import os import logging import requests from typing import Dict, Optional logger = logging.getLogger(__name__) # Biomechanical knowledge graph templates BIOMECHANICAL_TEMPLATES = { "warrior_2": { "knee_l": { "issue_low": { "en": "Bend your left knee more to bring it directly over your ankle.", "hi": "अपने बाएं घुटने को थोड़ा और मोड़ें ताकि वह टखने के ठीक ऊपर रहे।", "bn": "আপনার বাম হাঁটু আরও বাঁকুন যাতে এটি গোড়ালির ঠিক উপরে থাকে।" } }, "knee_r": { "issue_low": { "en": "Bend your right knee more to align it directly over your ankle.", "hi": "अपने दाएं घुटने को थोड़ा और मोड़ें ताकि वह टखने के ठीक ऊपर रहे।", "bn": "আপনার ডান হাঁটু আরও বাঁকুন যাতে এটি গোড়ালির ঠিক উপরে থাকে।" } } }, "chair_pose": { "knee_l": { "issue_low": { "en": "Sit deeper to bring your thighs closer to parallel to the floor.", "hi": "जांघों को फर्श के समानांतर लाने के लिए थोड़ा नीचे बैठें।", "bn": "উরু মেঝের সমান্তরাল করার জন্য আর একটু নিচে বসুন।" } }, "knee_r": { "issue_low": { "en": "Sit deeper to align both thighs towards parallel to the floor.", "hi": "दोनों जांघों को फर्श के समानांतर करने के लिए थोड़ा नीचे बैठें।", "bn": "দুই উরু মেঝের সমান্তরাল করার জন্য আর একটু নিচে বসুন।" } } }, "cobra_pose": { "neck": { "issue_low": { "en": "Elongate your neck and gaze forward, relaxing your shoulders.", "hi": "अपनी गर्दन को लंबी करें और सामने की ओर देखें, कंधों को ढीला छोड़ें।", "bn": "আপনার ঘাড় সোজা করুন এবং সামনের দিকে তাকান, কাঁধ শিথিল রাখুন।" } } }, "plank": { "elbow_l": { "issue_low": { "en": "Extend your left elbow to keep your arm straight.", "hi": "अपने बाएं हाथ को सीधा रखने के लिए अपनी बाईं कोहनी को फैलाएं।", "bn": "আপনার বাম হাত সোজা রাখতে বাম কনুই প্রসারিত করুন।" } }, "elbow_r": { "issue_low": { "en": "Extend your right elbow to keep your arm straight.", "hi": "अपने दाएं हाथ को सीधा रखने के लिए अपनी दाईं कोहनी को फैलाएं।", "bn": "আপনার ডান হাত সোজা রাখতে ডান কনুই প্রসারিত করুন।" } }, "trunk_l": { "issue_low": { "en": "Keep your spine and body in a straight line, do not let your hips sag.", "hi": "अपनी रीढ़ और शरीर को एक सीधी रेखा में रखें, अपने नितंबों को नीचे न झुकने दें।", "bn": "আপনার মেরুদণ্ড এবং শরীর সোজা রাখুন, নিতম্ব ঝুলে যেতে দেবেন না।" } } }, "tree_pose": { "knee_l": { "issue_low": { "en": "Bend your left knee more and place your foot on your inner thigh.", "hi": "अपने बाएं घुटने को अधिक मोड़ें और अपने पैर को अपनी आंतरिक जांघ पर रखें।", "bn": "আপনার বাম হাঁটু আরও বাঁকুন এবং পায়ের পাতাটি উরুর ভেতরের দিকে রাখুন।" } }, "hip_abduct_l": { "issue_low": { "en": "Open your left hip outward to bring your knee to the side.", "hi": "अपने बाएं कूल्हे को बाहर की ओर खोलें ताकि आपका घुटना बगल की ओर हो सके।", "bn": "আপনার বাম হাঁটু পাশের দিকে আনতে বাম হিপ বাইরের দিকে প্রসারিত করুন।" } }, "knee_r": { "issue_low": { "en": "Keep your standing right leg fully straight and strong.", "hi": "अपने खड़े हुए दाएं पैर को पूरी तरह से सीधा और मजबूत रखें।", "bn": "দাঁড়িয়ে থাকা ডান পা সম্পূর্ণ সোজা এবং শক্ত রাখুন।" } } }, "mountain_pose": { "knee_l": { "issue_low": { "en": "Keep your left leg straight and engage your thigh muscles.", "hi": "अपने बाएं पैर को सीधा रखें और अपनी जांघ की मांसपेशियों को सक्रिय करें।", "bn": "আপনার বাম পা সোজা রাখুন এবং উরুর পেশী সক্রিয় করুন।" } }, "knee_r": { "issue_low": { "en": "Keep your right leg straight and engage your thigh muscles.", "hi": "अपने दाएं पैर को सीधा रखें और अपनी जांघ की मांसपेशियों को सक्रिय करें।", "bn": "আপনার ডান পা সোজা রাখুন এবং উরুর পেশী সক্রিয় করুন।" } }, "trunk_l": { "issue_low": { "en": "Lengthen your spine and stand tall with shoulders relaxed.", "hi": "अपनी रीढ़ को लंबा करें और कंधों को आराम देते हुए सीधे खड़े हों।", "bn": "মেরুদণ্ড সোজা করে বুক টানটান করে দাঁড়ান এবং কাঁধ শিথিল রাখুন।" } } }, "downward_dog": { "hip_l": { "issue_low": { "en": "Lift your hips higher to form a taller inverted V shape.", "hi": "अपने कूल्हों को ऊंचा उठाएं ताकि एक बड़ा उल्टा वी आकार बने।", "bn": "একটি বড় উল্টানো ভি আকৃতি তৈরি করতে আপনার নিতম্ব আরও উঁচু করুন।" } }, "knee_l": { "issue_low": { "en": "Straighten your legs as much as comfortable, keeping heels reaching down.", "hi": "जितना आरामदायक हो अपने पैरों को सीधा करें, एड़ियों को नीचे की ओर ले जाते हुए।", "bn": "যতটা আরামদায়ক পা সোজা করুন, গোড়ালি নিচের দিকে রাখুন।" } }, "shoulder_l": { "issue_low": { "en": "Press firmly through your hands and extend your arms fully.", "hi": "अपने हाथों से मजबूती से दबाएं और अपनी बाहों को पूरी तरह फैलाएं।", "bn": "হাতের উপর দৃঢ়ভাবে চাপ দিন এবং বাহু সম্পূর্ণ প্রসারিত করুন।" } } } } DEFAULT_CORRECTIONS = { "en": "Maintain your posture, breathing steadily.", "hi": "अपनी मुद्रा बनाए रखें, लगातार सांस लेते रहें।", "bn": "আপনার অঙ্গভঙ্গি বজায় রাখুন, নিয়মিত শ্বাস নিন।" } # Escalation wrappers for the correction-efficacy loop. # # When a cue has been delivered and the targeted joint did NOT measurably # improve, repeating the identical sentence is the least useful thing the # system can do -- it is what makes automated coaching feel deaf. These are # pose-agnostic so they compose with any base cue in any of the three # languages, instead of needing 3 hand-written variants per pose per joint. # # Tier 2 adds the measured magnitude, which turns a vague instruction into a # quantified one. Tier 3 stops asking for more and backs off, because a cue # that has failed twice usually means the range of motion is not available # today -- pushing harder there is exactly the injury risk this system exists # to avoid. _ESCALATION = { 1: { "en": "{base} You're about {deg} degrees off.", "hi": "{base} आप लगभग {deg} डिग्री दूर हैं।", "bn": "{base} আপনি প্রায় {deg} ডিগ্রি দূরে আছেন।", }, 2: { "en": "Ease out of the shape and reset. Come back only as far as feels comfortable today.", "hi": "मुद्रा से धीरे-धीरे बाहर आएं और फिर से शुरू करें। आज जितना सहज लगे, उतना ही करें।", "bn": "ভঙ্গি থেকে ধীরে বেরিয়ে আসুন এবং আবার শুরু করুন। আজ যতটা স্বচ্ছন্দ, ততটুকুই করুন।", }, } def _escalate(text: str, language: str, attempt: int, deg: float) -> str: """attempt counts PRIOR deliveries of this cue that produced no measured improvement. 0 -> the plain cue.""" tier = min(int(attempt), 2) if tier <= 0: return text tmpl = _ESCALATION[tier].get(language) or _ESCALATION[tier]["en"] return tmpl.format(base=text, deg=int(round(deg))) def generate_safe_correction( pose_id: str, deviations: Dict[str, float], language: str = "en", groq_api_key: Optional[str] = None, attempt: int = 0, ) -> tuple: """ Enforces a 3-stage validation pipeline: Stage 1: Symbolic Rules Engine + Local template matching. Stage 2: Optional Groq API translation/paraphrase inside safe boundaries. Stage 3: Post-generation safety validation screening. """ pose_templates = BIOMECHANICAL_TEMPLATES.get(pose_id, {}) # Stage 1: Find highest deviation joint exceeding safe threshold (10 degrees) target_joint = None max_dev = 0.0 for joint, dev_val in deviations.items(): if joint in pose_templates and dev_val > max_dev: if dev_val > 10.0: max_dev = dev_val target_joint = joint # Retrieve pre-approved template if target_joint: correction_text = pose_templates[target_joint]["issue_low"].get(language, "Adjust your alignment.") else: correction_text = DEFAULT_CORRECTIONS.get(language, "Adjust your alignment.") is_safe = True # Tier 2 replaces the cue outright with a back-off instruction, so there is # nothing left to paraphrase and no reason to spend an LLM call on it. if target_joint and attempt >= 2: return _escalate(correction_text, language, attempt, max_dev), True, target_joint # Stage 2: Paraphrase using LLM if Groq API key is available api_key = groq_api_key or os.environ.get("GROQ_API_KEY") if api_key: try: headers = { "Authorization": f"Bearer {api_key}", "Content-Type": "application/json" } # Map language code to full language name to instruct the LLM lang_names = {"en": "English", "hi": "Hindi", "bn": "Bengali"} target_lang_name = lang_names.get(language, "English") # qwen/qwen3.6-27b was correct when this was written but has since # been decommissioned: Groq now answers it with 404 model_not_found. # Because the non-200 branch below used to be silent, the service # had been quietly returning the Stage-1 template for every request # instead of the paraphrase -- verified by the live response being # byte-identical to the template string. payload = { "model": "qwen/qwen3.8-27b", # A system-only message list is rejected by this model with # 400 "No user query found in messages", so the instruction to # rewrite goes in the system role and the text to be rewritten # goes in the user role -- which is also the correct shape. "messages": [ { "role": "system", "content": f"You are a professional yoga instructor. Translate or paraphrase the user's instruction into {target_lang_name}. Do NOT suggest stretching further or pushing deeper. Keep it safe, concise (under 15 words), and respond ONLY in the {target_lang_name} language, with no preamble." }, {"role": "user", "content": correction_text}, ], "temperature": 0.2, "max_tokens": 120, "reasoning_format": "hidden" } response = requests.post("https://api.groq.com/openai/v1/chat/completions", json=payload, headers=headers, timeout=8) if response.status_code != 200: # Log it. The template fallback is a correct and safe outcome, # which is exactly why a silent one is dangerous: it looks # identical to success from the outside. logger.warning( "Groq correction call returned %s, falling back to template: %s", response.status_code, response.text[:200]) if response.status_code == 200: candidate_text = response.json()["choices"][0]["message"]["content"].strip() # Stage 3: Post-Generation Safety Validation forbidden_words = ["push", "force", "hurt", "pain", "stretch more", "further"] if not any(word in candidate_text.lower() for word in forbidden_words): correction_text = candidate_text else: is_safe = False # Flag candidate as unsafe; fallback to rule-based template except Exception as e: logger.warning(f"Groq correction call failed, falling back to template: {e}") if target_joint and attempt >= 1: correction_text = _escalate(correction_text, language, attempt, max_dev) return correction_text, is_safe, target_joint