyright 2017, GeoSolutions Sas. * All rights reserved. * * This source code is licensed under the BSD-style license found in the * LICENSE file in the root directory of this source tree. */ import React from 'react'; import PropTypes", "input_sha256": 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"math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9321362194693953, "noise_severity": 0.3090931607695409, "missing_content_severity": 0.019112661396572098, "factual_density": 3.473843448467088, "pretraining_utility": 0.4966964368332762, "math_density": 0.08525196466491453, "code_density": 3.690016686668489, "reasoning_density": 0.6952000885129452, "education_level": 2.6533990827268408, "creative_writing_quality": -0.10809712452080546}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.6067 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.40880512333666574, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9956106223365672, "secondary": 0.589567567592215, "tertiary": 0.7478447605128937, "structured_records": 0.007454847845381915, "procedural_steps": 0.004322598382698454, "worked_solution": 0.001696399844873789, "contextual_dialogue": 6.209519126604708e-06, "content_quality": 0.5640477945815936, "noise_severity": 0.7424223660253826, "missing_content_severity": 0.9840727821695232, "factual_density": 0.5615362070559069, "pretraining_utility": 0.44618308324359146, "math_density": 0.9289566961125713, "code_density": 0.741680572223741, "reasoning_density": 0.42066659290587893, "education_level": 0.41823585284826426, "creative_writing_quality": 0.9988544043571829, "primary_topic": 0.9892113901345526, "secondary_topic": 0.8654741531512028}, "probability_margin": {"primary": 0.9932801599418833, "secondary": 0.25712281345434856, "tertiary": 0.531506864168855, "education_level": 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0.00036902504691680376, "Technical Writing": 0.9957935130725435, "Transcript / Interview": 1.2263375705383121e-05, "Tutorial / How-To Guide": 8.3677743358402e-05, "User Reviews": 6.160422984458994e-06, "Other / Unknown": 0.0009511723430211432}, "secondary": {"Academic Writing": 0.0007569004468784631, "Content Listing": 0.00061322503393173, "Creative Writing": 0.0005858966904562195, "Customer Support Page": 0.000326398063618733, "Q&A Forum": 0.000346230697281595, "Discussion Thread": 0.00036103019479781853, "Comment Section": 0.0006404871487954713, "Missing Content": 0.000654875342025572, "Knowledge Article": 0.001031446687818382, "Legal Notices": 0.6066689189425393, "News Article": 0.0005498127396213958, "Organizational Announcement": 0.0006636132616308067, "Nonfiction Writing": 0.0005177070183161643, "Organizational About Page": 0.0006671865437272305, "Personal About Page": 0.0003844976660501974, "Personal Blog": 0.00031945034412160094, "Product Page": 0.0004152033268112752, "Spam / 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0.0045133264231667115, "2": 0.4054690748062174, "3": 0.5151965440402202, "4": 0.07233918913871393}, "creative_writing_quality": {"not_applicable": 0.9990453369643191, "-2": 6.729094318302737e-05, "-1": 0.0001441056906505256, "0": 0.0006000840477497705, "1": 0.00011087346022221839, "2": 3.2308893875470695e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.072118205656681e-05, "Art & Design": 0.0003253711022410822, "Law & Crime": 0.0003294644895467422, "Education & Careers": 3.819327982418843e-05, "Entertainment & Media": 3.397615650940897e-05, "Fashion & Beauty": 3.194660590562663e-05, "Business, Economics & Finance": 7.609855357598975e-05, "Food & Dining": 2.792937290847268e-05, "Games & Gaming": 0.00013316266644943997, "Electronics & Hardware": 6.89045160402129e-05, "Health, Healthcare & Wellness": 3.641997850565414e-05, "History & Geography": 0.003639166724754624, "Home, Pets & Hobbies": 3.265694923925567e-05, "Industry & Production": 4.074196394656153e-05, "Language, 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"decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:16.761253+00:00", "scoring_key_slot": "primary", "dataset_uid": "a11cf3d5-9eae-458c-83aa-040c682f6dba", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "ad0636251c3b00c6b9bec5a834fd61d2e83a2fed47f80d6dc44c8b99485013c3", "source_metadata": {"uuid": "a11cf3d5-9eae-458c-83aa-040c682f6dba", "repo_name": "geosolutions-it/MapStore2", "relative_path": "web/client/components/map/openlayers/Feature.jsx", "category": "WEB", "algo_rel_score": 0.9631286859512329, "quality_score": 7.470836162567139, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 5231, "repo_created_at": "2015-07-20T12:20:59.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 581, "forks": 429, "issues": 623, "pull_requests": 33}, "file_meta": {"content_id": "2206eced7936fe59f4c5a7dec7451806b9bf992f", "size_bytes": 5609, "file_timestamp": 1752760820, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e75d26e98431cd4ba9f854a268c5d1035893cc4e"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 5231, "repo_created_at": "2015-07-20T12:20:59.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 581, "forks": 429, "issues": 623, "pull_requests": 33}, "file_meta": {"content_id": "2206eced7936fe59f4c5a7dec7451806b9bf992f", "size_bytes": 5609, "file_timestamp": 1752760820, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "e75d26e98431cd4ba9f854a268c5d1035893cc4e", "url": "https://github.com/geosolutions-it/MapStore2/blob/e75d26e98431cd4ba9f854a268c5d1035893cc4e/web/client/components/map/openlayers/Feature.jsx"}, "gpt4o_tokens": 1108, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.9321362194693954, "noise_severity": 0.3090931607695409, "missing_content_severity": 0.019112661396572098, "factual_density": 3.473843448467088, "pretraining_utility": 0.4966964368332762, "math_density": 0.08525196466491453, "code_density": 3.690016686668489, "reasoning_density": 0.6952000885129452, "education_level": 2.6533990827268408, "creative_writing_quality": -0.10809712452080546}, "score_applicability": {"education_level": 0.9988689157106971, "creative_writing_quality": 0.0009546630356810125}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/vietanh203/TrafficMobile/blob/07eb3f2802295f06dbbad0f374ceaf666dd8ad11/components/MyCar.js", "id": "javascript-0-7b800db573f91326a899272074a6bbfa", "text": "import React, { Component } from 'react';\nimport {\n StyleSheet, Text, Dimensions,\n View, TouchableOpacity, Alert\n} from 'react-native';\nimport { Ionicons } from '@expo/vector-icons';\nimport { Table, TableWrapper, Row, Cell } from 'react-native-table-component';\nimport { SearchBar } from 'react-native-elements';\nimport { TextInput } from 'react-native-gesture-handler';\nimport AppProvider from'./Context';\nconst { width: WIDTH } = Dimensions.get('window')\nclass MyCar extends Component {\n constructor(props) {\n super(props);\n this.state = {\n search: \"\",\n tableHead: ['Biển số', 'Tên xe', 'Màu', 'Chủ xe', ''],\n tableData: [],\n car: {\n Plate: \"\",\n label: \"\",\n color: \"\",\n number: \"\",\n manaUsername: \"\",\n name: \"\"\n }\n }\n }\n updateSearch = search => {\n this.setState({ search });\n const url = \"http://apismarttraffic.servehttp.com/cars/\" + search;\n fetch(url, {\n method: 'GET',\n headers: {\n Accept: 'application/json',\n 'Content-Type': 'application/json',\n }\n }).then(response => response.json())\n .then((responseJson) => {\n this.setState({\n tableData: responseJson\n })\n })\n .catch(error => console.log(error))\n };\n addCar = () => {\n fetch('http://apismarttraffic.servehttp.com/users/login', {\n method: 'POST',\n headers: {\n Accept: 'application/json',\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify(this.state.car)\n }).then(response => response.json())\n .then((resJson) => {\n console.log(resJson);\n })\n .catch(error => console.log(error))\n }\n componentDidMount() {\n var data = [];\n const plates = this.props.plate;\n for (var i in plates) {\n fetch(`http://apismarttraffic.servehttp.com/cars/${plates[i]}`, {\n method: 'GET',\n headers: {\n Accept: 'application/json',\n 'Content-Type': 'application/json',\n }\n })\n .then(response => response.json())\n .then((responseJson) => {\n data = data.concat(responseJson);\n this.setState({ tableData: data });\n })\n .catch(error => console.log(error)) //to catch the errors if any\n }\n }\n\n _alertIndex(index) {\n Alert.alert(`This is row ${index + 1}`);\n }\n\n render() {\n const state = this.state;\n console.log(state.tableData);\n const listBtn = (\n \n \n \n \n \n \n \n \n );\n const table = [];\n for (var i in state.tableData) {\n row = (\n \n \n \n \n \n \n );\n table.push(row);\n }\n\n return (\n \n \n \n \n {table}\n
\n \n Thêm xe mới\n \n \n \n \n \"\n underlineColorAndroid='transparent'\n onChangeText={(manaUsername) => this.setState(currentState => ({ car: { ...currentState.car, manaUsername } }))}\n />\n \n \n \n this.setState(currentState => ({ car: { ...currentState.car, Plate } }))}\n\n />\n \n \n \n \n \n \"\n underlineColorAndroid='transparent'\n onChangeText={(label) => this.setState(currentState => ({ car: { ...currentState.car, label } }))}\n />\n \n \n \n \"\n underlineColorAndroid='transparent'\n onChangeText={(name) => this.setState(currentState => ({ car: { ...currentState.car, name } }))}\n />\n \n \n \n \n \n \"\n underlineColorAndroid='transparent'\n onChangeText={(color) => this.setState(currentState => ({ car: { ...currentState.car, color } }))}\n />\n \n \n \n this.setState(currentState => ({ car: { ...currentState.car, number } }))}\n />\n \n \n \n Thêm\n \n \n )\n }\n}\n\nconst styles = StyleSheet.create({\n container: { flex: 1, padding: 0, paddingTop: 40, backgroundColor: '#fff' },\n head: { height: 40, backgroundColor: '#808B97' },\n text: { margin: 0, textAlign: 'center' },\n row: { flexDirection: 'row', backgroundColor: '#FFF1C1', height: 35 },\n btn: { width: 58, height: 18, backgroundColor: '#78B7BB', borderRadius: 2 },\n btnText: { textAlign: 'center', color: '#fff' },\n lockBtn: {\n position: 'relative',\n top: 4,\n },\n deleteBtn: { position: 'relative', top: 4 },\n detailBtn: { position: 'relative', top: 4 },\n btnContainer: { flex: 1, flexDirection: 'row' },\n logoText: {\n color: 'black',\n fontSize: 20,\n marginTop: 10,\n fontWeight: '500',\n opacity: 0.5,\n textAlign: 'center'\n },\n inputContainer: {\n marginTop: 10,\n flexDirection: 'row'\n },\n textInput: {\n width: WIDTH / 2 - 25,\n height: 35,\n borderRadius: 25,\n fontSize: 16,\n paddingLeft: 35,\n backgroundColor: 'rgba(0,0,0,0.35)',\n color: 'rgba(255,255,255,0.7)',\n marginHorizontal: 12\n },\n inputIcon: {\n position: 'absolute',\n top: 4,\n left: 20\n },\n loginBtn: {\n width: WIDTH / 2 - 100,\n height: 25,\n borderRadius: 25,\n justifyContent: 'center',\n backgroundColor: '#003366',\n marginTop: 20,\n alignItems: 'center',\n marginLeft: WIDTH / 2 - 45\n },\n loginText: {\n color: 'rgba(255,225,255,0.7)',\n fontSize: 16,\n textAlign: \"center\"\n }\n\n});\nconst WrapperContext = (props) => {\n return (\n {context => { return }}\n )\n}\nexport default WrapperContext;", "text_preview": "import React, { Component } from 'react'; import { StyleSheet, Text, Dimensions, View, TouchableOpacity, Alert } from 'react-native'; import { Ionicons } from '@expo/vector-icons'; import { Table, TableWrapper, Row, Cell } from 'react-nativ", "input_sha256": "096a38db773fda3b89ca586f6cd7fcf06fe3b6192830a2ce5080b3812d37d7cb", "text_sha256": "7b800db573f91326a899272074a6bbfa9e44ae58ee1c1d91ac412e0c60fed81b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8073, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Vehicles & Transportation", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2973150918412203, "noise_severity": 0.17825290960815315, "missing_content_severity": 0.02950758493295496, "factual_density": 3.042155403470101, "pretraining_utility": 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"2020-02-13T02:54:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 14}, "file_meta": {"content_id": "50c5a28ea9f09c597bd13dba81cfa5a717f9e98b", "size_bytes": 10335, "file_timestamp": 1583143603, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "07eb3f2802295f06dbbad0f374ceaf666dd8ad11", "url": "https://github.com/vietanh203/TrafficMobile/blob/07eb3f2802295f06dbbad0f374ceaf666dd8ad11/components/MyCar.js"}, "gpt4o_tokens": 2189, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2973150918412203, "noise_severity": 0.17825290960815318, "missing_content_severity": 0.02950758493295496, "factual_density": 3.042155403470101, "pretraining_utility": 0.34037479679244537, "math_density": 0.16199692620687023, "code_density": 3.889636027928367, "reasoning_density": 0.09399762922560936, "education_level": 2.1154046427244744, "creative_writing_quality": -0.5575794285981273}, "score_applicability": {"education_level": 0.9985547076555823, "creative_writing_quality": 0.0008411534187414151}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/phanthuyen12/fullnetworkapisever/blob/b2a4e73824e9f02e9393ba115685cb6991d15c42/chaincode/UserChaincode/lib/HospitalChaincode.js", "id": "javascript-0-2d7ebe35b2e6bbdd3ca27a755bcdd833", "text": "'use strict';\nconst { Contract } = require('fabric-contract-api');\nconst crypto = require('crypto'); // Import module crypto\n\nclass HospitalContract extends Contract {\n async initLedger(ctx) {\n const hospitals = [\n {\n hospitalId: 'gfdgdfgdf6546dfdfg',\n namehospital: 'Hospital 1',\n address: 'quan 12',\n type: \"kgfdjhydkfgjdfgjh\",\n organization: 'Org1',\n doctors: [],\n admin: 'admin1'\n },\n {\n hospitalId: 'gfdgdfgdf6546dfdfg4544',\n namehospital: 'Hospital 2',\n address: 'quan 12',\n type: \"kgfdjhydkfgjdfgjhd\",\n organization: 'Org2',\n doctors: [],\n admin: 'admin2'\n }\n ];\n\n for (let i = 0; i < hospitals.length; i++) {\n await ctx.stub.putState(hospitals[i].hospitalId, Buffer.from(JSON.stringify(hospitals[i])));\n console.log('Added hospital:', hospitals[i]);\n }\n }\n\n async createHospital(ctx, type, namehospital, address, organization) {\n try {\n console.log('createHospital called with:', { type, namehospital, address, organization });\n \n let tokenhospital = this.generateToken(namehospital);\n console.log('Generated token:', tokenhospital);\n \n const hospital = {\n hospitalId: tokenhospital,\n namehospital,\n type,\n address,\n organization,\n doctors: [],\n admin: ctx.clientIdentity.getID()\n };\n \n await ctx.stub.putState(tokenhospital, Buffer.from(JSON.stringify(hospital)));\n console.log('Created new hospital:', hospital);\n } catch (error) {\n console.error('Error in createHospital:', error);\n throw new Error(`Failed to create hospital: ${error}`);\n }\n }\n \n \n generateToken(namehospital) {\n const hash = crypto.createHash('sha256');\n hash.update(namehospital);\n const result = hash.digest('hex');\n console.log('Generated token:', result);\n return result;\n }\n \n\n async createDoctor(ctx, namedoctor, specialization, hospitalId) {\n console.log('Starting createDoctor transaction');\n console.log(`Parameters: namedoctor=${namedoctor}, specialization=${specialization}, hospitalId=${hospitalId}`);\n\n try {\n const hospitalAsBytes = await ctx.stub.getState(hospitalId);\n if (!hospitalAsBytes || hospitalAsBytes.length === 0) {\n throw new Error(`Hospital ${hospitalId} does not exist`);\n }\n\n let hospital = JSON.parse(hospitalAsBytes.toString());\n\n const doctortoken = this.generateToken(namedoctor); // Generate token for the doctor\n\n const doctor = {\n doctorId: doctortoken,\n namedoctor,\n specialization,\n hospitalId,\n admincreate: ctx.clientIdentity.getID(),\n };\n\n if (!hospital.doctors) {\n hospital.doctors = [];\n }\n\n hospital.doctors.push(doctor);\n\n await ctx.stub.putState(hospitalId, Buffer.from(JSON.stringify(hospital))); // Save the hospital state with the new doctor\n console.log('Added doctor:', doctor);\n } catch (error) {\n console.error('Failed to create doctor:', error);\n throw error; // Rethrow the error to indicate transaction failure\n }\n }\n\n async createMedicalBook(ctx, doctorId, hospitalId, patientName, patientYear, appointment, typeDisease) {\n const medicalBookId = this.generateToken(patientName);\n\n const medicalBook = {\n medicalBookId,\n patientName,\n patientYear,\n medicalhistory: [\n {\n doctorId,\n hospitalId,\n appointment,\n typeDisease,\n adminhospital: ctx.clientIdentity.getID()\n }\n ],\n };\n\n await ctx.stub.putState(medicalBookId, Buffer.from(JSON.stringify(medicalBook)));\n console.log('Created new medical book:', medicalBook);\n }\n\n async addMedicalHistory(ctx, medicalBookId, doctorId, hospitalId, appointment, typeDisease) {\n const medicalBookBytes = await ctx.stub.getState(medicalBookId);\n if (!medicalBookBytes || medicalBookBytes.length === 0) {\n throw new Error(`Medical book with ID ${medicalBookId} does not exist`);\n }\n\n const medicalBook = JSON.parse(medicalBookBytes.toString());\n\n const newEntry = {\n doctorId,\n hospitalId,\n appointment,\n typeDisease,\n admin: ctx.clientIdentity.getID()\n };\n\n medicalBook.medicalhistory.push(newEntry);\n\n await ctx.stub.putState(medicalBookId, Buffer.from(JSON.stringify(medicalBook)));\n console.log('Updated medical book with new entry:', newEntry);\n }\n\n async getMedicalBook(ctx, medicalBookId) {\n const medicalBookBytes = await ctx.stub.getState(medicalBookId);\n if (!medicalBookBytes || medicalBookBytes.length === 0) {\n throw new Error(`Medical book with ID ${medicalBookId} does not exist`);\n }\n const medicalBook = JSON.parse(medicalBookBytes.toString());\n return medicalBook;\n }\n\n async getAllHospitals(ctx) {\n const iterator = await ctx.stub.getStateByRange('', '');\n const hospitals = [];\n\n while (true) {\n const result = await iterator.next();\n if (result.value && result.value.value.toString()) {\n const strValue = result.value.value.toString('utf8');\n let record;\n try {\n record = JSON.parse(strValue);\n } catch (err) {\n console.log(err);\n record = strValue;\n }\n hospitals.push(record);\n }\n if (result.done) {\n await iterator.close();\n return JSON.stringify(hospitals);\n }\n }\n }\n}\n\nmodule.exports = HospitalContract;\n", "text_preview": "'use strict'; const { Contract } = require('fabric-contract-api'); const crypto = require('crypto'); // Import module crypto class HospitalContract extends Contract { async initLedger(ctx) { const hospitals = [ { hospitalId: 'gfdgdfgdf6546d", "input_sha256": "282e1e2db4ad1c83f25a5f1c8164261d27fe9b1ad52e0934c9e9539951bbd884", "text_sha256": "2d7ebe35b2e6bbdd3ca27a755bcdd833551eaaa9f427b8180692efccdc81468c", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8071, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3893108710555598, "noise_severity": 0.28231073137437557, "missing_content_severity": 0.010161405588746805, "factual_density": 3.194181324570939, "pretraining_utility": 0.41947694036651345, "math_density": 0.20986142981597813, "code_density": 3.810669226512269, "reasoning_density": 0.3797595849977557, "education_level": 2.473439753294926, "creative_writing_quality": -0.5531075973596884}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.045602518357371946, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9949330993533037, "secondary": 0.9093562111247768, "tertiary": 0.9569414476715999, "structured_records": 0.5147995621345203, "procedural_steps": 0.02345399500962341, "worked_solution": 0.011706550967834751, "contextual_dialogue": 8.46888781577114e-06, "content_quality": 0.35421067385974414, "noise_severity": 0.7647410571880203, "missing_content_severity": 0.9915321620093777, "factual_density": 0.49228887283232325, "pretraining_utility": 0.41679961013551736, "math_density": 0.8251154751533516, "code_density": 0.8422243554268906, "reasoning_density": 0.6835336791685369, "education_level": 0.4924145893340643, "creative_writing_quality": 0.9992699037870996, "primary_topic": 0.9698173891561682, "secondary_topic": 0.5447422663556195}, "probability_margin": {"primary": 0.9920775965094907, "secondary": 0.8479158294057444, "tertiary": 0.9319800879979234, "education_level": 0.22690706916626152, "creative_writing_quality": 0.9991741736739312, "primary_topic": 0.9474407638133999, "secondary_topic": 0.2269528337760099}, "raw_probabilities": {"primary": {"Academic Writing": 3.179006703167479e-05, "Content Listing": 1.8655286650437738e-05, "Creative Writing": 1.691769832334779e-05, "Customer Support Page": 8.203450713533403e-06, "Q&A Forum": 1.2050439886590982e-05, "Discussion Thread": 7.129161140657178e-06, "Comment Section": 1.1015350933071133e-05, "Missing Content": 2.1925702751948242e-05, "Knowledge Article": 5.1158158072696026e-05, "Legal Notices": 3.694120325955964e-05, "News Article": 4.6306762498201804e-06, "Organizational Announcement": 9.591262887584343e-06, "Nonfiction Writing": 1.0368195176311031e-05, "Organizational About Page": 1.4690832587704408e-05, "Personal About Page": 1.1507325799825864e-05, "Personal Blog": 7.011579003725179e-06, "Product Page": 7.549704686798418e-06, "Spam / Ads": 1.4080164569873809e-05, "Structured Data": 0.0030666237040919606, "Technical Writing": 0.9951442202135826, "Transcript / Interview": 1.537246669108674e-05, "Tutorial / How-To Guide": 0.00027752552130588966, "User Reviews": 5.706130596593034e-06, "Other / Unknown": 0.001195335704006686}, "secondary": {"Academic Writing": 0.00029999124816409013, "Content Listing": 0.0002423257371734612, "Creative Writing": 0.0002809845742969114, "Customer Support Page": 0.00011850629363384474, "Q&A Forum": 0.00011176127993793674, "Discussion Thread": 9.494418983250761e-05, "Comment Section": 0.0001699468865230566, "Missing Content": 0.00037417672914497247, "Knowledge Article": 0.00042572668332845575, "Legal Notices": 0.00026701399809344256, "News Article": 0.00015865230877395444, "Organizational Announcement": 0.00014200731419048012, "Nonfiction Writing": 0.00015150653498110993, "Organizational About Page": 0.0001814398559449154, "Personal About Page": 0.00012642726345194372, "Personal Blog": 0.00013021192862135752, "Product Page": 0.00010659592440187153, "Spam / Ads": 0.00021593273093129667, "Structured Data": 0.06521720625550007, "Technical Writing": 0.01708022813995401, "Transcript / Interview": 0.0002160753842100585, "Tutorial / How-To Guide": 0.0006955390115669746, "User Reviews": 5.976406609887573e-05, "NONE": 0.9131330356612445}, "tertiary": {"Academic Writing": 0.00015462621337057148, "Content Listing": 0.0001438684798854564, "Creative Writing": 0.00019352845378219695, "Customer Support Page": 6.752156225471136e-05, "Q&A Forum": 4.819057084905295e-05, "Discussion Thread": 4.375914632399513e-05, "Comment Section": 0.00010563680272451857, "Missing Content": 0.00028679276794481236, "Knowledge Article": 0.0006237477339841129, "Legal Notices": 0.0002633188116290145, "News Article": 7.935441433427831e-05, "Organizational Announcement": 6.903879223516575e-05, "Nonfiction Writing": 0.00010179013378953067, "Organizational About Page": 0.0001355944904335907, "Personal About Page": 6.6176499884202e-05, "Personal Blog": 6.300974438922973e-05, "Product Page": 5.726334117281437e-05, "Spam / Ads": 0.0003923040341942254, "Structured Data": 0.02675546602069322, "Technical Writing": 0.010339359478343059, "Transcript / Interview": 0.00010147242000021348, "Tutorial / How-To Guide": 0.0011395883797241273, "User Reviews": 3.3037689441382317e-05, "NONE": 0.9587355540186167}, "structured_records": {"false": 0.48520043786547973, "true": 0.5147995621345203}, "procedural_steps": {"false": 0.9765460049903766, "true": 0.02345399500962341}, "worked_solution": {"false": 0.9882934490321652, "true": 0.011706550967834751}, "contextual_dialogue": {"false": 0.9999915311121842, "true": 8.46888781577114e-06}, "content_quality": {"-2": 0.02044115383566334, "-1": 0.12497101492813054, "0": 0.38155266879312255, "1": 0.3909061312311502, "2": 0.08212903121193339}, "noise_severity": {"0": 0.7810258932709964, "1": 0.16768088763874317, "2": 0.04257170030231471, "3": 0.005399632020780365, "4": 0.0033218867671654705}, "missing_content_severity": {"0": 0.9943798291858258, "1": 0.0021770129278445402, "2": 0.002532373173012061, "3": 0.0007234925383924818, "4": 0.00018729217492517434}, "factual_density": {"0": 0.006885263902088883, "1": 0.02123442562511861, "2": 0.14441137105863264, "3": 0.4257516008280842, "4": 0.4017173385860756}, "pretraining_utility": {"-2": 0.010743990527250957, "-1": 0.09678956355716535, "0": 0.41437066503934933, "1": 0.4184370767742882, "2": 0.059658704101946264}, "math_density": {"0": 0.8575568945805632, "1": 0.10975104889383842, "2": 0.012273056274409921, "3": 0.0061117326314338175, "4": 0.014307267619754597}, "code_density": {"0": 0.0002970433864240633, "1": 0.0006186551246410813, "2": 0.021216429274855255, "3": 0.14385377601840132, "4": 0.8340140961956783}, "reasoning_density": {"0": 0.7768213824424834, "1": 0.0945938931676731, "2": 0.10544954971875369, "3": 0.01827410629178385, "4": 0.0048610683793059205}, "education_level": {"none": 0.0010106409585408328, "0": 0.0014173705517789416, "1": 0.005072852006019586, "2": 0.5770121577783869, "3": 0.35010508861212536, "4": 0.06538189009314833}, "creative_writing_quality": {"not_applicable": 0.9993915864892496, "-2": 0.0001286074121111672, "-1": 0.00018343796095537801, "0": 0.00021741281531838675, "1": 5.377599468544055e-05, "2": 2.5179327679984462e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.6901766482990925e-05, "Art & Design": 7.584475838865977e-05, "Law & Crime": 8.534391105454477e-05, "Education & Careers": 0.00010516463680963707, "Entertainment & Media": 5.09102934480764e-05, "Fashion & Beauty": 5.5672292946353954e-05, "Business, Economics & Finance": 0.001039977364775625, "Food & Dining": 9.883822064956098e-05, "Games & Gaming": 8.362216435548927e-05, "Electronics & Hardware": 0.00011875107449310379, "Health, Healthcare & Wellness": 0.023537494990607942, "History & Geography": 6.809929038242964e-05, "Home, Pets & Hobbies": 4.309005765094127e-05, "Industry & Production": 7.970293141854244e-05, "Language, Literature & Philosophy": 5.077130965778073e-05, "Politics & Government": 8.005598235648092e-05, "Religion & Spirituality": 6.611689731134699e-05, "Science, Mathematics & Engineering": 0.0003369116931264613, "Relationships & Community": 5.540032565134957e-05, 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Tourism": 6.490972455818535e-05, "Other / Uncovered Subject": 0.002040794926109683, "Unknown / Insufficient Content": 8.37930799178625e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0009482957404643829, "Art & Design": 0.0007063950544734409, "Law & Crime": 0.0014003519622332288, "Education & Careers": 0.000784682809256581, "Entertainment & Media": 0.0005725735687210451, "Fashion & Beauty": 0.00044739266450448764, "Business, Economics & Finance": 0.004671901555167721, "Food & Dining": 0.0005914931375561055, "Games & Gaming": 0.00046374191786571257, "Electronics & Hardware": 0.0008037036647884992, "Health, Healthcare & Wellness": 0.33529934541208584, "History & Geography": 0.0005299888357619727, "Home, Pets & Hobbies": 0.0004407979086877121, "Industry & Production": 0.0004998654501491773, "Language, Literature & Philosophy": 0.0002588831699584274, "Politics & Government": 0.0006000642516021669, "Religion & Spirituality": 0.0006908518673232816, "Science, Mathematics & Engineering": 0.0044623130814869984, "Relationships & Community": 0.00036061275843301535, "Software Usage & Applications": 0.0012177206058893433, "Programming & Software Engineering": 0.07796177301430568, "Sports & Fitness": 0.00038896105584995054, "Vehicles & Transportation": 0.0002406174583265004, "Travel & Tourism": 0.0005495605345642492, "Other / Uncovered Subject": 0.0028559333324488247, "NONE": 0.5622521791880958}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9926, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9926, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:06.767344+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "bf544059-c2ed-4057-964f-3c8811ad55b9", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "69b266e0774cd5825c6ef527028515fb135b48a2f53167babcf4895fc81bebf7", "source_metadata": {"uuid": "bf544059-c2ed-4057-964f-3c8811ad55b9", "repo_name": "phanthuyen12/fullnetworkapisever", "relative_path": "chaincode/UserChaincode/lib/HospitalChaincode.js", "category": "TOOL", "algo_rel_score": 0.9453607797622681, "quality_score": 7.743732929229736, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2024-09-30T20:19:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a8a5d0b990bdfaa59ff685872db9b5bae59825d0", "size_bytes": 6373, "file_timestamp": 1728283080, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b2a4e73824e9f02e9393ba115685cb6991d15c42"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 5, "repo_created_at": "2024-09-30T20:19:09.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "a8a5d0b990bdfaa59ff685872db9b5bae59825d0", "size_bytes": 6373, "file_timestamp": 1728283080, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "b2a4e73824e9f02e9393ba115685cb6991d15c42", "url": "https://github.com/phanthuyen12/fullnetworkapisever/blob/b2a4e73824e9f02e9393ba115685cb6991d15c42/chaincode/UserChaincode/lib/HospitalChaincode.js"}, "gpt4o_tokens": 1254, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.3893108710555598, "noise_severity": 0.28231073137437557, "missing_content_severity": 0.010161405588746805, "factual_density": 3.194181324570939, "pretraining_utility": 0.41947694036651345, "math_density": 0.20986142981597813, "code_density": 3.810669226512269, "reasoning_density": 0.3797595849977557, "education_level": 2.473439753294926, "creative_writing_quality": -0.5531075973596884}, "score_applicability": {"education_level": 0.9989893590414592, "creative_writing_quality": 0.000608413510750357}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/sushantBansal/OnlineQuiz/blob/3b94d139d353f0d4191b0b0f9d8fa9da5ca2e52e/frontend/src/containers/Auth/Register/index.js", "id": "javascript-0-e9fe12971b0c58bac6c060012afc6454", "text": "import React from 'react';\nimport { Row, Col, Card, Input, Button, Select, Form, Alert } from 'antd';\nimport { Link, withRouter } from 'react-router-dom';\nimport './index.scss';\nimport { useStore, useDispatch } from 'react-redux';\nimport Axios from 'axios';\nimport { API_URL, login } from '../../../actions';\nimport { authHeader } from '../../../utils/auth-header';\n\nconst RegisterContainer = (props) => {\n const store = useStore();\n const dispatch = useDispatch();\n const [auth, setAuth] = React.useState(store.getState().auth)\n\n const [state, setState] = React.useState({\n userName: '',\n firstName: '',\n lastName: '',\n email: '',\n password: '',\n rePassword: '',\n language: 'en-us'\n })\n\n const [registerError, setRegisterError] = React.useState('')\n\n React.useEffect(() => {\n let isUnmounted = false;\n\n if (!isUnmounted) {\n scrollToTop();\n setState({\n ...state,\n userName: auth.userName,\n email: auth.email,\n firstName: auth.firstName\n })\n }\n return () => {\n isUnmounted = true;\n };\n }, [auth])\n\n const handleChange = (e) => {\n\n if (auth.email && e.target.name == 'email') {\n return\n }\n setState({\n ...state,\n [e.target.name]: e.target.value\n })\n }\n const handleLanguage = e => {\n setState({\n ...state,\n language: e\n })\n }\n\n const handleRegister = async (data) => {\n\n console.log(data)\n const { userName, firstName, lastName, email, password, rePassword, language } = data;\n\n if (!userName || !firstName || !lastName || !email || !password || !rePassword || !language) {\n return setState({\n ...state,\n error: true\n })\n }\n\n setState({\n ...state,\n loading: true,\n error: false\n })\n try {\n if ((userName && firstName && lastName && email && password && rePassword && language)) {\n if (auth && auth.role === 'guest') {\n const token = auth.token ? auth.token : auth.tempToken;\n const response = await Axios.put(`${API_URL}/auth/updatedetails`, {\n ...data,\n role: 'user'\n }, authHeader(token))\n\n const authMe = await getMe(token);\n dispatch(login({\n ...response.data,\n ...authMe.data.data\n }));\n return props.history.push('/dashboard')\n }\n\n const response = await Axios.post(`${API_URL}/auth/register`, data)\n const authMe = await getMe(response.data.token);\n dispatch(login({\n ...response.data,\n ...authMe.data.data\n }));\n return props.history.push('/')\n }\n } catch (error) {\n setState({\n ...state,\n loading: false\n })\n scrollToTop();\n setRegisterError(error.response.data.error)\n }\n\n setState({\n ...state,\n loading: false\n })\n }\n\n const scrollToTop = () => {\n window.scrollTo({\n behavior: 'smooth',\n top: 200\n })\n }\n\n const getMe = async (token) => {\n return Axios.get(`${API_URL}/auth/me`, authHeader(token))\n }\n\n const handleFinish = async values => {\n if (values[0]?.errors?.length > 0) {\n setState({\n ...state,\n error: true\n })\n } else {\n setState({\n ...state,\n error: false\n })\n await handleRegister(values)\n }\n };\n\n const closeAlert = () => {\n setRegisterError(\"\")\n }\n\n const { userName, firstName, lastName, email, password, rePassword, language } = state;\n\n return (\n <>\n
\n
\n

Register

\n
    \n
  • \n Home\n
    • \n
  • \n Register\n
    • \n
\n
\n
\n
\n
\n
\n \n
\n

Register Yourself!

\n {\n registerError && \n }\n
\n\n
\n \n \n \n
\n
\n \n \"\n size=\"large\"\n onChange={handleChange}\n value={state.firstName}\n />\n \n
\n
\n \"\n rules={[{ required: true, message: 'Lastname cannot be blank' }]}\n >\n \"\n size=\"large\"\n onChange={handleChange}\n value={state.lastName}\n />\n \n
\n
\n \n \n \n
\n
\n \n \n \n \n \n \n
\n
\n \n \n \n
\n
\n ({\n validator(rule, value) {\n if (!value || getFieldValue('password') === value) {\n return Promise.resolve();\n }\n return Promise.reject('The two passwords that you entered do not match!');\n },\n }),\n ]}\n dependencies={['password']}\n hasFeedback\n >\n \n \n
\n\n
\n
\n \n Sign Up\n \n
\n
\n Already have an account? Login\n
\n \n
\n
\n
\n
\n \n );\n}\n\nexport default withRouter(RegisterContainer);", "text_preview": "import React from 'react'; import { Row, Col, Card, Input, Button, Select, Form, Alert } from 'antd'; import { Link, withRouter } from 'react-router-dom'; import './index.scss'; import { useStore, useDispatch } from 'react-redux'; import Ax", "input_sha256": "27f9e92a0af3e3b9fe3e74d37f7c6a688af187d62da755bca12a5754e7760609", "text_sha256": "e9fe12971b0c58bac6c060012afc64544d15fcde56263731f1bf9830834d5d24", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8072, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.28097166692155073, "noise_severity": 0.2802031517860744, "missing_content_severity": 0.1575635099247863, "factual_density": 2.7925172689352884, "pretraining_utility": 0.30937059462567346, "math_density": 0.0331046816241846, "code_density": 3.8926539066636536, "reasoning_density": 0.4585036471842568, "education_level": 2.1621729127313714, "creative_writing_quality": -0.46964727850667515}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", 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"cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:06.824282+00:00", "scoring_key_slot": "primary", "dataset_uid": "99e9a768-aca4-4d14-90e2-84e498a9bdfa", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "8b79406df702e6389aea05cb06a749821dff7e786bb8ff519e98eceb8bc66f87", "source_metadata": {"uuid": "99e9a768-aca4-4d14-90e2-84e498a9bdfa", "repo_name": "sushantBansal/OnlineQuiz", "relative_path": "frontend/src/containers/Auth/Register/index.js", "category": "WEB", "algo_rel_score": 0.9654401540756226, "quality_score": 4.761961460113525, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2020-10-01T18:11:56.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ee70d2c2a90fd01d4d9fed7f404170b814b0362f", "size_bytes": 14819, "file_timestamp": 1602409337, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "3b94d139d353f0d4191b0b0f9d8fa9da5ca2e52e"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 3, "repo_created_at": "2020-10-01T18:11:56.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ee70d2c2a90fd01d4d9fed7f404170b814b0362f", "size_bytes": 14819, "file_timestamp": 1602409337, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "3b94d139d353f0d4191b0b0f9d8fa9da5ca2e52e", "url": "https://github.com/sushantBansal/OnlineQuiz/blob/3b94d139d353f0d4191b0b0f9d8fa9da5ca2e52e/frontend/src/containers/Auth/Register/index.js"}, "gpt4o_tokens": 1983, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2809716669215508, "noise_severity": 0.2802031517860744, "missing_content_severity": 0.1575635099247863, "factual_density": 2.7925172689352884, "pretraining_utility": 0.30937059462567346, "math_density": 0.033104681624184604, "code_density": 3.8926539066636536, "reasoning_density": 0.4585036471842568, "education_level": 2.1621729127313714, "creative_writing_quality": -0.46964727850667515}, "score_applicability": {"education_level": 0.9977485597342587, "creative_writing_quality": 0.0009646522822331685}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/salmanbuali/TrackTix/blob/e78c5634734ccdc4161eec4f41af9166c50b8fda/src/components/Tickets.jsx", "id": "javascript-0-94979d9d204abfdb1d0848b90a802c7e", "text": "import { Fragment, useEffect, useRef, useState } from 'react'\nimport { Link, useNavigate } from 'react-router-dom'\nimport { Menu, Transition } from '@headlessui/react'\nimport { EllipsisVerticalIcon } from '@heroicons/react/20/solid'\nimport AssignMemberDialog from './AssignMemberDialog'\nimport Client from '../services/api'\nimport moment from 'moment'\nimport { Pagination } from 'flowbite-react'\nimport { Spinner } from 'flowbite-react'\n\nfunction classNames(...classes) {\n return classes.filter(Boolean).join(' ')\n}\n\nconst Tickets = ({ teamId, user, members, manager }) => {\n const [tickets, setTickets] = useState([])\n const [update, setUpdate] = useState(false)\n const [sorted, setSorted] = useState(false)\n const [unsortedTickets, setUnsortedTickets] = useState([])\n const orderRef = useRef(null)\n const ticketToAssign = useRef(null)\n const [open, setOpen] = useState(false)\n\n const [loadingReq, setLoadingReq] = useState(true)\n const [currentPage, setCurrentPage] = useState(1)\n const [pageIndex, setPageIndex] = useState(0)\n\n const onPageChange = (page) => {\n setCurrentPage(page)\n setPageIndex((page - 1) * 6)\n }\n\n let navigate = useNavigate()\n // don't delete\n const statuses = {\n Complete: 'text-green-900 dark:text-black bg-green-200 ring-green-600/20',\n Processing:\n 'text-indigo-900 dark:text-black bg-indigo-200 ring-indigo-400/30',\n Pending: 'text-yellow-800 dark:text-black bg-yellow-50 ring-yellow-600/20'\n }\n // don't delete\n const prios = {\n Low: 'text-white dark:text-black bg-amber-400 ring-yellow-600/20',\n Mid: 'text-white dark:text-black bg-orange-400 ring-indigo-400/30',\n High: 'text-white dark:text-black bg-orange-600 ring-yellow-600/20',\n Urgent: 'text-white dark:text-black bg-red-600 ring-yellow-600/20'\n }\n\n useEffect(() => {\n const getTickets = async () => {\n const response = await Client.get(`/tickets/team/${teamId}`)\n setUnsortedTickets(response.data)\n setTickets(response.data)\n setLoadingReq(false)\n }\n getTickets()\n }, [update, open])\n\n useEffect(() => {\n return\n }, [sorted])\n\n const assign = async (ticketId) => {\n await Client.put(`/tickets/${ticketId}/assign`, { member: user.id })\n setUpdate((prev) => !prev)\n }\n\n const edit = (ticketId) => {\n navigate(`/teams/${teamId}/edit/${ticketId}`)\n }\n\n const sort = () => {\n let temp = tickets\n const order = orderRef.current.value\n let severity\n switch (order) {\n case 'd':\n temp = temp.sort((a, b) => new Date(a.due) - new Date(b.due))\n setTickets(temp)\n setSorted(!sorted)\n break\n case 'p':\n severity = { Low: 1, Mid: 2, High: 3, Urgent: 4 }\n temp.sort((a, b) => severity[b.priority] - severity[a.priority])\n setTickets(temp)\n setSorted(!sorted)\n break\n case 's':\n severity = { Complete: 1, Processing: 2, Pending: 3 }\n temp.sort((a, b) => severity[b.status] - severity[a.status])\n setTickets(temp)\n setSorted(!sorted)\n break\n case 'n':\n temp = temp.sort(\n (a, b) => new Date(b.createdAt) - new Date(a.createdAt)\n )\n setTickets(temp)\n setSorted(!sorted)\n break\n }\n }\n\n if (loadingReq) {\n return (\n
\n \n
\n )\n }\n\n return tickets ? (\n
\n {open && (\n \n )}\n
\n \n \n \n \n \n \n \n
\n
    \n {tickets?.slice(pageIndex, pageIndex + 6).map((ticket) => (\n \n
    \n
    \n

    \n {ticket.subject}\n

    \n \n {ticket.status}\n

    \n
    \n
    \n {ticket.due && (\n <>\n

    \n Due on{' '}\n \n

    \n \n \n \n \n )}\n\n

    Created by {ticket.createdBy.name}

    \n {ticket.solvedBy && (\n <>\n \n \n \n

    Closed by {ticket.solvedBy.name}

    {' '}\n \n )}\n
    \n
    \n
    \n
    \n {ticket.member.map((member) => (\n \n ))}\n
    \n \n {ticket.priority}\n

    \n\n \n View ticket, {ticket.name}\n \n {!ticket.solvedBy && (\n \n \n Open options\n \n \n \n \n {(manager || user.id === ticket.createdBy._id) && (\n \n {({ active }) => (\n {\n edit(ticket._id)\n }}\n className={classNames(\n active ? 'bg-gray-50' : '',\n 'block px-3 py-1 text-sm leading-6 text-gray-900'\n )}\n >\n Edit\n \n )}\n \n )}\n {manager && true && (\n \n {({ active }) => (\n {\n ticketToAssign.current = ticket\n setOpen(true)\n }}\n className={classNames(\n active ? 'bg-gray-50' : '',\n 'block px-3 py-1 text-sm leading-6 text-gray-900'\n )}\n >\n Assign to member\n \n )}\n \n )}\n {!ticket.solvedBy &&\n !ticket.member.some(\n (member) => member._id === user.id\n ) && (\n \n {({ active }) => (\n {\n assign(ticket._id)\n }}\n className={classNames(\n active ? 'bg-gray-50' : '',\n 'block px-3 py-1 text-sm leading-6 text-gray-900'\n )}\n >\n Assign\n , {ticket.name}\n \n )}\n \n )}\n \n \n \n )}\n
    \n \n ))}\n
\n
\n \n
\n
\n ) : (\n <>\n )\n}\n\nexport default Tickets\n", "text_preview": "import { Fragment, useEffect, useRef, useState } from 'react' import { Link, useNavigate } from 'react-router-dom' import { Menu, Transition } from '@headlessui/react' import { EllipsisVerticalIcon } from '@heroicons/react/20/solid' import ", "input_sha256": "2905a002a141620464f8343197aeb2b305680c0446b27ec360c2740214ab9287", "text_sha256": "94979d9d204abfdb1d0848b90a802c7e79e2634809d1228e1dab1f8ddc56a4ed", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8075, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.66221045188742, "noise_severity": 0.11909031209902707, "missing_content_severity": 0.034272928605494905, "factual_density": 3.085260637563664, "pretraining_utility": 0.5325974050961118, "math_density": 0.24095583129532297, "code_density": 3.798175072086994, "reasoning_density": 0.2479010178978242, "education_level": 2.161191623968166, "creative_writing_quality": -0.3241697153858696}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.012954038433643511, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.99603643798664, "secondary": 0.9567364167165264, "tertiary": 0.9702536742125023, "structured_records": 0.19832575227425836, "procedural_steps": 0.0010987970157613854, "worked_solution": 0.001759085888112772, "contextual_dialogue": 9.341737894902854e-06, "content_quality": 0.4713278248474554, "noise_severity": 0.9007580732508108, "missing_content_severity": 0.9714392261620876, "factual_density": 0.23771719796971946, "pretraining_utility": 0.5012055099999257, "math_density": 0.7992034739205641, "code_density": 0.8318125600724949, "reasoning_density": 0.7934158184184797, "education_level": 0.7641895073359404, "creative_writing_quality": 0.998429535958675, "primary_topic": 0.9863080806319228, "secondary_topic": 0.8879936573408045}, "probability_margin": {"primary": 0.9943202511893985, "secondary": 0.9444589786840895, "tertiary": 0.9595918254948549, "education_level": 0.6354840659954651, "creative_writing_quality": 0.99799415196452, "primary_topic": 0.9799185462607143, "secondary_topic": 0.8550675757340483}, "raw_probabilities": {"primary": {"Academic Writing": 4.066060142175723e-05, "Content Listing": 0.00037726897799154, "Creative Writing": 2.6126938521136366e-05, "Customer Support Page": 1.0736430960669068e-05, "Q&A Forum": 1.6873847405371862e-05, "Discussion Thread": 1.0328241734239336e-05, "Comment Section": 1.8052524761142688e-05, "Missing Content": 5.3624106765650466e-05, "Knowledge Article": 7.549770068979019e-05, "Legal Notices": 6.248497443130177e-05, "News Article": 7.049965734558927e-06, "Organizational Announcement": 1.5078976037447584e-05, "Nonfiction Writing": 1.536700738307391e-05, "Organizational About Page": 1.6288307376915276e-05, "Personal About Page": 1.877948937299642e-05, "Personal Blog": 9.637369179046528e-06, "Product Page": 1.2344205111672212e-05, "Spam / Ads": 1.3857292603954808e-05, "Structured Data": 0.00074476367235067, "Technical Writing": 0.9962015864038634, "Transcript / Interview": 1.7102898977834136e-05, "Tutorial / How-To Guide": 0.0003460050832088902, "User Reviews": 9.149769652149657e-06, "Other / Unknown": 0.0018813352144648602}, "secondary": {"Academic Writing": 0.00021712578649540985, "Content Listing": 0.013510297829665256, "Creative Writing": 0.00024101067859034705, "Customer Support Page": 0.00011678597312452103, "Q&A Forum": 7.472815836774399e-05, "Discussion Thread": 6.58037530761898e-05, "Comment Section": 0.00023449428384167284, "Missing Content": 0.0003932247542279568, "Knowledge Article": 0.00037020915041249656, "Legal Notices": 0.0002729498587978838, "News Article": 0.00011216228060079979, "Organizational Announcement": 0.00010043361536219607, "Nonfiction Writing": 9.889162499292034e-05, "Organizational About Page": 0.0001198208562607746, "Personal About Page": 0.00010964343186767937, "Personal Blog": 9.87749376111388e-05, "Product Page": 8.481383430138678e-05, "Spam / Ads": 0.0001104801411818642, "Structured Data": 0.0098730868572965, "Technical Writing": 0.014080087335915018, "Transcript / Interview": 0.00011741386535020272, "Tutorial / How-To Guide": 0.001006419184240038, "User Reviews": 5.227578841561083e-05, "NONE": 0.9585390660200045}, "tertiary": {"Academic Writing": 0.00011536599557771187, "Content Listing": 0.011901278958793162, "Creative Writing": 0.00015076669056297818, "Customer Support Page": 8.544858966708923e-05, "Q&A Forum": 4.704576146594822e-05, "Discussion Thread": 4.265772848529462e-05, "Comment Section": 0.0004079254283397091, "Missing Content": 0.0006033296559158649, "Knowledge Article": 0.00042444118632942163, "Legal Notices": 0.00036445253352613806, "News Article": 7.275438471861282e-05, "Organizational Announcement": 6.666624497337556e-05, "Nonfiction Writing": 7.896754764600525e-05, "Organizational About Page": 0.0001014724206299965, "Personal About Page": 7.727179154165351e-05, "Personal Blog": 5.904389970531251e-05, "Product Page": 7.008009916925139e-05, "Spam / Ads": 8.643785821725669e-05, "Structured Data": 0.0063988398152571265, "Technical Writing": 0.0064396480503733184, "Transcript / Interview": 6.895118164351452e-05, "Tutorial / How-To Guide": 0.0008088347365493628, "User Reviews": 3.5214987263856095e-05, "NONE": 0.971493104453648}, "structured_records": {"false": 0.8016742477257417, "true": 0.19832575227425836}, "procedural_steps": {"false": 0.9989012029842386, "true": 0.0010987970157613854}, "worked_solution": {"false": 0.9982409141118872, "true": 0.001759085888112772}, "contextual_dialogue": {"false": 0.9999906582621051, "true": 9.341737894902854e-06}, "content_quality": {"-2": 0.011249046086412211, "-1": 0.07535568626823397, "0": 0.30163956835211214, "1": 0.463447168258005, "2": 0.1483085310352367}, "noise_severity": {"0": 0.9059330509157438, "1": 0.07505468660905897, "2": 0.01464876362507238, "3": 0.0027158971606753624, "4": 0.0016476016894493093}, "missing_content_severity": {"0": 0.9810332284056622, "1": 0.00749091691038835, "2": 0.00824029962164463, "3": 0.0026408077974024334, "4": 0.0005947472649025013}, "factual_density": {"0": 0.024574207974699037, "1": 0.049800326344166895, "2": 0.15498759191908287, "3": 0.35706636766687405, "4": 0.41357150609517723}, "pretraining_utility": {"-2": 0.00913010460901308, "-1": 0.07697203521260476, "0": 0.35164360719973997, "1": 0.4966788564305419, "2": 0.06557539654810045}, "math_density": {"0": 0.7941752665334275, "1": 0.18619688604425996, "2": 0.010369600939612323, "3": 0.0030132425589628475, "4": 0.006245003923737464}, "code_density": {"0": 0.0003583025874886929, "1": 0.0006873527008968651, "2": 0.019244532569448518, "3": 0.15984059432146366, "4": 0.8198692178207022}, "reasoning_density": {"0": 0.8636495091466441, "1": 0.04599570078579492, "2": 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"javascript-0-56f70e454df475a2c4cb30ed97191512", "text": "function layoutDashboardAndPreview(\n container,\n movieName,\n sceneNum,\n dashboard_mode\n) {\n console.log(\"laying out dashboard\", movieName);\n console.log(data[movieName]);\n d3.select(\"#films-page\").style(\"--shrinkSize\", `${shrinkSize}px`); //sync it with the css\n d3.select(\"#films-page\").style(\"--hoverImageSize\", `${hoverImageSize}px`);\n d3.select(\"#films-page\").style(\n \"--selectedImageSize\",\n `${selectedImageSize}px`\n );\n\n //the dashboard\n const dashboard = container\n .append(\"div\")\n .attr(\"class\", \"dashboard full\")\n .attr(\"id\", `dashboard-${movieName}`);\n container\n .append(\"p\")\n .text(\"expand dashboard\")\n .attr(\"class\", \"hidden\")\n .attr(\"id\", \"expand-dash-text\");\n dashboard.append(\"div\").attr(\"class\", \"plots-container\");\n layoutScenePreviews(container, movieName);\n\n //the preview\n const previewContainer = layoutPreview(container, sceneNum, movieName);\n\n //grow and shrink elements\n // dashboard.on(\"click\", () => expandDashboard());\n console.log(sceneNum);\n d3.select(\"#displayed-video\").attr(\"sceneNum\", sceneNum + 1);\n changeDisplayedVideo(movieName, sceneNum);\n if (dashboard_mode) {\n expandDashboard(movieName);\n } else {\n expandPreviewContainer(movieName);\n }\n}\n\nfunction layoutPreview(container, sceneNum, movieName) {\n const previewContainer = container\n .append(\"div\")\n .attr(\"class\", \"preview-container full\");\n previewContainer.append(\"p\").text(\"Watch Clip\");\n //the video player\n const videoWrapper = previewContainer\n .append(\"div\")\n .attr(\"class\", \"video-wrapper-outer full\");\n videoWrapper.append(\"div\").attr(\"class\", \"bottom-container visible\");\n makeBottomContainer(sceneNum, movieName);\n\n //place in the background sketch\n const sketch_container = previewContainer\n .append(\"div\")\n .attr(\"id\", \"sketches-container\")\n .attr(\"class\", \"hidden\");\n d3.select(\"p#expand-dash-text\").attr(\"class\", \"visible\");\n makeSingleVideoPlayer(videoWrapper, movieName, sceneNum);\n return previewContainer;\n}\nfunction removeTrailingNumber(url) {\n return url.replace(/\\/\\d+$/, \"\");\n}\n//put motion and transitions into these?\nfunction expandDashboard() {\n console.log(\"expand dashboard function\");\n\n //shrink preview container\n const movieName = d3.select(\"#displayed-video\").attr(\"movieName\");\n const sceneNum = d3.select(\"#displayed-video\").attr(\"sceneNum\");\n\n d3.select(\".preview-container\").attr(\"class\", \"preview-container small\");\n d3.select(\"p#expand-dash-text\").attr(\"class\", \"hidden\");\n // the buttons\n\n d3.select(\".dashboard.small\").on(\"click\", () => null);\n\n d3.select(\".small.preview-container\").on(\"click\", () => {\n console.log(\"HERE\");\n expandPreviewContainer(movieName);\n });\n // .style(\n // \"background-image\",\n // `url(${metaData[movieName].imgDir}${sceneNum}-005.png)`\n // );\n d3.select(\"#sketches-container\").attr(\"class\", \"hidden\");\n d3.select(\".scene-preview-container\").attr(\n \"class\",\n \"scene-preview-container full\"\n );\n //grow shrink wrap\n d3.select(\".video-wrapper-outer\").attr(\"class\", \"video-wrapper-outer small\");\n d3.select(\".video-wrapper-outer .bottom-container\").attr(\n \"class\",\n \"bottom-container hidden\"\n );\n //grow dashboard\n d3.select(\".dashboard\")\n .attr(\"class\", \"dashboard full\")\n .style(\"background-image\", `none`);\n console.log(\"HEHHEHH\");\n if (!window.location.href.endsWith(movieName)) {\n console.log(\"dashboard link\", removeTrailingNumber(window.location.href));\n window.location.href = removeTrailingNumber(window.location.href);\n }\n}\nfunction expandPreviewContainer(movieName) {\n d3.select(\".preview-container\").attr(\"class\", \"preview-container full\");\n d3.select(\"#sketches-container\").attr(\"class\", \"visible\");\n d3.select(\".scene-preview-container\").attr(\n \"class\",\n \"scene-preview-container small hidden\"\n );\n d3.select(\".video-wrapper-outer\")\n .select(\".bottom-container\")\n .attr(\"class\", \"bottom-container visible\");\n\n d3.select(\".video-wrapper-outer\").attr(\n \"class\",\n \"video-wrapper-outer full visible\"\n );\n\n const sceneNum = d3.select(\"#displayed-video\").attr(\"sceneNum\");\n\n d3.select(\".dashboard\").attr(\"class\", \"dashboard small\");\n\n // .style(\n // \"background-image\",\n // `url(${metaData[movieName].imgDir}${sceneNum}-005.png)`\n // );\n makeBottomContainer(sceneNum, movieName);\n\n // the buttons\n console.log(\"expand preview container\");\n d3.select(\".preview-container\").on(\"click\", null);\n d3.select(\".dashboard.small\").on(\"click\", () => expandDashboard());\n\n showSketches(movieName, sceneNum);\n showInstruments(d3.select(\"#instrument-plotcontainer\"), movieName, sceneNum);\n let current = window.location.href;\n if (current.endsWith(movieName)) {\n console.log(\"preview link\");\n window.location.href = current + \"/\" + sceneNum;\n } else if (current.endsWith(movieName + \"/\")) {\n console.log(\"preview link2\");\n window.location.href = current + sceneNum;\n }\n}\n\nfunction layoutDashboard(dashboard, movieName) {\n dashboard.append(\"div\").attr(\"class\", \"plots-container\");\n // changeDisplayedVideo(\n // movieName,\n // d3.select(\"#displayed-video\").attr(\"sceneNum\")\n // );\n layoutScenePreviews(dashboard, movieName);\n}\n\nfunction layoutScenePreviews(outer_container, movieName) {\n const container = outer_container\n .append(\"div\")\n .attr(\"class\", \"scene-preview-container small\");\n // console.log(data);\n for (let sceneNum = 0; sceneNum < data[movieName].numSamples; sceneNum++) {\n const filename = `${sceneNum}-001.jpg`;\n const sceneImg = container\n .append(\"img\")\n .attr(\"class\", `sceneImg sceneImg-${sceneNum}`)\n .attr(\"src\", metaData[movieName].imgDir + filename)\n .attr(\"next\", false)\n .attr(\"prev\", false)\n .attr(\"selected\", false);\n //TODO change this to make the scene selection\n sceneImg.on(\"click\", () => {\n changeDisplayedVideo(movieName, sceneNum);\n });\n }\n // for (const { filename, colors, sceneNum } of data[movieName].imageSceneData) {\n // const sceneImg = container\n // .append(\"img\")\n // .attr(\"class\", `sceneImg sceneImg-${sceneNum}`)\n // .attr(\"src\", metaData[movieName].imgDir + filename)\n // .attr(\"next\", false)\n // .attr(\"prev\", false)\n // .attr(\"selected\", false);\n // //TODO change this to make the scene selection\n // sceneImg.on(\"click\", () => {\n // changeDisplayedVideo(movieName, sceneNum);\n // });\n // }\n}\n\nfunction changeDisplayedVideo(movieName, sceneNum) {\n console.log(\"CHANGE VIDEO\");\n\n if (d3.select(\"#displayed-video\").attr(\"sceneNum\") == sceneNum) {\n console.log(\"same video\", sceneNum);\n return;\n }\n const numSamples = data[movieName].numSamples;\n //RESIZE ALL OF THE IMAGES IN THE PREVIEW\n d3.selectAll(\".sceneImg\").attr(\"selected\", false);\n d3.selectAll(\".sceneImg\").attr(\"prev\", false);\n d3.selectAll(\".sceneImg\").attr(\"next\", false);\n d3.select(`.sceneImg-${sceneNum}`).attr(\"selected\", true);\n d3.select(`.sceneImg-${sceneNum - 1}`).attr(\"prev\", true);\n d3.select(`.sceneImg-${sceneNum + 1}`).attr(\"next\", true);\n\n let remainingW, imgW;\n if (sceneNum != 0 && sceneNum != numSamples - 1) {\n //cuz now two hovers for the near by ones\n remainingW =\n window.innerWidth -\n 20 -\n 2 * hoverImageSize -\n selectedImageSize -\n numSamples * 0.5;\n imgW = remainingW / (numSamples - 2);\n } else {\n remainingW =\n window.innerWidth -\n 20 -\n hoverImageSize -\n selectedImageSize -\n numSamples * 0.5;\n imgW = remainingW / (numSamples - 3);\n }\n d3.select(\"#films-page\").style(\"--prevImageSize\", `${imgW}px`);\n\n //PLACE THE VIDEO WRAPPER WHERE THE SLEECTED IMAGE IS\n // d3.select(\".video-wrapper-outer\")\n // .style(\"visibility\", \"hidden\")\n // .style(\"opacity\", 0);\n const selectedSceneImg = d3.select(`.sceneImg-${sceneNum}`).node();\n selectedSceneImg.addEventListener(\n \"transitionend\",\n () => {\n const rect = selectedSceneImg.getBoundingClientRect();\n console.log(\"setting visibility\");\n d3.select(\".video-wrapper-outer\")\n .style(\"height\", `${rect.height}px`)\n .style(\"width\", `${selectedVideoSize}px`)\n .style(\"top\", `${rect.top + window.scrollY}px`)\n .style(\"visibility\", \"visible\")\n .style(\"opacity\", 1)\n .style(\"left\", `${rect.left + window.scrollX - overlap}px`);\n },\n { once: true }\n );\n\n //set the displayed video\n d3.select(\"#displayed-video\").attr(\n \"src\",\n metaData[movieName].videoDir + sceneNum + \".mp4\"\n );\n d3.select(\"#displayed-video\").attr(\"sceneNum\", sceneNum);\n\n //update plots\n layoutPlots(movieName, sceneNum);\n\n //show the sketches\n console.log(\"change displayed video\");\n showSketches(movieName, sceneNum);\n}\n\nfunction makeBottomContainer(sceneNum, movieName) {\n const container = d3.select(\".bottom-container\");\n console.log(container);\n container.selectAll(\"*\").remove();\n const captionParamContainer = container\n .append(\"div\")\n .attr(\"class\", \"section\");\n\n //label the caption\n const c1 = captionParamContainer.append(\"div\").attr(\"class\", \"subsection\");\n c1.append(\"div\").attr(\"class\", \"label\").text(\"CAPTION\");\n c1.append(\"input\")\n .attr(\"autocomplete\", \"off\")\n .attr(\"id\", \"caption-text\")\n .property(\"value\", \"captiontext\");\n\n //select the caption type\n\n const c2 = captionParamContainer.append(\"div\").attr(\"class\", \"subsection\");\n c2.append(\"div\").attr(\"class\", \"label\").text(\"TEXT TYPE\");\n const options = [\"flow\", \"blur\", \"swirl\", \"strings\", \"wiggly\"];\n const radioGroup = c2.append(\"div\").attr(\"class\", \"radio-group\");\n options.forEach((opt, i) => {\n const label = radioGroup.append(\"label\").attr(\"for\", `text-type-${i}`);\n label\n .append(\"input\")\n .attr(\"type\", \"radio\")\n .attr(\"name\", \"text-type\")\n .attr(\"id\", `text-type-${i}`)\n .attr(\"value\", opt)\n .property(\"checked\", i === 0);\n label.append(\"span\").text(opt);\n });\n //set up color mode\n const c5 = captionParamContainer.append(\"div\").attr(\"class\", \"subsection\");\n c5.append(\"div\").attr(\"class\", \"label\").text(\"COLOR MODE\");\n const bgModeRadioGroup = c5.append(\"div\").attr(\"class\", \"radio-group\");\n [\"simple\", \"sync with image\"].forEach((opt, i) => {\n const label = bgModeRadioGroup\n .append(\"label\")\n .attr(\"for\", `sketch-bgMode-${i}`);\n label\n .append(\"input\")\n .attr(\"type\", \"radio\")\n .attr(\"name\", \"sketch-bgMode\")\n .attr(\"id\", `sketch-bgMode-${i}`)\n .attr(\"value\", opt)\n .property(\"checked\", i === 0);\n label.append(\"span\").text(opt);\n });\n const c6 = captionParamContainer.append(\"div\").attr(\"class\", \"subsection\");\n c6.append(\"div\").attr(\"class\", \"label\").text(\"FADE\");\n c6.append(\"input\")\n .attr(\"type\", \"checkbox\")\n .attr(\"class\", \"hidden my-toggle\")\n .attr(\"id\", \"fade-mode\");\n c6.append(\"label\").attr(\"for\", \"fade-mode\").attr(\"class\", \"toggle\");\n\n //select the feature\n const pltoContainer2 = container.append(\"div\").attr(\"class\", \"section\");\n const c4 = pltoContainer2.append(\"div\").attr(\"class\", \"subsection\");\n c4.append(\"div\").attr(\"class\", \"label \").text(\"AUDIO INPUT\");\n const audioRadioGroup = c4.append(\"div\").attr(\"class\", \"radio-group\");\n [\"mfcc\", \"chroma\"].forEach((opt, i) => {\n const label = audioRadioGroup\n .append(\"label\")\n .attr(\"for\", `audio-feature-${i}`);\n label\n .append(\"input\")\n .attr(\"type\", \"radio\")\n .attr(\"name\", \"audio-feature\")\n .attr(\"id\", `audio-feature-${i}`)\n .attr(\"value\", opt)\n .property(\"checked\", i === 0);\n label.append(\"span\").text(opt);\n });\n\n //toggle dark mode or light mode\n const c3 = pltoContainer2.append(\"div\").attr(\"class\", \"subsection\");\n c3.append(\"div\").attr(\"class\", \"label \").text(\"DARK MODE\");\n c3.append(\"input\")\n .attr(\"type\", \"checkbox\")\n .attr(\"class\", \"hidden my-toggle\")\n .attr(\"id\", \"light-mode\")\n .on(\"change\", function () {\n const on = d3.select(this).property(\"checked\");\n d3.select(\"body\").classed(\"light-mode\", on);\n if (on) {\n d3.selectAll(\".icon\").style(\"filter\", \"invert(100%\");\n d3.select(\"#menu-btn\").style(\"filter\", \"none\");\n d3.select(\"#films-page\")\n .style(\"--text-color\", \"black\")\n .style(\"--bgColor\", \"#EFEDE3\");\n } else {\n d3.selectAll(\".icon\").style(\"filter\", \"none\");\n d3.select(\"#films-page\")\n .style(\"--text-color\", \"#EFEDE3\")\n .style(\"--bgColor\", \"black\");\n }\n });\n c3.append(\"label\").attr(\"for\", \"light-mode\").attr(\"class\", \"toggle\");\n\n //create the instrument plot section\n const plotContainer3 = container\n .append(\"div\")\n 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"4": 0.007111412173212486}, "education_level": {"none": 0.0014287285173240317, "0": 0.0017308179104980452, "1": 0.03761582337882271, "2": 0.8390880268846361, "3": 0.11128748384728485, "4": 0.008849119461434322}, "creative_writing_quality": {"not_applicable": 0.9994958371564816, "-2": 8.950800242804496e-05, "-1": 0.0001597872134548699, "0": 0.0001993707242436802, "1": 3.9795922989240885e-05, "2": 1.5700980402373958e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.657875663706214e-05, "Art & Design": 0.0028274556390959927, "Law & Crime": 6.348940969592114e-05, "Education & Careers": 7.99589237820499e-05, "Entertainment & Media": 0.019655390379962712, "Fashion & Beauty": 5.936675821282154e-05, "Business, Economics & Finance": 5.466014338868499e-05, "Food & Dining": 4.0238822213917824e-05, "Games & Gaming": 0.0007990503238423913, "Electronics & Hardware": 8.635691414372331e-05, "Health, Healthcare & Wellness": 3.9127506185779923e-05, "History & Geography": 5.3378286793802366e-05, "Home, Pets & Hobbies": 4.567396153737618e-05, "Industry & Production": 3.8809113302100036e-05, "Language, Literature & Philosophy": 4.6656487850154976e-05, "Politics & Government": 2.5621723334141953e-05, "Religion & Spirituality": 4.5918123304095514e-05, "Science, Mathematics & Engineering": 0.0008755775085728243, "Relationships & Community": 3.0166053376147678e-05, "Software Usage & Applications": 0.0005852344560696947, "Programming & Software Engineering": 0.9735767921231497, "Sports & Fitness": 6.792866476742895e-05, "Vehicles & Transportation": 2.914652929701808e-05, "Travel & Tourism": 4.758378328245116e-05, "Other / Uncovered Subject": 0.0006108565101122804, "Unknown / Insufficient Content": 0.00013898309808998616}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0015004031802943027, "Art & Design": 0.08176906466249599, "Law & Crime": 0.0008265217279388321, "Education & Careers": 0.0005667862283611386, "Entertainment & Media": 0.2249572799941265, "Fashion & Beauty": 0.0005472467896782559, "Business, Economics & Finance": 0.0007323002666150994, "Food & Dining": 0.0003540203600046093, "Games & Gaming": 0.0013193579046802408, "Electronics & Hardware": 0.0005442434648747767, "Health, Healthcare & Wellness": 0.0004701039320577012, "History & Geography": 0.00045037407371938825, "Home, Pets & Hobbies": 0.0004226064026812512, "Industry & Production": 0.000262063167854644, "Language, Literature & Philosophy": 0.0003294697603772038, "Politics & Government": 0.0002953054939835036, "Religion & Spirituality": 0.0004532962361789617, "Science, Mathematics & Engineering": 0.0019871824550151576, "Relationships & Community": 0.00026656845336449505, "Software Usage & Applications": 0.0030402397148345814, "Programming & Software Engineering": 0.029149763819435643, "Sports & Fitness": 0.0003686252996016517, "Vehicles & Transportation": 0.00019634190929511904, "Travel & Tourism": 0.00046986749954364745, "Other / Uncovered Subject": 0.0013365692814141625, "NONE": 0.6473843979215732}}, "probabilities": {"primary": {"Academic Writing": 2.0604289807940376e-05, "Content Listing": 1.4642265305853214e-05, "Creative Writing": 1.237988160051028e-05, "Customer Support Page": 4.815382320497677e-06, "Q&A Forum": 6.150386480611937e-06, "Discussion Thread": 3.831238960165716e-06, "Comment Section": 9.06704184628298e-06, "Missing Content": 2.8314032119257714e-05, "Knowledge Article": 3.895822212534341e-05, "Legal Notices": 1.791308370031617e-05, "News Article": 2.8248572259405763e-06, "Organizational Announcement": 6.336471152763821e-06, "Nonfiction Writing": 7.564715005843556e-06, "Organizational About Page": 7.933281907433744e-06, "Personal About Page": 1.0505441681910584e-05, "Personal Blog": 4.142039535026037e-06, "Product Page": 4.990688815430105e-06, "Spam / Ads": 6.148366159602089e-06, "Structured Data": 0.00023656064369319033, "Technical Writing": 0.9980432151591454, "Transcript / Interview": 9.172832088877804e-06, "Tutorial / How-To Guide": 0.0002809548660840746, "User Reviews": 4.290800463970865e-06, "Other / Unknown": 0.0012186840127736992}, "secondary": {"Academic Writing": 8.21577230078332e-05, "Content Listing": 0.000126358155372444, "Creative Writing": 0.00010170306370234689, "Customer Support Page": 3.8202444390630933e-05, "Q&A Forum": 2.5571414202959637e-05, "Discussion Thread": 2.1439635313506643e-05, "Comment Section": 0.0001171417021896587, "Missing Content": 0.00045714623440415924, "Knowledge Article": 0.00022813843393022686, "Legal Notices": 6.907212720895853e-05, "News Article": 3.283808792651685e-05, "Organizational Announcement": 3.683793924213554e-05, "Nonfiction Writing": 3.466976250539004e-05, "Organizational About Page": 4.4680329854006806e-05, "Personal About Page": 4.7824746279383025e-05, "Personal Blog": 3.412428358090391e-05, "Product Page": 2.3844875654267345e-05, "Spam / Ads": 4.334826134244034e-05, "Structured Data": 0.0011323864045378138, "Technical Writing": 0.006827223484942087, "Transcript / Interview": 4.811712499162553e-05, "Tutorial / How-To Guide": 0.0010781179215558667, "User Reviews": 2.0363699522341536e-05, "NONE": 0.9893286921443425}, "tertiary": {"Academic Writing": 4.1593603850976796e-05, "Content Listing": 0.00014392005215111087, "Creative Writing": 8.104993352816916e-05, "Customer Support Page": 2.301379311691884e-05, "Q&A Forum": 1.557996984946008e-05, "Discussion Thread": 1.513044981543083e-05, "Comment Section": 0.00015948434831085807, "Missing Content": 0.0007959398267470643, "Knowledge Article": 0.00019616124181996627, "Legal Notices": 7.468781371480003e-05, "News Article": 2.2621254503733903e-05, "Organizational Announcement": 2.379752381074926e-05, "Nonfiction Writing": 3.100250000530123e-05, "Organizational About Page": 3.551196346452123e-05, "Personal About Page": 3.52061494044167e-05, "Personal Blog": 2.156626732725765e-05, "Product Page": 1.9691678189665774e-05, "Spam / Ads": 3.302560277432332e-05, "Structured Data": 0.0006785535395760654, "Technical Writing": 0.0025176723773325397, "Transcript / Interview": 2.7937743450969035e-05, "Tutorial / How-To Guide": 0.0007966293584213412, "User Reviews": 1.517818566873427e-05, "NONE": 0.9941950448231657}, "structured_records": {"false": 0.9767046913330735, "true": 0.023295308666926525}, "procedural_steps": {"false": 0.99706994477597, "true": 0.002930055224029979}, "worked_solution": {"false": 0.9985477153829807, "true": 0.0014522846170192901}, "contextual_dialogue": {"false": 0.9999962451262664, "true": 3.7548737336564113e-06}, "content_quality": {"-2": 0.019199144834546784, "-1": 0.16079639958700306, "0": 0.4071706262747884, "1": 0.33372102888501703, "2": 0.07911280041864481}, "noise_severity": {"0": 0.7678137199991437, "1": 0.1920046932082689, "2": 0.03537830515598449, "3": 0.0030111776709126382, "4": 0.0017921039656903525}, "missing_content_severity": {"0": 0.9081968230429585, "1": 0.05407501935042806, "2": 0.02781287954831597, "3": 0.008530734588672444, "4": 0.0013845434696250656}, "factual_density": {"0": 0.03026509874858817, "1": 0.04903734019968074, "2": 0.1590491106607414, "3": 0.3533480993684772, "4": 0.40830035102251244}, "pretraining_utility": {"-2": 0.011213638913290396, "-1": 0.14336281884366478, "0": 0.4512722824088044, "1": 0.34417660168088615, "2": 0.049974658153354286}, "math_density": {"0": 0.5523382566360027, "1": 0.3341574952797945, "2": 0.08122706979776644, "3": 0.008829820948290998, "4": 0.023447357338145507}, "code_density": {"0": 0.00019640205365491234, "1": 0.0003860419552946506, "2": 0.010055727458469858, "3": 0.08035131937038992, "4": 0.9090105091621907}, "reasoning_density": {"0": 0.7318647743010693, "1": 0.14425916306445513, "2": 0.10571189404942631, "3": 0.011052756411836801, "4": 0.007111412173212486}, "education_level": {"none": 0.0014287285173240317, "0": 0.0017308179104980452, "1": 0.03761582337882271, "2": 0.8390880268846361, "3": 0.11128748384728485, "4": 0.008849119461434322}, "creative_writing_quality": {"not_applicable": 0.9994958371564818, "-2": 8.950800242804497e-05, "-1": 0.00015978721345486993, "0": 0.00019937072424368026, "1": 3.979592298924089e-05, "2": 1.570098040237396e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 7.657875663706213e-05, "Art & Design": 0.0028274556390959923, "Law & Crime": 6.348940969592112e-05, "Education & Careers": 7.995892378204989e-05, "Entertainment & Media": 0.01965539037996271, "Fashion & Beauty": 5.936675821282153e-05, "Business, Economics & Finance": 5.4660143388684974e-05, "Food & Dining": 4.023882221391782e-05, "Games & Gaming": 0.0007990503238423911, "Electronics & Hardware": 8.63569141437233e-05, "Health, Healthcare & Wellness": 3.912750618577992e-05, "History & Geography": 5.337828679380235e-05, "Home, Pets & Hobbies": 4.567396153737617e-05, "Industry & Production": 3.880911330210003e-05, "Language, Literature & Philosophy": 4.665648785015496e-05, "Politics & Government": 2.5621723334141947e-05, "Religion & Spirituality": 4.59181233040955e-05, "Science, Mathematics & Engineering": 0.000875577508572824, "Relationships & Community": 3.016605337614767e-05, "Software Usage & Applications": 0.0005852344560696946, "Programming & Software Engineering": 0.9735767921231495, "Sports & Fitness": 6.792866476742893e-05, "Vehicles & Transportation": 2.914652929701807e-05, "Travel & Tourism": 4.758378328245115e-05, "Other / Uncovered Subject": 0.0006108565101122803, "Unknown / Insufficient Content": 0.00013898309808998613}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0015004031802943027, "Art & Design": 0.08176906466249599, "Law & Crime": 0.0008265217279388321, "Education & Careers": 0.0005667862283611386, "Entertainment & Media": 0.2249572799941265, "Fashion & Beauty": 0.0005472467896782559, "Business, Economics & Finance": 0.0007323002666150994, "Food & Dining": 0.0003540203600046093, "Games & Gaming": 0.0013193579046802408, "Electronics & Hardware": 0.0005442434648747767, "Health, Healthcare & Wellness": 0.0004701039320577012, "History & Geography": 0.00045037407371938825, "Home, Pets & Hobbies": 0.0004226064026812512, "Industry & Production": 0.000262063167854644, "Language, Literature & Philosophy": 0.0003294697603772038, "Politics & Government": 0.0002953054939835036, "Religion & Spirituality": 0.0004532962361789617, "Science, Mathematics & Engineering": 0.0019871824550151576, "Relationships & Community": 0.00026656845336449505, "Software Usage & Applications": 0.0030402397148345814, "Programming & Software Engineering": 0.029149763819435643, "Sports & Fitness": 0.0003686252996016517, "Vehicles & Transportation": 0.00019634190929511904, "Travel & Tourism": 0.00046986749954364745, "Other / Uncovered Subject": 0.0013365692814141625, "NONE": 0.6473843979215732}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12542, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12542, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:09.034551+00:00", "scoring_key_slot": "primary", "dataset_uid": "f6cb9f79-650d-451b-b1d9-127266268bb8", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "afc93e6643f06a79995bd87311ec0221479bd423db34ed421a3068095d9daf26", "source_metadata": {"uuid": "f6cb9f79-650d-451b-b1d9-127266268bb8", "repo_name": "chuik633/SoundStories", "relative_path": "docs/js/components/movieDashboard.js", "category": "WEB", "algo_rel_score": 0.9031383395195007, "quality_score": 4.6882829666137695, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 50, "repo_created_at": "2025-04-08T14:44:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "66b09a83236b9b0b397710bb8011ae52bdf19f8c", "size_bytes": 13590, "file_timestamp": 1750867344, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0ea2dcf6deacb78a412906f3ea147ea765e5871e"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 50, "repo_created_at": "2025-04-08T14:44:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "66b09a83236b9b0b397710bb8011ae52bdf19f8c", "size_bytes": 13590, "file_timestamp": 1750867344, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "0ea2dcf6deacb78a412906f3ea147ea765e5871e", "url": "https://github.com/chuik633/SoundStories/blob/0ea2dcf6deacb78a412906f3ea147ea765e5871e/docs/js/components/movieDashboard.js"}, "gpt4o_tokens": 3718, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.29275194046621, "noise_severity": 0.2789632523957372, "missing_content_severity": 0.1408311560915776, "factual_density": 3.060381263716645, "pretraining_utility": 0.27833582131734913, "math_density": 0.6168905270727824, "code_density": 3.897593491632167, "reasoning_density": 0.4172868690916681, "education_level": 2.088034040314227, "creative_writing_quality": -0.5307914654113247}, "score_applicability": {"education_level": 0.9985712714826761, "creative_writing_quality": 0.00050416284351821}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/WeilerWebServices/Yahoo/blob/648c832d0dadea16d5ae32c516d954b5dad3b279/fluxible/packages/fluxible-plugin-devtools/tests/unit/lib/devtools-plugin.js", "id": "javascript-0-c278bd45db22e1fc570529bf680a98ca", "text": "/**\n * Copyright 2016, Yahoo! Inc.\n * Copyrights licensed under the New BSD License. See the accompanying LICENSE file for terms.\n */\n/*globals describe,it,beforeEach */\n'use strict';\nvar expect = require('chai').expect;\nvar async = require('async');\nvar devToolsPlugin = require('../../../dist/lib/devtools-plugin')['default'];\nvar FluxibleApp = require('fluxible');\n\ndescribe('devToolsPlugin', function () {\n var app,\n pluginInstance;\n\n beforeEach(function () {\n app = new FluxibleApp();\n pluginInstance = devToolsPlugin();\n app.plug(pluginInstance);\n context = app.createContext({\n debug: true\n });\n });\n\n describe('plugContext', function () {\n it('should be enabled based on debug flag', function () {\n var ctx;\n ctx = app.createContext();\n expect(ctx.dehydrate().plugins[pluginInstance.name].enableDebug).to.equal(false);\n ctx = app.createContext({debug: false});\n expect(ctx.dehydrate().plugins[pluginInstance.name].enableDebug).to.equal(false);\n ctx = app.createContext({debug: true});\n expect(ctx.dehydrate().plugins[pluginInstance.name].enableDebug).to.equal(true);\n });\n });\n\n describe('context', function () {\n it('should dehydrate context correctly', function () {\n const dehydrated = context.dehydrate().plugins[pluginInstance.name];\n expect(dehydrated).to.have.keys(['enableDebug', 'actionHistory']);\n expect(dehydrated)\n .to.have.property('enableDebug')\n .that.is.a('boolean');\n expect(dehydrated)\n .to.have.property('actionHistory')\n .that.is.an('array')\n .that.has.lengthOf(0);\n });\n it('should rehydrate correctly and override context upon rehydrating debug:true flag', function () {\n let ctx;\n ctx = app.createContext();\n expect(ctx.dehydrate().plugins[pluginInstance.name].enableDebug).to.equal(false);\n expect(ctx.dehydrate().plugins[pluginInstance.name].actionHistory).to.have.lengthOf(0);\n expect(ctx._createSubActionContext.name).to.equal('createSubActionContext');\n ctx.rehydrate({\n plugins: {\n DevToolsPlugin: {\n enableDebug: true,\n actionHistory: [{name: 'navigateAction'}]\n }\n }\n });\n expect(ctx.dehydrate().plugins[pluginInstance.name].enableDebug).to.equal(true);\n expect(ctx.dehydrate().plugins[pluginInstance.name].actionHistory).to.have.lengthOf(1);\n expect(ctx.dehydrate().plugins[pluginInstance.name].actionHistory[0].name).to.equal('navigateAction');\n expect(ctx._createSubActionContext.name).to.equal('createDevSubActionContext');\n });\n });\n\n describe('componentContext', function () {\n var componentContext;\n beforeEach(function() {\n componentContext = context.getComponentContext();\n })\n it('should plug componentContext with a devtools namespace', function () {\n expect(componentContext.devtools).to.be.an('object');\n expect(componentContext.devtools.getActionHistory).to.be.a('function');\n });\n });\n describe('Action History', function () {\n it('#getActionHistory should return array of actions', function() {\n const componentContext = context.getComponentContext();\n const actionContext = context.getActionContext();\n const getActionHistory = componentContext.devtools.getActionHistory;\n expect(getActionHistory()).to.be.an('array');\n expect(getActionHistory()).to.have.lengthOf(0);\n function MockActionFromComponent (ctx, payload, cb) {\n cb();\n }\n componentContext.executeAction(MockActionFromComponent);\n expect(getActionHistory()).to.be.an('array');\n expect(getActionHistory()).to.have.lengthOf(1);\n expect(getActionHistory()[0].name).to.equal(MockActionFromComponent.name);\n function MockActionFromAction (ctx, payload, cb) {\n cb();\n }\n actionContext.executeAction(MockActionFromAction);\n expect(getActionHistory()).to.be.an('array');\n expect(getActionHistory()).to.have.lengthOf(2);\n expect(getActionHistory()[0].name).to.equal(MockActionFromComponent.name);\n expect(getActionHistory()[1].name).to.equal(MockActionFromAction.name);\n });\n it('#getActionHistory actionContext nested executeAction', function (done) {\n var actionOne = function (context, payload, callback) {\n async.series([\n function (cb) {\n context.executeAction(actionTwo, payload, function actionOneFirstCallback () {\n context.executeAction(actionTwo, payload, function actionOneSecondCallback () {\n cb();\n });\n });\n },\n function (cb) {\n context.executeAction(actionThree, payload, function actionOneThirdcallback () {\n cb();\n });\n },\n async.apply(context.executeAction, actionFour, payload)\n ], function (err) {\n callback(err)\n });\n };\n actionOne.displayName = 'One';\n var actionTwo = function (context, payload, callback) {\n callback();\n };\n actionTwo.displayName = 'Two';\n var actionThree = function (context, payload, callback) {\n callback();\n };\n actionThree.displayName = 'Three';\n var actionFour = function (context, payload, callback) {\n context.executeAction(actionFive, payload, callback);\n };\n actionFour.displayName = 'Four';\n var actionFive = function (context, payload, callback) {\n callback();\n };\n actionFive.displayName = 'Five';\n var cb = function () {\n var expectedHeirarchy = {\n name: 'One',\n actionCalls: [{\n name: 'Two'\n },{\n name: 'Two'\n },{\n name: 'Three'\n }, {\n name: 'Four',\n actionCalls: [{\n name: 'Five'\n }]\n }]\n };\n var heirarchy = context.getComponentContext().devtools.getActionHistory()[0];\n function compareHeirarchy(expected, actual) {\n expect(actual).to.contain.keys(['name', 'startTime', 'endTime', 'duration', 'failed', 'rootId']);\n expect(expected.name).to.equal(actual.name);\n if (expected.actionCalls) {\n expect(actual).to.contain.keys(['actionCalls']);\n expect(expected.actionCalls.length).to.equal(actual.actionCalls.length);\n expected.actionCalls.forEach((a,index) => {\n compareHeirarchy(expected.actionCalls[index], actual.actionCalls[index]);\n });\n }\n }\n compareHeirarchy(expectedHeirarchy, heirarchy);\n done();\n }\n var actionContext = context.getActionContext();\n actionContext.executeAction(actionOne, {}, cb);\n });\n it('#getActionHistory componentContext nested executeAction', function (done) {\n var actionOne = function (context, payload, callback) {\n context.dispatch('ONE_START');\n async.series([\n function (cb) {\n context.executeAction(actionTwo, payload, function actionOneFirstCallback () {\n context.executeAction(actionTwo, payload, function actionOneSecondCallback () {\n cb();\n });\n });\n },\n function (cb) {\n context.executeAction(actionThree, payload, function actionOneThirdcallback () {\n cb();\n });\n },\n async.apply(context.executeAction, actionFour, payload)\n ], function (err) {\n context.dispatch('ONE_STOP');\n callback(err)\n });\n };\n actionOne.displayName = 'One';\n var actionTwo = function (context, payload, callback) {\n callback();\n };\n actionTwo.displayName = 'Two';\n var actionThree = function (context, payload, callback) {\n callback();\n };\n actionThree.displayName = 'Three';\n var actionFour = function (context, payload, callback) {\n context.executeAction(actionFive, payload, callback);\n };\n actionFour.displayName = 'Four';\n var actionFive = function (context, payload, callback) {\n context.dispatch('FIVE');\n callback();\n };\n actionFive.displayName = 'Five';\n var cb = function () {\n var expectedHeirarchy = {\n name: 'One',\n dispatchCalls: [{\n name: 'ONE_START'\n }, {\n name: 'ONE_STOP'\n }],\n actionCalls: [{\n name: 'Two'\n },{\n name: 'Two'\n },{\n name: 'Three'\n }, {\n name: 'Four',\n actionCalls: [{\n name: 'Five',\n dispatchCalls: [{\n name: 'FIVE'\n }]\n }]\n }]\n };\n var heirarchy = context.getComponentContext().devtools.getActionHistory()[0];\n function compareHeirarchy(expected, actual) {\n expect(actual).to.contain.keys(['name', 'startTime', 'endTime', 'duration', 'failed', 'rootId']);\n expect(expected.name).to.equal(actual.name);\n if (expected.dispatchCalls) {\n expect(actual).to.contain.keys(['dispatchCalls']);\n expect(expected.dispatchCalls.length).to.equal(actual.dispatchCalls.length);\n expected.dispatchCalls.forEach((a,index) => {\n expect(expected.dispatchCalls[index].name).to.equal(actual.dispatchCalls[index].name);\n });\n }\n if (expected.actionCalls) {\n expect(actual).to.contain.keys(['actionCalls']);\n expect(expected.actionCalls.length).to.equal(actual.actionCalls.length);\n expected.actionCalls.forEach((a,index) => {\n compareHeirarchy(expected.actionCalls[index], actual.actionCalls[index]);\n });\n }\n }\n compareHeirarchy(expectedHeirarchy, heirarchy);\n done();\n }\n var componentContext = context.getComponentContext();\n componentContext.executeAction(actionOne, {});\n setTimeout(function() {\n cb();\n }, 1000);\n });\n })\n});\n", "text_preview": "/** * Copyright 2016, Yahoo! Inc. * Copyrights licensed under the New BSD License. See the accompanying LICENSE file for terms. */ /*globals describe,it,beforeEach */ 'use strict'; var expect = require('chai').expect; var async = require('a", "input_sha256": "250694ad088a400f28a8bf6d6d20c0fe261887b1379a413fd17fe8d88b4b0a7b", "text_sha256": "c278bd45db22e1fc570529bf680a98ca15e75bf6bd26f517c69c46ae8c0559b5", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8076, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.1381250077810934, "noise_severity": 0.2848795685489301, "missing_content_severity": 0.023234267562022924, "factual_density": 3.0975238666303246, "pretraining_utility": 0.3908642505688422, "math_density": 0.025455682297435764, "code_density": 3.8738467854665557, "reasoning_density": 1.0761753100196763, "education_level": 2.6926685942950987, "creative_writing_quality": -0.2537260977709444}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.5433 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.2932444870043876, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9933776806506351, "secondary": 0.5233939759892656, "tertiary": 0.682717174984028, "structured_records": 0.1447507579067062, "procedural_steps": 0.01476261438050003, "worked_solution": 0.19732827209518128, "contextual_dialogue": 1.2315309552907097e-05, "content_quality": 0.549741854313498, "noise_severity": 0.7626003595425582, "missing_content_severity": 0.9806381103649809, "factual_density": 0.5231336743485445, "pretraining_utility": 0.4584210853791101, "math_density": 0.9787869314188036, "code_density": 0.8948723212221295, "reasoning_density": 0.10318724165026971, "education_level": 0.38694309906907254, "creative_writing_quality": 0.999194383110156, "primary_topic": 0.9978720425372021, "secondary_topic": 0.955389323266846}, "probability_margin": {"primary": 0.99026763466707, "secondary": 0.14055975463440717, "tertiary": 0.41240647228007765, "education_level": 0.09316872650855279, "creative_writing_quality": 0.999000068356922, "primary_topic": 0.9970817474965552, "secondary_topic": 0.9343996028591348}, "raw_probabilities": {"primary": {"Academic Writing": 5.770966217187005e-05, "Content Listing": 1.8675262088535943e-05, "Creative Writing": 1.903188813361442e-05, "Customer Support Page": 1.2244890621727528e-05, "Q&A Forum": 1.4921605736020641e-05, "Discussion Thread": 9.114559901044644e-06, "Comment Section": 1.7432642935839603e-05, "Missing Content": 2.6453681028439712e-05, "Knowledge Article": 8.369675789760503e-05, "Legal Notices": 0.0011054375226778523, "News Article": 7.245195054326394e-06, "Organizational Announcement": 2.414926860254059e-05, "Nonfiction Writing": 1.3148506460091093e-05, "Organizational About Page": 1.693518332470178e-05, "Personal About Page": 9.941965874250355e-06, "Personal Blog": 7.808064494789902e-06, "Product Page": 7.873695409029342e-06, "Spam / Ads": 1.0529679654834567e-05, "Structured Data": 0.0011677372958573946, "Technical Writing": 0.9936536106235253, "Transcript / Interview": 2.436123376262739e-05, "Tutorial / How-To Guide": 0.0002966886120357572, "User Reviews": 9.276246296674522e-06, "Other / Unknown": 0.0033859759564553405}, "secondary": {"Academic Writing": 0.0007965515481438964, "Content Listing": 0.0005828131916473734, "Creative Writing": 0.0006387053808643125, "Customer Support Page": 0.0003636977474412296, "Q&A Forum": 0.0004476427655868251, "Discussion Thread": 0.00036645895055307365, "Comment Section": 0.0005998170895703052, "Missing Content": 0.0007421073619470681, "Knowledge Article": 0.0010947143821606765, "Legal Notices": 0.5432525603230463, "News Article": 0.0006090066502798499, "Organizational Announcement": 0.0006611685065109026, "Nonfiction Writing": 0.0005076188135974516, "Organizational About Page": 0.0006768231375413307, "Personal About Page": 0.00039546967996664534, "Personal Blog": 0.0003199375200725167, "Product Page": 0.0004109493633528006, "Spam / Ads": 0.00044459589899879863, "Structured Data": 0.0047811237831129035, "Technical Writing": 0.037641184246885964, "Transcript / Interview": 0.0007586510498973641, "Tutorial / How-To Guide": 0.0008787758301696205, "User Reviews": 0.00033682109001367285, "NONE": 0.4026928056886391}, "tertiary": {"Academic Writing": 0.0005170131558007805, "Content Listing": 0.000322443833728611, "Creative Writing": 0.0005103172271548222, "Customer Support Page": 0.0002135235450471192, "Q&A Forum": 0.000226526739813686, "Discussion Thread": 0.00018987564346795242, "Comment Section": 0.00031052844761039717, "Missing Content": 0.0004077446358626528, "Knowledge Article": 0.0006423857423508535, "Legal Notices": 0.2835308204129491, "News Article": 0.0003224384524114689, "Organizational Announcement": 0.0003822420205792016, "Nonfiction Writing": 0.00026153281926217343, "Organizational About Page": 0.0004233768090548262, "Personal About Page": 0.00019967116837392638, "Personal Blog": 0.00019144161313060432, "Product Page": 0.00022920122767552446, "Spam / Ads": 0.00029327153659878895, "Structured Data": 0.0027894053735912825, "Technical Writing": 0.010567068220491738, "Transcript / Interview": 0.00043922359399929206, "Tutorial / How-To Guide": 0.0009359113628850841, "User Reviews": 0.0001567437251333774, "NONE": 0.6959372926930267}, "structured_records": {"false": 0.8552492420932938, "true": 0.1447507579067062}, "procedural_steps": {"false": 0.9852373856194999, "true": 0.01476261438050003}, "worked_solution": {"false": 0.8026717279048188, "true": 0.19732827209518128}, "contextual_dialogue": {"false": 0.9999876846904471, "true": 1.2315309552907097e-05}, "content_quality": {"-2": 0.005442563590830698, "-1": 0.01965614144782765, "0": 0.14545240985320707, "1": 0.49023149380568676, "2": 0.3392173913024479}, "noise_severity": {"0": 0.7247249380311082, "1": 0.26955457804359617, "2": 0.0031591108504865537, "3": 0.0012387234948760875, "4": 0.001322649579933156}, "missing_content_severity": {"0": 0.9817785666522886, "1": 0.014414992318778857, "2": 0.0028618629146273636, "3": 0.0006827630432315745, "4": 0.00026181507107365357}, "factual_density": {"0": 0.00905117570796071, "1": 0.026065707671953032, "2": 0.15807291960792283, "3": 0.4719284683061278, "4": 0.3348817287060356}, "pretraining_utility": {"-2": 0.010343755011431626, "-1": 0.10882771346524957, "0": 0.42221883021892503, "1": 0.3968399285518324, "2": 0.06176977275256132}, "math_density": {"0": 0.9830325826640907, "1": 0.013132129982034491, "2": 0.0011161021589237321, "3": 0.0007853927822506712, "4": 0.0019337924127004488}, "code_density": {"0": 0.00022873202050277539, "1": 0.00039764652130608877, "2": 0.01244900365146638, "3": 0.09914733958458247, "4": 0.8877772782221424}, "reasoning_density": {"0": 0.5762544450814125, "1": 0.08126625578671567, "2": 0.131933063587159, "3": 0.11114201512020877, "4": 0.09940422042450406}, "education_level": {"none": 0.0014774748973915956, "0": 0.0014383658196737942, "1": 0.006208699478709838, "2": 0.3959505227156744, "3": 0.48911924922422717, "4": 0.10580568786432318}, "creative_writing_quality": {"not_applicable": 0.9993286525917966, "-2": 9.322724437311898e-05, "-1": 0.00013400247121626477, "0": 0.0003285842348746289, "1": 8.094831364861259e-05, "2": 3.458514409088776e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.5571368034638887e-05, "Art & Design": 3.06142323874568e-05, "Law & Crime": 0.0003131619301586214, "Education & Careers": 2.2973871395457505e-05, "Entertainment & Media": 1.8294440738907936e-05, "Fashion & Beauty": 9.425315755701883e-06, "Business, Economics & Finance": 6.108726089650298e-05, "Food & Dining": 1.2202622671710562e-05, "Games & Gaming": 5.040497160480086e-05, "Electronics & Hardware": 5.131265208773338e-05, "Health, Healthcare & Wellness": 1.3937081851895805e-05, "History & Geography": 1.7766771052117197e-05, "Home, Pets & Hobbies": 1.186437719507303e-05, "Industry & Production": 1.5187727282468982e-05, "Language, Literature & Philosophy": 2.346737431929864e-05, "Politics & Government": 1.3866365343249271e-05, "Religion & Spirituality": 1.5635891842462804e-05, "Science, Mathematics & Engineering": 0.000167471545837249, "Relationships & Community": 1.017166020411224e-05, "Software Usage & Applications": 0.000872139558446779, "Programming & Software Engineering": 0.997953887055002, "Sports & Fitness": 2.5011629400419307e-05, "Vehicles & Transportation": 9.779336328147475e-06, "Travel & Tourism": 2.2090606425994504e-05, "Other / Uncovered Subject": 0.00017196546282856787, "Unknown / Insufficient Content": 6.0708890908850486e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00029856995045390557, "Art & Design": 0.00025808623595638516, "Law & Crime": 0.02270551566667872, "Education & Careers": 0.0002391641258674572, "Entertainment & Media": 0.0002356620061225364, "Fashion & Beauty": 0.00010333496145764177, "Business, Economics & Finance": 0.001142806858089446, "Food & Dining": 7.601011388010592e-05, "Games & Gaming": 0.0001887146106851118, "Electronics & Hardware": 0.00021352519205418953, "Health, Healthcare & Wellness": 9.955701460267684e-05, "History & Geography": 0.00010733147992431228, "Home, Pets & Hobbies": 8.745273666107086e-05, "Industry & Production": 8.714153713699402e-05, "Language, Literature & Philosophy": 0.00012544534315759472, "Politics & Government": 0.00017460157296572378, "Religion & Spirituality": 0.00015365107011705377, "Science, Mathematics & Engineering": 0.0005137944426228108, "Relationships & Community": 6.0183851879234535e-05, "Software Usage & Applications": 0.004559067673463823, "Programming & Software Engineering": 0.010708329702649714, "Sports & Fitness": 9.252397495530234e-05, "Vehicles & Transportation": 4.963792643914752e-05, "Travel & Tourism": 0.00013169422335318362, "Other / Uncovered Subject": 0.0004830792030123923, "NONE": 0.9571051185258135}}, "probabilities": {"primary": {"Academic Writing": 5.7709662171870036e-05, "Content Listing": 1.867526208853594e-05, "Creative Writing": 1.9031888133614416e-05, "Customer Support Page": 1.2244890621727524e-05, "Q&A Forum": 1.4921605736020638e-05, "Discussion Thread": 9.114559901044642e-06, "Comment Section": 1.74326429358396e-05, "Missing Content": 2.6453681028439706e-05, "Knowledge Article": 8.369675789760502e-05, "Legal Notices": 0.001105437522677852, "News Article": 7.245195054326392e-06, "Organizational Announcement": 2.4149268602540582e-05, "Nonfiction Writing": 1.314850646009109e-05, "Organizational About Page": 1.6935183324701776e-05, "Personal About Page": 9.941965874250354e-06, "Personal Blog": 7.8080644947899e-06, "Product Page": 7.87369540902934e-06, "Spam / Ads": 1.0529679654834566e-05, "Structured Data": 0.0011677372958573944, "Technical Writing": 0.9936536106235251, "Transcript / Interview": 2.4361233762627385e-05, "Tutorial / How-To Guide": 0.0002966886120357571, "User Reviews": 9.27624629667452e-06, "Other / Unknown": 0.0033859759564553397}, "secondary": {"Academic Writing": 0.0007965515481438965, "Content Listing": 0.0005828131916473735, "Creative Writing": 0.0006387053808643126, "Customer Support Page": 0.00036369774744122963, "Q&A Forum": 0.00044764276558682516, "Discussion Thread": 0.0003664589505530737, "Comment Section": 0.0005998170895703053, "Missing Content": 0.0007421073619470682, "Knowledge Article": 0.0010947143821606767, "Legal Notices": 0.5432525603230464, "News Article": 0.00060900665027985, "Organizational Announcement": 0.0006611685065109027, "Nonfiction Writing": 0.0005076188135974517, "Organizational About Page": 0.0006768231375413308, "Personal About Page": 0.0003954696799666454, "Personal Blog": 0.00031993752007251675, "Product Page": 0.00041094936335280067, "Spam / Ads": 0.0004445958989987987, "Structured Data": 0.004781123783112904, "Technical Writing": 0.03764118424688597, "Transcript / Interview": 0.0007586510498973642, "Tutorial / How-To Guide": 0.0008787758301696206, "User Reviews": 0.0003368210900136729, "NONE": 0.40269280568863913}, "tertiary": {"Academic Writing": 0.0005170131558007805, "Content Listing": 0.000322443833728611, "Creative Writing": 0.0005103172271548222, "Customer Support Page": 0.0002135235450471192, "Q&A Forum": 0.000226526739813686, "Discussion Thread": 0.00018987564346795242, "Comment Section": 0.00031052844761039717, "Missing Content": 0.0004077446358626528, "Knowledge Article": 0.0006423857423508535, "Legal Notices": 0.2835308204129491, "News Article": 0.0003224384524114689, "Organizational Announcement": 0.0003822420205792016, "Nonfiction Writing": 0.00026153281926217343, "Organizational About Page": 0.0004233768090548262, "Personal About Page": 0.00019967116837392638, "Personal Blog": 0.00019144161313060432, "Product Page": 0.00022920122767552446, "Spam / Ads": 0.00029327153659878895, "Structured Data": 0.0027894053735912825, "Technical Writing": 0.010567068220491738, "Transcript / Interview": 0.00043922359399929206, "Tutorial / How-To Guide": 0.0009359113628850841, "User Reviews": 0.0001567437251333774, "NONE": 0.6959372926930267}, "structured_records": {"false": 0.8552492420932938, "true": 0.1447507579067062}, "procedural_steps": {"false": 0.9852373856194999, "true": 0.01476261438050003}, "worked_solution": {"false": 0.8026717279048188, "true": 0.19732827209518128}, "contextual_dialogue": {"false": 0.9999876846904471, "true": 1.2315309552907097e-05}, "content_quality": {"-2": 0.005442563590830698, "-1": 0.01965614144782765, "0": 0.14545240985320707, "1": 0.49023149380568676, "2": 0.3392173913024479}, "noise_severity": {"0": 0.724724938031108, "1": 0.2695545780435961, "2": 0.003159110850486553, "3": 0.0012387234948760873, "4": 0.0013226495799331557}, "missing_content_severity": {"0": 0.9817785666522886, "1": 0.014414992318778857, "2": 0.0028618629146273636, "3": 0.0006827630432315745, "4": 0.00026181507107365357}, "factual_density": {"0": 0.00905117570796071, "1": 0.026065707671953032, "2": 0.15807291960792283, "3": 0.4719284683061278, "4": 0.3348817287060356}, "pretraining_utility": {"-2": 0.010343755011431626, "-1": 0.10882771346524957, "0": 0.42221883021892503, "1": 0.3968399285518324, "2": 0.06176977275256132}, "math_density": {"0": 0.9830325826640907, "1": 0.013132129982034491, "2": 0.0011161021589237321, "3": 0.0007853927822506712, "4": 0.0019337924127004488}, "code_density": {"0": 0.00022873202050277539, "1": 0.00039764652130608877, "2": 0.01244900365146638, "3": 0.09914733958458247, "4": 0.8877772782221424}, "reasoning_density": {"0": 0.5762544450814125, "1": 0.08126625578671567, "2": 0.131933063587159, "3": 0.11114201512020877, "4": 0.09940422042450406}, "education_level": {"none": 0.0014774748973915956, "0": 0.0014383658196737942, "1": 0.006208699478709838, "2": 0.3959505227156744, "3": 0.48911924922422717, "4": 0.10580568786432318}, "creative_writing_quality": {"not_applicable": 0.9993286525917966, "-2": 9.322724437311898e-05, "-1": 0.00013400247121626477, "0": 0.0003285842348746289, "1": 8.094831364861259e-05, "2": 3.458514409088776e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.557136803463888e-05, "Art & Design": 3.0614232387456794e-05, "Law & Crime": 0.00031316193015862136, "Education & Careers": 2.29738713954575e-05, "Entertainment & Media": 1.8294440738907933e-05, "Fashion & Beauty": 9.425315755701881e-06, "Business, Economics & Finance": 6.108726089650296e-05, "Food & Dining": 1.2202622671710559e-05, "Games & Gaming": 5.040497160480085e-05, "Electronics & Hardware": 5.1312652087733365e-05, "Health, Healthcare & Wellness": 1.3937081851895801e-05, "History & Geography": 1.7766771052117193e-05, "Home, Pets & Hobbies": 1.1864377195073026e-05, "Industry & Production": 1.5187727282468978e-05, "Language, Literature & Philosophy": 2.3467374319298635e-05, "Politics & Government": 1.3866365343249267e-05, "Religion & Spirituality": 1.56358918424628e-05, "Science, Mathematics & Engineering": 0.00016747154583724897, "Relationships & Community": 1.0171660204112238e-05, "Software Usage & Applications": 0.0008721395584467788, "Programming & Software Engineering": 0.9979538870550018, "Sports & Fitness": 2.50116294004193e-05, "Vehicles & Transportation": 9.779336328147474e-06, "Travel & Tourism": 2.20906064259945e-05, "Other / Uncovered Subject": 0.00017196546282856784, "Unknown / Insufficient Content": 6.070889090885047e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00029856995045390557, "Art & Design": 0.00025808623595638516, "Law & Crime": 0.02270551566667872, "Education & Careers": 0.0002391641258674572, "Entertainment & Media": 0.0002356620061225364, "Fashion & Beauty": 0.00010333496145764177, "Business, Economics & Finance": 0.001142806858089446, "Food & Dining": 7.601011388010592e-05, 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1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, 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"03fb35802b0699bfaa5b27cf58bfc6b5045249c1", "size_bytes": 12078, "file_timestamp": 1602761220, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "648c832d0dadea16d5ae32c516d954b5dad3b279"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 1, "repo_created_at": "2020-10-05T21:04:18.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 2, "forks": 2, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "03fb35802b0699bfaa5b27cf58bfc6b5045249c1", "size_bytes": 12078, "file_timestamp": 1602761220, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "648c832d0dadea16d5ae32c516d954b5dad3b279", "url": "https://github.com/WeilerWebServices/Yahoo/blob/648c832d0dadea16d5ae32c516d954b5dad3b279/fluxible/packages/fluxible-plugin-devtools/tests/unit/lib/devtools-plugin.js"}, "gpt4o_tokens": 2093, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.1381250077810934, "noise_severity": 0.2848795685489301, "missing_content_severity": 0.023234267562022924, "factual_density": 3.0975238666303246, "pretraining_utility": 0.3908642505688422, "math_density": 0.025455682297435764, "code_density": 3.8738467854665557, "reasoning_density": 1.0761753100196763, "education_level": 2.6926685942950987, "creative_writing_quality": -0.2537260977709444}, "score_applicability": {"education_level": 0.9985225251026084, "creative_writing_quality": 0.000671347408203513}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/cornerstonejs/cornerstoneTools/blob/dbf874f744a91fcf3242659052d6d64b84bb61a7/src/eventDispatchers/mouseEventHandlers/mouseDown.js", "id": "javascript-0-4698ca00b55e9772ef5fbdecc3e22ca9", "text": "// State\nimport { getters, state } from './../../store/index.js';\nimport { getToolState } from './../../stateManagement/toolState.js';\n// Util\nimport getToolsWithMoveableHandles from '../../store/getToolsWithMoveableHandles.js';\nimport { findHandleDataNearImagePoint } from '../../util/findAndMoveHelpers.js';\nimport getInteractiveToolsForElement from './../../store/getInteractiveToolsForElement.js';\nimport getToolsWithDataForElement from './../../store/getToolsWithDataForElement.js';\nimport filterToolsUseableWithMultiPartTools from './../../store/filterToolsUsableWithMultiPartTools.js';\n\n/**\n * MouseDown is called before MouseDownActivate. If MouseDown\n * finds an existing tool to interact with, it can prevent the\n * event from bubbling to MouseDownActivate.\n *\n * @private\n * @param {mousedown} evt\n * @listens {CornerstoneTools.event:cornerstonetoolsmousedown}\n * @returns {undefined}\n */\nexport default function(evt) {\n if (state.isToolLocked) {\n return;\n }\n\n const eventData = evt.detail;\n const element = evt.detail.element;\n const coords = evt.detail.currentPoints.canvas;\n\n // High level filtering\n const activeAndPassiveTools = getInteractiveToolsForElement(\n element,\n getters.mouseTools()\n );\n\n // ACTIVE TOOL W/ PRE CALLBACK?\n // Note: In theory, this should only ever be a single tool.\n let activeTools = activeAndPassiveTools.filter(\n tool =>\n tool.mode === 'active' &&\n Array.isArray(tool.options.mouseButtonMask) &&\n tool.options.mouseButtonMask.includes(eventData.buttons) &&\n tool.options.isMouseActive\n );\n\n if (state.isMultiPartToolActive) {\n activeTools = filterToolsUseableWithMultiPartTools(activeTools);\n }\n\n // If any tools are active, check if they have a special reason for dealing with the event.\n if (activeTools.length > 0) {\n // TODO: If length > 1, you could assess fitness and select the ideal tool\n // TODO: But because we're locking this to 'active' tools, that should rarely be an issue\n // Super-Meta-TODO: ^ I think we should just take the approach of one active tool per mouse button?\n const firstActiveToolWithCallback = activeTools.find(\n tool => typeof tool.preMouseDownCallback === 'function'\n );\n\n if (firstActiveToolWithCallback) {\n const consumedEvent = firstActiveToolWithCallback.preMouseDownCallback(\n evt\n );\n\n if (consumedEvent) {\n return;\n }\n }\n }\n\n if (state.isMultiPartToolActive) {\n // Don't fire events to Annotation Tools during a multi part loop.\n return;\n }\n\n // Annotation tool specific\n const annotationTools = getToolsWithDataForElement(\n element,\n activeAndPassiveTools\n );\n\n // NEAR HANDLES?\n const annotationToolsWithMoveableHandles = getToolsWithMoveableHandles(\n element,\n annotationTools,\n coords,\n 'mouse'\n );\n\n if (annotationToolsWithMoveableHandles.length > 0) {\n const firstToolWithMoveableHandles = annotationToolsWithMoveableHandles[0];\n const toolState = getToolState(element, firstToolWithMoveableHandles.name);\n\n const { handle, data } = findHandleDataNearImagePoint(\n element,\n toolState,\n firstToolWithMoveableHandles.name,\n coords\n );\n\n firstToolWithMoveableHandles.handleSelectedCallback(\n evt,\n data,\n handle,\n 'mouse'\n );\n\n return;\n }\n\n // NEAR TOOL?\n const annotationToolsWithPointNearClick = activeAndPassiveTools.filter(\n tool => {\n const toolState = getToolState(element, tool.name);\n const isNearPoint =\n toolState &&\n toolState.data &&\n tool.pointNearTool &&\n toolState.data.some(data =>\n tool.pointNearTool(element, data, coords, 'mouse')\n );\n\n return isNearPoint;\n }\n );\n\n if (annotationToolsWithPointNearClick.length > 0) {\n const firstToolNearPoint = annotationToolsWithPointNearClick[0];\n const toolState = getToolState(element, firstToolNearPoint.name);\n const firstAnnotationNearPoint = toolState.data.find(data =>\n firstToolNearPoint.pointNearTool(element, data, coords)\n );\n\n firstToolNearPoint.toolSelectedCallback(\n evt,\n firstAnnotationNearPoint,\n 'mouse'\n );\n\n return;\n }\n\n // ACTIVE TOOL W/ POST CALLBACK?\n // If any tools are active, check if they have a special reason for dealing with the event.\n if (activeTools.length > 0) {\n // TODO: If length > 1, you could assess fitness and select the ideal tool\n // TODO: But because we're locking this to 'active' tools, that should rarely be an issue\n // Super-Meta-TODO: ^ I think we should just take the approach of one active tool per mouse button?\n const firstActiveToolWithCallback = activeTools.find(\n tool => typeof tool.postMouseDownCallback === 'function'\n );\n\n if (firstActiveToolWithCallback) {\n const consumedEvent = firstActiveToolWithCallback.postMouseDownCallback(\n evt\n );\n\n if (consumedEvent) {\n return;\n }\n }\n }\n}\n", "text_preview": "// State import { getters, state } from './../../store/index.js'; import { getToolState } from './../../stateManagement/toolState.js'; // Util import getToolsWithMoveableHandles from '../../store/getToolsWithMoveableHandles.js'; import { fi", "input_sha256": "c734ad93c128703755fd02b07da1a02145bdc7f268bd0c45f10eb1164ac27081", "text_sha256": "4698ca00b55e9772ef5fbdecc3e22ca9031d56faa834eeaeb392281f6f463011", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8077, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0503448921517993, "noise_severity": 0.040300669181457915, "missing_content_severity": 0.004190507274248949, "factual_density": 3.3312844277546505, "pretraining_utility": 0.4296593524632819, "math_density": 0.02129997268668281, "code_density": 3.856502169394735, "reasoning_density": 0.9678620027982284, "education_level": 2.4538917048908004, "creative_writing_quality": -0.14987120837136814}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008832124930630791, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9983452440490946, "secondary": 0.9835421816566317, "tertiary": 0.9927583120190291, "structured_records": 0.00026091068876890134, "procedural_steps": 0.0008987443857401985, "worked_solution": 0.0007406721738252825, "contextual_dialogue": 5.5072147315744075e-06, "content_quality": 0.5695356882647766, "noise_severity": 0.9664161090154517, "missing_content_severity": 0.9965079106047925, "factual_density": 0.44273702312887575, "pretraining_utility": 0.47013089930231333, "math_density": 0.9822500227610976, "code_density": 0.8804184744956124, "reasoning_density": 0.1934483310014763, "education_level": 0.49179344254811896, "creative_writing_quality": 0.9995966961639187, "primary_topic": 0.9945883526334556, "secondary_topic": 0.9616901653251592}, "probability_margin": {"primary": 0.9974750279618882, "secondary": 0.9715092157450606, "tertiary": 0.9886540317935866, "education_level": 0.22377107511937894, "creative_writing_quality": 0.9994959912096091, "primary_topic": 0.9935168061982906, "secondary_topic": 0.9496686363929998}, "raw_probabilities": {"primary": {"Academic Writing": 2.9172842327847346e-05, "Content Listing": 1.7703819072015158e-05, "Creative Writing": 1.257391524831926e-05, "Customer Support Page": 8.901449269234062e-06, "Q&A Forum": 1.0085518826803008e-05, "Discussion Thread": 5.8148401852185305e-06, "Comment Section": 1.0734965330184967e-05, "Missing Content": 2.3559190290872285e-05, "Knowledge Article": 8.795391393782606e-05, "Legal Notices": 2.8210362847351553e-05, "News Article": 3.492843299518659e-06, "Organizational 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"commit_id": "dbf874f744a91fcf3242659052d6d64b84bb61a7", "url": "https://github.com/cornerstonejs/cornerstoneTools/blob/dbf874f744a91fcf3242659052d6d64b84bb61a7/src/eventDispatchers/mouseEventHandlers/mouseDown.js"}, "gpt4o_tokens": 1149, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.0503448921517993, "noise_severity": 0.04030066918145792, "missing_content_severity": 0.004190507274248949, "factual_density": 3.331284427754651, "pretraining_utility": 0.4296593524632819, "math_density": 0.021299972686682815, "code_density": 3.856502169394735, "reasoning_density": 0.9678620027982284, "education_level": 2.4538917048908004, "creative_writing_quality": -0.14987120837136814}, "score_applicability": {"education_level": 0.9986744142043273, "creative_writing_quality": 0.00033608653006787515}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/ericoporto/ash2doc/blob/6c12e8822cf971c4c0c72bc17ffe0e74dde41674/src/ash2doc.js", "id": "javascript-0-c434b65ecdfe821016bf653e0c93070f", "text": "// comment is the content text when documenting methods, properties and functions\nexports.handleComment = function(cur, hl, lastComment){\n if (lastComment === undefined || lastComment === null) {\n lastComment = \"\";\n }\n\n if(cur.nodeType == 'comment'){\n lastComment = String(cur.nodeText);\n\n if(lastComment.startsWith('///')){\n const strlen = lastComment.length;\n lastComment = lastComment.substr(3,strlen-3);\n } else {\n lastComment = \"\";\n }\n }\n\n return lastComment;\n}\nvar handleComment = exports.handleComment;\n\n// it's a method from a struct, so we handle it\nexports.handleMethodDeclaration = function(cur, hl, structName , lastComment){\n var func_text = \"\";\n var reportText = \"\"\n\n for(var notEnd = cur.gotoFirstChild();\n notEnd;\n notEnd = cur.gotoNextSibling()) {\n\n if(cur.nodeType == 'function_access_specifier') {\n if(cur.nodeText != 'import'){\n func_text = func_text + cur.nodeText + \" \";\n }\n }\n\n if(cur.nodeType == 'type_identifier') {\n func_text = func_text + cur.nodeText + \" \";\n }\n\n if(cur.nodeType == 'function_field_declarator') {\n var txt = cur.nodeText;\n var asterisk = \"\";\n\n if(txt.startsWith('* ')){\n const strlen = txt.length;\n txt = txt.substr(2,strlen-2);\n asterisk = \"* \";\n }\n\n func_text = func_text + asterisk +\n structName + \".\" + txt + \" \";\n }\n\n }\n\n reportText = reportText + \"\\n\\n\" + hl + \"# \" +\n func_text +\n \"\\n\" + lastComment;\n return reportText;\n}\nvar handleMethodDeclaration = exports.handleMethodDeclaration;\n\nexports.handlePropertyDeclaration = function(cur, hl, structName , lastComment){\n var prop_text = \"\";\n var reportText = \"\"\n\n for(var notEnd = cur.gotoFirstChild();\n notEnd;\n notEnd = cur.gotoNextSibling()) {\n\n if(cur.nodeType == 'field_access_specifier') {\n if(cur.nodeText != 'import'){\n prop_text = prop_text + cur.nodeText + \" \";\n }\n }\n\n if(cur.nodeType == 'type_identifier') {\n prop_text = prop_text + cur.nodeText + \" \";\n }\n\n if(cur.nodeType == 'primitive_type') {\n prop_text = prop_text + cur.nodeText + \" \";\n }\n\n if(cur.nodeType == 'field_identifier') {\n var txt = cur.nodeText;\n var asterisk = \"\";\n\n if(txt.startsWith('* ')){\n const strlen = txt.length;\n txt = txt.substr(2,strlen-2);\n asterisk = \"* \";\n }\n\n prop_text = prop_text + asterisk +\n structName + \".\" + txt + \" \";\n }\n\n }\n\n reportText = reportText + \"\\n\\n\" + hl + \"# \" +\n prop_text +\n \"\\n\" + lastComment;\n return reportText;\n}\nvar handlePropertyDeclaration = exports.handlePropertyDeclaration;\n\n// fields can be methods and properties\nexports.handleFieldDeclarationList = function(cur, hl, structName){\n var reportText = \"\";\n var lastComment = \"\";\n\n for(var notEnd = cur.gotoFirstChild();\n notEnd;\n notEnd = cur.gotoNextSibling()) {\n\n lastComment = handleComment(cur, hl, lastComment);\n\n\n if(cur.nodeType == 'field_declaration'){\n\n reportText += handlePropertyDeclaration(cur.currentNode.walk(),\n hl,\n structName,\n lastComment);\n\n lastComment = \"\";\n\n }\n\n if(cur.nodeType == 'field_function_declaration'){\n\n reportText += handleMethodDeclaration(cur.currentNode.walk(),\n hl,\n structName,\n lastComment);\n\n lastComment = \"\";\n }\n }\n\n return reportText;\n}\nvar handleFieldDeclarationList = exports.handleFieldDeclarationList;\n\n// do parsing for the contents of a struct node\nexports.handleStructDeclaration = function(cur, hl){\n var reportText = \"\";\n var struct_name = \"\";\n\n for(var notEnd = cur.gotoFirstChild();\n notEnd;\n notEnd= cur.gotoNextSibling()) {\n\n if(cur.nodeType == 'type_identifier') {\n struct_name = cur.nodeText;\n reportText = reportText + \"\\n\\n\" + hl + \" \" + struct_name + \"\\n\";\n }\n\n if(cur.nodeType == 'field_declaration_list') {\n\n reportText += handleFieldDeclarationList(cur.currentNode.walk(),\n hl,\n struct_name);\n\n }\n }\n\n return reportText;\n}\nvar handleStructDeclaration = exports.handleStructDeclaration;\n\n// traverse the script header for the interesting nodes\nexports.handleScriptHeader = function(cur, hl){\n let lastComment = \"\";\n let reportText = \"\";\n\n for(var notEnd = cur.gotoFirstChild();\n notEnd;\n notEnd = cur.gotoNextSibling()) {\n\n lastComment = handleComment(cur, hl, lastComment);\n\n if(cur.nodeType == 'import_declaration'){\n var func_text = cur.nodeText;\n func_text = func_text.replace('import', '');\n func_text = func_text.replace(';', '');\n\n reportText = reportText + \"\\n\\n\" + hl + \" \" + func_text +\n \"\\n\" + lastComment;\n\n lastComment = \"\";\n }\n\n if(cur.nodeType == 'struct_declaration'){\n\n reportText += handleStructDeclaration(cur.currentNode.walk(),\n hl);\n\n lastComment = \"\";\n }\n }\n\n return reportText;\n}\nvar handleScriptHeader = exports.handleScriptHeader;\n\n// 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"1": 0.00027089764233671824, "2": 0.006829810318171547, "3": 0.05476799252032882, "4": 0.9379980973761698}, "reasoning_density": {"0": 0.8131559976043187, "1": 0.058272997473575304, "2": 0.08023005456239528, "3": 0.029742051405528578, "4": 0.018598898954182215}, "education_level": {"none": 0.0013752080906827007, "0": 0.0019357695161126805, "1": 0.034775848172413325, "2": 0.7483515298018528, "3": 0.18534361360600007, "4": 0.02821803081293839}, "creative_writing_quality": {"not_applicable": 0.9996357294897487, "-2": 6.426328273496454e-05, "-1": 9.570389948301528e-05, "0": 0.00014467701409133634, "1": 4.3416951675337926e-05, "2": 1.620936226665586e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 4.252104422889482e-05, "Art & Design": 3.8188568951038864e-05, "Law & Crime": 2.249347008392296e-05, "Education & Careers": 2.5839813823615666e-05, "Entertainment & Media": 8.062188158533183e-05, "Fashion & Beauty": 1.19763294525926e-05, "Business, Economics & Finance": 2.5029119070897233e-05, "Food & Dining": 1.537661357207936e-05, "Games & Gaming": 0.005743692805722381, "Electronics & Hardware": 5.16290609005197e-05, "Health, Healthcare & Wellness": 1.3426309730216948e-05, "History & Geography": 1.9202171164175827e-05, "Home, Pets & Hobbies": 2.4929067142581667e-05, "Industry & Production": 1.9346778309004493e-05, "Language, Literature & Philosophy": 0.00010900075588916793, "Politics & Government": 1.3595280081434334e-05, "Religion & Spirituality": 2.4687604552540396e-05, "Science, Mathematics & Engineering": 0.00015558380614295598, "Relationships & Community": 1.2966361730415245e-05, "Software Usage & Applications": 0.0003303858077728522, "Programming & Software Engineering": 0.9929002030692917, "Sports & Fitness": 3.379543472594251e-05, "Vehicles & Transportation": 1.3207506469437574e-05, "Travel & Tourism": 2.9193574668091843e-05, "Other / Uncovered Subject": 0.00015600851837556558, "Unknown / Insufficient Content": 8.709924656257372e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0003304894221991988, "Art & Design": 0.00018759210841056052, "Law & Crime": 0.0001704315366102883, "Education & Careers": 0.0001382089404380926, "Entertainment & Media": 0.0008536836627127847, "Fashion & Beauty": 8.442373536263904e-05, "Business, Economics & Finance": 0.0001671244133377748, "Food & Dining": 7.861573906979933e-05, "Games & Gaming": 0.014796987981339353, "Electronics & Hardware": 0.00018916305626950715, "Health, Healthcare & Wellness": 7.531687863035534e-05, "History & Geography": 9.032644189682951e-05, "Home, Pets & Hobbies": 9.206138346252887e-05, "Industry & Production": 5.77976466116147e-05, "Language, Literature & Philosophy": 0.0006717941318331505, "Politics & Government": 9.62796330194377e-05, "Religion & Spirituality": 0.00011198725760002815, "Science, Mathematics & Engineering": 0.0005741771939953528, "Relationships & Community": 5.578706018207077e-05, "Software Usage & Applications": 0.0015574418448811034, "Programming & Software Engineering": 0.006831127123176763, "Sports & Fitness": 9.851984454132423e-05, "Vehicles & Transportation": 3.8197339731874584e-05, "Travel & Tourism": 0.00012329341012782093, "Other / Uncovered Subject": 0.00029130425049277847, "NONE": 0.972237867964067}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 9899, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 9899, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:10.279528+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "00f7e841-8962-449c-a67a-2ab93208e2bd", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "dfcd3fbb2d7cdf6e4abe29f72fb4f4373ffbd0fb2c684fc7e6c9a92b481ef552", "source_metadata": {"uuid": "00f7e841-8962-449c-a67a-2ab93208e2bd", "repo_name": "ericoporto/ash2doc", "relative_path": "src/ash2doc.js", "category": "TOOL", "algo_rel_score": 0.9396408796310425, "quality_score": 4.034041881561279, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 30, "repo_created_at": "2019-10-18T01:59:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 1, "pull_requests": 1}, "file_meta": {"content_id": "cbbfe6b7e3271313b437270b2d2dd99b8d9e7f7c", "size_bytes": 5723, "file_timestamp": 1689146953, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6c12e8822cf971c4c0c72bc17ffe0e74dde41674"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 30, "repo_created_at": "2019-10-18T01:59:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 1, "pull_requests": 1}, "file_meta": {"content_id": "cbbfe6b7e3271313b437270b2d2dd99b8d9e7f7c", "size_bytes": 5723, "file_timestamp": 1689146953, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "6c12e8822cf971c4c0c72bc17ffe0e74dde41674", "url": "https://github.com/ericoporto/ash2doc/blob/6c12e8822cf971c4c0c72bc17ffe0e74dde41674/src/ash2doc.js"}, "gpt4o_tokens": 1267, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6658842299084804, "noise_severity": 0.035015947709401446, "missing_content_severity": 0.02704083800146433, "factual_density": 3.067978707263026, "pretraining_utility": 0.22419603129522944, "math_density": 0.023496935189439357, "code_density": 3.9302268853443456, "reasoning_density": 0.38235485663168045, "education_level": 2.2034120218854776, "creative_writing_quality": -0.40737524605523867}, "score_applicability": {"education_level": 0.9986247919093173, "creative_writing_quality": 0.00036427051025130993}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/AmitKumarn/MedEase_MERN/blob/80706894efd7e76719320f9e711d6a8e1ea94c77/frontend/src/pages/StartConsultationPage.js", "id": "javascript-0-373cf2064ebab3be8a449e3e91c43a10", "text": "import React, { useState, useEffect } from 'react';\nimport { useNavigate } from 'react-router-dom';\nimport useFetchUser from '../utils/useFetchUser';\nimport { BASE_URL } from '../config'\n\n// Create a separate component for showing doctor details\nconst DoctorDetails = ({ doctor}) => (\n
\n {doctor.name}\n
\n
{doctor.name}
\n

Speciality: {doctor.speciality}

\n

Experience: {doctor.experience} years

\n

Location: {doctor.location}

\n

{doctor.isAvailable}

\n
\n
\n);\n\nconst StartConsultation = (props) => {\n const [doctors, setDoctors] = useState([]);\n const [selectedDoctor, setSelectedDoctor] = useState(null);\n const [problem, setProblem] = useState('');\n const [symptoms, setSymptoms] = useState('');\n const {userDetails, fetchUser, authToken} = useFetchUser();\n\n \n useEffect(() => {\n fetchUser();\n // Fetch the list of available doctors from the server\n fetch(`${BASE_URL}/api/doctors`)\n .then(response => response.json())\n .then(data => setDoctors(data))\n .catch(error => console.error(error));\n }, [fetchUser]);\n \n const navigate = useNavigate();\n\n // If not authenticated, redirect to the login page\n if (!authToken) {\n navigate('/login');\n return null;\n }\n\n const handleStartConsultation = async () => {\n if (selectedDoctor && problem && symptoms) {\n const selectedDoctorData = doctors.find(doctor => doctor._id === selectedDoctor);\n if(selectedDoctorData.isAvailable !==\"Available\"){\n props.showAlert(\"The Selected Doctor is not available\", 'danger')\n return;\n }// Send a POST request to create a new consultation session\n const response = await fetch(`${BASE_URL}/api/consultations`, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n 'auth-token': authToken,\n },\n body: JSON.stringify({\n doctorId: selectedDoctor,\n doctorName: selectedDoctorData.name,\n patientId : userDetails._id,\n patientName : userDetails.name,\n problem,\n symptoms,\n }),\n });\n \n if (response.ok) {\n props.showAlert(\"Successfully started the session\", 'success')\n navigate('/profile');\n } else {\n console.error('Failed to start consultation session');\n }\n }\n };\n return (\n
\n

Start Consultation

\n
\n
\n \n \n \n setProblem(e.target.value)} />\n \n \n
\n \n \n\n {/* Comments List */}\n
\n {comments.length > 0 ? (\n comments.map((comment, index) => (\n
\n
\n {comment.userId}\n \n {new Date(comment.timestamp).toLocaleDateString()}\n \n
\n

{comment.text}

\n
\n ))\n ) : (\n

No comments yet. Be the first to comment!

\n )}\n
\n
\n
\n \n \n );\n};\n\nexport default LocationDetail; \n", "text_preview": "/* I/We declare that the lab work here submitted is original except for source material explicitly acknowledged, and that the same or closely related material has not been previously submitted for another course. I/We also acknowledge that ", "input_sha256": "1b378090e9212e6cc4c0be6b86ebc7a3ba874a66e2207910634b25cddcd0c202", "text_sha256": "52cf3177c8c9c77092dcbca290440681b4368a39264d76fdc76bba2912ea0052", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8114, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": {"main_format": "Legal Notices", "sub_format": null}, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7516105624181143, "noise_severity": 0.47001230084119866, "missing_content_severity": 0.3577233618253995, "factual_density": 2.6643483772904553, "pretraining_utility": 0.5770030985650109, "math_density": 0.044113843050010336, "code_density": 3.5057956439181486, "reasoning_density": 0.2089134853423369, "education_level": 2.026409966574001, "creative_writing_quality": -0.25485408171176477}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "Legal Notices", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["Duplicate Document Type slots", "secondary probability 0.5151 below 0.70", "tertiary 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"9e472455dcac2cb965778f5e9b3fcc4fce6a0268", "size_bytes": 7925, "file_timestamp": 1734530867, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "507489b13aae0a5b120035aa619e9eca6948b51f"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 47, "repo_created_at": "2025-06-11T05:09:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "9e472455dcac2cb965778f5e9b3fcc4fce6a0268", "size_bytes": 7925, "file_timestamp": 1734530867, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "507489b13aae0a5b120035aa619e9eca6948b51f", "url": "https://github.com/SuryanshManav/MuseLocator/blob/507489b13aae0a5b120035aa619e9eca6948b51f/client/src/components/LocationDetail.js"}, "gpt4o_tokens": 1440, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7516105624181143, "noise_severity": 0.47001230084119866, "missing_content_severity": 0.3577233618253995, "factual_density": 2.6643483772904553, "pretraining_utility": 0.5770030985650109, "math_density": 0.044113843050010336, "code_density": 3.5057956439181486, "reasoning_density": 0.2089134853423369, "education_level": 2.026409966574001, "creative_writing_quality": -0.25485408171176477}, "score_applicability": {"education_level": 0.9984675610953366, "creative_writing_quality": 0.001655509636970216}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Innoverenit/hrcrm-nuboxed/blob/ad7cb22bbb2d57133453d6b2eb04986e41b629c6/src/Containers/Main/Account/AccountActionLeft.js", "id": "javascript-0-1247af7557d7292f7167b4ddc4cc68e7", "text": "import React, { useEffect, useState,useRef } from \"react\";\nimport { connect } from \"react-redux\";\nimport { bindActionCreators } from \"redux\";\nimport { Tooltip, Badge, Avatar, Input } from \"antd\";\nimport TocIcon from '@mui/icons-material/Toc';\nimport SpeechRecognition, { useSpeechRecognition } from 'react-speech-recognition';\nimport { inputDataSearch,ClearSearchedDataOfAccount,getAllDistributorsList, getRecords,getCustomerByUser, getAccountRecords, getAllRecords, getDistributorCount ,} from \"./AccountAction\";\nimport MicIcon from '@mui/icons-material/Mic';\nimport DeleteOutlineIcon from '@mui/icons-material/DeleteOutline';\n\nconst AccountActionLeft = (props) => {\n const [currentData, setCurrentData] = useState(\"\");\n const [searchOnEnter, setSearchOnEnter] = useState(false); //Code for Search\n const [pageNo, setPage] = useState(0);\n const [page, setpage] = useState(0);\n const [startTime, setStartTime] = useState(null);\n const [isRecording, setIsRecording] = useState(false); \n const minRecordingTime = 3000; // 3 seconds\n const timerRef = useRef(null);\n const dummy = [\"cloud\", \"azure\", \"fgfdg\"];\n const {\n transcript,\n listening,\n resetTranscript,\n browserSupportsSpeechRecognition\n } = useSpeechRecognition();\n\n const { user, } = props;\n useEffect(() => {\n if (props.viewType === \"list\") {\n props.getRecords(props.userId);\n } else if (props.viewType === \"card\") {\n props.getDistributorCount(props.userId);\n }\n else if (props.viewType === \"all\") {\n props.getAccountRecords();\n }\n }, [props.viewType, props.userId]);\n\n \n useEffect(() => {\n // props.getCustomerRecords();\n if (transcript) {\n console.log(\">>>>>>>\", transcript);\n setCurrentData(transcript);\n }\n }, [transcript]);\n const handleChange = (e) => {\n setCurrentData(e.target.value);\n\n if (searchOnEnter && e.target.value.trim() === \"\") { //Code for Search\n // setPage(pageNo + 1);\n if (props.viewType === \"list\") {\n props.getCustomerByUser(props.userId, pageNo); \n }\n else if (props.viewType === \"all\") {\n props.getAllDistributorsList(props.orgId,page);\n }\n props.ClearSearchedDataOfAccount()\n setSearchOnEnter(false);\n }\n };\n const handleSearch = () => {\n if (currentData.trim() !== \"\") {\n // Perform the search\n // props.inputDataSearch(currentData);\n if (props.viewType === \"list\") {\n props.inputDataSearch(currentData,'user')\n }\n else if (props.viewType === \"all\") {\n props.inputDataSearch(currentData,'all')\n }\n setSearchOnEnter(true); //Code for Search\n } else {\n console.error(\"Input is empty. Please provide a value.\");\n }\n };\n const handleStartListening = () => {\n setStartTime(Date.now());\n setIsRecording(true);\n SpeechRecognition.startListening();\n if (timerRef.current) {\n clearTimeout(timerRef.current);\n }\n timerRef.current = setTimeout(() => {\n SpeechRecognition.stopListening();\n setIsRecording(false);\n }, minRecordingTime);\n };\n const suffix = (\n \n );\n const handleStopListening = () => {\n SpeechRecognition.stopListening();\n setIsRecording(false);\n if (transcript.trim() !== \"\") {\n setCurrentData(transcript);\n if (props.viewType === \"list\") {\n props.inputDataSearch(transcript,'user')\n }\n else if (props.viewType === \"all\") {\n props.inputDataSearch(transcript,'all')\n }\n // props.inputDataSearch(transcript);\n setSearchOnEnter(true);\n }\n };\n useEffect(() => {\n if (!listening && isRecording) {\n handleStopListening();\n }\n }, [listening]);\n useEffect(() => {\n if (isRecording && !listening) {\n // If recording was stopped but less than 5 seconds have passed, restart listening\n const elapsedTime = Date.now() - startTime;\n if (elapsedTime < minRecordingTime) {\n SpeechRecognition.startListening();\n } else {\n setIsRecording(false);\n }\n }\n }, [listening, isRecording, startTime]);\n \n console.log(currentData)\n const teamCount = props.teamsAccessInd && props.leadsTeamCountData ? props.leadsTeamCountData.leadsTeam : 0;\n \n\n return (\n
\n
\n {user.functionName !== \"Customer Care\" && (\n\n \n \n props.setDistributorViewType(\"list\")}\n style={{\n color: props.viewType === \"list\" && \"#1890ff\",\n }}\n >\n \n \n\n \n \n \n\n )}\n
\n\n {user.accountFullListInd === true && user.erpInd === true && (\n
\n \n \n props.setDistributorViewType(\"all\")}\n style={{\n color: props.viewType === \"all\" && \"#1890ff\",\n }}\n >\n \n \n \n
ALL
\n\n \n \n \n
\n )}\n \n \n props.setDistributorViewType(\"dashboard\")}\n style={{\n color: props.viewType === \"dashboard\" && \"#1890ff\",\n }}\n >\n \n \n\n \n \n \n\n
\n
\n \n
\n );\n};\n\nconst mapStateToProps = ({ auth, distributor }) => ({\n user: auth.userDetails,\n customerListByUser: distributor.customerListByUser,\n allDistributorCount: distributor.allDistributorCount,\n accountRecordData: distributor.accountRecordData,\n recordData: distributor.recordData,\n recordAllData: distributor.recordAllData,\n userId: auth.userDetails.userId,\n distributorsByUserId: distributor.distributorsByUserId,\n allDistributors: distributor.allDistributors,\n orgId: auth.userDetails.organizationId,\n});\nconst mapDispatchToProps = (dispatch) =>\n bindActionCreators(\n {\n getCustomerByUser,\n inputDataSearch,\n getRecords,\n getAccountRecords,\n getAllRecords,\n getDistributorCount,\n ClearSearchedDataOfAccount,\n getAllDistributorsList\n },\n dispatch\n );\n\nexport default connect(mapStateToProps, mapDispatchToProps)(AccountActionLeft);\n", "text_preview": "import React, { useEffect, useState,useRef } from \"react\"; import { connect } from \"react-redux\"; import { bindActionCreators } from \"redux\"; import { Tooltip, Badge, Avatar, Input } from \"antd\"; import TocIcon from '@mui/icons-material/Toc", "input_sha256": "7e561f6936702dde841530ba28f958efca6a903682e839ff191ddb39e17c6066", "text_sha256": "1247af7557d7292f7167b4ddc4cc68e724e2af12f44a02b076d4ccaf302fe548", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8115, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.30701822784061494, "noise_severity": 0.12860306858847395, "missing_content_severity": 0.02501183065719378, "factual_density": 3.1303960067630605, "pretraining_utility": 0.3187463216080611, "math_density": 0.14334310008048304, "code_density": 3.8812396537085094, "reasoning_density": 0.246794509933886, "education_level": 2.3273411578447694, "creative_writing_quality": -0.5842294212864381}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006202872126669923, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997155979536136, "secondary": 0.9832962271672566, "tertiary": 0.9897687893863905, "structured_records": 0.004297383869526568, "procedural_steps": 0.0010526337213661988, "worked_solution": 0.0014799562166353001, "contextual_dialogue": 8.67935755475253e-06, "content_quality": 0.42412810282833346, "noise_severity": 0.8928307761762717, "missing_content_severity": 0.9791568077856718, "factual_density": 0.2753300056358836, "pretraining_utility": 0.47629563376732387, "math_density": 0.8805474165995975, "code_density": 0.9010330447570911, "reasoning_density": 0.7943379083884283, "education_level": 0.6081102322459964, "creative_writing_quality": 0.9992414749606224, "primary_topic": 0.9855991162775228, "secondary_topic": 0.8423633757889976}, "probability_margin": {"primary": 0.9955422159166608, "secondary": 0.9759679138183683, "tertiary": 0.9861246818318592, "education_level": 0.38099844400256005, "creative_writing_quality": 0.9991345919173207, "primary_topic": 0.9795256458011311, "secondary_topic": 0.7856704287562257}, "raw_probabilities": {"primary": {"Academic Writing": 3.856969087060368e-05, "Content Listing": 2.997637831250093e-05, "Creative Writing": 2.0301436075879458e-05, "Customer Support Page": 8.872424498787058e-06, "Q&A Forum": 1.509460405153444e-05, "Discussion Thread": 8.146213651089202e-06, "Comment Section": 1.7535656780012944e-05, "Missing Content": 4.2076466130863625e-05, "Knowledge Article": 5.740786829799993e-05, "Legal Notices": 5.9749927815739155e-05, "News Article": 4.9731395207311394e-06, "Organizational Announcement": 1.0857713595132201e-05, "Nonfiction Writing": 1.082224460184567e-05, "Organizational About Page": 1.2553807865129705e-05, "Personal About Page": 1.1197696062911328e-05, "Personal Blog": 7.656842496093727e-06, "Product Page": 8.742704225684671e-06, "Spam / Ads": 1.1223981076107405e-05, "Structured Data": 0.0003868707916605486, "Technical Writing": 0.997274480388797, "Transcript / Interview": 1.5987553528634188e-05, "Tutorial / How-To Guide": 0.000207419634291889, "User Reviews": 7.218363657383508e-06, "Other / Unknown": 0.001732264472136113}, "secondary": {"Academic Writing": 0.00012424574895596922, "Content Listing": 0.000555464017522114, "Creative Writing": 0.00011721659014665413, "Customer Support Page": 5.915399012120501e-05, "Q&A Forum": 4.349842566107588e-05, "Discussion Thread": 3.514422399924926e-05, "Comment Section": 0.000218067022620845, "Missing Content": 0.00036052298613328094, "Knowledge Article": 0.0002890456427466117, "Legal Notices": 0.0004591559350620858, "News Article": 5.5688124795923107e-05, "Organizational Announcement": 5.635890768953278e-05, "Nonfiction Writing": 5.388048553893429e-05, "Organizational About Page": 6.28030300577559e-05, "Personal About Page": 6.200423121536249e-05, "Personal Blog": 5.0906974901411264e-05, "Product Page": 3.8207951001636823e-05, "Spam / Ads": 6.933999688952028e-05, "Structured Data": 0.004203297894457889, "Technical Writing": 0.008024303883585875, "Transcript / Interview": 9.74777177979577e-05, "Tutorial / How-To Guide": 0.00093874068737423, "User Reviews": 3.325782977064118e-05, "NONE": 0.9839922177019542}, "tertiary": {"Academic Writing": 7.69385799754273e-05, "Content Listing": 0.0005031642333173794, "Creative Writing": 8.725727855247007e-05, "Customer Support Page": 4.698005807671792e-05, "Q&A Forum": 3.869950935502962e-05, "Discussion Thread": 2.587663912694655e-05, "Comment Section": 0.00034067832118343566, "Missing Content": 0.0003609475967744447, "Knowledge Article": 0.00027275469448706356, "Legal Notices": 0.0005897640086615707, "News Article": 4.233866405964745e-05, "Organizational Announcement": 3.660061734714229e-05, "Nonfiction Writing": 4.348503886646688e-05, "Organizational About Page": 5.4984238744054285e-05, "Personal About Page": 4.484185412374362e-05, "Personal Blog": 3.616034153549438e-05, "Product Page": 3.686657828496175e-05, "Spam / Ads": 7.823131684525331e-05, "Structured Data": 0.0017370087538304887, "Technical Writing": 0.004070407996764986, "Transcript / Interview": 7.922722989379437e-05, "Tutorial / How-To Guide": 0.0011764661249499377, "User Reviews": 2.5230496619360958e-05, "NONE": 0.9901950898286241}, "structured_records": {"false": 0.9957026161304734, "true": 0.004297383869526568}, "procedural_steps": {"false": 0.9989473662786338, "true": 0.0010526337213661988}, "worked_solution": {"false": 0.9985200437833647, "true": 0.0014799562166353001}, "contextual_dialogue": {"false": 0.9999913206424452, "true": 8.67935755475253e-06}, "content_quality": {"-2": 0.018248221945623342, "-1": 0.15551758049144573, "0": 0.40866615526948324, "1": 0.33610383236358815, "2": 0.08146420992985964}, "noise_severity": {"0": 0.8951968773821445, "1": 0.08514882908514786, "2": 0.01663868246188977, "3": 0.0018855697037239657, "4": 0.0011300413670936602}, "missing_content_severity": {"0": 0.9818534043061669, "1": 0.013420625571496854, "2": 0.0029899576906612267, "3": 0.0013327600223254263, "4": 0.0004032524093495476}, "factual_density": {"0": 0.023880014995239597, "1": 0.04086552153269045, "2": 0.14596273166587098, "3": 0.35956190532616794, "4": 0.42972982648003094}, "pretraining_utility": {"-2": 0.011216363738414198, "-1": 0.1324167314587468, "0": 0.43574157870100627, "1": 0.367654871660029, "2": 0.052970454441803615}, "math_density": {"0": 0.8711483554985627, "1": 0.12109371646514751, "2": 0.003735031898734854, "3": 0.0013122647323538497, "4": 0.002710631405201066}, "code_density": {"0": 0.00021499139564420453, "1": 0.0004063348285630934, "2": 0.011508879131815156, "3": 0.09366361795959424, "4": 0.8942061766843833}, "reasoning_density": {"0": 0.8451574033064385, "1": 0.07482557586154878, "2": 0.070974554417595, "3": 0.0061500404205234805, "4": 0.002892425993894195}, "education_level": {"none": 0.0014154250653720573, "0": 0.0015335279842890264, "1": 0.008293356784880293, "2": 0.6734251935383303, "3": 0.29242674953577025, "4": 0.022905747091358022}, "creative_writing_quality": {"not_applicable": 0.9993678958005187, "-2": 0.00014384295779494048, "-1": 0.00018012945467439325, "0": 0.00023330388319800128, "1": 5.1134308019356015e-05, "2": 2.369359579461407e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.89009858874447e-05, "Art & Design": 0.001424291105895091, "Law & Crime": 6.863302160613589e-05, "Education & Careers": 0.00033255708256026037, "Entertainment & Media": 4.126258308316854e-05, "Fashion & Beauty": 4.872408360971274e-05, "Business, Economics & Finance": 0.006627350619563875, "Food & Dining": 7.227392457898464e-05, "Games & Gaming": 0.00010868034385813752, "Electronics & Hardware": 0.00013462667634094844, "Health, Healthcare & Wellness": 0.0001422859442638654, "History & Geography": 4.87481826588186e-05, "Home, Pets & Hobbies": 3.498455078111235e-05, "Industry & Production": 5.58337560829392e-05, "Language, Literature & Philosophy": 6.517000269081784e-05, "Politics & Government": 4.0468274472870836e-05, "Religion & Spirituality": 4.368048499099201e-05, "Science, Mathematics & Engineering": 0.00010572003783857285, "Relationships & Community": 8.721238132070853e-05, "Software Usage & Applications": 0.0032289025582911807, "Programming & Software Engineering": 0.986152996420695, "Sports & Fitness": 6.463562532641232e-05, "Vehicles & Transportation": 3.547999308062908e-05, "Travel & Tourism": 5.0828608575909165e-05, "Other / Uncovered Subject": 0.000732810616762648, "Unknown / Insufficient Content": 0.00019294213518382752}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008186054699339784, "Art & Design": 0.03027865918605877, "Law & Crime": 0.0008025222828670943, "Education & Careers": 0.0007798103013031051, "Entertainment & Media": 0.0005449222855864299, "Fashion & Beauty": 0.00036703973664352466, "Business, Economics & Finance": 0.06275589411781032, "Food & Dining": 0.0003988929090464412, "Games & Gaming": 0.0005786992110752736, "Electronics & Hardware": 0.0006608416718803468, "Health, Healthcare & Wellness": 0.0007178350504412497, "History & Geography": 0.00027710091785483403, "Home, Pets & Hobbies": 0.00029984520729632434, "Industry & Production": 0.00027823016654316557, "Language, Literature & Philosophy": 0.00036743549094595706, "Politics & Government": 0.00032598384124898834, "Religion & Spirituality": 0.00038407939086435467, "Science, Mathematics & Engineering": 0.0007821180173741716, "Relationships & Community": 0.0004516481015180055, "Software Usage & Applications": 0.027978276476720538, "Programming & Software Engineering": 0.018172974328080484, "Sports & Fitness": 0.00024339935117202808, "Vehicles & Transportation": 0.00016497503756309693, "Travel & Tourism": 0.00031916636372353096, "Other / Uncovered Subject": 0.002824722212411934, "NONE": 0.8484263228740361}}, "probabilities": {"primary": {"Academic Writing": 3.8569690870603676e-05, "Content Listing": 2.9976378312500923e-05, "Creative Writing": 2.0301436075879455e-05, "Customer Support Page": 8.872424498787056e-06, "Q&A Forum": 1.5094604051534437e-05, "Discussion Thread": 8.1462136510892e-06, "Comment Section": 1.753565678001294e-05, "Missing Content": 4.207646613086362e-05, "Knowledge Article": 5.7407868297999915e-05, "Legal Notices": 5.974992781573914e-05, "News Article": 4.973139520731139e-06, "Organizational Announcement": 1.08577135951322e-05, "Nonfiction Writing": 1.0822244601845669e-05, "Organizational About Page": 1.2553807865129702e-05, "Personal About Page": 1.1197696062911326e-05, "Personal Blog": 7.656842496093725e-06, "Product Page": 8.74270422568467e-06, "Spam / Ads": 1.1223981076107404e-05, "Structured Data": 0.0003868707916605485, "Technical Writing": 0.9972744803887967, "Transcript / Interview": 1.5987553528634185e-05, "Tutorial / How-To Guide": 0.00020741963429188895, "User Reviews": 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"1": 0.040865521532690456, "2": 0.145962731665871, "3": 0.359561905326168, "4": 0.429729826480031}, "pretraining_utility": {"-2": 0.0112163637384142, "-1": 0.13241673145874683, "0": 0.4357415787010063, "1": 0.3676548716600291, "2": 0.05297045444180362}, "math_density": {"0": 0.8711483554985627, "1": 0.12109371646514751, "2": 0.003735031898734854, "3": 0.0013122647323538497, "4": 0.002710631405201066}, "code_density": {"0": 0.00021499139564420453, "1": 0.0004063348285630934, "2": 0.011508879131815156, "3": 0.09366361795959424, "4": 0.8942061766843833}, "reasoning_density": {"0": 0.8451574033064385, "1": 0.07482557586154878, "2": 0.070974554417595, "3": 0.0061500404205234805, "4": 0.002892425993894195}, "education_level": {"none": 0.0014154250653720573, "0": 0.0015335279842890264, "1": 0.008293356784880293, "2": 0.6734251935383303, "3": 0.29242674953577025, "4": 0.022905747091358022}, "creative_writing_quality": {"not_applicable": 0.9993678958005187, "-2": 0.00014384295779494048, "-1": 0.00018012945467439325, "0": 0.00023330388319800128, "1": 5.1134308019356015e-05, "2": 2.369359579461407e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.89009858874447e-05, "Art & Design": 0.001424291105895091, "Law & Crime": 6.863302160613589e-05, "Education & Careers": 0.00033255708256026037, "Entertainment & Media": 4.126258308316854e-05, "Fashion & Beauty": 4.872408360971274e-05, "Business, Economics & Finance": 0.006627350619563875, "Food & Dining": 7.227392457898464e-05, "Games & Gaming": 0.00010868034385813752, "Electronics & Hardware": 0.00013462667634094844, "Health, Healthcare & Wellness": 0.0001422859442638654, "History & Geography": 4.87481826588186e-05, "Home, Pets & Hobbies": 3.498455078111235e-05, "Industry & Production": 5.58337560829392e-05, "Language, Literature & Philosophy": 6.517000269081784e-05, "Politics & Government": 4.0468274472870836e-05, "Religion & Spirituality": 4.368048499099201e-05, "Science, Mathematics & Engineering": 0.00010572003783857285, "Relationships & Community": 8.721238132070853e-05, "Software Usage & Applications": 0.0032289025582911807, "Programming & Software Engineering": 0.986152996420695, "Sports & Fitness": 6.463562532641232e-05, "Vehicles & Transportation": 3.547999308062908e-05, "Travel & Tourism": 5.0828608575909165e-05, "Other / Uncovered Subject": 0.000732810616762648, "Unknown / Insufficient Content": 0.00019294213518382752}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008186054699339784, "Art & Design": 0.03027865918605877, "Law & Crime": 0.0008025222828670943, "Education & Careers": 0.0007798103013031051, "Entertainment & Media": 0.0005449222855864299, "Fashion & Beauty": 0.00036703973664352466, "Business, Economics & Finance": 0.06275589411781032, "Food & Dining": 0.0003988929090464412, "Games & Gaming": 0.0005786992110752736, "Electronics & Hardware": 0.0006608416718803468, "Health, Healthcare & Wellness": 0.0007178350504412497, "History & Geography": 0.00027710091785483403, "Home, Pets & Hobbies": 0.00029984520729632434, "Industry & Production": 0.00027823016654316557, "Language, Literature & Philosophy": 0.00036743549094595706, "Politics & Government": 0.00032598384124898834, "Religion & Spirituality": 0.00038407939086435467, "Science, Mathematics & Engineering": 0.0007821180173741716, "Relationships & Community": 0.0004516481015180055, "Software Usage & Applications": 0.027978276476720538, "Programming & Software Engineering": 0.018172974328080484, "Sports & Fitness": 0.00024339935117202808, "Vehicles & Transportation": 0.00016497503756309693, "Travel & Tourism": 0.00031916636372353096, "Other / Uncovered Subject": 0.002824722212411934, "NONE": 0.8484263228740361}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10730, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10730, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:07.288789+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "25676d75-bf65-4d0c-9136-e854425081bf", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "a16390859b1b8c43db4a1e3f75391e34876a8a39f67e532913f41125a1c53b44", "source_metadata": {"uuid": "25676d75-bf65-4d0c-9136-e854425081bf", "repo_name": "Innoverenit/hrcrm-nuboxed", "relative_path": "src/Containers/Main/Account/AccountActionLeft.js", "category": "WEB", "algo_rel_score": 0.9650604128837585, "quality_score": 5.544775009155273, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 5232, "repo_created_at": "2023-09-23T05:47:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "6aeea7cb5d8edbf99285420dcc7732be69dbf63e", "size_bytes": 9749, "file_timestamp": 1735184776, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ad7cb22bbb2d57133453d6b2eb04986e41b629c6"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 5232, "repo_created_at": "2023-09-23T05:47:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "6aeea7cb5d8edbf99285420dcc7732be69dbf63e", "size_bytes": 9749, "file_timestamp": 1735184776, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "ad7cb22bbb2d57133453d6b2eb04986e41b629c6", "url": "https://github.com/Innoverenit/hrcrm-nuboxed/blob/ad7cb22bbb2d57133453d6b2eb04986e41b629c6/src/Containers/Main/Account/AccountActionLeft.js"}, "gpt4o_tokens": 2021, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.307018227840615, "noise_severity": 0.12860306858847395, "missing_content_severity": 0.02501183065719378, "factual_density": 3.1303960067630605, "pretraining_utility": 0.3187463216080611, "math_density": 0.14334310008048304, "code_density": 3.8812396537085094, "reasoning_density": 0.246794509933886, "education_level": 2.3273411578447694, "creative_writing_quality": -0.5842294212864381}, "score_applicability": {"education_level": 0.9985845749346279, "creative_writing_quality": 0.000632104199481305}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/DreamMall-Verlag/dreammall-nostr-client/blob/0ad1b4934a68f13be79ff6b1b2e47e7e953d1478/src/services/UIManager.js", "id": "javascript-0-8eb43a3f60770b275fe75807cab86dd8", "text": "// =================================================================\n// UI Manager - Professional Chat Interface\n// =================================================================\n\nexport class UIManager {\n constructor() {\n this.components = new Map();\n this.modals = new Map();\n this.toast = null;\n this.eventListeners = new Map();\n this.messages = [];\n this.services = null;\n }\n\n async init() {\n console.log('🎨 Initialisiere Professional Chat UI...');\n \n // Create the professional chat interface\n await this.createChatInterface();\n \n // Initialize event listeners\n this.setupEventListeners();\n \n // Create toast container\n this.createToastContainer();\n \n console.log('✅ Professional Chat UI initialisiert');\n }\n\n setServices(services) {\n this.services = services;\n }\n\n async createChatInterface() {\n const appContainer = document.getElementById('app');\n if (!appContainer) {\n console.error('❌ App container nicht gefunden');\n return;\n }\n\n appContainer.innerHTML = `\n
\n \n
\n
\n

🌟 NOSTR Chat

\n
\n \n Offline\n
\n
\n
\n \n \n
\n
\n\n \n
\n \n
\n
\n

🔑 Schlüssel einrichten

\n

Um NOSTR zu nutzen, benötigen Sie ein Schlüsselpaar.

\n \n
\n
\n

Neue Schlüssel generieren

\n

Erstellen Sie ein neues Schlüsselpaar

\n \n
\n \n
\n

Schlüssel importieren

\n

Importieren Sie einen vorhandenen privaten Schlüssel

\n \n \n
\n
\n
\n
\n\n \n
\n
\n \n
\n
\n

💬 Kontakte

\n \n
\n
\n
\n
🌐
\n
\n
Öffentlicher Chat
\n
Online
\n
\n
\n
\n
\n\n \n
\n
\n
\n

Öffentlicher Chat

\n Online\n
\n
\n\n
\n
\n
🎉
\n

Willkommen im NOSTR Chat!

\n

Beginnen Sie eine Unterhaltung oder verbinden Sie sich mit Relays.

\n
\n
\n\n
\n \n \n
\n
\n
\n
\n
\n\n \n
\n
\n

Wird geladen...

\n
\n
\n `;\n\n console.log('✅ Chat Interface HTML erstellt');\n }\n\n getComponent(name) {\n return this.components.get(name);\n }\n\n // =================================================================\n // Screen Management\n // =================================================================\n\n showLoading() {\n this.hideAllScreens();\n this.showComponent('loadingScreen');\n }\n\n showSetup() {\n this.hideAllScreens();\n this.showComponent('setupScreen');\n }\n\n showMainApp() {\n this.hideAllScreens();\n this.showComponent('mainApp');\n }\n\n hideAllScreens() {\n ['loadingScreen', 'setupScreen', 'mainApp'].forEach(screen => {\n this.hideComponent(screen);\n });\n }\n\n showComponent(name) {\n const component = this.getComponent(name);\n if (component) {\n component.style.display = 'block';\n component.classList.remove('hidden');\n }\n }\n\n hideComponent(name) {\n const component = this.getComponent(name);\n if (component) {\n component.style.display = 'none';\n component.classList.add('hidden');\n }\n }\n\n // =================================================================\n // Event Listeners\n // =================================================================\n\n setupEventListeners() {\n console.log('🔗 Setze Event Listeners...');\n \n // Key setup handlers\n this.addEventListenerSafe('generateKeysBtn', 'click', () => {\n this.handleGenerateKeys();\n });\n\n this.addEventListenerSafe('importKeysBtn', 'click', () => {\n this.handleImportKeys();\n });\n\n // Chat handlers\n this.addEventListenerSafe('sendMessageBtn', 'click', () => {\n this.handleSendMessage();\n });\n\n this.addEventListenerSafe('messageInput', 'keypress', (e) => {\n if (e.key === 'Enter') {\n this.handleSendMessage();\n }\n });\n\n // Settings handlers\n this.addEventListenerSafe('settingsBtn', 'click', () => {\n this.showSettings();\n });\n\n this.addEventListenerSafe('profileBtn', 'click', () => {\n this.showProfile();\n });\n\n console.log('✅ Event Listeners konfiguriert');\n }\n\n addEventListenerSafe(elementId, event, handler) {\n const element = document.getElementById(elementId);\n if (element) {\n element.addEventListener(event, handler);\n console.log(`✅ Event Listener für ${elementId} hinzugefügt`);\n } else {\n console.warn(`⚠️ Element ${elementId} nicht gefunden`);\n }\n }\n\n // Screen Management\n showScreen(screenId) {\n document.querySelectorAll('.screen').forEach(screen => {\n screen.classList.add('hidden');\n });\n const targetScreen = document.getElementById(screenId);\n if (targetScreen) {\n targetScreen.classList.remove('hidden');\n }\n }\n\n showKeySetup() {\n this.showScreen('keySetupScreen');\n console.log('🔑 Zeige Schlüssel-Setup Screen');\n }\n\n showChatInterface() {\n this.showScreen('chatInterface');\n console.log('💬 Zeige Chat Interface');\n }\n\n showLoading(text = 'Wird geladen...') {\n const overlay = document.getElementById('loadingOverlay');\n const loadingText = document.getElementById('loadingText');\n \n if (loadingText) loadingText.textContent = text;\n if (overlay) overlay.classList.remove('hidden');\n }\n\n hideLoading() {\n const overlay = document.getElementById('loadingOverlay');\n if (overlay) overlay.classList.add('hidden');\n }\n\n // Event Handlers\n async handleGenerateKeys() {\n if (!this.services || !this.services.key) {\n console.error('❌ KeyService nicht verfügbar');\n return;\n }\n\n try {\n this.showLoading('Generiere Schlüssel...');\n const keyPair = await this.services.key.generateKeyPair();\n this.hideLoading();\n \n if (keyPair) {\n this.services.toast?.success('✅ Schlüssel erfolgreich generiert!');\n this.showChatInterface();\n \n // Connect to relays\n if (this.services.relay) {\n await this.services.relay.connect();\n }\n }\n } catch (error) {\n this.hideLoading();\n console.error('❌ Fehler beim Generieren der Schlüssel:', error);\n this.services.toast?.error('Fehler beim Generieren der Schlüssel: ' + error.message);\n }\n }\n\n async handleImportKeys() {\n const nsecInput = document.getElementById('importNsecInput');\n if (!nsecInput) return;\n\n const nsec = nsecInput.value.trim();\n if (!nsec) {\n this.services.toast?.error('Bitte geben Sie einen privaten Schlüssel ein');\n return;\n }\n\n if (!this.services || !this.services.key) {\n console.error('❌ KeyService nicht verfügbar');\n return;\n }\n\n try {\n this.showLoading('Importiere Schlüssel...');\n const keyPair = await this.services.key.importKeys(nsec);\n this.hideLoading();\n \n if (keyPair) {\n this.services.toast?.success('✅ Schlüssel erfolgreich importiert!');\n this.showChatInterface();\n \n // Connect to relays\n if (this.services.relay) {\n await this.services.relay.connect();\n }\n }\n } catch (error) {\n this.hideLoading();\n console.error('❌ Fehler beim Importieren der Schlüssel:', error);\n this.services.toast?.error('Fehler beim Importieren der Schlüssel: ' + error.message);\n }\n }\n\n async handleSendMessage() {\n const messageInput = document.getElementById('messageInput');\n if (!messageInput) return;\n\n const message = messageInput.value.trim();\n if (!message) return;\n\n try {\n // Send message through NOSTR service\n if (this.services && this.services.nostr) {\n await this.services.nostr.sendMessage(message);\n }\n \n // Clear input\n messageInput.value = '';\n \n // Add to local messages\n this.addMessage({\n content: message,\n timestamp: Date.now(),\n sender: 'self',\n type: 'text'\n });\n \n } catch (error) {\n console.error('❌ Fehler beim Senden der Nachricht:', error);\n this.services.toast?.error('Fehler beim Senden der Nachricht: ' + error.message);\n }\n }\n\n addMessage(message) {\n this.messages.push(message);\n this.renderMessages();\n }\n\n renderMessages() {\n const container = document.getElementById('messagesContainer');\n if (!container) return;\n\n // Clear welcome message if it exists\n const welcomeMessage = container.querySelector('.welcome-message');\n if (welcomeMessage && this.messages.length > 0) {\n welcomeMessage.remove();\n }\n\n if (this.messages.length === 0) return;\n\n container.innerHTML = this.messages.map(msg => {\n const isOwnMessage = msg.sender === 'self';\n const time = new Date(msg.timestamp).toLocaleTimeString();\n \n return `\n
\n
\n
${this.escapeHtml(msg.content)}
\n
${time}
\n
\n
\n `;\n }).join('');\n\n // Scroll to bottom\n container.scrollTop = container.scrollHeight;\n }\n\n escapeHtml(text) {\n const div = document.createElement('div');\n div.textContent = text;\n return div.innerHTML;\n }\n\n updateConnectionStatus(status) {\n const statusElement = document.getElementById('connectionStatus');\n if (!statusElement) return;\n\n const dot = statusElement.querySelector('.status-dot');\n const text = statusElement.querySelector('.status-text');\n\n if (status.connected > 0) {\n if (dot) dot.className = 'status-dot online';\n if (text) text.textContent = `Online (${status.connected} Relays)`;\n } else {\n if (dot) dot.className = 'status-dot offline';\n if (text) text.textContent = 'Offline';\n }\n }\n\n showSettings() {\n this.services.toast?.info('Einstellungen werden geladen...');\n // TODO: Implement settings modal\n }\n\n showProfile() {\n this.services.toast?.info('Profil wird geladen...');\n // TODO: Implement profile modal\n }\n\n // =================================================================\n // Modal Management\n // =================================================================\n\n createModal(id, title, content, options = {}) {\n const modal = document.createElement('div');\n modal.className = 'modal';\n modal.id = `modal-${id}`;\n \n modal.innerHTML = `\n
\n
\n
\n

${title}

\n \n
\n
\n ${content}\n
\n ${options.footer ? `
${options.footer}
` : ''}\n
\n
\n `;\n\n document.body.appendChild(modal);\n this.modals.set(id, modal);\n\n // Add event listeners\n modal.addEventListener('click', (e) => {\n if (e.target.dataset.closeModal === id || e.target.closest(`[data-close-modal=\"${id}\"]`)) {\n this.closeModal(id);\n }\n });\n\n return modal;\n }\n\n showModal(titleOrId, content = null, buttons = null) {\n // If called with just ID (legacy usage)\n if (content === null && buttons === null) {\n const modal = this.modals.get(titleOrId);\n if (modal) {\n modal.style.display = 'flex';\n document.body.style.overflow = 'hidden';\n \n // Focus trap\n setTimeout(() => {\n const firstFocusable = modal.querySelector('input, button, select, textarea, [tabindex]:not([tabindex=\"-1\"])');\n if (firstFocusable) {\n firstFocusable.focus();\n }\n }, 100);\n }\n return;\n }\n\n // New usage: showModal(title, content, buttons)\n const modalId = 'dynamic-modal';\n \n // Remove existing modal if present\n this.removeModal(modalId);\n \n // Create new modal\n const modal = document.createElement('div');\n modal.className = 'modal-overlay';\n modal.id = `modal-${modalId}`;\n \n modal.innerHTML = `\n
\n
\n

${titleOrId}

\n \n
\n
\n ${content}\n
\n \n
\n `;\n \n // Add to DOM\n document.body.appendChild(modal);\n this.modals.set(modalId, modal);\n \n // Add buttons if provided\n if (buttons && Array.isArray(buttons)) {\n const footer = modal.querySelector(`#modal-footer-${modalId}`);\n buttons.forEach(button => {\n const btn = document.createElement('button');\n btn.className = `btn ${button.class || 'btn-secondary'}`;\n btn.textContent = button.text || 'OK';\n btn.addEventListener('click', () => {\n if (button.click) {\n button.click();\n }\n });\n footer.appendChild(btn);\n });\n }\n \n // Setup close handlers\n const closeButton = modal.querySelector('.modal-close');\n closeButton.addEventListener('click', () => {\n this.hideModal();\n });\n \n // Click outside to close\n modal.addEventListener('click', (e) => {\n if (e.target === modal) {\n this.hideModal();\n }\n });\n \n // ESC key to close\n const escHandler = (e) => {\n if (e.key === 'Escape') {\n this.hideModal();\n document.removeEventListener('keydown', escHandler);\n }\n };\n document.addEventListener('keydown', escHandler);\n \n // Show modal\n modal.style.display = 'flex';\n document.body.style.overflow = 'hidden';\n \n // Focus trap\n setTimeout(() => {\n const firstFocusable = modal.querySelector('input, button, select, textarea, [tabindex]:not([tabindex=\"-1\"])');\n if (firstFocusable) {\n firstFocusable.focus();\n }\n }, 100);\n }\n\n hideModal() {\n const modal = this.modals.get('dynamic-modal');\n if (modal) {\n modal.remove();\n this.modals.delete('dynamic-modal');\n document.body.style.overflow = '';\n }\n }\n\n closeModal(id) {\n const modal = this.modals.get(id);\n if (modal) {\n modal.style.display = 'none';\n document.body.style.overflow = '';\n }\n }\n\n removeModal(id) {\n const modal = this.modals.get(id);\n if (modal) {\n modal.remove();\n this.modals.delete(id);\n }\n }\n\n // =================================================================\n // Toast Notifications\n // =================================================================\n\n createToastContainer() {\n if (!document.getElementById('toast-container')) {\n const container = document.createElement('div');\n container.id = 'toast-container';\n container.className = 'toast-container';\n document.body.appendChild(container);\n this.toast = container;\n }\n }\n\n showToast(message, type = 'info', duration = 5000) {\n if (!this.toast) this.createToastContainer();\n\n const toast = document.createElement('div');\n toast.className = `toast toast-${type}`;\n \n const icon = this.getToastIcon(type);\n toast.innerHTML = `\n
\n \n ${message}\n \n
\n `;\n\n // Add to container\n this.toast.appendChild(toast);\n\n // Show toast\n setTimeout(() => toast.classList.add('show'), 100);\n\n // Auto remove\n if (duration > 0) {\n setTimeout(() => this.removeToast(toast), duration);\n }\n\n // Close button\n toast.querySelector('.toast-close').addEventListener('click', () => {\n this.removeToast(toast);\n });\n\n return toast;\n }\n\n removeToast(toast) {\n toast.classList.remove('show');\n setTimeout(() => {\n if (toast.parentNode) {\n toast.parentNode.removeChild(toast);\n }\n }, 300);\n }\n\n getToastIcon(type) {\n const icons = {\n success: 'fas fa-check-circle',\n error: 'fas fa-exclamation-circle',\n warning: 'fas fa-exclamation-triangle',\n info: 'fas fa-info-circle'\n };\n return icons[type] || icons.info;\n }\n\n // =================================================================\n // Message Handling\n // =================================================================\n\n handleMessageInput(e) {\n const input = e.target;\n const sendButton = this.getComponent('sendButton');\n \n if (sendButton) {\n sendButton.disabled = !input.value.trim();\n }\n\n // Auto-resize textarea\n input.style.height = 'auto';\n input.style.height = Math.min(input.scrollHeight, 120) + 'px';\n }\n\n sendMessage() {\n const messageInput = this.getComponent('messageInput');\n const message = messageInput?.value?.trim();\n \n if (!message) return;\n\n // Emit event for app to handle\n this.emit('sendMessage', { message });\n \n // Clear input\n messageInput.value = '';\n messageInput.style.height = 'auto';\n \n // Disable send button\n const sendButton = this.getComponent('sendButton');\n if (sendButton) {\n sendButton.disabled = true;\n }\n }\n\n // =================================================================\n // Room Management UI\n // =================================================================\n\n renderRoomList(rooms, currentRoomId = null) {\n const roomList = this.getComponent('roomList');\n if (!roomList) return;\n\n roomList.innerHTML = '';\n\n rooms.forEach(room => {\n const roomElement = document.createElement('div');\n roomElement.className = `room-item ${room.id === currentRoomId ? 'active' : ''}`;\n roomElement.dataset.roomId = room.id;\n \n roomElement.innerHTML = `\n
\n \n
\n
\n
${this.escapeHtml(room.name)}
\n
${room.lastMessage || 'Noch keine Nachrichten'}
\n
\n
\n ${room.unreadCount ? `${room.unreadCount}` : ''}\n ${this.formatTime(room.lastActivity)}\n
\n `;\n\n roomElement.addEventListener('click', () => {\n this.emit('roomSelected', { roomId: room.id });\n });\n\n roomList.appendChild(roomElement);\n });\n }\n\n // =================================================================\n // Message Display\n // =================================================================\n\n renderMessages(messages, currentUserPubkey) {\n const messageContainer = this.getComponent('messageContainer');\n if (!messageContainer) return;\n\n // Clear welcome message\n messageContainer.innerHTML = '';\n\n if (messages.length === 0) {\n messageContainer.innerHTML = `\n
\n

Willkommen im Chat! 🎉

\n

Schreibe eine Nachricht, um die Unterhaltung zu beginnen.

\n
\n `;\n return;\n }\n\n messages.forEach(message => {\n const messageElement = this.createMessageElement(message, currentUserPubkey);\n messageContainer.appendChild(messageElement);\n });\n\n // Scroll to bottom\n this.scrollToBottom();\n }\n\n createMessageElement(message, currentUserPubkey) {\n const isOwnMessage = message.authorPubkey === currentUserPubkey;\n \n const messageElement = document.createElement('div');\n messageElement.className = `message ${isOwnMessage ? 'own' : ''}`;\n messageElement.dataset.messageId = message.id;\n \n const avatar = isOwnMessage ? '👤' : '👥';\n const authorName = message.author || 'Unbekannt';\n const timestamp = new Date(message.timestamp).toLocaleTimeString();\n \n messageElement.innerHTML = `\n
${avatar}
\n
\n
\n ${this.escapeHtml(authorName)}\n ${timestamp}\n
\n
${this.escapeHtml(message.content)}
\n ${message.encrypted ? '
🔒 Verschlüsselt
' : ''}\n
\n `;\n\n return messageElement;\n }\n\n addMessage(message, currentUserPubkey) {\n const messageContainer = this.getComponent('messageContainer');\n if (!messageContainer) return;\n\n const messageElement = this.createMessageElement(message, currentUserPubkey);\n messageContainer.appendChild(messageElement);\n \n this.scrollToBottom();\n }\n\n scrollToBottom() {\n const messageContainer = this.getComponent('messageContainer');\n if (messageContainer) {\n messageContainer.scrollTop = messageContainer.scrollHeight;\n }\n }\n\n // =================================================================\n // Status Updates\n // =================================================================\n\n updateConnectionStatus(status, relayCount = 0) {\n const statusIndicator = this.getComponent('connectionStatus');\n const statusText = this.getComponent('connectionText');\n \n if (!statusIndicator || !statusText) return;\n\n const statusConfig = {\n connected: { class: 'online', text: `Verbunden (${relayCount} Relays)` },\n connecting: { class: 'connecting', text: 'Verbinde...' },\n disconnected: { class: 'offline', text: 'Getrennt' }\n };\n\n const config = statusConfig[status] || statusConfig.disconnected;\n \n statusIndicator.className = `status-indicator ${config.class}`;\n statusText.textContent = config.text;\n }\n\n updateUserProfile(profile) {\n const userName = this.getComponent('userName');\n const userNpub = this.getComponent('userNpub');\n \n if (userName) {\n userName.textContent = profile.name || 'DreamMall User';\n }\n \n if (userNpub) {\n userNpub.textContent = profile.npub ? profile.npub.slice(0, 16) + '...' : 'npub...';\n }\n }\n\n escapeHtml(text) {\n const div = document.createElement('div');\n div.textContent = text;\n return div.innerHTML;\n }\n\n // =================================================================\n // User Interface Updates\n // =================================================================\n\n updateUserInfo(profile) {\n const userNameElement = this.getComponent('user-name');\n const userNpubElement = this.getComponent('user-npub');\n \n if (userNameElement) {\n userNameElement.textContent = profile.displayName || profile.name || 'Benutzer';\n }\n \n if (userNpubElement) {\n const npub = profile.npub || 'npub...';\n userNpubElement.textContent = npub.substring(0, 16) + '...';\n userNpubElement.title = npub; // Full npub on hover\n }\n \n console.log('✅ Benutzer-Info aktualisiert:', profile);\n }\n\n updateRoomsList(rooms) {\n const roomsListElement = this.getComponent('rooms-list');\n if (!roomsListElement) return;\n \n roomsListElement.innerHTML = '';\n \n if (rooms.size === 0) {\n roomsListElement.innerHTML = `\n
\n

Keine Räume vorhanden

\n \n
\n `;\n return;\n }\n \n rooms.forEach(room => {\n const roomElement = document.createElement('div');\n roomElement.className = 'room-item';\n roomElement.dataset.roomId = room.id;\n \n roomElement.innerHTML = `\n
\n ${room.name.charAt(0).toUpperCase()}\n
\n
\n
${room.name}
\n
\n ${room.lastMessage || 'Noch keine Nachrichten'}\n
\n
\n
\n \n ${room.lastActivity ? this.formatTime(room.lastActivity) : ''}\n \n ${room.unreadCount > 0 ? `${room.unreadCount}` : ''}\n
\n `;\n \n roomElement.addEventListener('click', () => {\n this.selectRoom(room.id);\n });\n \n roomsListElement.appendChild(roomElement);\n });\n \n console.log('✅ Räume-Liste aktualisiert:', rooms.size, 'Räume');\n }\n\n selectRoom(roomId) {\n // Remove active class from all rooms\n document.querySelectorAll('.room-item').forEach(item => {\n item.classList.remove('active');\n });\n \n // Add active class to selected room\n const selectedRoom = document.querySelector(`[data-room-id=\"${roomId}\"]`);\n if (selectedRoom) {\n selectedRoom.classList.add('active');\n }\n \n // Update chat header\n const room = this.findRoomById(roomId);\n if (room) {\n const roomNameElement = document.getElementById('current-room-name');\n const roomUsersElement = document.getElementById('current-room-users');\n \n if (roomNameElement) {\n roomNameElement.textContent = room.name;\n }\n \n if (roomUsersElement) {\n roomUsersElement.textContent = `${room.members?.length || 0} Benutzer`;\n }\n }\n \n // Emit room change event\n this.emit('roomChanged', { roomId });\n }\n\n formatTime(timestamp) {\n if (!timestamp) return '';\n const date = new Date(timestamp);\n const now = new Date();\n const diff = now - date;\n \n if (diff < 60000) return 'gerade eben';\n if (diff < 3600000) return `${Math.floor(diff / 60000)}m`;\n if (diff < 86400000) return `${Math.floor(diff / 3600000)}h`;\n return date.toLocaleDateString();\n }\n\n findRoomById(roomId) {\n // This would need to be connected to the actual rooms data\n // For now, return a placeholder\n return { id: roomId, name: 'Raum', members: [] };\n }\n\n // =================================================================\n // Event System\n // =================================================================\n\n emit(eventName, data) {\n const event = new CustomEvent(eventName, { detail: data });\n document.dispatchEvent(event);\n console.log('📡 Event emitted:', eventName, data);\n }\n\n // =================================================================\n // Loading States\n // =================================================================\n\n showLoadingInComponent(componentName, message = 'Lädt...') {\n const component = this.getComponent(componentName);\n if (!component) return;\n\n const loader = document.createElement('div');\n loader.className = 'component-loader';\n loader.innerHTML = `\n
\n \n ${message}\n
\n `;\n\n component.appendChild(loader);\n return loader;\n }\n\n hideLoadingInComponent(componentName) {\n const component = this.getComponent(componentName);\n if (!component) return;\n\n const loader = component.querySelector('.component-loader');\n if (loader) {\n loader.remove();\n }\n }\n\n // =================================================================\n // Focus Management\n // =================================================================\n\n focusMessageInput() {\n const messageInput = this.getComponent('messageInput');\n if (messageInput) {\n messageInput.focus();\n }\n }\n\n clearMessageInput() {\n const messageInput = this.getComponent('messageInput');\n if (messageInput) {\n messageInput.value = '';\n messageInput.style.height = 'auto';\n }\n }\n\n // =================================================================\n // Loading State Management\n // =================================================================\n\n showLoadingState(message = 'Laden...') {\n const loadingScreen = this.getComponent('loadingScreen');\n if (loadingScreen) {\n const loadingMessage = loadingScreen.querySelector('.loading-message');\n if (loadingMessage) {\n loadingMessage.textContent = message;\n }\n loadingScreen.classList.add('active');\n }\n }\n\n hideLoadingState() {\n const loadingScreen = this.getComponent('loadingScreen');\n if (loadingScreen) {\n loadingScreen.classList.remove('active');\n }\n }\n}\n", "text_preview": "// ================================================================= // UI Manager - Professional Chat Interface // ================================================================= export class UIManager { constructor() { this.components =", "input_sha256": "bac01bda868f74da05eb09a2cbddcbe84d869d5d52d669326bfd1149aecd14f8", "text_sha256": "8eb43a3f60770b275fe75807cab86dd883c4da90cf40883516a65ce863a5c951", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8106, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3735552146161734, "noise_severity": 0.7172866619911047, "missing_content_severity": 0.2580443207490541, "factual_density": 2.608551221271643, "pretraining_utility": 0.3206499036356904, "math_density": 0.1359580431681069, "code_density": 3.6461333730207897, "reasoning_density": 0.24712014916207314, "education_level": 2.039944584171031, "creative_writing_quality": -0.21572217340215225}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.019925436094332977, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9966540141773953, "secondary": 0.9234350516712208, "tertiary": 0.9422278145094316, "structured_records": 0.04107235837998389, "procedural_steps": 0.03407813041791495, "worked_solution": 0.0049123981320909615, "contextual_dialogue": 9.254921309207347e-06, "content_quality": 0.32120492800877465, "noise_severity": 0.4022611150074129, "missing_content_severity": 0.7849630660424549, "factual_density": 0.31190119443694564, "pretraining_utility": 0.4357382919381837, "math_density": 0.8867016306932443, "code_density": 0.705111144183991, "reasoning_density": 0.794066542364939, "education_level": 0.7996281499522009, "creative_writing_quality": 0.9967193575514375, "primary_topic": 0.9890224398010672, "secondary_topic": 0.8158598342261236}, "probability_margin": {"primary": 0.9948144596470386, "secondary": 0.8847572329693156, "tertiary": 0.922692400116934, "education_level": 0.7369455669781431, "creative_writing_quality": 0.9954160538246436, "primary_topic": 0.9868852433393202, "secondary_topic": 0.7447218218189301}, "raw_probabilities": {"primary": {"Academic Writing": 3.265908745696752e-05, "Content Listing": 3.349002711078914e-05, "Creative Writing": 2.477470549593074e-05, "Customer Support Page": 1.7313775909144567e-05, "Q&A Forum": 1.1615270294982592e-05, 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"detected_licenses": []}, "commit_id": "0ad1b4934a68f13be79ff6b1b2e47e7e953d1478", "url": "https://github.com/DreamMall-Verlag/dreammall-nostr-client/blob/0ad1b4934a68f13be79ff6b1b2e47e7e953d1478/src/services/UIManager.js"}, "gpt4o_tokens": 6957, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.3735552146161734, "noise_severity": 0.7172866619911047, "missing_content_severity": 0.2580443207490541, "factual_density": 2.608551221271643, "pretraining_utility": 0.3206499036356904, "math_density": 0.1359580431681069, "code_density": 3.6461333730207897, "reasoning_density": 0.24712014916207317, "education_level": 2.039944584171031, "creative_writing_quality": -0.21572217340215225}, "score_applicability": {"education_level": 0.998382611298676, "creative_writing_quality": 0.002733868707135385}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/karem-Ismael/future-wensite/blob/d05587bff0093c368b976a153358f4a771462572/src/Components/ProfileInputs.js", "id": "javascript-0-0025dd1a5fba107f08c8784beec9c73b", "text": "import { FormGroup, Label, Input } from \"reactstrap\";\nimport { Button, Modal, ModalHeader, ModalBody, ModalFooter } from 'reactstrap';\nimport React from \"react\";\nimport ImageUpload from \"./ImageUpload\";\nimport { useState } from \"react\";\nimport axios from \"axios\";\nimport FileUpload from \"./FileUploader\";\nimport { useEffect } from \"react\";\nimport Select, { components } from \"react-select\";\nimport { useRouter } from \"next/router\";\nimport {AddProfileImage, GetProfileInfo} from \"../store/Profile/action\"\nimport { useDispatch } from \"react-redux\";\nconst client = axios.create({\n baseURL: \"https://estithmar.arabia-it.net/api\",\n});\nconst ProfileInputs = ({ownerDetails}) => {\n const [loader, setLoader] = useState(false);\n const [loader1, setLoader1] = useState(false);\n const [loader2, setLoader2] = useState(false);\n const [loader3, setLoader3] = useState(false);\n const [loader4, setLoader4] = useState(false);\n\n const dispatch=useDispatch()\n const [EnImage, setEnImage] = useState();\n const [firstImage,setFirstImage]=useState()\n const [secondImage,setSecondImage]=useState()\n const [thirdImage,setThirdImage]=useState()\n const [forthImage,setForthImage]=useState()\n const [data,setData]=useState()\n const [files,setFiles]=useState([])\n const [modal,setModal]=useState()\nconst toggle=()=>setModal(!modal)\n const history=useRouter()\n// const {id} =useParams()\nconst SavePassword =()=>{\n toggle()\n} \nuseEffect(()=>{\n if(localStorage.getItem(\"token\")){\n client.get(`/asset-owner/me?token=${localStorage.getItem(\"token\")}`).then((data)=>{\n dispatch(GetProfileInfo(data.data.data))\n setData({\n ...data.data.data.asset_owner ,\n name:data.data.data.name,\n email:data.data.data.email,\n }) \n const profile=data?.data?.data?.asset_owner?.files?.find(file=>file.title==\"profile\")?.path\n const firstImage=data?.data?.data?.asset_owner?.files?.find(file=>file.title==\"صك الوقفية\")?.path\n const secondImage =data?.data?.data?.asset_owner?.files?.find(file=>file.title==\"الشهادة الضريبية\")?.path\n const thirdImage =data?.data?.data?.asset_owner?.files?.find(file=>file.title==\"عقد التأسيس\")?.path\n const forthImage=data?.data?.data?.asset_owner?.files?.find(file=>file.title==\"دورة عمل الوقف\")?.path\n \n setEnImage(profile ? \"https://estithmar.arabia-it.net/\"+ profile : null)\n dispatch(AddProfileImage(\"https://estithmar.arabia-it.net/\"+ profile))\n setFirstImage(firstImage ? \"https://estithmar.arabia-it.net/\"+firstImage :null)\n setSecondImage(secondImage ? \"https://estithmar.arabia-it.net/\"+secondImage : null)\n setThirdImage(thirdImage ? \"https://estithmar.arabia-it.net/\"+ thirdImage: null)\n setForthImage(forthImage ? \"https://estithmar.arabia-it.net/\" + forthImage :null)\n })\n \n\n \n \n }\n},[])\n\n const uploadEnimage = (file) => {\n setLoader(true);\n const formdata = new FormData();\n formdata.append(\"title\", \"profile\");\n formdata.append(\"store_file\", true);\n formdata.append(\"file\", file);\n formdata.append(\"token\",localStorage.getItem(\"token\"))\n client\n .post(\"/asset-owner/files\", formdata, {\n headers: {\n \"Content-Type\": \"multipart/form-data; \",\n },\n })\n .then((res) => {\n setLoader(false);\n setFiles([...files,res.data.data.id])\n setEnImage(\"https://estithmar.arabia-it.net\" + res.data.data.path);\n });\n };\n const UploadFile=(file,name)=>{\n if(name==\"first\"){\n setLoader1(true)\n }else if(name==\"second\"){\n setLoader2(true);\n } else if(name==\"third\"){\n setLoader3(true);\n }\n else{\n setLoader4(true)\n }\n const formdata = new FormData();\n formdata.append(\"title\", name== \"first\" ? \"صك الوقفية\" : name==\"second\" ? \"الشهادة الضريبية\":name == \"third\" ?\"عقد التأسيس\" : \"دورة عمل الوقف\");\n formdata.append(\"store_file\", true);\n formdata.append(\"file\", file);\n formdata.append(\"token\",localStorage.getItem(\"token\"))\n\n client\n .post(\"/asset-owner/files\", formdata, {\n headers: {\n \"Content-Type\": \"multipart/form-data; \",\n },\n })\n .then((res) => {\n if(name==\"first\"){\n setLoader1(false)\n }else if(name ==\"second\"){\n setLoader2(false);\n \n } else if(name==\"third\"){\n setLoader3(false);\n \n }\n else{\n setLoader4(false)\n }\n if(name == \"first\"){\n setFirstImage(\"https://estithmar.arabia-it.net\" + res.data.data.path);\n setFiles([...files,res.data.data.id])\n \n }else if (name== \"second\"){\n setSecondImage(\"https://estithmar.arabia-it.net\" + res.data.data.path)\n setFiles([...files,res.data.data.id])\n } else if(name==\"third\"){\n setThirdImage(\"https://estithmar.arabia-it.net\" + res.data.data.path)\n setFiles([...files,res.data.data.id])\n }else{\n setForthImage(\"https://estithmar.arabia-it.net\" + res.data.data.path)\n setFiles([...files,res.data.data.id])\n }\n });\n }\n const AddAssetsOwner=()=>{\n client.post(\"/asset-owner\",{\n ...data,\n files_ids:files,\n password:\"\",\n password_confirmation : \"\",\n\n }).then(res=>{\n if(res.data.success){\n \n // swal({\n // title: \"\",\n // text:\" تم اضافه الوقف بنجاح\",\n // icon: \"success\",\n // });\n }\n }).then(()=>{\n setTimeout(()=>{\n // history.push(\"/app/owners-assets\")\n },2000)\n })\n }\n const EditAssetsOwner=()=>{\n\n client.post(`asset-owner/update`,{\n ...data,\n files_ids:files,\n type : \"update\",\n user:undefined,\n files:undefined,\n created_at:undefined,\n updated_at:undefined,\n token:localStorage.getItem(\"token\")\n }).then((res)=>{\n if(res.data.success){\n // swal({\n // title: \"\",\n // text:\" تم تعديل الوقف بنجاح\",\n // icon: \"success\",\n // });\n setTimeout(()=>{\n // history.push(\"/app/owners-assets\")\n },2000)\n }\n })\n }\n return (\n <>\n
\n
\n
\n

بيانات الحساب

\n \n \n setData({\n ...data,\n name:e.target.value\n })}\n />\n \n
\n
\n \n \n setData({\n ...data,\n email:e.target.value\n })}\n />\n \n
\n
\n \n \n setData({\n ...data,\n phone:e.target.value\n })}\n />\n \n
\n
\n
\n
\n تحديث كلمة المرور\n من خلال الزر أدناه سيتم\n
\n إعادة توجيهك إلى صفحة جديدة ويجب اتباع\n
\n التعليمات\n
\n
\n \n
\n
\n
\n {\n uploadEnimage(file);\n }}\n />\n
\n
\n
\n
\n

\n بيانات الوقف\n

\n \n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n asset_name_ar:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n asset_name_en:e.target.value\n })\n }}\n />\n \n
\n
\n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n owner_name:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n owner_family:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n asset_nom:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n export_date:e.target.value\n })\n }}\n />\n \n
\n
\n \n \n {\n \n setData({\n ...data,\n exported_by:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n
\n
\n
\n \n \n الوقف\"}\n type=\"text\"\n defaultValue={data?.manager_name}\n value={data?.manager_name}\n onChange={(e)=>{\n \n setData({\n ...data,\n manager_name:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n \n \n الوقفية\"}\n type=\"text\"\n defaultValue={data?.civil_registry_nom}\n value={data?.civil_registry_nom}\n onChange={(e)=>{\n \n setData({\n ...data,\n civil_registry_nom:e.target.value\n })\n }}\n \n />\n \n
\n
\n
\n
\n
\n
\n \n \n {\n setData({\n ...data,\n asset_type:sel.value\n })\n }}\n value={\n [\n {\n label:\"اسهم\",\n value:\"اسهم\"\n }\n ].find(option=>option.value == data?.asset_type)\n }\n />\n \n
\n
\n
\n
\n\n
\n
\n \n \n \n city.value == data?.city)\n }\n onChange={(sel)=>{\n setData({\n ...data,\n city:sel.value\n })\n \n }}\n />\n\n \n
\n
\n\n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n district:e.target.value\n })\n }}\n \n style={{ borderColor: \"#D4B265\" }}\n />\n \n \n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n street:e.target.value\n })\n }}\n \n />\n \n \n
\n
\n\n\n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n building_nom:e.target.value\n })\n }}\n \n />\n \n \n
\n
\n
\n
\n \n \n {\n \n setData({\n ...data,\n post_nom:e.target.value\n })\n }}\n \n style={{ borderColor: \"#D4B265\" }}\n />\n \n \n
\n
\n\n\n
\n
\n
\n \n \n
\n
\n {\n UploadFile(file,\"first\");\n }}\n />\n
\n
\n {\n UploadFile(file,\"second\");\n }}\n />\n
\n\n
\n
\n
\n {\n UploadFile(file,\"third\");\n }}\n />\n
\n
\n {\n UploadFile(file,\"forth\");\n }}\n />\n
\n\n
\n \n \n
\n تعيين كلمة مرور \n
\n \n \n
\n \n \n {\n setData({\n ...data,\n password:e.target.value\n\n })\n }}\n />\n \n
\n
\n \n \n {\n setData({\n ...data,\n password_confirmation:e.target.value\n })\n }}\n />\n \n
\n \n \n {' '}\n {/* */}\n \n \n
\n
\n \n
\n\n
\n \n );\n};\nexport default ProfileInputs;\n", "text_preview": "import { FormGroup, Label, Input } from \"reactstrap\"; import { Button, Modal, ModalHeader, ModalBody, ModalFooter } from 'reactstrap'; import React from \"react\"; import ImageUpload from \"./ImageUpload\"; import { useState } from \"react\"; imp", "input_sha256": "63c2f7500e19ff219375e6f5b9437b56dc8fc51878bc3da9fe2a181eda7ad2ba", "text_sha256": "0025dd1a5fba107f08c8784beec9c73bf2068a67f76414236452be0832602c94", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8113, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.08950603146939849, "noise_severity": 0.43524221403664687, "missing_content_severity": 0.2746514081571699, "factual_density": 2.5321535183087582, "pretraining_utility": 0.24461738630100427, "math_density": 0.008378353333002573, "code_density": 3.8053853817728194, "reasoning_density": 0.17801701410616574, "education_level": 2.14689115947784, "creative_writing_quality": -0.5575964478366774}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008597985208547727, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.988935107035061, "secondary": 0.9349498541715661, "tertiary": 0.9390006150083532, "structured_records": 0.16673462286535853, "procedural_steps": 0.01958800648566507, "worked_solution": 0.0014953569645594113, "contextual_dialogue": 1.0931209017140253e-05, "content_quality": 0.39624224573339495, "noise_severity": 0.6372981549694609, "missing_content_severity": 0.7711238265356917, "factual_density": 0.1795025436162453, "pretraining_utility": 0.4719224652019872, "math_density": 0.9930180388891645, "code_density": 0.8378211514773498, "reasoning_density": 0.8516524882448618, "education_level": 0.741935957443495, "creative_writing_quality": 0.9984993028530874, "primary_topic": 0.9375773798474826, "secondary_topic": 0.7131446627304623}, "probability_margin": {"primary": 0.9821148076412001, "secondary": 0.9072272219328067, "tertiary": 0.9197071862766089, "education_level": 0.6170520169808682, "creative_writing_quality": 0.9982156810962122, "primary_topic": 0.9155388255200212, "secondary_topic": 0.6183708652369213}, "raw_probabilities": {"primary": {"Academic Writing": 6.320815531702916e-05, "Content Listing": 0.00012918372266175728, "Creative Writing": 7.266917278074603e-05, "Customer Support Page": 0.0002529364456930924, "Q&A Forum": 3.62129043643115e-05, "Discussion Thread": 2.3761417290465445e-05, "Comment Section": 5.6592649787355144e-05, "Missing Content": 0.00026307663349351687, "Knowledge Article": 0.00010496704548234811, "Legal Notices": 0.00010980788680586315, "News Article": 1.7634577088239792e-05, "Organizational Announcement": 3.637962633264916e-05, "Nonfiction Writing": 3.4341139252586196e-05, "Organizational About Page": 0.00010377946928473813, "Personal About Page": 0.0003105703519694432, "Personal Blog": 2.56520171476853e-05, "Product Page": 6.809409517412049e-05, "Spam / Ads": 6.041216828044204e-05, "Structured Data": 0.001030986123759304, "Technical Writing": 0.9893961442419335, "Transcript / Interview": 5.1170734083559096e-05, "Tutorial / How-To Guide": 0.00045041822610548943, "User Reviews": 2.0664595178293834e-05, "Other / Unknown": 0.007281336600733379}, "secondary": {"Academic Writing": 0.0004390686448740652, "Content Listing": 0.00167020157545713, "Creative Writing": 0.0005711723852469097, "Customer Support Page": 0.0018327899229804, "Q&A Forum": 0.00022206726538206273, "Discussion Thread": 0.0001502317201991626, "Comment Section": 0.0005717375314485277, "Missing Content": 0.0038602426486142294, "Knowledge Article": 0.0007149504063218809, "Legal Notices": 0.000757906062616183, "News Article": 0.0002541107706540091, "Organizational Announcement": 0.00026931198428177777, "Nonfiction Writing": 0.000293290141453756, "Organizational About Page": 0.0006591392745102618, "Personal About Page": 0.0023039671806266305, "Personal Blog": 0.00024796143864087096, "Product Page": 0.0005680618624558659, "Spam / Ads": 0.00040591433711941285, "Structured Data": 0.013831379326344652, "Technical Writing": 0.030433054981610714, "Transcript / Interview": 0.0003037859620691558, "Tutorial / How-To Guide": 0.0018462339049182218, "User Reviews": 0.00013314375775664927, "NONE": 0.9376602769144174}, "tertiary": {"Academic Writing": 0.00032083674444087864, "Content Listing": 0.0011858005231391911, "Creative Writing": 0.00044359872012714657, "Customer Support Page": 0.007763024606832669, "Q&A Forum": 0.00016121695576086365, "Discussion Thread": 0.00012848795606571135, "Comment Section": 0.0004885492656795666, "Missing Content": 0.00501461422250103, "Knowledge Article": 0.0007750094001334694, "Legal Notices": 0.0009714442821119116, "News Article": 0.00019363080440644066, "Organizational Announcement": 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"reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:08.237011+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "83b96511-38eb-4a61-b710-107a682f5df4", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "060290d140666f01526dd4f12ca752a210ac7862186296596b1f9f1b8ffa265c", "source_metadata": {"uuid": "83b96511-38eb-4a61-b710-107a682f5df4", "repo_name": "karem-Ismael/future-wensite", "relative_path": "src/Components/ProfileInputs.js", "category": "WEB", "algo_rel_score": 0.9558025598526001, "quality_score": 5.023630619049072, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 66, "repo_created_at": "2023-04-13T03:38:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": 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"probability_weighted_scores": {"content_quality": 0.08950603146939849, "noise_severity": 0.43524221403664687, "missing_content_severity": 0.27465140815716993, "factual_density": 2.5321535183087582, "pretraining_utility": 0.24461738630100427, "math_density": 0.008378353333002573, "code_density": 3.80538538177282, "reasoning_density": 0.17801701410616577, "education_level": 2.14689115947784, "creative_writing_quality": -0.5575964478366774}, "score_applicability": {"education_level": 0.9949262273984051, "creative_writing_quality": 0.0012505809557606636}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/sennat26/NewsGroupClassifier-BigData-/blob/62de447fb59bfb4c9e403e9a812e29831d6f7c21/src/mapReducejs.js", "id": "javascript-0-5d7c015556f1dfddc1c70f3c933e7f7c", "text": "//db.system.js.save({_id: \"myfunc\", value: function(){return \"hello\";}}) \nload(\"stemmer.js\");\nvar mapfunction = function(){\n\tvar lines = this.contents.split(\"\\n\");\n\tif (lines){\n\t\tfor(var i=0;i+-]/g, \" \");\n\t\t\t\tvar words = trimLine.split(\" \");\n\t\t\t\tfor(var j=0;j1 ) {\n\t\t\t\t\t\t//if(!checkForStopWords(woNum)) {\n\t\t\t\t\t\t\t//var comp = stemmedWord + \"-\"+this.category_name;\n\t\t\t\t\t\t\temit({word:stemmedWord,category:this.category_name},1); \n\t\t\t\t\t\t\t//emit({word:'',category:this.category_name},1);\n\t\t\t\t\t\t\t//emit({word:stemmedWord,category:''},1);\n\t\t\t\t\t\t//}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n\n\n//db.system.js.save({_id: \"checkForStopWords\", value: function(trimLine) {\n\t//return trimLine.replace(/[^\\w\\s]/gi, '')\n//}})\n\ndb.system.js.save({_id: \"checkForEmail\", value: function(trimLine) {\n \tvar re = new RegExp(\"^[_A-Za-z0-9-\\\\+]+(\\\\.[_A-Za-z0-9-]+)@[A-Za-z0-9-]+(\\\\.[A-Za-z0-9]+)(\\\\.[A-Za-z]{2,})$\");\n\treturn re.test(trimLine);\n}})\n\ndb.system.js.save({_id: \"removeNumbers\", value: function(word) {\n\tif(word.length>0)\n\t\tword = word.replace(/\\d+/g,\"\");\n\treturn word;\n}})\n\ndb.system.js.save({_id: \"checkStartEnd\", value: function(trimLine) {\n\tvar checkStartWord = trimLine.indexOf('from') == 0 || trimLine.indexOf('to') == 0|| trimLine.indexOf('summary') == 0\n\t\t\t\t|| trimLine.indexOf('expires') == 0|| trimLine.indexOf('distribution') == 0\n\t\t\t\t|| trimLine.indexOf('organization') == 0|| trimLine.indexOf('supersedes') == 0|| trimLine.indexOf('lines') == 0\n\t\t\t\t|| trimLine.indexOf('archive-name') == 0|| trimLine.indexOf('last-modified') == 0|| trimLine.indexOf('version') == 0\n\t\t\t\t|| trimLine.indexOf('reply-to') == 0|| trimLine.indexOf('article-i.d.') == 0|| trimLine.indexOf('followup-to') == 0\n\t\t\t\t|| trimLine.indexOf('originator') == 0|| trimLine.indexOf('last-update') == 0\n\t\t\t\t|| trimLine.match('writes'+\"$\") == 'writes';\n\t\t\t\t\n\treturn checkStartWord;\n}})\n\n\n\n\ndb.system.js.save({_id: \"checkStopWord\", value: function(word) {\n\t//var result = files1.find({words:word})\n\tif(strStartsWith(word,'F'))\n\t\treturn true;\n\telse \n\t\treturn false;\n}})\n\ndb.system.js.save({_id: \"strStartsWith\",value:function (str, prefix) {\n return str.indexOf(prefix) == 0;\n}})\n\nfunction strEndsWith(str, suffix) {\n return str.match(suffix+\"$\")==suffix;\n}\n\n\nvar reduceFunction = function(k,v) {\n\tvar count = 0;\n\tfor (ve in v) {\n\t\tcount++;\n\t}\n\treturn count;\n}\n\ndb.files.mapReduce(mapfunction, reduceFunction, {out:\"word_category\"})\n\n\n\nvar map2 = function() {\n\temit(this._id.category, {count:this.value});\n}\n\n\nvar reduce2 = function(k,v) {\n\tvar results = {count:0};\n\tv.forEach(function(value){\n\t\tresults.count += value.count;\n\t});\n\treturn results;\n}\n\n\ndb.word_category.mapReduce(map2, reduce2, {out:\"category_count\"})\n\n\n\n\n\nvar map3 = function() {\n\temit(this._id.word, {count:1});\n}\n\n\nvar reduce3 = function(k,v) {\n\treturn {count:1};\n}\n\n\ndb.word_category.mapReduce(map3, reduce3, {out:\"unique_words\"})\n\n\n\n//var map4 = function() {\n\t//emit(this.category, {text_value: \"\",text_count1:0,text_count2:this.value.count});\n\t//emit(this.category, [{text_value:\"\", text_count1:0,text_count2:this.value.count}]);\n//}\n$total = {count:db.unique_words.count()};\n$cat1 ={name:\"alt.atheism\",count:36026};\n$cat2 ={name:\"comp.graphics\",count:22968};\n$cat3 ={name:\"comp.os.ms-windows.misc\",count:50495};\n$cat4 ={name:\"comp.sys.ibm.pc.hardware\",count:20254};\n$cat5 ={name:\"comp.sys.mac.hardware\",count:16768};\n$cat6 ={name:\"comp.windows.x\",count:24312};\n$cat7 ={name:\"misc.forsale\",count:18423};\n$cat8 ={name:\"rec.autos\",count:27969};\n$cat9 ={name:\"rec.motorcycles\",count:26560};\n$cat10 ={name:\"rec.sport.baseball\",count:24021};\n$cat11 ={name:\"arec.sport.hockey\",count:29944};\n$cat12 ={name:\"sci.crypt\",count:35737};\n$cat13 ={name:\"sci.electronics\",count:23398};\n$cat14 ={name:\"sci.med\",count:35932};\n$cat15 ={name:\"sci.space\",count:34889};\n$cat16 ={name:\"soc.religion.christian\",count:36268};\n$cat17 ={name:\"talk.politics.guns\",count:27533};\n$cat18 ={name:\"talk.politics.mideast\",count:53716};\n$cat19 ={name:\"talk.politics.misc\",count:27196};\n$cat20 ={name:\"talk.religion.misc\",count:21492};\n\nvar map5 = function() {\n\tvar prob = {textValue:this._id.word, textCat:this._id.category};\n\tvar count = {totalCount:this.value};\n\tvar catCount = {categoryCount:0};\n\t\n\tif(this._id.category==cat1.name) {\n\t\tcatCount.categoryCount = cat1.count;\n\t} else if(this._id.category == cat2.name) {\n\t\tcatCount.categoryCount = cat2.count;\n\t} else if(this._id.category ==cat3.name) {\n\t\tcatCount.categoryCount = cat3.count;\n\t} else if(this._id.category==cat4.name) {\n\t\tcatCount.categoryCount = cat4.count;\n\t} else if(this._id.category==cat5.name) {\n\t\tcatCount.categoryCount = cat5.count;\n\t} else if(this._id.category==cat6.name) {\n\t\tcatCount.categoryCount = cat6.count;\n\t} else if(this._id.category==cat7.name) {\n\t\tcatCount.categoryCount = cat7.count;\n\t} else if(this._id.category==cat8.name) {\n\t\tcatCount.categoryCount = cat8.count;\n\t} else if(this._id.category==cat9.name) {\n\t\tcatCount.categoryCount = cat9.count;\n\t} else if(this._id.category==cat10.name) {\n\t\tcatCount.categoryCount = cat10.count;\n\t} else if(this._id.category==cat11.name) {\n\t\tcatCount.categoryCount = cat11.count;\n\t} else if(this._id.category==cat12.name) {\n\t\tcatCount.categoryCount = cat12.count;\n\t} else if(this._id.category==cat13.name) {\n\t\tcatCount.categoryCount = cat13.count;\n\t} else if(this._id.category==cat14.name) {\n\t\tcatCount.categoryCount = cat14.count;\n\t} else if(this._id.category==cat15.name) {\n\t\tcatCount.categoryCount = cat15.count;\n\t} else if(this._id.category==cat16.name) {\n\t\tcatCount.categoryCount = cat16.count;\n\t} else if(this._id.category==cat17.name) {\n\t\tcatCount.categoryCount = cat17.count;\n\t} else if(this._id.category==cat18.name) {\n\t\tcatCount.categoryCount = cat18.count;\n\t} else if(this._id.category==cat19.name) {\n\t\tcatCount.categoryCount = cat19.count;\n\t} else {\n\t\tcatCount.categoryCount = cat20.count;\n\t}\n\t\n\tvar probValue = {value:0};\n\tprobValue.value = (count.totalCount + 1)/(catCount.categoryCount + total.count);\n\temit({word:this._id.word,category: this._id.category}, {prob:probValue.value});\n}\n\nvar reduce5 = function(k,values){\n\tvalues.forEach(function(value) {\n\t\treturn {prob:value};\n\t});\n}\n\ndb.word_category.mapReduce(map5, reduce5, {out:\"probJoin\",scope:{cat1:$cat1,cat2:$cat2,cat3:$cat3,cat4:$cat4,cat5:$cat5,cat6:$cat6,cat7:$cat7,cat8:$cat8,\ncat9:$cat9,cat10:$cat10,cat11:$cat11,cat12:$cat12,cat13:$cat13,cat14:$cat14,cat15:$cat15,cat16:$cat16,cat17:$cat17,cat18:$cat18,cat19:$cat19,\ncat20:$cat20,total:$total}})\ndb.probJoin.find();\n \n $category_count = new Array();\n var i =0;\n var c = db.category_count.find();\n while(c.hasNext()) { var cur = c.next(); $category_count[i] = {name:cur._id,count:cur.value.count}; i++;};\n \nvar map5 = function(){\n\tvar prob = {textValue:this._id.word, textCat:this._id.category};\n\tvar count = {totalCount:this.value};\n\tvar catCount = {categoryCount:0};\n\tfor(var i=0;i<20;i++)\n\t{\n\t\tif(this._id.category == \"kk\")\n\t\t{\n\t\t\tcatCount.categoryCount = 2;\n\t\t\tbreak;\n\t\t}\n\t}\n\tvar probValue = {value:0};\n\tprobValue.value = (count.totalCount + 1)/(catCount.categoryCount + total.count);\n\temit(word:this._id.word, category:this._id.category,{prob:probValue.value});\n}\n\n\nvar reduce5 = function(k,values){\n\tvalues.forEach(function(value) {\n\t\treturn {prob:value};\n\t});\n}\n\nvar maptest = function(){\n\tvar lines = this.contents.split(\"\\n\");\n\tif (lines){\n\t\tfor(var i=0;i+-]/g, \" \");\n\t\t\t\t//trimLine = trimLine.replace(\"/\",\" \");\n\t\t\t\t//trimLine = trimLine.replace(\"<\",\" \");\n\t\t\t\t//trimLine = trimLine.replace(\">\",\" \");\n\t\t\t\t//trimLine = trimLine.replace(\"\\\",\" \");\n\t\t\t\t//trimLine = trimLine.replace(\".\",\" \");\n\t\t\t\tvar words = trimLine.split(\" \");\n\t\t\t\tfor(var j=0;j1 ) {\n\t\t\t\t\t\t\temit({word:stemmedWord},{GCategory:this.category_name}); \n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n\nvar reducetest = function(k,values){\n\tvar cat;\n\tvalues.forEach(function(value) {\n\t\tcat = value.GCategory;\n\t});\n\t\n\treturn {GCategory:cat};\n}\ndb.test.mapReduce(maptest, reducetest, {out:\"testJoin\",scope:{category_count:$category_count,total:$total}});\n\n\n$category_count = new Array();\n var i =0;\n var c = db.category_count.find();\n while(c.hasNext()) { var cur = c.next(); $category_count[i] = {id:cur._id,count:cur.value.count}; i++; $count++};\n \n \nvar map6 = function() {\n\tfor(var i =0 ;i < category_count.length;i++){\n\t\temit({word:this._id.word, Tcategory: category_count[i].id},{GCategory:(this.value == null)?\"\":this.value.GCategory,basevalue:(1/(category_count[i].count+total.count))});\n\t}\n}\n\n\nvar reduce6 = function(k,v) {\n\tvar cat;\n\tvalues.forEach(function(value) {\n\t\tcat = value.GCategory;\n\t});\n\t\n\treturn {GCategory:cat};\n}\n\ndb.testJoin.mapReduce(map6, reduce6, {out:\"testcombinations\",scope:{category_count:$category_count,total:$total}});\n\n\n\n\nvar map7 = function() {\n\temit({word:this._id.word, category: this._id.Tcategory},{GCategory:this.value.GCategory,basevalue:this.value.basevalue,prob:0,id:1});\n}\n\nvar map8 = function() {\n\temit({word:this._id.word, category: this._id.category},{GCategory:null,basevalue:0,prob:this.value.prob,id:2});\n}\n\nvar reduce7 = function(k,v) {\n\tvar result = {GCategory:null,basevalue:0,prob:0};\n\tvar result1 = {GCategory:null,basevalue:0,prob:0};\n\tvar i;\n\tv.forEach(function(value) {\n\t\tif(value.basevalue==0){\n\t\t\tresult.prob = value.prob;\n\t\t\ti = i+ value.id;\n\t\t}else{\n\t\t\tresult.basevalue = value.basevalue;\n\t\t\tresult.GCategory = value.GCategory;\n\t\t\ti = i+ value.id;\n\t\t}\n\t\t\n\t});\n\tif (i == 3) { \n\t\treturn result;\n\t} else {\n\t\treturn result1;\n\t}\n}\n\ndb.testcombinations.mapReduce(map7, reduce7, {out:{reduce:\"condprob\"}});\ndb.probJoin.mapReduce(map8, reduce7, {out:{reduce:\"condprob\"}});\n\nvar map9 = function() {\n\t\tif (this.value.GCategory != null) {\n\t\t\temit({word:this._id.word, category: this._id.category},{GCategory:this.value.GCategory,basevalue:this.value.basevalue,prob:this.value.prob});\n\t\t}\n}\n\nvar reduce8 = function(k,v) {\n\tvar result = {GCategory:null,basevalue:0,prob:0};\n\t\n\tresult.GCategory = v.GCategory;\n\t\tresult.basevalue = v.basevalue;\n\t\tresult.prob = v.prob;\n\treturn result;\n\t\n}\n\ndb.condprob.mapReduce(map9, reduce8, {out:\"finalvalue\"});\n\n", "text_preview": "//db.system.js.save({_id: \"myfunc\", value: function(){return \"hello\";}}) load(\"stemmer.js\"); var mapfunction = function(){ var lines = this.contents.split(\"\\n\"); if (lines){ for(var i=0;i {\n // Inside this callback function we perform our side effects.\n let result = getTopPair(uniquePairs);\n setTopPair(result);\n }, [uniquePairs]);\n\n const changeHandler = (event) => {\n // Passing file data (event.target.files[0]) to parse using Papa.parse\n Papa.parse(event.target.files[0], {\n header: true,\n skipEmptyLines: true,\n complete: function ({ data }) {\n\n let uniqueEmp = {};\n let pairs = [];\n\n //Combine Emploers work\n for (let i = 0; i < data.length; i++) {\n let employee = data[i];\n let id = employee.EmpID;\n uniqueEmp[id] = {\n ...employee,\n projects: {\n ...(uniqueEmp.hasOwnProperty([id]) ? uniqueEmp[id].projects : {}),\n [employee.ProjectID]: {\n ProjectID: employee.ProjectID,\n duration: duration(employee.DateFrom, employee.DateTo),\n DateFrom: employee.DateFrom,\n DateTo: employee.DateTo\n },\n }\n\n };\n }\n let EmpArr = Object.values(uniqueEmp);\n\n // Make all possible pairs\n for (let i = 0; i < EmpArr.length; i++) {\n for (let j = i + 1; j < EmpArr.length; j++) {\n let e1 = EmpArr[i];\n let e2 = EmpArr[j];\n let commonProjects = intersectingKeys(e1.projects, e2.projects);\n let commonProjectsDeatils = [];\n let commonTime = commonProjects.reduce((sum, projId) => {\n\n let first = e1;\n let second = e2;\n //determine which employee went to work first\n if (moment(e1.projects[projId].DateFrom).isAfter(e2.projects[projId].DateFrom)) {\n first = e2;\n second = e1;\n }\n if (moment(first.projects[projId].DateTo).isBefore(second.projects[projId].DateFrom)) {\n //they didn't meet each other in this the project\n sum += 0;\n commonProjectsDeatils.push({ projId, days: 0 })\n }\n else {\n if (moment(first.projects[projId].DateTo).isAfter(second.projects[projId].DateTo)) {\n //the new employee left first \n //and the total time with another employee \n //equals his own time spent on the project\n sum += second.projects[projId].duration;\n commonProjectsDeatils.push({ projId, days: second.projects[projId].duration })\n\n }\n else {\n //the end date of the old employee minus the start date of the new employee\n sum += duration(first.projects[projId].DateTo, second.projects[projId].DateFrom)\n commonProjectsDeatils.push({ projId, days: duration(first.projects[projId].DateTo, second.projects[projId].DateFrom) })\n }\n }\n\n return sum;\n }, 0);\n pairs.push({ e1, e2, commonProjects, commonTime, commonProjectsDeatils });\n }\n }\n // Save all posible pairs in array format\n setUniquePairs(pairs);\n },\n });\n };\n\n const intersectingKeys = (...objects) => {\n return objects\n .map((object) => Object.keys(object))\n .sort((a, b) => a.length - b.length)\n .reduce((a, b) => a.filter((key) => b.includes(key)));\n };\n\n const duration = (from, to) => {\n let now = moment();\n let start = moment(from).isValid() ? moment(from) : now;\n let end = moment(to).isValid() ? moment(to) : now;\n\n //Difference in number of days\n let res = Math.abs(moment.duration(end.diff(start)).asDays());\n return res;\n };\n\n\n const getTopPair = (pairs) => pairs.sort((a, b) => b.commonTime - a.commonTime)[0];\n\n return (\n
\n {/* File Uploader */}\n \n\n {/* Table */}\n {topPair && \n \n \n {resultRows.map((rows, index) => )}\n \n \n \n {topPair.commonProjectsDeatils\n .map(({ projId, days }, index) => {\n return (\n \n \n \n \n \n \n );\n })}\n \n
{rows}
{topPair.e1.EmpID}{topPair.e2.EmpID}{projId}{Math.floor(days)}
}\n\n
\n );\n}\n\nexport default App;\n", "text_preview": "import \"./App.css\"; import { useState, useEffect } from \"react\"; import Papa from \"papaparse\"; import moment from \"moment/moment\"; function App() { // State to store parsed data const [uniquePairs, setUniquePairs] = useState([]); const [top", "input_sha256": "d4c11041d49baac6229f6fb2429ad8f2d7d073e2467265f4e5c0fef932e4e59a", "text_sha256": "6356ae8438dd75632c6311136aafbf67aa78e65ba4d6e8db1b7c06ef7a76989d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8118, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2766456220117982, "noise_severity": 0.029822285767095422, "missing_content_severity": 0.015239937230857729, "factual_density": 3.04588191029792, "pretraining_utility": 0.5810097426903083, "math_density": 1.5766382785887505, "code_density": 3.7986928727309865, "reasoning_density": 1.7872763574067994, "education_level": 2.0534856158064865, "creative_writing_quality": -0.4336942310179466}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010377400222976625, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9943409501531503, "secondary": 0.9438963041889439, "tertiary": 0.9535019822598969, "structured_records": 0.06585977773697076, "procedural_steps": 0.012575412721907247, "worked_solution": 0.022542587699551104, "contextual_dialogue": 7.609879848952964e-06, "content_quality": 0.3748156858419759, "noise_severity": 0.9751480951940872, "missing_content_severity": 0.9873000523076185, "factual_density": 0.2049015919149334, "pretraining_utility": 0.4898448177807253, "math_density": 0.0, "code_density": 0.8322440606091557, "reasoning_density": 0.0, "education_level": 0.8002745741576459, "creative_writing_quality": 0.9991799641056047, "primary_topic": 0.9508555923045905, "secondary_topic": 0.7400369706157386}, 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"is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d0f1cc85ae0ef5665250a9a4d1860de42df5e7fc"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 2, "repo_created_at": "2023-07-06T12:57:52.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "48bde913f8bdea2964272fe12692e78f15b838bf", "size_bytes": 5045, "file_timestamp": 1688649240, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "d0f1cc85ae0ef5665250a9a4d1860de42df5e7fc", "url": "https://github.com/RosenPelovski/rosen-pelovski-employees/blob/d0f1cc85ae0ef5665250a9a4d1860de42df5e7fc/src/App.js"}, "gpt4o_tokens": 1185, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.2766456220117982, "noise_severity": 0.029822285767095422, "missing_content_severity": 0.015239937230857729, "factual_density": 3.04588191029792, "pretraining_utility": 0.5810097426903083, "math_density": 1.5766382785887505, "code_density": 3.7986928727309865, "reasoning_density": 1.7872763574067994, "education_level": 2.0534856158064865, "creative_writing_quality": -0.4336942310179466}, "score_applicability": {"education_level": 0.9988066348936404, "creative_writing_quality": 0.0006833632453295307}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Dodosters/electrictraffic-web/blob/0cd0e6577031d906f42a08cdafa1aabd16455cef/src/components/ForecastUploader.jsx", "id": "javascript-0-d06d655372c6e93fec405d66c8bdea8e", "text": "import React, { useState, useEffect } from 'react';\nimport { \n BarChart, Bar, LineChart, Line, XAxis, YAxis, CartesianGrid, \n Tooltip, Legend, ResponsiveContainer\n} from 'recharts';\n\nconst ForecastUploader = ({ onForecastComplete }) => {\n const [file, setFile] = useState(null);\n const [monthsAhead, setMonthsAhead] = useState(6);\n const [loading, setLoading] = useState(false);\n const [error, setError] = useState(null);\n const [forecastData, setForecastData] = useState(null);\n const [formattedChartData, setFormattedChartData] = useState([]);\n const [showForecastResults, setShowForecastResults] = useState(false);\n const [apiResponse, setApiResponse] = useState(null);\n\n // Эффект для преобразования данных прогноза в формат для графика\n useEffect(() => {\n if (!forecastData || !forecastData.forecast) {\n console.log(\"No forecast data available\");\n setFormattedChartData([]);\n return;\n }\n\n // Вывод структуры ответа для отладки\n console.log(\"Raw forecast data:\", forecastData);\n \n try {\n // Преобразуем данные в формат для графика\n const chartData = forecastData.forecast\n .filter(item => item && typeof item === 'object')\n .map(item => {\n // Для каждого элемента проверяем наличие необходимых полей\n // API использует поле \"volume\" вместо \"prediction\"\n const formattedItem = {\n date: item.year && item.month ? `${item.year}-${item.month}` : item.date || `Период ${Math.random().toString(36).substring(7)}`,\n value: typeof item.volume === 'number' ? item.volume : \n typeof item.prediction === 'number' ? item.prediction : null,\n };\n\n // Добавляем фактические значения, если они есть\n if (typeof item.actual === 'number') {\n formattedItem.actual = item.actual;\n }\n\n return formattedItem;\n });\n \n console.log(\"Formatted chart data:\", chartData);\n setFormattedChartData(chartData);\n } catch (err) {\n console.error(\"Error formatting forecast data:\", err);\n setError(\"Ошибка при обработке данных прогноза: \" + err.message);\n setFormattedChartData([]);\n }\n }, [forecastData]);\n\n const handleFileChange = (event) => {\n const selectedFile = event.target.files[0];\n if (selectedFile && !selectedFile.name.endsWith('.csv')) {\n setError('Пожалуйста, загрузите файл в формате CSV');\n setFile(null);\n } else {\n setFile(selectedFile);\n setError(null);\n }\n };\n\n const handleMonthsChange = (event) => {\n const value = parseInt(event.target.value);\n if (value < 1) {\n setMonthsAhead(1);\n } else if (value > 24) {\n setMonthsAhead(24);\n } else {\n setMonthsAhead(value);\n }\n };\n\n const handleSubmit = async (event) => {\n event.preventDefault();\n \n if (!file) {\n setError('Пожалуйста, выберите файл');\n return;\n }\n\n setLoading(true);\n setError(null);\n setFormattedChartData([]);\n setApiResponse(null);\n\n try {\n const formData = new FormData();\n formData.append('file', file);\n \n const API_BASE_URL = 'http://localhost:8000';\n console.log(\"Sending request to:\", `${API_BASE_URL}/forecast/?months_ahead=${monthsAhead}`);\n \n const response = await fetch(`${API_BASE_URL}/forecast/?months_ahead=${monthsAhead}`, {\n method: 'POST',\n body: formData,\n });\n \n if (!response.ok) {\n let errorMessage = \"Ошибка при выполнении прогноза\";\n try {\n const errorData = await response.json();\n errorMessage = errorData.detail || errorMessage;\n } catch (e) {\n // Игнорируем ошибку парсинга JSON\n }\n throw new Error(errorMessage);\n }\n \n const data = await response.json();\n console.log(\"API Response data:\", data);\n setApiResponse(data);\n \n // Проверяем, есть ли в ответе данные прогноза\n if (!data) {\n throw new Error('Получен пустой ответ от API');\n }\n \n if (!data.forecast || !Array.isArray(data.forecast)) {\n console.warn('Некорректный формат данных от API: прогноз отсутствует или не является массивом');\n // Создаем заглушку для прогноза, если его нет\n data.forecast = [];\n }\n \n setForecastData(data);\n setShowForecastResults(true);\n \n // If callback provided, call it with the forecast data\n if (onForecastComplete) {\n // Форматируем данные для обратного вызова\n const formattedData = {\n ...data,\n forecast: data.forecast.map(item => ({\n ...item,\n prediction: item.volume !== undefined ? item.volume : item.prediction\n }))\n };\n onForecastComplete(formattedData);\n }\n } catch (err) {\n console.error('Error during forecast:', err);\n setError(err.message || 'Произошла ошибка при обработке прогноза');\n } finally {\n setLoading(false);\n }\n };\n\n const renderForecastChart = () => {\n if (formattedChartData.length === 0) {\n return (\n
\n
Нет данных для построения графика
\n

Формат данных, полученных от API:

\n 
\n            {JSON.stringify(apiResponse, null, 2)}\n
\n
\n );\n }\n \n // Определяем, есть ли фактические данные для отображения\n const hasActualData = formattedChartData.some(item => item.actual !== undefined);\n \n return (\n
\n \n \n \n \n \n value !== null ? value.toFixed(2) : 'Нет данных'} \n />\n \n {hasActualData && (\n \n )}\n \n \n \n
\n );\n };\n\n const renderForecastSummary = () => {\n if (!forecastData || !forecastData.forecast || !forecastData.forecast.length) {\n return (\n
\n Нет данных для отображения статистики прогноза.\n
\n );\n }\n \n // Фильтруем только валидные прогнозные значения - используем volume или prediction\n const predictions = forecastData.forecast\n .map(item => item.volume !== undefined ? item.volume : item.prediction)\n .filter(val => val !== undefined && val !== null && !isNaN(val));\n \n if (!predictions.length) {\n return (\n
\n Нет числовых данных для статистического анализа.\n
\n );\n }\n \n const average = predictions.reduce((sum, val) => sum + val, 0) / predictions.length;\n const max = Math.max(...predictions);\n const min = Math.min(...predictions);\n \n return (\n
\n

Результаты прогноза:

\n
\n
\n
\n
\n
Среднее значение
\n

{average.toFixed(2)}

\n
\n
\n
\n
\n
\n
\n
Максимум
\n

{max.toFixed(2)}

\n
\n
\n
\n
\n
\n
\n
Минимум
\n

{min.toFixed(2)}

\n
\n
\n
\n
\n \n
\n \n \n \n \n \n \n {forecastData.forecast.some(item => item.actual !== undefined) && \n \n }\n \n \n \n {forecastData.forecast.map((item, index) => (\n \n \n \n \n {forecastData.forecast.some(item => item.actual !== undefined) && \n \n }\n \n ))}\n \n
ГодМесяцПрогноз потребленияФактическое потребление
{item.year !== undefined ? item.year : '-'}{item.month !== undefined ? item.month : '-'}\n {(item.volume !== undefined && item.volume !== null) \n ? Number(item.volume).toFixed(2) \n : (item.prediction !== undefined && item.prediction !== null)\n ? Number(item.prediction).toFixed(2)\n : '-'}\n \n {item.actual !== undefined && item.actual !== null \n ? Number(item.actual).toFixed(2) \n : '-'}\n
\n
\n
\n );\n };\n\n const renderDownloadLink = () => {\n if (!forecastData || !forecastData.forecast || !forecastData.forecast.length) return null;\n \n try {\n const csvContent = \n \"year,month,forecast\\n\" + \n forecastData.forecast\n .filter(item => \n item && \n item.year !== undefined && \n item.month !== undefined && \n (item.volume !== undefined || item.prediction !== undefined)\n )\n .map(item => {\n const forecastValue = item.volume !== undefined ? item.volume : item.prediction;\n return `${item.year},${item.month},${forecastValue}`;\n })\n .join(\"\\n\");\n \n const blob = new Blob([csvContent], { type: 'text/csv;charset=utf-8;' });\n const url = URL.createObjectURL(blob);\n \n return (\n \n \n Скачать результаты в CSV\n \n );\n } catch (err) {\n console.error(\"Error creating download link:\", err);\n return (\n
\n Ошибка при создании файла для скачивания: {err.message}\n
\n );\n }\n };\n\n const renderSampleLink = () => {\n // Sample CSV data structure\n const sampleCsvContent = \n \"year,month,volume\\n\" +\n \"2022,1,1240\\n\" +\n \"2022,2,1180\\n\" +\n \"2022,3,1320\\n\" +\n \"2022,4,1090\\n\" +\n \"2022,5,980\\n\" +\n \"2022,6,1150\\n\" +\n \"2022,7,1340\\n\" +\n \"2022,8,1270\\n\" +\n \"2022,9,1180\\n\" +\n \"2022,10,1050\\n\" +\n \"2022,11,1120\\n\" +\n \"2022,12,1310\\n\" +\n \"2023,1,1290\\n\" +\n \"2023,2,1210\\n\" +\n \"2023,3,1350\\n\";\n \n const blob = new Blob([sampleCsvContent], { type: 'text/csv;charset=utf-8;' });\n const url = URL.createObjectURL(blob);\n \n return (\n \n Скачать пример CSV файла\n \n );\n };\n\n return (\n
\n
\n
\n

Прогнозирование потребления электроэнергии

\n
\n
\n {!showForecastResults ? (\n <>\n

\n Загрузите файл с историческими данными о потреблении электроэнергии, \n и мы построим прогноз на выбранное количество месяцев вперед.\n

\n \n
\n Требования к файлу:\n
    \n
  • Формат CSV
    • \n
  • Обязательные колонки: year, month, volume
    • \n
  • Минимум 3 записи для построения прогноза
    • \n
  • Данные должны быть ежемесячными
    • \n
\n
\n \n
\n
\n \n \n {renderSampleLink()}\n
\n \n
\n \n \n
\n \n {error && (\n
\n {error}\n
\n )}\n \n \n
\n \n ) : (\n <>\n
\n

Результаты прогнозирования

\n \n
\n \n {error && (\n
\n {error}\n
\n )}\n \n {renderForecastChart()}\n {renderForecastSummary()}\n {renderDownloadLink()}\n \n )}\n
\n
\n
\n );\n};\n\nexport default ForecastUploader;", "text_preview": "import React, { useState, useEffect } from 'react'; import { BarChart, Bar, LineChart, Line, XAxis, YAxis, CartesianGrid, Tooltip, Legend, ResponsiveContainer } from 'recharts'; const ForecastUploader = ({ onForecastComplete }) => { const [", "input_sha256": "0463a346296bbe23e00688ca6287a66b528b36ea49d32f72d9953a973f7c594d", "text_sha256": "d06d655372c6e93fec405d66c8bdea8e30b059f68df01aaa34686dc8fad3b982", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8117, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": ["structured_records"]}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8882025243370322, "noise_severity": 0.16158546522954226, "missing_content_severity": 0.010956170925736697, "factual_density": 2.6818573672133894, "pretraining_utility": 0.692419381493583, "math_density": 1.0124912946794553, "code_density": 3.7268862500846875, "reasoning_density": 0.8530655845339837, "education_level": 2.129071299845667, "creative_writing_quality": -0.16031807593591244}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.07043643225931184, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9944153598294367, "secondary": 0.7549398590079797, "tertiary": 0.6814409731721757, "structured_records": 0.735569304470627, "procedural_steps": 0.04337478053804525, "worked_solution": 0.030648023731108025, "contextual_dialogue": 5.2333445895065064e-06, "content_quality": 0.5551344591282952, "noise_severity": 0.8653454456420482, "missing_content_severity": 0.9908698575618861, "factual_density": 0.29322135890930423, "pretraining_utility": 0.5776925635142894, "math_density": 0.5571759220620038, "code_density": 0.7724052084039061, "reasoning_density": 0.289112012888347, "education_level": 0.7843445221440217, "creative_writing_quality": 0.9968887696596247, "primary_topic": 0.9276945957010069, "secondary_topic": 0.48168464294081753}, "probability_margin": {"primary": 0.9923105388656077, "secondary": 0.6043800304268562, "tertiary": 0.4736620188846623, "education_level": 0.6749882396679898, "creative_writing_quality": 0.9955858309856714, "primary_topic": 0.9032476070104501, "secondary_topic": 0.2975993250340039}, "raw_probabilities": {"primary": {"Academic Writing": 3.768786194121837e-05, "Content Listing": 2.8710458712088602e-05, "Creative Writing": 3.0406211587587362e-05, "Customer Support Page": 8.912948356076385e-05, "Q&A Forum": 1.5402242204235854e-05, "Discussion Thread": 1.1274481627089106e-05, "Comment Section": 2.2201861752876777e-05, "Missing Content": 3.3881880329106205e-05, "Knowledge Article": 0.00010219944383653106, "Legal Notices": 3.607352374007587e-05, "News Article": 7.613509281810719e-06, "Organizational Announcement": 1.7241740978469514e-05, "Nonfiction Writing": 1.6812351162028713e-05, "Organizational About Page": 2.28261781900274e-05, "Personal About Page": 1.901166834914658e-05, "Personal Blog": 1.085194570449423e-05, "Product Page": 3.564086303182488e-05, "Spam / Ads": 1.5841410281508162e-05, "Structured Data": 0.0007860389188956521, "Technical Writing": 0.9946480531698769, "Transcript / Interview": 1.9440303077460007e-05, "Tutorial / How-To Guide": 0.0016460966138982438, "User Reviews": 1.004957371171056e-05, "Other / Unknown": 0.0023375143042692064}, "secondary": {"Academic Writing": 0.0011353486184001828, "Content Listing": 0.001075179191295955, "Creative Writing": 0.0009945147655065728, "Customer Support Page": 0.008541116371192826, "Q&A Forum": 0.00042337535317684067, "Discussion Thread": 0.00039298013417089853, "Comment Section": 0.0011642339915074195, "Missing Content": 0.0010394884290373617, "Knowledge Article": 0.0024823800994439277, "Legal Notices": 0.0006642797472345421, "News Article": 0.0005192361557991121, "Organizational Announcement": 0.0005660508805301503, "Nonfiction Writing": 0.0006615448434011657, "Organizational About Page": 0.000706469963651373, "Personal About Page": 0.00048303327619269166, "Personal Blog": 0.0004979810958769547, "Product Page": 0.0006226206573669279, "Spam / Ads": 0.0006494851445134131, "Structured Data": 0.16077066778912427, "Technical Writing": 0.030870733514231037, "Transcript / Interview": 0.0007385740983547665, "Tutorial / How-To Guide": 0.019538582715800744, "User Reviews": 0.0003114249482105442, "NONE": 0.7651506982159805}, "tertiary": {"Academic Writing": 0.0009833288887817029, "Content Listing": 0.001110209419518428, "Creative Writing": 0.0011076643913168031, "Customer Support Page": 0.019826024228393527, "Q&A Forum": 0.0004716295599197277, "Discussion Thread": 0.00036321427229445743, "Comment Section": 0.0017719900428469005, "Missing Content": 0.0009609427080860421, "Knowledge Article": 0.004306475989192327, "Legal Notices": 0.000724608136432093, "News Article": 0.0005401646742775399, "Organizational Announcement": 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"reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:09.366823+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "71467c54-c9a7-48cf-bfec-9d1ad8de346d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "46bebc4d44ab61eb7ae7bd2d0cb78d55fb02ba43b19352cfb44c9ca5887f285f", "source_metadata": {"uuid": "71467c54-c9a7-48cf-bfec-9d1ad8de346d", "repo_name": "Dodosters/electrictraffic-web", "relative_path": "src/components/ForecastUploader.jsx", "category": "WEB", "algo_rel_score": 0.9306531548500061, "quality_score": 6.730010509490967, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 41, "repo_created_at": "2025-04-04T22:39:47.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "dcece45a40e465d10fb5cd50911d87570830285e", "size_bytes": 17677, "file_timestamp": 1744270784, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "0cd0e6577031d906f42a08cdafa1aabd16455cef"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 41, "repo_created_at": "2025-04-04T22:39:47.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "dcece45a40e465d10fb5cd50911d87570830285e", "size_bytes": 17677, "file_timestamp": 1744270784, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "0cd0e6577031d906f42a08cdafa1aabd16455cef", "url": "https://github.com/Dodosters/electrictraffic-web/blob/0cd0e6577031d906f42a08cdafa1aabd16455cef/src/components/ForecastUploader.jsx"}, "gpt4o_tokens": 3737, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.8882025243370322, "noise_severity": 0.16158546522954226, "missing_content_severity": 0.010956170925736697, "factual_density": 2.6818573672133894, "pretraining_utility": 0.692419381493583, "math_density": 1.0124912946794553, "code_density": 3.7268862500846875, "reasoning_density": 0.8530655845339837, "education_level": 2.129071299845667, "creative_writing_quality": -0.16031807593591244}, "score_applicability": {"education_level": 0.9985704428279158, "creative_writing_quality": 0.0025926919503125568}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/Ashish-11-06/techtrixF/blob/8194f03c3387ffccc33ef6a9bb3ddaaf0554ac26/src/components/User/CreateUserForm.jsx", "id": "javascript-0-5fb8896ed7a0758c4908ab0f80d09e64", "text": "import React, { useEffect, useState } from 'react';\nimport { Form, Input, Select, Button, Switch, message, Row, Col } from 'antd';\nimport { useDispatch, useSelector } from 'react-redux';\nimport { createUser, fetchUsers, updateUser } from '../../redux/slices/userSlice';\n\nconst { Option } = Select;\n\nconst CreateUserForm = ({ user, onClose, mode }) => {\n const dispatch = useDispatch();\n const [form] = Form.useForm();\n const isEditMode = Boolean(user); // Determine if it's edit mode\n const [loading, setLoading] = useState(false);\n\n const { users } = useSelector((state) => state.users);\n\n if (users.length === 0) {\n dispatch(fetchUsers());\n }\n\n const existingEmailIds = users.map(users => users.email?.toLowerCase());\n\n // console.log(existingEmailIds);\n\n const loggedInUser = JSON.parse(localStorage.getItem('user'));\n const userType = loggedInUser.userType;\n const userRole = loggedInUser.role;\n\n if (mode === 'edit') {\n if (user) {\n // console.log('setting user', user);\n form.setFieldsValue({\n ...user,\n userType: user.userType === 'Admin_User', // Convert to boolean for the Switch\n isActive: user.isActive, // Set isActive value\n });\n // console.log(form)\n }\n } else {\n form.setFieldValue(null);\n }\n\n const handleSubmit = async (values) => {\n setLoading(true);\n try {\n const userData = {\n ...values,\n userType: values.userType ? 'Admin_User' : 'Normal_User', // Map boolean to enum\n role: values.role || userRole, // Ensure the role is included\n isActive: values.isActive !== undefined ? values.isActive : true, // Set isActive value\n\n };\n\n if (!values.password) {\n delete userData.password; // Remove password if not entered\n }\n\n if (userData.userType === null) {\n userData.userType = 'Normal_User'; // Default to Normal_User if null\n }\n\n if (isEditMode) {\n await dispatch(updateUser({ ...userData, userId: user.userId }));\n message.success('User updated successfully!');\n } else {\n const result = await dispatch(createUser(userData));\n if (createUser.fulfilled.match(result)) {\n message.success('User added successfully!');\n } else {\n // console.log(result.payload);\n throw new Error(result.payload);\n }\n }\n\n form.resetFields();\n onClose(); // Close the modal after success\n\n } catch (error) {\n message.error(error.message || 'Failed to save user. Please try again later.');\n } finally {\n setLoading(false);\n form.resetFields();\n }\n };\n\n return (\n \n \n \n \n \n \n \n \n \"\n name=\"lastName\"\n rules={[{ required: true, message: 'Please input the last name!' }]}\n >\n \n \n \n \n \n \n {\n if (value && existingEmailIds.includes(value.toLowerCase())) {\n return Promise.reject(new Error('This Email is already in use!'));\n }\n return Promise.resolve();\n },\n },\n ]),\n ]}\n >\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n {userRole === 'Admin' && (\n \n \n \n \n \n )}\n {user && (\n \n \n \n \n \n )}\n \n\n \n \n \n \n \n \n \n \n \n \n \n );\n};\n\nexport default CreateUserForm;\n", "text_preview": "import React, { useEffect, useState } from 'react'; import { Form, Input, Select, Button, Switch, message, Row, Col } from 'antd'; import { useDispatch, useSelector } from 'react-redux'; import { createUser, fetchUsers, updateUser } from '.", "input_sha256": "a3188f28afd92600e74c2006662326c6c511972c0706885056e0a9c4e233afe1", "text_sha256": "5fb8896ed7a0758c4908ab0f80d09e6455e62417d61b4fc8a84d413cabef1046", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8119, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4136575654999015, "noise_severity": 0.15565340890902102, "missing_content_severity": 0.04038615784886906, "factual_density": 3.2222564851921627, "pretraining_utility": 0.555030544825466, "math_density": 0.05176003877934631, "code_density": 3.8789204653107157, "reasoning_density": 0.6583963757845843, "education_level": 2.17474454369712, "creative_writing_quality": -0.3244477041388341}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007606626570732078, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967159059485838, "secondary": 0.9781699986783267, "tertiary": 0.9861073481434384, "structured_records": 0.02617858364313734, "procedural_steps": 0.005865621969130326, "worked_solution": 0.006402307480130034, "contextual_dialogue": 8.543217000581415e-06, "content_quality": 0.3459687325988102, "noise_severity": 0.8702888259091491, "missing_content_severity": 0.9663448684592758, "factual_density": 0.3518804043268021, "pretraining_utility": 0.5089432670951537, "math_density": 0.9568666343505448, "code_density": 0.8991003877589296, "reasoning_density": 0.4513363535128465, "education_level": 0.7447267661042658, "creative_writing_quality": 0.9990842388612786, "primary_topic": 0.9953164697975126, "secondary_topic": 0.9587011626686558}, "probability_margin": {"primary": 0.9953300221449964, "secondary": 0.966814821990413, "tertiary": 0.9809416506256584, "education_level": 0.6046552074422632, "creative_writing_quality": 0.9988127199291301, "primary_topic": 0.9939840699798496, "secondary_topic": 0.9477207709843138}, "raw_probabilities": {"primary": {"Academic Writing": 3.201538354135672e-05, "Content Listing": 2.42275155029661e-05, "Creative Writing": 2.064891370278075e-05, "Customer Support Page": 9.453198106168366e-06, "Q&A Forum": 1.3059161455110308e-05, "Discussion Thread": 8.052267036592691e-06, "Comment Section": 1.5658437355775593e-05, "Missing Content": 3.158393478542952e-05, "Knowledge Article": 0.00010170737678428847, "Legal Notices": 4.812618725444845e-05, "News Article": 4.880795759985147e-06, "Organizational Announcement": 9.986652614245475e-06, "Nonfiction Writing": 1.283483659266968e-05, "Organizational About Page": 1.252551316020041e-05, "Personal About Page": 1.4261459858572326e-05, "Personal Blog": 6.763851269773901e-06, "Product Page": 8.914505008954134e-06, "Spam / Ads": 1.1006396011607668e-05, "Structured Data": 0.0004675735864630722, "Technical Writing": 0.9968527432007261, "Transcript / Interview": 1.3757463999879938e-05, "Tutorial / How-To Guide": 0.000750731042829252, "User Reviews": 6.767264451109604e-06, "Other / Unknown": 0.0015227210557297436}, "secondary": {"Academic Writing": 0.00012008568911565337, "Content Listing": 0.00015073673193946073, "Creative Writing": 0.00012696695387288246, "Customer Support Page": 7.831507592967857e-05, "Q&A Forum": 4.693142947460959e-05, "Discussion Thread": 3.820331690116265e-05, "Comment Section": 0.00019588527964606444, "Missing Content": 0.0003703920968069015, "Knowledge Article": 0.00037599052344336443, "Legal Notices": 0.00026559498597494955, "News Article": 6.140929945584324e-05, "Organizational Announcement": 5.9722893409829944e-05, "Nonfiction Writing": 5.4741967789335904e-05, "Organizational About Page": 7.282332387022102e-05, "Personal About Page": 6.6739598124303e-05, "Personal Blog": 5.2631633406821586e-05, "Product Page": 5.347598787449169e-05, "Spam / Ads": 7.03041565175639e-05, "Structured Data": 0.0035462209958327727, "Technical Writing": 0.012264760076316727, "Transcript / Interview": 8.340278284721416e-05, "Tutorial / How-To Guide": 0.0027310063819457986, "User Reviews": 3.4076752774705564e-05, "NONE": 0.9790795820667297}, "tertiary": {"Academic Writing": 7.873317135917629e-05, "Content Listing": 0.0001682706414907982, "Creative Writing": 9.781630376916611e-05, "Customer Support Page": 5.694555696966972e-05, "Q&A Forum": 3.597222064834579e-05, "Discussion Thread": 2.942366346572983e-05, "Comment Section": 0.0003745914987793649, "Missing Content": 0.0006748227626841285, "Knowledge Article": 0.00038788489277952345, "Legal Notices": 0.0003411027826425718, "News Article": 4.6392141426647024e-05, "Organizational Announcement": 4.082487052877475e-05, "Nonfiction Writing": 6.0455395708180304e-05, "Organizational About Page": 7.166809157370706e-05, "Personal About Page": 5.006098051467926e-05, "Personal Blog": 4.077967415342528e-05, "Product Page": 5.55464204669318e-05, "Spam / Ads": 5.556872665947856e-05, "Structured Data": 0.0020156989055002007, "Technical 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0.00011783813981896826, "Politics & Government": 0.00014123352683734344, "Religion & Spirituality": 0.00014478534017797634, "Science, Mathematics & Engineering": 0.000241093827024841, "Relationships & Community": 0.00025830033738525975, "Software Usage & Applications": 0.012568808504778307, "Programming & Software Engineering": 0.009711838913343936, "Sports & Fitness": 0.00015331595007899118, "Vehicles & Transportation": 0.0008340080883526223, "Travel & Tourism": 0.00022096042928960516, "Other / Uncovered Subject": 0.0007365375829037541, "NONE": 0.9602895794890921}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10212, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10212, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:10.189326+00:00", "scoring_key_slot": "primary", "dataset_uid": "915ad37a-fdc6-4694-9f85-7fd6918d285b", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "73efe5c4bd0ebc73aad8fd27c38e00c6e6aeeefeb8ff3cdb02a61ff0570a2ea8", "source_metadata": {"uuid": "915ad37a-fdc6-4694-9f85-7fd6918d285b", "repo_name": "Ashish-11-06/techtrixF", "relative_path": "src/components/User/CreateUserForm.jsx", "category": "WEB", "algo_rel_score": 0.9418653845787048, "quality_score": 7.301383018493652, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 381, "repo_created_at": "2024-09-25T09:34:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "d3407fba7f556d00513bffb3400e5c517fdfcdf9", "size_bytes": 9058, "file_timestamp": 1752302263, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8194f03c3387ffccc33ef6a9bb3ddaaf0554ac26"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 381, "repo_created_at": "2024-09-25T09:34:25.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "d3407fba7f556d00513bffb3400e5c517fdfcdf9", "size_bytes": 9058, "file_timestamp": 1752302263, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "8194f03c3387ffccc33ef6a9bb3ddaaf0554ac26", "url": "https://github.com/Ashish-11-06/techtrixF/blob/8194f03c3387ffccc33ef6a9bb3ddaaf0554ac26/src/components/User/CreateUserForm.jsx"}, "gpt4o_tokens": 1564, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.4136575654999015, "noise_severity": 0.15565340890902102, "missing_content_severity": 0.04038615784886906, "factual_density": 3.2222564851921627, "pretraining_utility": 0.555030544825466, "math_density": 0.05176003877934631, "code_density": 3.8789204653107157, "reasoning_density": 0.6583963757845843, "education_level": 2.17474454369712, "creative_writing_quality": -0.3244477041388341}, "score_applicability": {"education_level": 0.9987778968063945, "creative_writing_quality": 0.0007631342822679788}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/magedo992/E-commerce/blob/aa65c7f3c937d13b41c830d86d6c8d44e94d38c0/Controller/ProductController.js", "id": "javascript-0-c4cd24e5b1cf1d05f7de0938f990520c", "text": "const ProductModel=require('../Model/ProductModel');\nconst { asyncWarpper}=require('../middelware/asyncWapper');\nconst { cloudinary } = require('../middelware/uploadImage'); \nconst streamifier = require('streamifier'); \nconst CategoryModel=require('../Model/CategoryModel');\nconst { ErrorHandler } = require('../utils/ErrorHandler');\nconst ApiFeture=require('../utils/apiFeture');\n\nexports.createProduct = asyncWarpper(async (req, res, next) => {\n let { name, price, description, category } = req.body;\n name=name.toString();\n description=description.toString();\n // Ensure category exists\n const Category = await CategoryModel.findById(category);\n if (!Category) {\n return res.status(404).json({\n 'status': 'error',\n 'message': 'Cannot find this category'\n });\n }\n\n // Ensure a file has been uploaded\n if (!req.file || !req.file.buffer) {\n return res.status(400).json({\n 'status': 'error',\n 'message': 'Image file is required'\n });\n }\n\n // Helper function to upload image buffer to Cloudinary\n const uploadFromBuffer = (fileBuffer) => {\n return new Promise((resolve, reject) => {\n const uploadStream = cloudinary.uploader.upload_stream(\n { folder: 'Product' }, \n (error, result) => {\n if (error) {\n return reject(error);\n }\n resolve(result);\n }\n );\n streamifier.createReadStream(fileBuffer).pipe(uploadStream);\n });\n };\n\n // Upload the image buffer\n let result;\n try {\n result = await uploadFromBuffer(req.file.buffer);\n } catch (error) {\n return res.status(500).json({\n 'status': 'error',\n 'message': 'Failed to upload image to Cloudinary',\n 'error': error.message\n });\n }\n\n // Create product with uploaded image URL\n const product = await ProductModel.create({\n name,\n price,\n description,\n \n category,\n images: result.secure_url, // Image URL\n imagePublicIds: result.public_id // Public ID for the image\n });\n\n // Respond with the created product\n res.status(201).json({\n 'status': 'success',\n 'data': product\n });\n});\n\nexports.updateProduct = asyncWarpper(async (req, res, next) => {\n const { name, price, description, category } = req.body;\n\n const productId = req.params.Id;\n const product = await ProductModel.findById(productId);\n\n if (!product) {\n return res.status(404).json({\n 'status': 'error',\n 'message': 'Cannot find the product'\n });\n }\n\n \n const Category = await CategoryModel.findById(category);\n if (!Category) {\n return res.status(404).json({\n 'status': 'error',\n 'message': 'Cannot find this category'\n });\n }\n\n \n \n \n const uploadFromBuffer = (fileBuffer) => {\n return new Promise((resolve, reject) => {\n const uploadStream = cloudinary.uploader.upload_stream({ folder: 'Product' }, (error, result) => {\n if (error) {\n return reject(error);\n }\n resolve(result);\n });\n streamifier.createReadStream(fileBuffer).pipe(uploadStream);\n });\n };\n\n const uploadedImages = [];\n const imagePublicIds = [];\n \n if (product.imagePublicIds && product.imagePublicIds.length > 0) {\n for (const publicId of product.imagePublicIds) {\n try {\n await cloudinary.uploader.destroy(publicId);\n } catch (error) {\n return res.status(500).json({\n message: 'Error deleting old image from Cloudinary',\n error: error.message\n });\n }\n }\n }\n\n\n \n if (req.file) {\n \n \n \n const result = await uploadFromBuffer(req.file.buffer);\n uploadedImages.push(result.secure_url);\n imagePublicIds.push(result.public_id); \n \n }\n\n \n product.name = name;\n product.price = price;\n product.description = description;\n \n product.category = category;\n product.images = uploadedImages; \n product.imagePublicIds = imagePublicIds; \n\n await product.save();\n\n \n res.status(200).json({\n 'status': 'success',\n 'data': product\n });\n});\n\nexports.getAllProducts=asyncWarpper(async (req,res,next)=>{\n \n\n const countDoc=await ProductModel.countDocuments();\n\n const apiFeture=new ApiFeture(ProductModel.find({},{'__v':0,'imagePublicIds':0}),req.query)\n .pagination(countDoc).sort().search(\"Products\");\n const products = await apiFeture.mongoQuery; \n \n res.status(200).json({\n 'success':'success',\n 'data':products,\n results: products.length,\n pagination: apiFeture.paginationResult\n\n });\n})\nexports.getProduct=asyncWarpper(async (req,res,next)=>{\n const productId=req.params.Id;\n const product=await ProductModel.findById(productId);\n\n if(!product)\n {\n return next(new ErrorHandler('Not found Product'));\n }\n res.status(200).json({\n 'status':'success',\n data:product\n })\n})\n\nexports.deleteProduct=asyncWarpper(async (req,res,next)=>{\n const product=await ProductModel.findByIdAndDelete(req.params.Id);\n if(!product)\n {\n return res.status(404).json({\n 'status':'error',\n 'message':'can not found this product'\n })\n }\n if (product.imagePublicIds && product.imagePublicIds.length > 0) {\n for (const publicId of product.imagePublicIds) {\n try {\n await cloudinary.uploader.destroy(publicId);\n } catch (error) {\n return res.status(500).json({\n message: 'Error deleting old image from Cloudinary',\n error: error.message\n });\n }\n }\n }\n \n res.status(204).json({\n 'status':'success',\n 'message':'deleted success'\n });\n \n})", "text_preview": "const ProductModel=require('../Model/ProductModel'); const { asyncWarpper}=require('../middelware/asyncWapper'); const { cloudinary } = require('../middelware/uploadImage'); const streamifier = require('streamifier'); const CategoryModel=re", "input_sha256": "15f6189d41126c23c3f099313e467aa29f0d77d47797c53f8ee32daa6a2bb09b", "text_sha256": "c4cd24e5b1cf1d05f7de0938f990520c2d69783b8180912198110900e1f0149f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8120, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.170942689086338, "noise_severity": 0.07813727798696198, "missing_content_severity": 0.026131822310729382, "factual_density": 3.248534707874326, "pretraining_utility": 0.5494539773345622, "math_density": 0.026190270689373532, "code_density": 3.8910550800718684, "reasoning_density": 0.9362549065684667, "education_level": 2.2276704767270985, "creative_writing_quality": -0.4674947635815673}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006276638349503538, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9971299606066321, "secondary": 0.9771727634922063, "tertiary": 0.9837222991612533, "structured_records": 0.007442586616085804, "procedural_steps": 0.026010046315253582, "worked_solution": 0.0074703033391667045, "contextual_dialogue": 7.2453800985232e-06, "content_quality": 0.4396841466333046, "noise_severity": 0.9348856016775317, "missing_content_severity": 0.9782234814077255, "factual_density": 0.37377892322860484, "pretraining_utility": 0.4978012271269183, "math_density": 0.978174774425522, "code_density": 0.909212566726557, "reasoning_density": 0.21978757785961112, "education_level": 0.7033228139842915, "creative_writing_quality": 0.9988700187839602, "primary_topic": 0.9909345650568766, "secondary_topic": 0.9011242844545583}, "probability_margin": {"primary": 0.996070753688312, "secondary": 0.9693456532316066, "tertiary": 0.9782422484616379, "education_level": 0.5305696396776688, "creative_writing_quality": 0.9986478533089862, "primary_topic": 0.9854986501452444, "secondary_topic": 0.8493462626486592}, "raw_probabilities": {"primary": {"Academic Writing": 3.105421427290599e-05, "Content Listing": 2.8337001913191437e-05, "Creative Writing": 1.3942155703128884e-05, "Customer Support Page": 8.552737902230495e-06, "Q&A Forum": 1.5646940263970913e-05, "Discussion Thread": 6.326639772286912e-06, "Comment Section": 1.1666600967611571e-05, "Missing Content": 2.605112724809439e-05, "Knowledge Article": 7.6130372978965e-05, "Legal Notices": 2.692518208753156e-05, "News Article": 4.031706956619722e-06, "Organizational Announcement": 9.429048315438209e-06, "Nonfiction Writing": 1.0042464502314049e-05, "Organizational About Page": 1.1054789301832028e-05, "Personal About Page": 1.0094517159657404e-05, "Personal Blog": 6.997380833060844e-06, "Product Page": 9.683013067185238e-06, "Spam / Ads": 8.574179695032762e-06, "Structured Data": 0.0005277804317266132, "Technical Writing": 0.9972495455813557, "Transcript / Interview": 1.3284667753748253e-05, "Tutorial / How-To Guide": 0.0007099129857445317, "User Reviews": 6.144367434754747e-06, "Other / Unknown": 0.0011787918930436625}, "secondary": {"Academic Writing": 0.00015261956190719173, "Content Listing": 0.00019791476803057202, "Creative Writing": 0.00012121257613489284, "Customer Support Page": 7.132566223276316e-05, "Q&A Forum": 6.003268031331108e-05, "Discussion Thread": 4.261373774162879e-05, "Comment Section": 0.00017955363314902485, "Missing Content": 0.00028019533963389746, "Knowledge Article": 0.0006161182736233522, "Legal Notices": 0.00018847914566504247, "News Article": 6.974839616639282e-05, "Organizational Announcement": 6.190891277646348e-05, "Nonfiction Writing": 5.685162323762324e-05, "Organizational About Page": 7.391949016093507e-05, "Personal About Page": 6.21119324819808e-05, "Personal Blog": 6.172074065112368e-05, "Product Page": 5.978996738108526e-05, "Spam / Ads": 6.768171232610989e-05, "Structured Data": 0.008054341173744893, "Technical Writing": 0.00877824511509108, "Transcript / Interview": 8.575968077866963e-05, "Tutorial / How-To Guide": 0.002496253792345659, "User Reviews": 3.770373772865976e-05, "NONE": 0.9781238983466977}, "tertiary": {"Academic Writing": 8.669500670768566e-05, "Content Listing": 0.0002241245728146076, "Creative Writing": 9.234318292786642e-05, "Customer Support Page": 6.563770902297871e-05, "Q&A Forum": 4.056274398401618e-05, "Discussion Thread": 3.103145966478685e-05, "Comment Section": 0.00018921344576805962, "Missing Content": 0.00042604300923506657, "Knowledge Article": 0.0007302411876805401, "Legal Notices": 0.0002494570552271587, "News Article": 5.476390988831935e-05, "Organizational Announcement": 4.587202249961176e-05, "Nonfiction Writing": 5.5279503736478735e-05, "Organizational About Page": 7.100716666223794e-05, "Personal About Page": 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"detected_licenses": []}, "commit_id": "aa65c7f3c937d13b41c830d86d6c8d44e94d38c0"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 9, "repo_created_at": "2024-09-24T17:06:01.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "8bc160212099eb8ea9e335352ba5db0e4e6e2d94", "size_bytes": 6212, "file_timestamp": 1727985855, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "aa65c7f3c937d13b41c830d86d6c8d44e94d38c0", "url": "https://github.com/magedo992/E-commerce/blob/aa65c7f3c937d13b41c830d86d6c8d44e94d38c0/Controller/ProductController.js"}, "gpt4o_tokens": 1311, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.170942689086338, "noise_severity": 0.07813727798696198, "missing_content_severity": 0.026131822310729382, "factual_density": 3.248534707874326, "pretraining_utility": 0.5494539773345622, "math_density": 0.026190270689373536, "code_density": 3.8910550800718684, "reasoning_density": 0.9362549065684667, "education_level": 2.2276704767270985, "creative_writing_quality": -0.4674947635815673}, "score_applicability": {"education_level": 0.9989459834833725, "creative_writing_quality": 0.0009416510133665415}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/RowenTey/tiktok-product-scanner/blob/a0cedbdbd751053a2597851bcd1cc4825fdf46d7/frontend/src/components/SwipeableVideo.jsx", "id": "javascript-0-5a8117a71fb636cf766ed52b8673fb11", "text": "import { useState, useEffect, useRef, useCallback } from \"react\";\nimport ReactPlayer from \"react-player\";\nimport { getProducts, getVideos } from \"../api\";\nimport ProductsList from \"./ProductsList\";\n\nconst limit = 2;\n\nconst SwipeableVideoList = () => {\n const [currentIndex, setCurrentIndex] = useState(0);\n const [currentPage, setCurrentPage] = useState(1);\n const [totalPages, setTotalPages] = useState(1);\n const [playing, setPlaying] = useState(false);\n const [played, setPlayed] = useState(0);\n const [videos, setVideos] = useState([]);\n const [products, setProducts] = useState([]);\n const [showProducts, setShowProducts] = useState(false);\n const containerRef = useRef(null);\n\n const handleTouchStart = useCallback(\n (e) => {\n if (showProducts) {\n return;\n }\n const touch = e.touches[0];\n containerRef.current.startX = touch.clientX;\n containerRef.current.startY = touch.clientY;\n },\n [showProducts]\n );\n\n const handleTouchMove = useCallback(\n (e) => {\n if (!containerRef.current.startX || !containerRef.current.startY) {\n return;\n }\n\n if (showProducts) {\n return;\n }\n\n const touch = e.touches[0];\n const deltaX = containerRef.current.startX - touch.clientX;\n const deltaY = containerRef.current.startY - touch.clientY;\n\n // Check if the swipe is vertical (more vertical movement than horizontal)\n if (Math.abs(deltaX) > Math.abs(deltaY)) {\n return;\n }\n\n // Check if the swipe distance is significant\n const threshold = 50;\n if (Math.abs(deltaY) > threshold) {\n if (deltaY > 0) {\n handleSwipedUp();\n } else {\n handleSwipedDown();\n }\n\n containerRef.current.startX = null;\n containerRef.current.startY = null;\n }\n },\n [showProducts, videos]\n );\n\n useEffect(() => {\n const container = containerRef.current;\n container.addEventListener(\"touchstart\", handleTouchStart);\n container.addEventListener(\"touchmove\", handleTouchMove);\n\n return () => {\n container.removeEventListener(\"touchstart\", handleTouchStart);\n container.removeEventListener(\"touchmove\", handleTouchMove);\n };\n }, [handleTouchStart, handleTouchMove]);\n\n const handleSwipedUp = () => {\n setCurrentIndex((prevIndex) =>(prevIndex + 1) % videos.length);\n setPlaying(true);\n setPlayed(0);\n };\n\n const handleSwipedDown = () => {\n setCurrentIndex(\n (prevIndex) => (prevIndex - 1 + videos.length) % videos.length\n );\n setPlaying(true);\n setPlayed(0);\n setShowProducts(false);\n };\n\n const togglePlay = () => {\n setPlaying((prevPlaying) => !prevPlaying);\n };\n\n const handleVideoClick = (e) => {\n // If click is from the products list, do nothing\n if (e.target.closest(\".products-list-container\")) {\n return;\n } else if (e.target.closest(\".bg-stone-900\")) {\n return;\n }\n\n togglePlay();\n };\n\n const handleProgress = (state) => {\n setPlayed(state.played);\n };\n\n const fetchVideos = async (page) => {\n try {\n const response = await getVideos(page, limit);\n console.log(response.data.videos)\n const newVideos = response.data.videos.map(video => ({\n id: video._id,\n username: video.username,\n src: video.presignedUrl,\n title: video.title,\n }));\n\n console.log(\"New Videos\", newVideos);\n \n if (newVideos.length === 0) {\n console.log(\"No more videos found\");\n return [];\n }\n \n setTotalPages(response.data.totalPages);\n console.log(`Fetched ${newVideos.length} videos for page ${page}`);\n return newVideos;\n } catch (error) {\n console.error(\"Error fetching videos:\", error);\n return [];\n }\n };\n \n useEffect(() => {\n const initializeVideos = async () => {\n const initialVideos = await fetchVideos(1);\n setVideos(initialVideos);\n if (initialVideos.length > 0) {\n setPlaying(true);\n }\n };\n \n initializeVideos();\n }, []);\n \n // Effect to prefetch next batch\n useEffect(() => {\n const prefetchNextBatch = async () => {\n if (currentIndex === videos.length - 1 && currentPage < totalPages) {\n const nextPage = currentPage + 1;\n const newVideos = await fetchVideos(nextPage);\n setVideos(prevVideos => [...prevVideos, ...newVideos]);\n setCurrentPage(nextPage);\n }\n };\n \n prefetchNextBatch();\n }, [currentIndex, videos.length, currentPage, totalPages]);\n \n useEffect(() => {\n const fetchProducts = async () => {\n try {\n console.log(\"video Id: \", videos[currentIndex].id);\n const resp = await getProducts(videos[currentIndex].id);\n console.log(resp);\n setProducts(resp.data);\n console.log(\"Products fetched: \", resp.data.length);\n console.log(\"SUCCESSFUL GET products\");\n } catch (error) {\n console.error(\"Error fetching products:\", error);\n }\n }\n \n if (videos.length > 0) {\n setProducts([]);\n fetchProducts();\n }\n }, [currentIndex, videos]);\n\n return (\n \n
\n {videos.map((video, index) => (\n \n {index === currentIndex && (\n
\n \n
\n )}\n
\n {products.length > 0 && (\n {setShowProducts(true)}}\n >\n \n \n \n Products\n \n )}\n
\n \n {video.username || \"Anonymous\"}\n \n {video.title}\n
\n
\n
\n
\n
\n \n \n setShowProducts(false)\n }\n />\n \n \n ))}\n \n \n );\n};\n\nexport default SwipeableVideoList;\n", "text_preview": "import { useState, useEffect, useRef, useCallback } from \"react\"; import ReactPlayer from \"react-player\"; import { getProducts, getVideos } from \"../api\"; import ProductsList from \"./ProductsList\"; const limit = 2; const SwipeableVideoList ", "input_sha256": "1c69df31d706afa017178d73f60eebf15f89ea3caea51f6246b0e946b3a1f4fb", "text_sha256": "5a8117a71fb636cf766ed52b8673fb11b7db7a32e78aa7b59ca7e6632b38bae9", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8121, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6898552828541641, "noise_severity": 0.03768123776033765, "missing_content_severity": 0.0399687701334632, "factual_density": 3.1174685043272037, "pretraining_utility": 0.5909664519103339, "math_density": 0.4752239588844351, "code_density": 3.862355620145187, "reasoning_density": 0.41743508733850926, "education_level": 2.135358334499882, "creative_writing_quality": -0.18589805724366573}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004649018814529171, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9970360724238561, "secondary": 0.9859108313747417, "tertiary": 0.9907619814420764, "structured_records": 0.0121787839018963, "procedural_steps": 0.002548884643229804, "worked_solution": 0.005873212161827238, "contextual_dialogue": 8.586245376752934e-06, "content_quality": 0.4876884502745581, "noise_severity": 0.9685989685330519, "missing_content_severity": 0.9666926915554473, "factual_density": 0.26455708693933644, "pretraining_utility": 0.49449485383509595, "math_density": 0.6039800342629708, "code_density": 0.8852963501209891, "reasoning_density": 0.652137427217909, "education_level": 0.7660219891890933, "creative_writing_quality": 0.9984152770831451, "primary_topic": 0.9867242594917794, "secondary_topic": 0.7615756480189949}, "probability_margin": {"primary": 0.9954838481051361, "secondary": 0.979397408773621, "tertiary": 0.9873803347709974, "education_level": 0.6532211366686749, "creative_writing_quality": 0.9978140900009937, "primary_topic": 0.9828059381265338, "secondary_topic": 0.6455235519559358}, "raw_probabilities": {"primary": {"Academic Writing": 3.269296363415369e-05, "Content Listing": 4.445242139242468e-05, "Creative Writing": 2.113199139461524e-05, "Customer Support Page": 7.281923912886712e-06, "Q&A Forum": 1.3971869777475282e-05, "Discussion Thread": 8.247319373668863e-06, "Comment Section": 1.4300775300629554e-05, "Missing Content": 3.867979817968467e-05, "Knowledge Article": 6.0740137445935057e-05, "Legal Notices": 4.4856429920572584e-05, "News Article": 5.03646942438318e-06, "Organizational Announcement": 1.09769737485014e-05, "Nonfiction Writing": 1.2477200756529349e-05, "Organizational About Page": 1.2747126520629497e-05, "Personal About Page": 1.5125725277301038e-05, "Personal Blog": 8.12900191142744e-06, "Product Page": 1.2642904768112014e-05, "Spam / Ads": 1.265496169847227e-05, "Structured Data": 0.0003448650379817562, "Technical Writing": 0.9971595694061954, "Transcript / Interview": 1.5989385868665886e-05, "Tutorial / How-To Guide": 0.000419382828015623, "User Reviews": 8.326046441814005e-06, "Other / Unknown": 0.0016757213010593144}, "secondary": {"Academic Writing": 7.940434262823567e-05, "Content Listing": 0.0012830338592299852, "Creative Writing": 9.216495222054357e-05, "Customer Support Page": 3.156101794546177e-05, "Q&A Forum": 2.850627434088521e-05, "Discussion Thread": 2.2173794010611803e-05, "Comment Section": 9.528193905803577e-05, "Missing Content": 0.00030753735007913374, "Knowledge Article": 0.0001836768592642995, "Legal Notices": 0.000142819767839701, "News Article": 4.315208717388298e-05, "Organizational Announcement": 3.7166694278827025e-05, "Nonfiction Writing": 3.6024844359420204e-05, "Organizational About Page": 4.165041457907167e-05, "Personal About Page": 3.938071701482642e-05, "Personal Blog": 3.557688530021727e-05, "Product Page": 4.1901249854184404e-05, "Spam / Ads": 4.6896237115232965e-05, "Structured Data": 0.0031301615704515226, "Technical Writing": 0.007100471293839717, "Transcript / Interview": 4.5416149473635265e-05, "Tutorial / How-To Guide": 0.0006169099126483929, "User Reviews": 2.1251719833428396e-05, "NONE": 0.9864978800674608}, "tertiary": {"Academic Writing": 5.304029944209738e-05, "Content Listing": 0.0012671841705708764, "Creative Writing": 8.372097799026721e-05, "Customer Support Page": 2.6185671124354052e-05, "Q&A Forum": 2.225385033252856e-05, "Discussion Thread": 1.7747130886109342e-05, "Comment Section": 0.00015861047777630883, "Missing Content": 0.00031639719187683907, "Knowledge Article": 0.00017288717440805447, "Legal Notices": 0.00014772001791629435, "News Article": 3.510610967831288e-05, "Organizational Announcement": 3.212598965093565e-05, "Nonfiction Writing": 3.805057949854636e-05, "Organizational About Page": 4.580848738328571e-05, "Personal About Page": 3.464049117347817e-05, "Personal Blog": 2.6762889651195346e-05, "Product Page": 4.3325729552569055e-05, "Spam / Ads": 4.5108960127573755e-05, "Structured Data": 0.001705659222747255, "Technical Writing": 0.003766564110992551, "Transcript / Interview": 3.713562079430808e-05, "Tutorial / How-To Guide": 0.0007564559388072972, "User Reviews": 2.0610025629004985e-05, "NONE": 0.9911468988819899}, "structured_records": {"false": 0.9878212160981037, "true": 0.0121787839018963}, "procedural_steps": {"false": 0.9974511153567702, "true": 0.002548884643229804}, "worked_solution": {"false": 0.9941267878381728, "true": 0.005873212161827238}, "contextual_dialogue": {"false": 0.9999914137546232, "true": 8.586245376752934e-06}, "content_quality": {"-2": 0.009891090891750701, "-1": 0.06475665895677497, "0": 0.303272697819381, "1": 0.4697649810697462, "2": 0.15231457126234715}, "noise_severity": {"0": 0.9675710040680652, "1": 0.028441691949454607, "2": 0.0030720014440900347, "3": 0.0005656672308570328, "4": 0.0003496353075329666}, "missing_content_severity": {"0": 0.9802566076285283, "1": 0.0036985587037572857, "2": 0.01228230363521377, "3": 0.0033445159707241588, "4": 0.0004180140617764737}, "factual_density": {"0": 0.022726888263037863, "1": 0.04453253504104172, "2": 0.15756056208917155, "3": 0.34290521331917645, "4": 0.43227480128757245}, "pretraining_utility": {"-2": 0.009490243871453584, "-1": 0.064603114643699, "0": 0.35014290084201677, "1": 0.47697742698872125, "2": 0.0987863136541094}, "math_density": {"0": 0.6103244358786469, "1": 0.3307250463551746, "2": 0.042657662173693815, "3": 0.005987834188065724, "4": 0.010305021404418926}, "code_density": {"0": 0.0002457046845377715, "1": 0.0004412350191933402, "2": 0.011535633928707174, "3": 0.11226658820166772, "4": 0.875510838165894}, "reasoning_density": {"0": 0.7849384412882235, "1": 0.07500667525554308, "2": 0.09455545213341668, "3": 0.028680217475133656, "4": 0.01681921384768295}, "education_level": {"none": 0.0013616461376080042, "0": 0.002060762102733272, "1": 0.03067526622109183, "2": 0.8050183243242444, "3": 0.1517971876555695, "4": 0.00908681355875303}, "creative_writing_quality": {"not_applicable": 0.9986793975692876, "-2": 0.00010699162908785882, "-1": 0.0002089176283817652, "0": 0.0008653075682938031, "1": 0.00010136774960084814, "2": 3.80178553479817e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.38124960293436e-05, "Art & Design": 0.0007726045357943144, "Law & Crime": 4.713230284986681e-05, "Education & Careers": 5.601106911514168e-05, "Entertainment & Media": 0.004428926769407933, "Fashion & Beauty": 0.00015517554380761294, "Business, Economics & Finance": 0.002213809851054258, "Food & Dining": 3.717896269818298e-05, "Games & Gaming": 0.0003001897475983924, "Electronics & Hardware": 9.319313776335171e-05, "Health, Healthcare & Wellness": 3.132027854610603e-05, "History & Geography": 3.718906920880454e-05, "Home, Pets & Hobbies": 3.579915084656094e-05, "Industry & Production": 3.9914830637138354e-05, "Language, Literature & Philosophy": 4.978678438719669e-05, "Politics & Government": 2.5173391953232082e-05, "Religion & Spirituality": 4.1274812045570955e-05, "Science, Mathematics & Engineering": 7.90292952468917e-05, "Relationships & Community": 0.0001563300457747052, "Software Usage & Applications": 0.002475113510381709, "Programming & Software Engineering": 0.9872348648959418, "Sports & Fitness": 6.489879609297534e-05, "Vehicles & Transportation": 2.3624916510135827e-05, "Travel & Tourism": 6.198027535141861e-05, "Other / Uncovered Subject": 0.0013012095280446857, "Unknown / Insufficient Content": 0.00015445600291282338}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0013871776351767038, "Art & Design": 0.023808433669394457, "Law & Crime": 0.0007853352052182907, "Education & Careers": 0.0004917781670518988, "Entertainment & Media": 0.12522226344694387, "Fashion & Beauty": 0.0008774212431063888, "Business, Economics & Finance": 0.02531703477881502, "Food & Dining": 0.0003264378580790116, "Games & Gaming": 0.0013206859428454875, "Electronics & Hardware": 0.0005826408746751361, "Health, Healthcare & Wellness": 0.00040069896704601386, "History & Geography": 0.0003582760975227051, "Home, Pets & Hobbies": 0.0003786337344420485, "Industry & Production": 0.00029046391427688817, "Language, Literature & Philosophy": 0.00030190350624334175, "Politics & Government": 0.00033282230485194586, "Religion & Spirituality": 0.0003961166151765761, "Science, Mathematics & Engineering": 0.0006125466326562109, "Relationships & Community": 0.0011359878915477072, "Software Usage & Applications": 0.019227498859233663, "Programming & Software Engineering": 0.022298291260976582, "Sports & Fitness": 0.00037093884351167546, "Vehicles & Transportation": 0.00015712497859671714, "Travel & Tourism": 0.0004753739344287806, "Other / Uncovered Subject": 0.002398298235303159, "NONE": 0.7707458154028797}}, "probabilities": {"primary": {"Academic Writing": 3.269296363415369e-05, "Content Listing": 4.445242139242468e-05, "Creative Writing": 2.113199139461524e-05, "Customer Support Page": 7.281923912886712e-06, "Q&A Forum": 1.3971869777475282e-05, "Discussion Thread": 8.247319373668863e-06, "Comment Section": 1.4300775300629554e-05, "Missing Content": 3.867979817968467e-05, "Knowledge Article": 6.0740137445935057e-05, "Legal Notices": 4.4856429920572584e-05, "News Article": 5.03646942438318e-06, "Organizational Announcement": 1.09769737485014e-05, "Nonfiction Writing": 1.2477200756529349e-05, "Organizational About Page": 1.2747126520629497e-05, "Personal About Page": 1.5125725277301038e-05, "Personal Blog": 8.12900191142744e-06, "Product Page": 1.2642904768112014e-05, "Spam / Ads": 1.265496169847227e-05, "Structured Data": 0.0003448650379817562, "Technical Writing": 0.9971595694061954, "Transcript / Interview": 1.5989385868665886e-05, "Tutorial / How-To Guide": 0.000419382828015623, "User Reviews": 8.326046441814005e-06, "Other / Unknown": 0.0016757213010593144}, "secondary": {"Academic Writing": 7.940434262823567e-05, "Content Listing": 0.0012830338592299852, "Creative Writing": 9.216495222054357e-05, "Customer Support Page": 3.156101794546177e-05, "Q&A Forum": 2.850627434088521e-05, "Discussion Thread": 2.2173794010611803e-05, "Comment Section": 9.528193905803577e-05, "Missing Content": 0.00030753735007913374, "Knowledge Article": 0.0001836768592642995, "Legal Notices": 0.000142819767839701, "News Article": 4.315208717388298e-05, "Organizational Announcement": 3.7166694278827025e-05, "Nonfiction Writing": 3.6024844359420204e-05, "Organizational About Page": 4.165041457907167e-05, "Personal About Page": 3.938071701482642e-05, "Personal Blog": 3.557688530021727e-05, "Product Page": 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0.02531703477881502, "Food & Dining": 0.0003264378580790116, "Games & Gaming": 0.0013206859428454875, "Electronics & Hardware": 0.0005826408746751361, "Health, Healthcare & Wellness": 0.00040069896704601386, "History & Geography": 0.0003582760975227051, "Home, Pets & Hobbies": 0.0003786337344420485, "Industry & Production": 0.00029046391427688817, "Language, Literature & Philosophy": 0.00030190350624334175, "Politics & Government": 0.00033282230485194586, "Religion & Spirituality": 0.0003961166151765761, "Science, Mathematics & Engineering": 0.0006125466326562109, "Relationships & Community": 0.0011359878915477072, "Software Usage & Applications": 0.019227498859233663, "Programming & Software Engineering": 0.022298291260976582, "Sports & Fitness": 0.00037093884351167546, "Vehicles & Transportation": 0.00015712497859671714, "Travel & Tourism": 0.0004753739344287806, "Other / Uncovered Subject": 0.002398298235303159, "NONE": 0.7707458154028797}}, "probability_diagnostics": {"primary": 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"response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:12.266999+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "7fa1a7ab-e66e-4d8c-951f-ff2d885dd864", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "c08ac24109aceb0a2bead12a67f70816d99f08b6ef2d5c89c305132bd17036d9", "source_metadata": {"uuid": "7fa1a7ab-e66e-4d8c-951f-ff2d885dd864", "repo_name": "RowenTey/tiktok-product-scanner", "relative_path": "frontend/src/components/SwipeableVideo.jsx", "category": "WEB", "algo_rel_score": 0.9514369964599609, "quality_score": 7.095921516418457, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 86, "repo_created_at": "2024-06-14T14:55:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 2, "issues": 3, "pull_requests": 0}, "file_meta": {"content_id": "218c1f6a7bceb25a95c8f962f2b6a2e3c78f1dd1", "size_bytes": 10159, "file_timestamp": 1720400949, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a0cedbdbd751053a2597851bcd1cc4825fdf46d7"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 86, "repo_created_at": "2024-06-14T14:55:04.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 4, "forks": 2, "issues": 3, "pull_requests": 0}, "file_meta": {"content_id": "218c1f6a7bceb25a95c8f962f2b6a2e3c78f1dd1", "size_bytes": 10159, "file_timestamp": 1720400949, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a0cedbdbd751053a2597851bcd1cc4825fdf46d7", "url": "https://github.com/RowenTey/tiktok-product-scanner/blob/a0cedbdbd751053a2597851bcd1cc4825fdf46d7/frontend/src/components/SwipeableVideo.jsx"}, "gpt4o_tokens": 2007, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.6898552828541641, "noise_severity": 0.03768123776033765, "missing_content_severity": 0.0399687701334632, "factual_density": 3.1174685043272037, "pretraining_utility": 0.5909664519103339, "math_density": 0.4752239588844351, "code_density": 3.862355620145187, "reasoning_density": 0.41743508733850926, "education_level": 2.135358334499882, "creative_writing_quality": -0.18589805724366573}, "score_applicability": {"education_level": 0.998638353862392, "creative_writing_quality": 0.0013206024307122572}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/harshalkardile/Ecommerce-Node-React-App/blob/b19a3ab83af5d56efead438ab1fb8d6a9d19656f/frontend/src/components/AddProduct.js", "id": "javascript-0-5766b38edc7bcda8cbbcd8f2796c3f01", "text": "import React, { useState } from \"react\";\nimport { useNavigate } from \"react-router-dom\";\nimport { ClipLoader } from \"react-spinners\";\nimport { toast, ToastContainer } from \"react-toastify\";\nimport \"react-toastify/dist/ReactToastify.css\";\nimport { baseurl } from \"./baseURL\";\n\nconst AddProduct = () => {\n const [name, setName] = useState(\"\");\n const [desc, setDesc] = useState(\"\");\n const [stock, setStock] = useState(\"\");\n const [price, setPrice] = useState(\"\");\n const [category, setCategory] = useState(\"\");\n const [company, setCompany] = useState(\"\");\n const [error, setError] = useState(false);\n const [isLoading, setIsLoading] = useState(false);\n const navigate = useNavigate();\n\n const addProduct = async (e) => {\n e.preventDefault();\n if (!name || !desc || !stock || !price || !company || !category) {\n setError(true);\n return false;\n }\n\n setIsLoading(true);\n setError(false);\n\n try {\n const userId = JSON.parse(localStorage.getItem(\"user\"))._id;\n let result = await fetch(`${baseurl}/add-product`, {\n method: \"POST\",\n body: JSON.stringify({\n name,\n desc,\n stock,\n price,\n category,\n company,\n userId,\n }),\n headers: {\n \"Content-type\": \"application/json\",\n authorization: `bearer ${JSON.parse(localStorage.getItem(\"token\"))}`,\n },\n });\n result = await result.json();\n console.warn(result);\n\n if (result) {\n toast.success(\"Product added successfully\");\n navigate(\"/\"); // Redirect to product list or dashboard\n } else {\n toast.error(\"Failed to add product\");\n }\n } catch (error) {\n console.error(\"Error adding product:\", error);\n toast.error(\"An error occurred while adding the product\");\n } finally {\n setIsLoading(false);\n }\n };\n\n return (\n
\n

Add Product

\n
\n setName(e.target.value)}\n required\n />\n {error && !name && (\n Enter valid name\n )}\n\n setDesc(e.target.value)}\n required\n />\n {error && !desc && (\n Enter valid description\n )}\n\n setStock(e.target.value)}\n required\n />\n {error && !stock && (\n Enter valid stock\n )}\n\n setPrice(e.target.value)}\n required\n />\n {error && !price && (\n Enter valid price\n )}\n\n setCategory(e.target.value)}\n required\n />\n {error && !category && (\n Enter valid category\n )}\n\n setCompany(e.target.value)}\n required\n />\n {error && !company && (\n Enter valid company\n )}\n\n \n \n {isLoading && (\n
\n \n
\n )}\n \n \n
\n );\n};\n\nexport default AddProduct;\n", "text_preview": "import React, { useState } from \"react\"; import { useNavigate } from \"react-router-dom\"; import { ClipLoader } from \"react-spinners\"; import { toast, ToastContainer } from \"react-toastify\"; import \"react-toastify/dist/ReactToastify.css\"; im", "input_sha256": "0323d74c3a572a1b918c9443cb81d3879bcb76036f3b7fcf2df33899e10529ff", "text_sha256": "5766b38edc7bcda8cbbcd8f2796c3f01c27cbb70ce67266912a223b0dce7f251", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8122, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.797149765941219, "noise_severity": 0.05662908156387357, "missing_content_severity": 0.018176263503589685, "factual_density": 3.0555545470882928, "pretraining_utility": 0.5777185014810612, "math_density": 0.013730779248621509, "code_density": 3.8157859942289165, "reasoning_density": 0.31915730836750295, "education_level": 2.075915131283905, "creative_writing_quality": -0.21329490025973197}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.00026101676033859003, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9948408721742616, "secondary": 0.9735832554316148, "tertiary": 0.9734714052794385, "structured_records": 0.01492870235467972, "procedural_steps": 0.007384969846174654, "worked_solution": 0.006557635450141361, "contextual_dialogue": 6.756496897513979e-06, "content_quality": 0.5436129290685592, "noise_severity": 0.952809098696772, "missing_content_severity": 0.9848531137470086, "factual_density": 0.4228275501383979, "pretraining_utility": 0.529216925777456, "math_density": 0.9885576839594821, "code_density": 0.8464883285240971, "reasoning_density": 0.7340355763604142, "education_level": 0.8143996360108963, "creative_writing_quality": 0.9990306149796329, "primary_topic": 0.9864014357826637, "secondary_topic": 0.843869209202023}, "probability_margin": {"primary": 0.992353145664637, "secondary": 0.9602938605068462, "tertiary": 0.9602335329685515, "education_level": 0.7384289425523612, "creative_writing_quality": 0.9986940945059325, "primary_topic": 0.978378197421748, "secondary_topic": 0.7724026334158831}, "raw_probabilities": {"primary": {"Academic Writing": 4.697805262213383e-05, "Content Listing": 3.8244132249008485e-05, "Creative Writing": 3.293258501155733e-05, "Customer Support Page": 1.4303986862600195e-05, "Q&A Forum": 2.0727164399911233e-05, "Discussion Thread": 1.2157536028943235e-05, "Comment Section": 2.4587535236535625e-05, "Missing Content": 6.352030772917998e-05, "Knowledge Article": 0.00012233600297178386, "Legal Notices": 5.54697734369758e-05, "News Article": 7.0916849677282345e-06, "Organizational Announcement": 1.412196529521609e-05, "Nonfiction Writing": 1.758990942253153e-05, "Organizational About Page": 2.1257481758106308e-05, "Personal About Page": 2.0289312492889094e-05, "Personal Blog": 9.70896742079649e-06, "Product Page": 3.7592047835918354e-05, "Spam / Ads": 1.3385686073988094e-05, "Structured Data": 0.0004103706846366507, "Technical Writing": 0.9950558358336674, "Transcript / Interview": 2.0631073296086963e-05, "Tutorial / How-To Guide": 0.001227635262922763, "User Reviews": 1.0542844630616504e-05, "Other / Unknown": 0.0027026901690304587}, "secondary": {"Academic Writing": 0.00015591721312795367, "Content Listing": 0.0003125407798208619, "Creative Writing": 0.00023296180675047928, "Customer Support Page": 8.422344293811432e-05, "Q&A Forum": 4.4801666261484365e-05, "Discussion Thread": 3.964456114814571e-05, "Comment Section": 0.000113936979733039, "Missing Content": 0.0002848018490335247, "Knowledge Article": 0.0004990385727311698, "Legal Notices": 0.0002984304970012189, "News Article": 7.527561293335086e-05, "Organizational Announcement": 7.47726639757586e-05, "Nonfiction 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"https://github.com/Chain-Fox/chain-fox-frontend/blob/56a554bb5d16c0b22a7ea86d5a3aa0ab0833bf52/src/services/solanaRpcService.js", "id": "javascript-0-f42eb152c7d90e2495c959cb37bd78fd", "text": "/**\n * Solana RPC Service - 简化的 Solana RPC 调用\n * 提供直接的 API 函数用于与 Solana 网络交互\n */\n\nimport { Connection, PublicKey } from '@solana/web3.js';\n\n// Helius API 配置\nconst HELIUS_API_KEY = import.meta.env.VITE_HELIUS_API_KEY;\nconst HELIUS_RPC_URL = `https://mainnet.helius-rpc.com/?api-key=${HELIUS_API_KEY}`;\n\n// CFX Token 地址\nconst CFX_TOKEN_ADDRESS = import.meta.env.VITE_CFX_TOKEN;\n\n// SPL Token 程序 ID - 直接使用常量而不是导入\nconst TOKEN_PROGRAM_ID = '';\n\n// 创建连接实例\nlet connection = null;\nlet isInitialized = false;\n\n/**\n * 初始化 Solana 连接\n * @returns {Promise} 初始化是否成功\n */\nasync function initialize() {\n try {\n if (isInitialized) return true;\n\n if (!HELIUS_API_KEY) {\n console.error(\"未配置 Helius API 密钥。请在 .env 文件中添加 VITE_HELIUS_API_KEY\");\n return false;\n }\n\n // 创建连接\n connection = new Connection(HELIUS_RPC_URL, 'confirmed');\n\n // 测试连接\n try {\n await connection.getVersion();\n // 移除日志输出\n } catch (error) {\n console.error(\"Solana RPC 连接测试失败\", error);\n return false;\n }\n\n isInitialized = true;\n return true;\n } catch (error) {\n // 只在开发环境中输出详细错误\n if (import.meta.env.DEV) {\n console.error(\"初始化 Solana RPC 服务失败\", error);\n }\n return false;\n }\n}\n\n/**\n * 获取 Solana 连接实例\n * @returns {Connection} Solana 连接实例\n */\nasync function getConnection() {\n if (!isInitialized) {\n await initialize();\n }\n return connection;\n}\n\n/**\n * 执行 JSON-RPC 请求\n * @param {string} method - RPC 方法名\n * @param {Array} params - RPC 参数\n * @returns {Promise} - RPC 响应\n */\nasync function callJsonRpc(method, params = []) {\n if (!HELIUS_API_KEY) {\n throw new Error(\"未配置 Helius API 密钥。请在 .env 文件中添加 VITE_HELIUS_API_KEY\");\n }\n\n // 移除详细调试日志\n\n try {\n const response = await fetch(HELIUS_RPC_URL, {\n method: 'POST',\n headers: {\n 'Content-Type': 'application/json',\n },\n body: JSON.stringify({\n jsonrpc: '2.0',\n id: '1',\n method,\n params,\n }),\n });\n\n if (!response.ok) {\n const errorText = await response.text();\n throw new Error(`HTTP error! status: ${response.status}, response: ${errorText}`);\n }\n\n const data = await response.json();\n\n if (data.error) {\n throw new Error(`RPC error: ${JSON.stringify(data.error)}`);\n }\n\n // 检查结果是否存在\n if (data.result === undefined) {\n throw new Error(`RPC result is undefined for method: ${method}`);\n }\n\n // 对于 getBalance 方法，检查 value 字段\n if (method === 'getBalance' && data.result && typeof data.result === 'object') {\n // 如果结果是一个对象并且有 value 字段，返回 value\n if (data.result.value !== undefined) {\n return data.result.value;\n }\n }\n\n return data.result;\n } catch (error) {\n // 记录详细错误信息\n if (import.meta.env.DEV) {\n console.error(`RPC call failed for method ${method}:`, error);\n }\n throw error;\n }\n}\n\n/**\n * 获取钱包余额\n * @param {string} address - 钱包地址\n * @returns {Promise<{success: boolean, balance?: number, error?: Error, message?: string}>} - 余额结果\n */\nasync function getBalance(address) {\n try {\n if (!address) {\n return {\n success: false,\n message: \"未提供钱包地址\"\n };\n }\n\n // 验证地址格式\n const publicKey = new PublicKey(address);\n\n // 调用 getBalance RPC 方法\n const result = await callJsonRpc('getBalance', [publicKey.toString()]);\n\n // 移除详细调试日志\n\n // 处理不同格式的响应\n let lamports;\n\n // 如果结果是一个对象并且有 value 字段（Helius API 格式）\n if (result && typeof result === 'object' && 'value' in result) {\n lamports = result.value;\n }\n // 如果结果直接是一个数值\n else if (typeof result === 'number') {\n lamports = result;\n }\n // 其他情况\n else {\n // 移除错误日志\n return {\n success: false,\n message: \"获取余额返回了意外的格式\"\n };\n }\n\n // 检查 lamports 是否为有效数值\n if (lamports === null || lamports === undefined || isNaN(lamports)) {\n // 移除错误日志\n return {\n success: false,\n message: \"获取到的余额不是有效数值\"\n };\n }\n\n // 将 lamports 转换为 SOL (1 SOL = 10^9 lamports)\n const solBalance = lamports / 1000000000;\n\n // 再次检查转换后的值是否有效\n if (isNaN(solBalance)) {\n // 移除错误日志\n return {\n success: false,\n message: \"余额转换失败\"\n };\n }\n\n // 移除成功日志\n\n return {\n success: true,\n balance: solBalance\n };\n } catch (error) {\n // 只在开发环境中输出详细错误\n if (import.meta.env.DEV) {\n console.error(\"获取余额失败:\", error);\n }\n return {\n success: false,\n error,\n message: error.message || \"获取钱包余额失败\"\n };\n }\n}\n\n/**\n * 获取账户信息\n * @param {string} address - 账户地址\n * @returns {Promise<{success: boolean, result?: Object, error?: Error, message?: string}>} - 账户信息结果\n */\nasync function getAccountInfo(address) {\n try {\n if (!address) {\n return {\n success: false,\n message: \"未提供账户地址\"\n };\n }\n\n // 验证地址格式\n const publicKey = new PublicKey(address);\n\n // 调用 getAccountInfo RPC 方法\n const result = await callJsonRpc('getAccountInfo', [publicKey.toString(), { encoding: 'jsonParsed' }]);\n\n return {\n success: true,\n result\n };\n } catch (error) {\n // 只在开发环境中输出详细错误\n if (import.meta.env.DEV) {\n console.error(\"获取账户信息失败:\", error);\n }\n return {\n success: false,\n error,\n message: error.message || \"获取账户信息失败\"\n };\n }\n}\n\n/**\n * 获取交易信息\n * @param {string} signature - 交易签名\n * @returns {Promise<{success: boolean, result?: Object, error?: Error, message?: string}>} - 交易信息结果\n */\nasync function getTransaction(signature) {\n try {\n if (!signature) {\n return {\n success: false,\n message: \"未提供交易签名\"\n };\n }\n\n const result = await callJsonRpc('getTransaction', [signature, { encoding: 'jsonParsed' }]);\n\n return {\n success: true,\n result\n };\n } catch (error) {\n // 只在开发环境中输出详细错误\n if (import.meta.env.DEV) {\n console.error(\"获取交易信息失败:\", error);\n }\n return {\n success: false,\n error,\n message: error.message || \"获取交易信息失败\"\n };\n }\n}\n\n/**\n * 获取用户的 CFX 代币余额\n * @param {string} ownerAddress - 钱包地址\n * @returns {Promise<{success: boolean, balance?: number, error?: Error, message?: string}>} - CFX 代币余额结果\n */\nasync function getCfxTokenBalance(ownerAddress) {\n try {\n if (!ownerAddress) {\n return {\n success: false,\n message: \"未提供钱包地址\"\n };\n }\n\n if (!CFX_TOKEN_ADDRESS) {\n return {\n success: false,\n message: \"未配置 CFX 代币地址。请在 .env 文件中添加 VITE_CFX_TOKEN\"\n };\n }\n\n // 验证地址格式\n const publicKey = new PublicKey(ownerAddress);\n\n // 第一步：获取用户的所有代币账户\n try {\n // 使用 getTokenAccountsByOwner RPC 方法\n const tokenAccountsResult = await callJsonRpc('getTokenAccountsByOwner', [\n publicKey.toString(),\n {\n programId: TOKEN_PROGRAM_ID\n },\n {\n encoding: 'jsonParsed'\n }\n ]);\n\n if (!tokenAccountsResult || !tokenAccountsResult.value || !Array.isArray(tokenAccountsResult.value)) {\n return {\n success: false,\n message: \"获取代币账户失败：无效的响应格式\"\n };\n }\n\n // 第二步：查找 CFX 代币账户\n const cfxTokenAccounts = tokenAccountsResult.value.filter(item => {\n try {\n const parsedData = item.account.data.parsed;\n return parsedData.info.mint === CFX_TOKEN_ADDRESS;\n } catch (e) {\n return false;\n }\n });\n\n // 如果没有找到 CFX 代币账户，返回余额为 0\n if (cfxTokenAccounts.length === 0) {\n return {\n success: true,\n balance: 0,\n message: \"用户没有 CFX 代币账户\"\n };\n }\n\n // 第三步：获取 CFX 代币余额\n const cfxTokenAccount = cfxTokenAccounts[0];\n const tokenAccountAddress = cfxTokenAccount.pubkey;\n\n // 使用 getTokenAccountBalance RPC 方法\n const balanceResult = await callJsonRpc('getTokenAccountBalance', [tokenAccountAddress]);\n\n if (!balanceResult || !balanceResult.value) {\n return {\n success: false,\n message: \"获取 CFX 代币余额失败：无效的响应格式\"\n };\n }\n\n // 解析余额\n const { amount, decimals, uiAmount, uiAmountString } = balanceResult.value;\n\n return {\n success: true,\n balance: uiAmount,\n rawAmount: amount,\n decimals: decimals,\n uiAmountString: uiAmountString\n };\n } catch (error) {\n if (import.meta.env.DEV) {\n console.error(\"获取代币账户失败:\", error);\n }\n return {\n success: false,\n error,\n message: error.message || \"获取代币账户失败\"\n };\n }\n } catch (error) {\n // 只在开发环境中输出详细错误\n if (import.meta.env.DEV) {\n console.error(\"获取 CFX 代币余额失败:\", error);\n }\n return {\n success: false,\n error,\n message: error.message || \"获取 CFX 代币余额失败\"\n };\n }\n}\n\n// 导出所有函数\nconst solanaRpcService = {\n initialize,\n getConnection,\n getBalance,\n getAccountInfo,\n getTransaction,\n getCfxTokenBalance\n};\n\nexport { getBalance, getAccountInfo, getTransaction, getCfxTokenBalance };\nexport default solanaRpcService;\n", "text_preview": "/** * Solana RPC Service - 简化的 Solana RPC 调用 * 提供直接的 API 函数用于与 Solana 网络交互 */ import { Connection, PublicKey } from '@solana/web3.js'; // Helius API 配置 const HELIUS_API_KEY = import.meta.env.VITE_HELIUS_API_KEY; const HELIUS_RPC_URL = `http", "input_sha256": "bcb802cf50a0babcaefddfdd7c447931a220a3e92913a183de2b39c53012fcc6", "text_sha256": "f42eb152c7d90e2495c959cb37bd78fd3e0dcc64fe8ea9615586b896f3bd089d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8123, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": 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3.7989068605170218, "reasoning_density": 1.4569213721176593, "education_level": 2.467053488813566, "creative_writing_quality": -0.1865559272206281}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01235235828127049, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9954002133857967, "secondary": 0.9445430345769129, "tertiary": 0.9527194288867923, "structured_records": 0.00827625792780317, "procedural_steps": 0.13252168049452928, "worked_solution": 0.00583674742403739, "contextual_dialogue": 3.1918695636910973e-06, "content_quality": 0.3243295549091755, "noise_severity": 0.6714893964859789, "missing_content_severity": 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Content": 0.00012746685279743246, "Knowledge Article": 0.00017175038592000692, "Legal Notices": 3.889492220294307e-05, "News Article": 8.776144552546701e-06, "Organizational Announcement": 1.8171613706503086e-05, "Nonfiction Writing": 1.858192003440559e-05, "Organizational About Page": 1.872723120230657e-05, "Personal About Page": 1.8147844113864365e-05, "Personal Blog": 1.4746967759265417e-05, "Product Page": 1.491020307359791e-05, "Spam / Ads": 2.4208204197843692e-05, "Structured Data": 0.0004358605331998971, "Technical Writing": 0.9955918711613885, "Transcript / Interview": 2.4502966603112953e-05, "Tutorial / How-To Guide": 0.002043241561781446, "User Reviews": 1.2148753187648675e-05, "Other / Unknown": 0.001228626966283164}, "secondary": {"Academic Writing": 0.0002188609703980404, "Content Listing": 0.0002031189255062343, "Creative Writing": 0.0002010662667664223, "Customer Support Page": 0.00013548624412037825, "Q&A Forum": 8.995125842196762e-05, "Discussion Thread": 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"size_bytes": 10306, "file_timestamp": 1746801539, "language": "javascript", "is_vendor": false, "license_type": "permissive", "detected_licenses": ["Apache-2.0"]}, "commit_id": "56a554bb5d16c0b22a7ea86d5a3aa0ab0833bf52", "url": "https://github.com/Chain-Fox/chain-fox-frontend/blob/56a554bb5d16c0b22a7ea86d5a3aa0ab0833bf52/src/services/solanaRpcService.js"}, "gpt4o_tokens": 2566, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.38992956976300547, "noise_severity": 0.3942127242168252, "missing_content_severity": 0.6389840740124841, "factual_density": 3.084217171394414, "pretraining_utility": 0.6249080729997439, "math_density": 0.5843168298940701, "code_density": 3.7989068605170218, "reasoning_density": 1.4569213721176593, "education_level": 2.467053488813566, "creative_writing_quality": -0.1865559272206281}, "score_applicability": {"education_level": 0.9991289147005759, "creative_writing_quality": 0.001170498836447796}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/arao/workflow_webapp/blob/a16b018f1af64d7bc0fdf6e8d5d8d3e7ae74a0c0/src/containers/task/index.js", "id": "javascript-0-5ccfc4b1d8adb6533bf66888efd155a2", "text": "import _ from 'lodash';\nimport ApiConstants from 'constants/api';\nimport React from 'react';\nimport PropTypes from 'prop-types';\nimport { connect } from 'react-redux';\nimport { Container, Row, Col, Form } from 'react-bootstrap';\nimport { push } from 'connected-react-router';\n\nimport TaskForm from 'components/taskForm';\nimport UserConst from 'constants/user';\nimport TaskConst from 'constants/task';\nimport { updateWorkflowAction } from 'actions/workflow';\nimport { Link } from 'react-router-dom';\nimport { getTask, makeUpdateTask, makeTaskComplete, makeFetchWorkflowPermissions } from 'services/workflow';\nimport { getEmployee, getAllEmployees } from 'services/employees';\nimport { errorParser } from 'utils/helpers/errorHandler';\nimport { toast } from 'react-toastify';\nimport { showLoader } from 'utils/helpers/loader';\n\nexport class Task extends React.Component {\n constructor(props) {\n super(props);\n const { match, currentUser } = this.props;\n const { id: taskId } = match.params;\n this.taskId = taskId;\n this.state = {\n task: {\n assignee: currentUser.employeeId,\n },\n editable: true,\n };\n\n // bind functions.\n this.fetchTask = this.fetchTask.bind(this);\n this.markComplete = this.markComplete.bind(this);\n this.submitForm = this.submitForm.bind(this);\n this.fetchPermissions = this.fetchPermissions.bind(this);\n this.permission = this.permission.bind(this);\n }\n\n componentDidMount() {\n const { task } = this.state;\n const { activeEmployees } = this.props;\n // fetch all active employees, if only assignee missing then only assignee will be fetched\n if (Object.keys(activeEmployees).length === 0) {\n getAllEmployees();\n } else if (!Object.hasOwnProperty.call(task, 'assignee')) {\n getEmployee(task.assignee);\n }\n this.fetchTask();\n }\n\n async fetchTask() {\n showLoader(true);\n const { redirect } = this.props;\n try {\n const task = await getTask(this.taskId);\n this.setState({ task });\n await this.fetchPermissions();\n } catch (error) {\n // redirect(ApiConstants.DASHBOARD_PAGE);\n } finally {\n showLoader(false);\n }\n }\n\n async markComplete() {\n try {\n const res = await makeTaskComplete(this.taskId);\n const { response, body } = res;\n if (!response.ok) {\n const errMsg = errorParser(body, 'failed to mark complete the task');\n toast.error(errMsg);\n return;\n }\n toast.success('Task Completed');\n this.fetchTask();\n } catch (err) {\n const errMsg = errorParser(err, 'failed to complete task');\n toast.error(errMsg);\n } finally {\n showLoader(false);\n }\n }\n\n async fetchPermissions() {\n showLoader(true);\n const { task } = this.state;\n try {\n const res = await makeFetchWorkflowPermissions(task.workflow);\n const { response, body } = res;\n if (!response.ok) {\n throw new Error(body);\n }\n this.setState({ permissions: body }, () => {\n this.permission();\n });\n // this.permission();\n } catch (err) {\n const errMsg = errorParser(err, 'failed to fetch permission');\n toast.error(errMsg);\n } finally {\n showLoader(false);\n }\n }\n\n permission() {\n const { currentUser, redirect } = this.props;\n const { task, permissions } = this.state;\n\n const userPermission = permissions.filter(perm => perm.employee === currentUser.employeeId);\n if (currentUser.employeeId !== task.assignee && !currentUser.isAdmin && !userPermission.length) {\n redirect(ApiConstants.DASHBOARD_PAGE);\n }\n\n if (userPermission.length > 0 && userPermission[0].permission === UserConst.PERMISSION.READ) {\n this.setState({ editable: false });\n }\n }\n\n possibleParents(workflow) {\n return [];\n }\n\n submitForm(taskInformation, taskId) {\n const { task } = this.state;\n const { currentUser } = this.props;\n const {\n assignee,\n taskDetail,\n taskDurationDays,\n taskDurationTime,\n taskStartDeltaDays,\n taskStartDeltaTime,\n taskTitle,\n } = taskInformation;\n const submitData = {\n title: taskTitle,\n description: taskDetail,\n assignee,\n start_delta: `${taskStartDeltaDays}:${taskStartDeltaTime}`,\n duration: `${taskDurationDays}:${taskDurationTime}`,\n };\n if (task.status && task.status !== TaskConst.STATUS.UPCOMMING) {\n delete submitData.start_delta;\n }\n if (!currentUser.isAdmin) {\n delete submitData.assignee;\n }\n\n showLoader(true);\n makeUpdateTask(taskId, submitData)\n .then(res => {\n const { response, body } = res;\n if (!response.ok) {\n const errMsg = errorParser(body, 'failed to update task');\n toast.error(errMsg);\n return;\n }\n toast.success('task updated successfully');\n })\n .finally(() => {\n showLoader(false);\n });\n }\n\n render() {\n const { activeEmployees } = this.props;\n const { task, editable } = this.state;\n return (\n \n \n \n \n {'<< Workflow'}\n \n
\n \n \n \n
\n \n \n \n );\n }\n}\n\nTask.propTypes = {\n currentUser: PropTypes.object.isRequired,\n match: PropTypes.object.isRequired,\n activeEmployees: PropTypes.object,\n\n redirect: PropTypes.func.isRequired,\n};\n\nTask.defaultProps = {\n activeEmployees: {},\n};\n\nconst mapStateToProps = state => ({\n currentUser: state.currentUser,\n activeEmployees: state.employees.activeEmployees,\n workflows: state.workflows,\n upcommingTasks: state.tasks.upcomming,\n ongoingTasks: state.tasks.ongoing,\n completeTasks: state.tasks.complete,\n});\n\nconst mapDispatchToProps = dispatch => ({\n updateWorkflowAction: (...args) => dispatch(updateWorkflowAction(...args)),\n redirect: url => dispatch(push(url)),\n});\n\nexport default connect(\n mapStateToProps,\n mapDispatchToProps\n)(Task);\n", "text_preview": "import _ from 'lodash'; import ApiConstants from 'constants/api'; import React from 'react'; import PropTypes from 'prop-types'; import { connect } from 'react-redux'; import { Container, Row, Col, Form } from 'react-bootstrap'; import { pu", "input_sha256": "24ece6a9ca1005f784710daa4c86385ddef4e3f98591cbaeed6ff4e1300a74e0", "text_sha256": "5ccfc4b1d8adb6533bf66888efd155a2c013364fd9b4489285f4832e4f53d662", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8125, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7536420572428634, "noise_severity": 0.05415987421853027, "missing_content_severity": 0.03838127442162609, "factual_density": 3.345717246398315, "pretraining_utility": 0.5105765877522838, "math_density": 0.019356632272521043, "code_density": 3.8408791360747285, "reasoning_density": 0.6375956756745352, "education_level": 2.350449637519302, "creative_writing_quality": -0.2829927602428432}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006174919987282701, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9962880081915038, "secondary": 0.9853404795665781, "tertiary": 0.9917838743359165, "structured_records": 0.007724905700478561, "procedural_steps": 0.0034657842672085356, "worked_solution": 0.0019695518554405282, "contextual_dialogue": 9.039701275594906e-06, "content_quality": 0.5290513858114201, "noise_severity": 0.9548667714845581, "missing_content_severity": 0.9680156046486449, "factual_density": 0.454764371998596, "pretraining_utility": 0.46631765836688843, "math_density": 0.9838694731062325, "code_density": 0.8673992800622738, "reasoning_density": 0.46867027027122066, "education_level": 0.5823417674669734, "creative_writing_quality": 0.999348667948802, "primary_topic": 0.9884059288070015, "secondary_topic": 0.9042098743369417}, "probability_margin": {"primary": 0.9943736699878735, "secondary": 0.9776647931774619, "tertiary": 0.9893519099401268, "education_level": 0.3307933140855923, "creative_writing_quality": 0.999161447441894, "primary_topic": 0.982174545435345, "secondary_topic": 0.860511716744367}, "raw_probabilities": {"primary": {"Academic Writing": 3.487154089423632e-05, "Content Listing": 2.6300288498463223e-05, "Creative Writing": 1.8091867614284524e-05, "Customer Support Page": 8.51220867725303e-06, "Q&A Forum": 1.2721287967097061e-05, "Discussion Thread": 7.742734023910267e-06, "Comment Section": 1.5668805301523724e-05, "Missing Content": 3.7525708095209404e-05, "Knowledge Article": 6.838891074768683e-05, "Legal Notices": 4.755160174116746e-05, "News Article": 4.780680239818446e-06, "Organizational Announcement": 1.0217183418542277e-05, "Nonfiction Writing": 1.114740509055292e-05, "Organizational About Page": 1.2311529919181138e-05, "Personal About Page": 1.5011893330330752e-05, "Personal Blog": 6.675526060042676e-06, "Product Page": 7.102567884726056e-06, "Spam / Ads": 8.97674049869063e-06, "Structured Data": 0.0007454947004101749, "Technical Writing": 0.9964426745168578, "Transcript / Interview": 1.21777781712001e-05, "Tutorial / How-To Guide": 0.00037076037900166727, "User Reviews": 6.28961657208125e-06, "Other / Unknown": 0.0020690045289842494}, "secondary": {"Academic Writing": 8.86770223215659e-05, "Content Listing": 0.00020745340787712223, "Creative Writing": 8.609359300385596e-05, "Customer Support Page": 4.1507064960880656e-05, "Q&A Forum": 3.156638673695473e-05, "Discussion Thread": 2.7694397342583884e-05, "Comment Section": 9.440916312999037e-05, "Missing Content": 0.00028577368906975147, "Knowledge Article": 0.00022860237572317027, "Legal Notices": 0.00021259978097083092, "News Article": 4.646017929042315e-05, "Organizational Announcement": 4.1658651779666365e-05, "Nonfiction Writing": 3.385905619257749e-05, "Organizational About Page": 4.694323651075809e-05, "Personal About Page": 4.828887854921356e-05, "Personal Blog": 3.539918274735285e-05, "Product Page": 2.9248644495153103e-05, "Spam / Ads": 5.144458576579892e-05, "Structured Data": 0.0032643835898809727, "Technical Writing": 0.008286499740508813, "Transcript / Interview": 4.900592781447478e-05, "Tutorial / How-To Guide": 0.0007910000872908378, "User Reviews": 2.0138440066704258e-05, "NONE": 0.9859512929179707}, "tertiary": {"Academic Writing": 6.655936348876032e-05, "Content Listing": 0.00028002142081599125, "Creative Writing": 8.12799601217903e-05, "Customer Support Page": 3.68345973399422e-05, "Q&A Forum": 2.4011661747440317e-05, "Discussion Thread": 2.179823354708797e-05, "Comment Section": 0.00015743854107663703, "Missing Content": 0.0003763937468861329, "Knowledge Article": 0.0002625225967146241, "Legal Notices": 0.0004622685198730237, "News Article": 3.9360779992206724e-05, "Organizational Announcement": 3.6465748529301575e-05, "Nonfiction Writing": 4.063515498544901e-05, "Organizational About Page": 5.0695415120161595e-05, "Personal About Page": 4.26535401123609e-05, "Personal Blog": 3.0379967147442697e-05, "Product Page": 2.7008947306951377e-05, "Spam / Ads": 5.1044516029991274e-05, "Structured Data": 0.002205842502808308, "Technical Writing": 0.002774302965126616, "Transcript / Interview": 3.774402905390833e-05, "Tutorial / How-To Guide": 0.0007475185834427982, "User Reviews": 2.1006303479811463e-05, "NONE": 0.9921262129052534}, "structured_records": {"false": 0.9922750942995214, "true": 0.007724905700478561}, "procedural_steps": {"false": 0.9965342157327914, "true": 0.0034657842672085356}, "worked_solution": {"false": 0.9980304481445594, "true": 0.0019695518554405282}, "contextual_dialogue": {"false": 0.9999909602987244, "true": 9.039701275594906e-06}, "content_quality": {"-2": 0.008150117934097409, "-1": 0.0421853490627493, "0": 0.29692708796392536, "1": 0.4933472479046485, "2": 0.15939019713457955}, "noise_severity": {"0": 0.9523439612267379, "1": 0.04309562623975418, "2": 0.0031780388166867415, "3": 0.0008213245218820182, "4": 0.0005610491949391373}, "missing_content_severity": {"0": 0.9690127350081289, "1": 0.02582188636704729, "2": 0.0033948735171319654, "3": 0.0013123794104527317, "4": 0.0004581256972391674}, "factual_density": {"0": 0.008719381072648736, "1": 0.014550246660168345, "2": 0.11182696638627053, "3": 0.35210055655804373, "4": 0.5128028493228687}, "pretraining_utility": {"-2": 0.008388528873797754, "-1": 0.07865423980333447, "0": 0.38244704487566283, "1": 0.455012487591196, "2": 0.07549769885600889}, "math_density": {"0": 0.9846508196573436, "1": 0.013244693867655448, "2": 0.0009539617409046868, "3": 0.00039808401332932765, "4": 0.0007524407207670594}, "code_density": {"0": 0.000271517870282738, "1": 0.0005070832028302746, "2": 0.013629490329207182, "3": 0.12925456217723524, "4": 0.8563373464204446}, "reasoning_density": {"0": 0.6513836377894898, "1": 0.1384922328233398, "2": 0.146661080163878, "3": 0.04807091436973018, "4": 0.015392134853562235}, "education_level": {"none": 0.0010611285380718108, "0": 0.0012628221263111326, "1": 0.005895861476332444, "2": 0.6519514728891445, "3": 0.32115815880355214, "4": 0.01867055616658794}, "creative_writing_quality": {"not_applicable": 0.9994572232906683, "-2": 6.600825170587975e-05, "-1": 0.000110127543119926, "0": 0.0002957758487743934, "1": 5.3187964100362455e-05, "2": 1.7677101631024987e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.411668058476204e-05, "Art & Design": 0.00013624554374786835, "Law & Crime": 3.29123452146798e-05, "Education & Careers": 0.0004069864845862192, "Entertainment & Media": 2.336214139077473e-05, "Fashion & Beauty": 2.1024513802935945e-05, "Business, Economics & Finance": 0.006677309186771888, "Food & Dining": 3.235465735000446e-05, "Games & Gaming": 8.42379641389751e-05, "Electronics & Hardware": 6.743177114504773e-05, "Health, Healthcare & Wellness": 2.8230422086557528e-05, "History & Geography": 2.648224347226793e-05, "Home, Pets & Hobbies": 3.223604957489519e-05, "Industry & Production": 4.6451463335037986e-05, "Language, Literature & Philosophy": 3.282233885017646e-05, "Politics & Government": 2.111108933891747e-05, "Religion & Spirituality": 2.420308296292992e-05, "Science, Mathematics & Engineering": 7.543074742515074e-05, "Relationships & Community": 6.367447482634121e-05, "Software Usage & Applications": 0.001953307839975818, "Programming & Software Engineering": 0.9888518546221169, "Sports & Fitness": 3.7138017094222294e-05, "Vehicles & Transportation": 3.077227922058038e-05, "Travel & Tourism": 3.9528425572040714e-05, "Other / Uncovered Subject": 0.0011435551847376667, "Unknown / Insufficient Content": 8.722043067715057e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00041637335665092976, "Art & Design": 0.004716664021946302, "Law & Crime": 0.0005084585992209885, "Education & Careers": 0.0008903503202293899, "Entertainment & Media": 0.00037636651557336183, "Fashion & Beauty": 0.00022219182432473838, "Business, Economics & Finance": 0.047382393195000046, "Food & Dining": 0.00024175574732127972, "Games & Gaming": 0.000546440955125237, "Electronics & Hardware": 0.000432341080565214, "Health, Healthcare & Wellness": 0.00024535404041925587, "History & Geography": 0.00018051108567832065, "Home, Pets & Hobbies": 0.0002646034479949005, "Industry & Production": 0.00020868332729688014, "Language, Literature & Philosophy": 0.00019752531069979734, "Politics & Government": 0.0002646232578591307, "Religion & Spirituality": 0.0002458831887458841, "Science, Mathematics & Engineering": 0.00046058055420779677, "Relationships & Community": 0.0002832940269782624, "Software Usage & Applications": 0.016861988512391873, "Programming & Software Engineering": 0.013203243853542278, "Sports & Fitness": 0.0001840993492448099, "Vehicles & Transportation": 0.00014506430651351412, "Travel & Tourism": 0.0002705514898670943, "Other / Uncovered Subject": 0.0033565486932357466, "NONE": 0.907894109939367}}, "probabilities": {"primary": {"Academic Writing": 3.487154089423633e-05, "Content Listing": 2.6300288498463226e-05, "Creative Writing": 1.8091867614284528e-05, "Customer Support Page": 8.512208677253031e-06, "Q&A Forum": 1.2721287967097063e-05, "Discussion Thread": 7.742734023910269e-06, "Comment Section": 1.5668805301523728e-05, "Missing Content": 3.752570809520941e-05, "Knowledge Article": 6.838891074768684e-05, "Legal Notices": 4.7551601741167466e-05, "News Article": 4.780680239818447e-06, "Organizational Announcement": 1.0217183418542279e-05, "Nonfiction Writing": 1.1147405090552921e-05, "Organizational About Page": 1.231152991918114e-05, "Personal About Page": 1.5011893330330754e-05, "Personal Blog": 6.675526060042677e-06, "Product Page": 7.102567884726057e-06, "Spam / Ads": 8.976740498690632e-06, "Structured Data": 0.000745494700410175, "Technical Writing": 0.9964426745168579, "Transcript / Interview": 1.2177778171200102e-05, "Tutorial / How-To Guide": 0.0003707603790016673, "User Reviews": 6.289616572081251e-06, "Other / Unknown": 0.00206900452898425}, "secondary": {"Academic Writing": 8.867702232156589e-05, "Content Listing": 0.00020745340787712218, "Creative Writing": 8.609359300385594e-05, "Customer Support Page": 4.150706496088065e-05, "Q&A Forum": 3.1566386736954726e-05, "Discussion Thread": 2.7694397342583877e-05, "Comment Section": 9.440916312999035e-05, "Missing Content": 0.0002857736890697514, "Knowledge Article": 0.00022860237572317022, "Legal Notices": 0.00021259978097083087, "News Article": 4.646017929042314e-05, "Organizational Announcement": 4.165865177966636e-05, "Nonfiction Writing": 3.385905619257748e-05, "Organizational About Page": 4.6943236510758075e-05, "Personal About Page": 4.8288878549213545e-05, "Personal Blog": 3.5399182747352845e-05, "Product Page": 2.9248644495153096e-05, "Spam / Ads": 5.144458576579891e-05, "Structured Data": 0.003264383589880972, "Technical Writing": 0.008286499740508812, "Transcript / Interview": 4.900592781447477e-05, "Tutorial / How-To Guide": 0.0007910000872908376, "User Reviews": 2.0138440066704254e-05, "NONE": 0.9859512929179705}, "tertiary": {"Academic Writing": 6.65593634887603e-05, "Content Listing": 0.0002800214208159912, "Creative Writing": 8.127996012179029e-05, "Customer Support Page": 3.683459733994219e-05, "Q&A Forum": 2.401166174744031e-05, "Discussion Thread": 2.1798233547087967e-05, "Comment Section": 0.000157438541076637, "Missing Content": 0.0003763937468861328, "Knowledge Article": 0.00026252259671462406, "Legal Notices": 0.0004622685198730236, "News Article": 3.936077999220672e-05, "Organizational Announcement": 3.646574852930157e-05, "Nonfiction Writing": 4.0635154985449e-05, "Organizational About Page": 5.069541512016158e-05, "Personal About Page": 4.2653540112360896e-05, "Personal Blog": 3.037996714744269e-05, "Product Page": 2.700894730695137e-05, "Spam / Ads": 5.104451602999126e-05, "Structured Data": 0.0022058425028083075, "Technical Writing": 0.0027743029651266155, "Transcript / Interview": 3.7744029053908323e-05, "Tutorial / How-To Guide": 0.000747518583442798, "User Reviews": 2.100630347981146e-05, "NONE": 0.9921262129052532}, "structured_records": {"false": 0.9922750942995214, "true": 0.007724905700478561}, "procedural_steps": {"false": 0.9965342157327914, "true": 0.0034657842672085356}, "worked_solution": {"false": 0.9980304481445594, "true": 0.0019695518554405282}, "contextual_dialogue": {"false": 0.9999909602987244, "true": 9.039701275594906e-06}, "content_quality": {"-2": 0.008150117934097407, "-1": 0.042185349062749296, "0": 0.2969270879639253, "1": 0.4933472479046484, "2": 0.15939019713457953}, "noise_severity": {"0": 0.9523439612267379, "1": 0.04309562623975418, "2": 0.0031780388166867415, "3": 0.0008213245218820182, "4": 0.0005610491949391373}, "missing_content_severity": {"0": 0.9690127350081289, "1": 0.02582188636704729, "2": 0.0033948735171319654, "3": 0.0013123794104527317, "4": 0.0004581256972391674}, "factual_density": {"0": 0.008719381072648736, "1": 0.014550246660168345, "2": 0.11182696638627053, "3": 0.35210055655804373, "4": 0.5128028493228687}, "pretraining_utility": {"-2": 0.008388528873797754, "-1": 0.07865423980333447, "0": 0.38244704487566283, "1": 0.455012487591196, "2": 0.07549769885600889}, "math_density": {"0": 0.9846508196573436, "1": 0.013244693867655448, "2": 0.0009539617409046868, "3": 0.00039808401332932765, "4": 0.0007524407207670594}, "code_density": {"0": 0.000271517870282738, "1": 0.0005070832028302746, "2": 0.013629490329207182, "3": 0.12925456217723524, "4": 0.8563373464204446}, "reasoning_density": {"0": 0.6513836377894898, "1": 0.1384922328233398, "2": 0.146661080163878, "3": 0.04807091436973018, "4": 0.015392134853562235}, "education_level": {"none": 0.001061128538071811, "0": 0.0012628221263111328, "1": 0.0058958614763324445, "2": 0.6519514728891446, "3": 0.3211581588035522, "4": 0.018670556166587943}, "creative_writing_quality": {"not_applicable": 0.9994572232906684, "-2": 6.600825170587977e-05, "-1": 0.00011012754311992601, "0": 0.00029577584877439345, "1": 5.318796410036246e-05, "2": 1.767710163102499e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.4116680584762047e-05, "Art & Design": 0.00013624554374786838, "Law & Crime": 3.2912345214679806e-05, "Education & Careers": 0.0004069864845862193, "Entertainment & Media": 2.3362141390774737e-05, "Fashion & Beauty": 2.102451380293595e-05, "Business, Economics & Finance": 0.00667730918677189, "Food & Dining": 3.235465735000447e-05, "Games & Gaming": 8.423796413897512e-05, "Electronics & Hardware": 6.743177114504775e-05, "Health, Healthcare & Wellness": 2.8230422086557535e-05, "History & Geography": 2.6482243472267936e-05, "Home, Pets & Hobbies": 3.2236049574895195e-05, "Industry & Production": 4.6451463335038e-05, "Language, Literature & Philosophy": 3.2822338850176466e-05, "Politics & Government": 2.1111089338917474e-05, "Religion & Spirituality": 2.4203082962929927e-05, "Science, Mathematics & Engineering": 7.543074742515076e-05, "Relationships & Community": 6.367447482634122e-05, "Software Usage & Applications": 0.0019533078399758185, "Programming & Software Engineering": 0.9888518546221171, "Sports & Fitness": 3.71380170942223e-05, "Vehicles & Transportation": 3.077227922058039e-05, "Travel & Tourism": 3.952842557204072e-05, "Other / Uncovered Subject": 0.0011435551847376669, "Unknown / Insufficient Content": 8.722043067715058e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00041637335665092976, "Art & Design": 0.004716664021946302, "Law & Crime": 0.0005084585992209885, "Education & Careers": 0.0008903503202293899, "Entertainment & Media": 0.00037636651557336183, "Fashion & Beauty": 0.00022219182432473838, "Business, Economics & Finance": 0.047382393195000046, "Food & Dining": 0.00024175574732127972, "Games & Gaming": 0.000546440955125237, "Electronics & Hardware": 0.000432341080565214, "Health, Healthcare & Wellness": 0.00024535404041925587, "History & Geography": 0.00018051108567832065, "Home, Pets & Hobbies": 0.0002646034479949005, "Industry & Production": 0.00020868332729688014, "Language, Literature & Philosophy": 0.00019752531069979734, "Politics & Government": 0.0002646232578591307, "Religion & Spirituality": 0.0002458831887458841, "Science, Mathematics & Engineering": 0.00046058055420779677, "Relationships & Community": 0.0002832940269782624, "Software Usage & Applications": 0.016861988512391873, "Programming & Software Engineering": 0.013203243853542278, "Sports & Fitness": 0.0001840993492448099, "Vehicles & Transportation": 0.00014506430651351412, "Travel & Tourism": 0.0002705514898670943, "Other / Uncovered Subject": 0.0033565486932357466, "NONE": 0.907894109939367}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10131, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10131, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:15.119261+00:00", "scoring_key_slot": "primary", "dataset_uid": "c89926bb-2ebc-45f5-9a7d-6d5d58158a04", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "3985c038345768b58a5aee8a541bc35641b0960e03e2b3b0423bed3d9a43a07d", "source_metadata": {"uuid": "c89926bb-2ebc-45f5-9a7d-6d5d58158a04", "repo_name": "arao/workflow_webapp", "relative_path": "src/containers/task/index.js", "category": "WEB", "algo_rel_score": 0.9297828674316406, "quality_score": 6.344211578369141, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 154, "repo_created_at": "2019-07-17T19:47:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 25}, "file_meta": {"content_id": "0dc7d85e93062a7fbe2ed4b6b88ef0ecf640134f", "size_bytes": 7672, "file_timestamp": 1563393855, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a16b018f1af64d7bc0fdf6e8d5d8d3e7ae74a0c0"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 154, "repo_created_at": "2019-07-17T19:47:12.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 25}, "file_meta": {"content_id": "0dc7d85e93062a7fbe2ed4b6b88ef0ecf640134f", "size_bytes": 7672, "file_timestamp": 1563393855, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a16b018f1af64d7bc0fdf6e8d5d8d3e7ae74a0c0", "url": "https://github.com/arao/workflow_webapp/blob/a16b018f1af64d7bc0fdf6e8d5d8d3e7ae74a0c0/src/containers/task/index.js"}, "gpt4o_tokens": 1489, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 0.7536420572428635, "noise_severity": 0.05415987421853027, "missing_content_severity": 0.03838127442162609, "factual_density": 3.345717246398315, "pretraining_utility": 0.5105765877522838, "math_density": 0.019356632272521043, "code_density": 3.8408791360747285, "reasoning_density": 0.6375956756745352, "education_level": 2.350449637519302, "creative_writing_quality": -0.2829927602428432}, "score_applicability": {"education_level": 0.9989388714619283, "creative_writing_quality": 0.0005427767093315867}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/PeterArnaudov/SoftUni/blob/39b5cacc74083c8b40584fdbf49e34050067dd22/JS%20Core/JS%20Advanced/8.%20Exam/03.%20PizzUni.Tests.js", "id": "javascript-0-fe73d1380f9fe72b575dd03a00332942", "text": "const assert = require('chai').assert;\nconst PizzUni = require('./03. PizzUni');\nlet pizzUni = new PizzUni();\n\ndescribe(\"PizzUni Tests\", function() {\n beforeEach(function () {\n pizzUni = new PizzUni();\n });\n\n describe(\"constructor tests\", function() {\n it('should have three properties', function () {\n assert.equal(Object.entries(pizzUni).length, 3);\n });\n\n it('should have three exact properties', function () {\n assert.equal(Object.entries(pizzUni)[0][0], 'registeredUsers');\n assert.equal(Object.entries(pizzUni)[1][0], 'availableProducts');\n assert.equal(Object.entries(pizzUni)[2][0], 'orders');\n });\n\n it(\"should initialize empty registeredUsers\", function() {\n assert.equal(Object.entries(pizzUni.registeredUsers).length, 0);\n });\n\n it('should initialize availableProducts object with 2 properties', function () {\n assert.equal(Object.entries(pizzUni.availableProducts).length, 2);\n });\n\n it('should initialize availableProducts object with correct property names', function () {\n assert.equal(Object.entries(pizzUni.availableProducts)[0][0], 'pizzas');\n assert.equal(Object.entries(pizzUni.availableProducts)[1][0], 'drinks');\n });\n\n it('should initialize availableProducts object with correct property pizzas', function () {\n assert.equal(Object.entries(pizzUni.availableProducts)[0][1][0], 'Italian Style');\n assert.equal(Object.entries(pizzUni.availableProducts)[0][1][1], 'Barbeque Classic');\n assert.equal(Object.entries(pizzUni.availableProducts)[0][1][2], 'Classic Margherita');\n });\n\n it('should initialize availableProducts object with correct property drinks', function () {\n assert.equal(Object.entries(pizzUni.availableProducts)[1][1][0], 'Coca-Cola');\n assert.equal(Object.entries(pizzUni.availableProducts)[1][1][1], 'Fanta');\n assert.equal(Object.entries(pizzUni.availableProducts)[1][1][2], 'Water');\n });\n\n it('should initialize empty orders', function () {\n assert.equal(Object.entries(pizzUni.orders).length, 0);\n });\n });\n\n describe(\"registerUser tests\", function () {\n it('should register user', function () {\n pizzUni.registerUser('meow');\n assert.equal(pizzUni.registeredUsers.length, 1);\n });\n\n it('should throw error registering existing user', function () {\n pizzUni.registerUser('meow');\n\n assert.throws(() => pizzUni.registerUser('meow'), 'This email address (meow) is already being used!');\n });\n\n it('should register user as an object', function () {\n pizzUni.registerUser('meow');\n\n assert.isObject(pizzUni.registeredUsers[0])\n });\n\n it('should register user as an object with empty array named orderHistory', function () {\n pizzUni.registerUser('meow');\n\n assert.isEmpty(pizzUni.registeredUsers[0].orderHistory);\n });\n\n it('should return the current user object', function () {\n let currentUser = {\n email: 'meow',\n orderHistory: []\n };\n\n let actual = pizzUni.registerUser('meow');\n\n assert.deepEqual(actual, currentUser);\n });\n })\n\n describe(\"makeAnOrder tests\", function () {\n it('should throw error if user does not exist', function () {\n assert.throws(() => pizzUni.makeAnOrder('meow'), \"You must be registered to make orders!\")\n });\n\n it('should throw error if ordered pizza is not available', function () {\n pizzUni.registerUser('meow');\n\n assert.throws(() => pizzUni.makeAnOrder('meow', 'pizza'), \"You must order at least 1 Pizza to finish the order.\")\n });\n\n it('should push new object in the user orderHistory', function () {\n pizzUni.registerUser('meow');\n\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n let object = {\n orderedPizza: 'Italian Style'\n };\n\n assert.deepEqual(pizzUni.registeredUsers[0].orderHistory[0], object);\n });\n\n it('should push new object in the user orderHistory, drink included', function () {\n pizzUni.registerUser('meow');\n\n pizzUni.makeAnOrder('meow', 'Italian Style', 'Fanta');\n\n let object = {\n orderedPizza: 'Italian Style',\n orderedDrink: 'Fanta'\n };\n\n assert.deepEqual(pizzUni.registeredUsers[0].orderHistory[0], object);\n });\n\n it('should push new object in the user orderHistory, invalid drink included', function () {\n pizzUni.registerUser('meow');\n\n pizzUni.makeAnOrder('meow', 'Italian Style', 'Vodichka');\n\n let object = {\n orderedPizza: 'Italian Style'\n };\n\n assert.deepEqual(pizzUni.registeredUsers[0].orderHistory[0], object);\n });\n\n it('should return proper index', function () {\n pizzUni.registerUser('meow');\n\n pizzUni.makeAnOrder('meow', 'Italian Style');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n let actual = pizzUni.makeAnOrder('meow', 'Italian Style');\n\n assert.equal(actual, 2);\n });\n\n it('should push object to orders', function () {\n pizzUni.registerUser('meow');\n\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n let object = {\n orderedPizza: 'Italian Style',\n email: 'meow',\n status: 'pending'\n };\n\n assert.deepEqual(pizzUni.orders[0], object);\n });\n });\n\n describe(\"completeOrder tests\", function () {\n it('should complete order', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n pizzUni.completeOrder();\n\n let object = {\n orderedPizza: 'Italian Style',\n email: 'meow',\n status: 'completed'\n };\n\n assert.deepEqual(pizzUni.orders[0], object);\n });\n\n it('should complete the first order', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n pizzUni.completeOrder();\n\n let complete = {\n orderedPizza: 'Italian Style',\n email: 'meow',\n status: 'completed'\n };\n\n let incomplete = {\n orderedPizza: 'Italian Style',\n email: 'meow',\n status: 'pending'\n };\n\n assert.deepEqual(pizzUni.orders[0], complete);\n assert.deepEqual(pizzUni.orders[1], incomplete);\n });\n\n it('should return undefined if no orders', function () {\n assert.isUndefined(pizzUni.completeOrder());\n });\n });\n\n describe(\"detailsAboutMyOrder tests\", function () {\n it('should return correct order status with valid index, pending', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n assert.equal(pizzUni.detailsAboutMyOrder(0), 'Status of your order: pending');\n });\n\n it('should return correct order status with valid index, completed', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n pizzUni.completeOrder();\n\n assert.equal(pizzUni.detailsAboutMyOrder(0), 'Status of your order: completed');\n });\n\n it('should return undefined with invalid index', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n assert.isUndefined(pizzUni.detailsAboutMyOrder(1));\n });\n\n it('should return undefined with negative index', function () {\n pizzUni.registerUser('meow');\n pizzUni.makeAnOrder('meow', 'Italian Style');\n\n assert.isUndefined(pizzUni.detailsAboutMyOrder(-1));\n });\n\n it('should return undefined with no orders in the records', function () {\n assert.isUndefined(pizzUni.detailsAboutMyOrder(0));\n });\n\n it('should return undefined with no index given', function () {\n assert.isUndefined(pizzUni.detailsAboutMyOrder());\n });\n });\n});\n", "text_preview": "const assert = require('chai').assert; const PizzUni = require('./03. PizzUni'); let pizzUni = new PizzUni(); describe(\"PizzUni Tests\", function() { beforeEach(function () { pizzUni = new PizzUni(); }); describe(\"constructor tests\", functio", "input_sha256": "c16093f5cd920754b4abacaf90e93a5b465f9d66dd0d54ab5ea2614e5ec04911", "text_sha256": "fe73d1380f9fe72b575dd03a00332942f50c5301360fa7f28df67f7d13e7811a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8124, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": 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"cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:37:16.126796+00:00", "scoring_key_slot": "primary", "dataset_uid": "6702ea36-0d66-42fa-92cb-d995d8d4c7f8", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "59c2e2d61b5cc9900128d95044f5da479e6f26e93d3753abf0272bf8e11b6843", "source_metadata": {"uuid": "6702ea36-0d66-42fa-92cb-d995d8d4c7f8", "repo_name": "PeterArnaudov/SoftUni", "relative_path": "JS Core/JS Advanced/8. Exam/03. PizzUni.Tests.js", "category": "TEST", "algo_rel_score": 0.9133425354957581, "quality_score": 6.833330154418945, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 278, "repo_created_at": "2019-01-17T13:52:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e4bf6d14b77b8f3a025e23cfd88a9999cb9a7492", "size_bytes": 8639, "file_timestamp": 1705412388, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "39b5cacc74083c8b40584fdbf49e34050067dd22"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 278, "repo_created_at": "2019-01-17T13:52:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 7, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "e4bf6d14b77b8f3a025e23cfd88a9999cb9a7492", "size_bytes": 8639, "file_timestamp": 1705412388, "language": "javascript", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "39b5cacc74083c8b40584fdbf49e34050067dd22", "url": "https://github.com/PeterArnaudov/SoftUni/blob/39b5cacc74083c8b40584fdbf49e34050067dd22/JS%20Core/JS%20Advanced/8.%20Exam/03.%20PizzUni.Tests.js"}, "gpt4o_tokens": 1800, "dataset_split": "javascript", "probability_weighted_scores": {"content_quality": 1.2823484684542483, "noise_severity": 0.02834350811002652, "missing_content_severity": 0.010578044441228703, "factual_density": 3.3170960685919684, "pretraining_utility": 0.4887815543839489, "math_density": 0.11477585912894414, "code_density": 3.9638513141296357, "reasoning_density": 0.7515397913518155, "education_level": 1.9426660975365975, "creative_writing_quality": -0.19460708673774368}, "score_applicability": {"education_level": 0.9987617482660235, "creative_writing_quality": 0.000640715385379535}} {"source_group": "javascript", "source": "openbmb/UltraData-Stack-v3-L2/javascript", "domain": "github.com", "language": "javascript", "url": "https://github.com/4GeeksAcademy/sp65-final-project-g4/blob/2c68709e524b5cbd8f9d92ad3e89ea463f514292/src/front/js/pages/Map.jsx", "id": "javascript-0-255eda563aadf8b6635fb52570c35ef6", "text": "import React, { useState, useEffect, useContext } from \"react\";\nimport { Context } from \"../store/appContext.js\";\nimport { MapContainer, TileLayer } from 'react-leaflet';\nimport 'leaflet/dist/leaflet.css';\nimport L from 'leaflet';\nimport \"../../styles/map.css\";\nimport { Flats } from \"../component/Flats.jsx\";\nimport { Universities } from \"../component/Universities.jsx\";\nimport { motion } from 'framer-motion'\nimport { useTranslation } from 'react-i18next';\n\nexport const Map = () => {\n const { t, i18n } = useTranslation();\n const { store, actions } = useContext(Context);\n const [lat, setLat] = useState(0);\n const [lon, setLon] = useState(0);\n const [distance, setDistance] = useState(100);\n const [price, setPrice] = useState({ min: 0, max: 1000 });\n const [surface, setSurface] = useState(false);\n\n delete L.Icon.Default.prototype._getIconUrl;\n L.Icon.Default.mergeOptions({\n iconRetinaUrl: 'https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.7.1/images/marker-icon-2x.png',\n iconUrl: 'https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.7.1/images/marker-icon.png',\n shadowUrl: 'https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.7.1/images/marker-shadow.png',\n });\n\n const handleUniversity = (coords) => {\n var str_array = coords.split(',');\n for (var i = 0; i < str_array.length; i++) {\n // Trim the excess whitespace.\n str_array[i] = str_array[i].replace(/^\\s*/, \"\").replace(/\\s*$/, \"\");\n }\n setLat(str_array[0])\n setLon(str_array[1])\n };\n\n const handleRadio = (distanceValue) => {\n setDistance(distanceValue)\n };\n\n const handleMinChange = (e) => {\n const newMin = parseInt(e.target.value, 10);\n if (newMin <= price.max) {\n setPrice({ ...price, min: newMin });\n }\n };\n const handleMaxChange = (e) => {\n const newMax = parseInt(e.target.value, 10);\n if (newMax >= price.min) {\n setPrice({ ...price, max: newMax });\n }\n };\n\n const handleSurface = (surfaceValue) => {\n setSurface(surfaceValue)\n };\n\n useEffect(() => {\n actions.getUniversities()\n }, []);\n\n\n return (\n \n
\n
\n
\n \n
\n \n \n
\n
\n
\n
\n \n \n
\n
\n
\n \n
\n \n
\n
\n
\n
\n \n \n {!store.flats ?\n \"\"\n :\n <>\n {store.flats.map((item, key) =>\n \n )}\n \n }\n {!store.universities ?\n \"\"\n :\n <>\n {store.universities.map((item, key) =>\n \n )}\n \n }\n \n \n );\n};\n", "text_preview": "import React, { useState, useEffect, useContext } from \"react\"; import { Context } from \"../store/appContext.js\"; import { MapContainer, TileLayer } from 'react-leaflet'; import 'leaflet/dist/leaflet.css'; import L from 'leaflet'; import \".", "input_sha256": "34ece6101e6bb6c5416f9ff3cbb2fa5a720c4b81592a1ad80c1d328702b156f1", "text_sha256": "255eda563aadf8b6635fb52570c35ef698aef5dfc012e0b725298898a30ba400", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 8126, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7880848086132666, "noise_severity": 0.07217616315304842, "missing_content_severity": 0.048359040280160616, "factual_density": 2.7700655162041894, "pretraining_utility": 0.4523028474254866, "math_density": 0.20019411565748563, "code_density": 3.8689207399913625, "reasoning_density": 0.17445465616557604, "education_level": 2.0799476677085007, "creative_writing_quality": -0.36831640112694886}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005293972220980315, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9963477992585781, "secondary": 0.9735584400091497, "tertiary": 0.9747831272137731, "structured_records": 0.020850525643227306, "procedural_steps": 0.0009396197445982516, "worked_solution": 0.0026288244211060416, "contextual_dialogue": 5.802102783235221e-06, "content_quality": 0.5515421026265818, "noise_severity": 0.9398531973724596, "missing_content_severity": 0.9597007997665328, "factual_density": 0.2790274875945101, "pretraining_utility": 0.43432146772099023, "math_density": 0.8331715702854288, "code_density": 0.8907672833261351, "reasoning_density": 0.8546211198620199, "education_level": 0.8203215337603325, "creative_writing_quality": 0.9992176962965655, "primary_topic": 0.9617862834769515, "secondary_topic": 0.6704952276541152}, "probability_margin": {"primary": 0.9941629692779653, "secondary": 0.9639474749212834, "tertiary": 0.9703250680347861, "education_level": 0.7451887764492996, "creative_writing_quality": 0.9990113299665273, "primary_topic": 0.9519049976658435, "secondary_topic": 0.6277482121132196}, "raw_probabilities": {"primary": {"Academic Writing": 2.942649234554819e-05, "Content Listing": 6.892304334775056e-05, "Creative Writing": 1.9279877320129145e-05, "Customer Support Page": 6.267466929991679e-06, "Q&A Forum": 1.149502011626609e-05, "Discussion Thread": 5.913575224258913e-06, "Comment Section": 1.1720062384773032e-05, "Missing Content": 8.360815713333787e-05, "Knowledge Article": 5.1879211937559424e-05, "Legal Notices": 3.6152546925514726e-05, "News Article": 4.900385181805189e-06, "Organizational Announcement": 1.1337968710550303e-05, "Nonfiction Writing": 1.1433750106990579e-05, "Organizational About Page": 1.3789670404639383e-05, "Personal About Page": 1.717642208013119e-05, "Personal Blog": 6.161178817683835e-06, "Product Page": 1.4582960277104121e-05, "Spam / Ads": 1.0373037172838448e-05, "Structured Data": 0.0003266711752237475, "Technical Writing": 0.9964999742894707, "Transcript / Interview": 1.366416776435067e-05, "Tutorial / How-To Guide": 0.0004011087073486131, "User Reviews": 7.155822270229752e-06, "Other / Unknown": 0.002337005011505335}, "secondary": {"Academic Writing": 0.00019851668697142927, "Content Listing": 0.004369708781686564, "Creative Writing": 0.00022384890118301627, "Customer Support Page": 9.392622813452224e-05, "Q&A Forum": 7.766970342236466e-05, "Discussion Thread": 5.848002050245036e-05, "Comment Section": 0.00013423244489092754, "Missing Content": 0.0006185000214897151, "Knowledge Article": 0.00048427714335366604, "Legal Notices": 0.0011453409210802762, "News Article": 0.00010764598227615292, "Organizational Announcement": 0.0001119377402317597, "Nonfiction Writing": 0.0001042339144549241, "Organizational About Page": 0.0001163908942758577, "Personal About Page": 0.0001255829427798516, "Personal Blog": 9.65323526327173e-05, "Product Page": 0.00014108269061945935, "Spam / Ads": 0.00022041333650170804, "Structured Data": 0.004414417488620704, "Technical Writing": 0.010712696754151742, "Transcript / Interview": 0.00011944586442422653, "Tutorial / How-To Guide": 0.0016030778951680923, "User Reviews": 6.186961571271868e-05, "NONE": 0.9746601716754352}, "tertiary": {"Academic Writing": 0.00013863231089061932, "Content Listing": 0.005508762211746412, "Creative Writing": 0.000201103399558235, "Customer Support Page": 8.358154622161016e-05, "Q&A Forum": 5.453286991021967e-05, "Discussion Thread": 4.521683968313744e-05, "Comment Section": 0.00016349356738227543, "Missing Content": 0.001211831761866514, "Knowledge Article": 0.0005232991638441726, "Legal Notices": 0.005230584409721857, "News Article": 9.55687559748266e-05, "Organizational Announcement": 0.00010516479198048115, "Nonfiction Writing": 0.0001113192154384021, "Organizational About Page": 0.0001249306534523548, "Personal About Page": 0.0001133370646663208, "Personal Blog": 6.723781164503962e-05, "Product Page": 0.0001643130227527245, "Spam / Ads": 0.0005072473137383512, "Structured Data": 0.003089213271813753, "Technical Writing": 0.005418724533171427, "Transcript / Interview": 8.991087449390353e-05, "Tutorial / How-To Guide": 0.0010673719363699886, "User Reviews": 5.079242714477068e-05, "NONE": 0.9758338302465326}, "structured_records": {"false": 0.9791494743567727, "true": 0.020850525643227306}, "procedural_steps": {"false": 0.9990603802554018, "true": 0.0009396197445982516}, "worked_solution": {"false": 0.997371175578894, "true": 0.0026288244211060416}, "contextual_dialogue": {"false": 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