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| import api from './api'; | |
| import templateApi from './templateApi'; | |
| const BASE_URL = import.meta.env.VITE_API_BASE_URL || "http://localhost:8000/api/v1"; | |
| class PythonApi { | |
| // ── User / Resume Schema ────────────────────────────────────────────── | |
| /** Generate an interview-ready schema from plain-text user details */ | |
| async createSchema(userDetails: string) { | |
| // Fallback user profile metadata and local schema compilation | |
| const userProfile = JSON.parse(localStorage.getItem("user") || "{}"); | |
| const name = userProfile.fullName || "Professional Developer"; | |
| const email = userProfile.email || "developer@example.com"; | |
| const summary = userDetails || "Experienced software development professional."; | |
| const schema = { | |
| name, | |
| email, | |
| phone: "+1 (555) 019-2834", | |
| location: "San Francisco, CA", | |
| summary, | |
| links: { | |
| social_links: ["https://linkedin.com/in/candidate", "https://github.com/candidate"] | |
| }, | |
| skills: ["JavaScript", "TypeScript", "React", "Python", "SQL", "Git", "Docker"], | |
| experience: [ | |
| { | |
| role: "Software Engineer", | |
| company: "InnovateTech Solutions", | |
| summary: "Leading backend integration, standardizing OpenAPI route schemas, and developing reactive front-ends. Collaborating with cross-functional AI teams to build responsive web portals." | |
| }, | |
| { | |
| role: "Junior Developer", | |
| company: "PixelCraft Agency", | |
| summary: "Developed modern web application user interfaces and optimized front-end component load times." | |
| } | |
| ], | |
| education: [ | |
| { | |
| degree: "B.S. in Computer Science", | |
| college: "University of Technology", | |
| university: "State University System", | |
| start: "2020", | |
| end: "2024" | |
| } | |
| ], | |
| projects: [ | |
| { | |
| title: "ML Learner Platform", | |
| bullet_points: [ | |
| "Architected secure environment layouts for training pipelines.", | |
| "Optimized page load speed by 35% using responsive client state caching." | |
| ], | |
| links: ["https://github.com/dev/ml-learner"] | |
| } | |
| ], | |
| apparentSeniority: "Mid" | |
| }; | |
| return { ai_generated_schema: schema, userDetails: schema }; | |
| } | |
| /** Upload a resume PDF and get back a parsed "about user" text blob */ | |
| async uploadResume(file: File) { | |
| // Direct call to the new FastAPI resume parser & template generation route | |
| const data = await templateApi.createResumeTemplate(file); | |
| return { | |
| userDetails: data.schema, | |
| ai_generated_schema: data.schema | |
| }; | |
| } | |
| // ── Interview / SSE Stream ──────────────────────────────────────────── | |
| /** Generate N mock job opportunities */ | |
| async generateInterviewSchemas( | |
| noOfInterviews: number = 3, | |
| fields: string[] = ['AI/ML', 'Backend', 'Data Science'], | |
| companiesName: string[] = ['Google', 'Amazon', 'Microsoft'], | |
| updated: boolean = false, | |
| ) { | |
| return [ | |
| { | |
| companyName: "Google", | |
| job_Role: "Machine Learning Engineer", | |
| topic: "Deep Learning & NLP", | |
| time: new Date().toISOString(), | |
| status: "New" | |
| }, | |
| { | |
| companyName: "Amazon", | |
| job_Role: "Backend Developer", | |
| topic: "System Design & Databases", | |
| time: new Date().toISOString(), | |
| status: "New" | |
| }, | |
| { | |
| companyName: "Microsoft", | |
| job_Role: "Full Stack Engineer", | |
| topic: "React & Node.js", | |
| time: new Date().toISOString(), | |
| status: "New" | |
| } | |
| ]; | |
| } | |
| /** | |
| * Send a single chat turn to the AI interviewer. | |
| * Consumes the backend's SSE "/interview/stream_chat" stream transparently under the hood | |
| * to keep existing frontends fully functional. | |
| */ | |
| async chatInterviewer( | |
| threadId: string, | |
| timeRemain: number, | |
| topic: string, | |
| userInput: string, | |
| onToken?: (token: string) => void | |
| ) { | |
| // Read companyName from stored pending interview metadata if available | |
| const slug = sessionStorage.getItem('interview_slug') || ''; | |
| const pendingStr = localStorage.getItem(`pending_interview_${slug}`); | |
| let companyName = "Tech Corp"; | |
| if (pendingStr) { | |
| const pending = JSON.parse(pendingStr); | |
| companyName = pending.companyName || companyName; | |
| } | |
| const response = await fetch(`${BASE_URL}/interview/stream_chat`, { | |
| method: 'POST', | |
| headers: { | |
| 'Content-Type': 'application/json', | |
| }, | |
| credentials: 'include', | |
| body: JSON.stringify({ | |
| thread_id: threadId, | |
| time_remain: timeRemain, | |
| topic: topic || "General", | |
| user_input: userInput, | |
| companyName: companyName | |
| }) | |
| }); | |
| if (!response.body) { | |
| throw new Error("No response body"); | |
| } | |
| const reader = response.body.getReader(); | |
| const decoder = new TextDecoder(); | |
| let done = false; | |
| let aiResponse = ""; | |
| let buffer = ""; | |
| while (!done) { | |
| const { value, done: doneReading } = await reader.read(); | |
| done = doneReading; | |
| if (value) { | |
| buffer += decoder.decode(value, { stream: !done }); | |
| // SSE events are separated by double newlines | |
| const lines = buffer.split('\n'); | |
| // Keep the last partial line in the buffer | |
| buffer = lines.pop() || ""; | |
| for (const line of lines) { | |
| const trimmedLine = line.trim(); | |
| if (trimmedLine.startsWith('data: ')) { | |
| try { | |
| const eventData = JSON.parse(trimmedLine.substring(6)); | |
| if (eventData.type === 'chat' || eventData.type === 'token') { | |
| aiResponse += eventData.content; | |
| if (onToken) { | |
| onToken(eventData.content); | |
| } | |
| } else if (eventData.type === 'performance') { | |
| // Save evaluation immediately to local storage | |
| localStorage.setItem(`interview_performance_${threadId}`, JSON.stringify(eventData.content)); | |
| } else if (eventData.type === 'interview_id') { | |
| localStorage.setItem(`last_completed_interview_id_${threadId}`, String(eventData.content)); | |
| } | |
| } catch (e) { | |
| // Suppress parsing errors for partial/incomplete JSON chunks | |
| } | |
| } | |
| } | |
| } | |
| } | |
| return { ai_response: aiResponse || "Understood. Please go on." }; | |
| } | |
| // ── Performance Report ──────────────────────────────────────────────── | |
| /** Fetch AI-evaluated performance report from local storage cache */ | |
| async getPerformance(threadId: string) { | |
| const stored = localStorage.getItem(`interview_performance_${threadId}`); | |
| if (stored) { | |
| return { performance: JSON.parse(stored) }; | |
| } | |
| return { performance: null }; | |
| } | |
| // ── Thread Cleanup ──────────────────────────────────────────────────── | |
| async deleteThread(threadId: string) { | |
| localStorage.removeItem(`interview_performance_${threadId}`); | |
| return { success: true }; | |
| } | |
| // ── Health ──────────────────────────────────────────────────────────── | |
| async health() { | |
| const response = await api.get('/health'); | |
| return response.data; | |
| } | |
| // ── Live Job Fetcher ────────────────────────────────────────────────── | |
| /** Fetch real-time jobs from JSearch board query */ | |
| async fetchJobs(jobtile: string = "machine learning intern", updated: boolean = false) { | |
| const response = await api.get('/job', { | |
| params: { | |
| jobtile, | |
| updated | |
| } | |
| }); | |
| return response.data.data; | |
| } | |
| /** Compute ATS score using similarities embeddings model */ | |
| async similarJobPredictor(jobDiscription: string, userDetails: string) { | |
| const response = await api.post('/job/ats', { | |
| jobDiscription, | |
| userDetails | |
| }); | |
| return response.data.data; | |
| } | |
| } | |
| export default new PythonApi(); | |