Spaces:
Sleeping
Sleeping
| import json | |
| import logging | |
| from typing import Dict | |
| from langchain import PromptTemplate, LLMChain | |
| from models import chat_model | |
| logger = logging.getLogger(__name__) | |
| # Updated prompt template with guidance connecting themes to question responses | |
| problem_prompt_template = PromptTemplate( | |
| input_variables=["responses", "internal_report"], | |
| template=( | |
| "You are a wellness analyst. You have the following user responses to health-related questions:\n" | |
| "{responses}\n\n" | |
| "You also have an internal analysis report:\n" | |
| "{internal_report}\n\n" | |
| "From these inputs, determine a 'problem severity percentage' for the user in the following areas: " | |
| "stress_management, low_therapy, balanced_weight, restless_night, lack_of_motivation, gut_health, anxiety, burnout.\n\n" | |
| "Consider the following connections between the questions and these themes:\n" | |
| "- stress_management is influenced by responses such as stress_level, stress_management, mood, mindfulness_frequency, and similar stress-related questions.\n" | |
| "- low_therapy relates to aspects of the user's mindset, wellness_goals, personal_growth_reflection, and similar therapeutic indicators.\n" | |
| "- balanced_weight depends on exercise, eating_habits, dietary_restrictions, activity_tracking, and other fitness/nutrition details.\n" | |
| "- restless_night is linked to answers about sleep duration, bedtime_routine, uninterrupted_sleep, and other sleep quality indicators.\n" | |
| "- lack_of_motivation correlates with mood, energy_rating, personal_growth_reflection, break_frequency, and similar motivation-related queries.\n" | |
| "- gut_health is connected to eating_habits, dietary_restrictions, health_issues, and other digestive or nutritional feedback.\n" | |
| "- anxiety is associated with responses about mood, stress_level, mindset, and related emotional well-being questions.\n" | |
| "- burnout may be reflected in high stress_level, low energy_rating, lack_of_motivation, and related fatigue or overwhelm indicators.\n\n" | |
| "Return your answer in JSON format with keys: stress_management, low_therapy, balanced_weight, restless_night, " | |
| "lack_of_motivation, gut_health, anxiety, burnout.\n" | |
| "Ensure severity percentages are numbers from 0 to 100.\n\n" | |
| "JSON Output:" | |
| ) | |
| ) | |
| problem_chain = LLMChain(llm=chat_model, prompt=problem_prompt_template) | |
| def analyze_problems_with_chain(responses: Dict[str, str], internal_report: str) -> Dict[str, float]: | |
| responses_str = "\n".join(f"{q}: {a}" for q, a in responses.items()) | |
| raw_text = problem_chain.run(responses=responses_str, internal_report=internal_report) | |
| try: | |
| # Extract JSON from the LLM output | |
| start_idx = raw_text.find('{') | |
| end_idx = raw_text.rfind('}') + 1 | |
| json_str = raw_text[start_idx:end_idx] | |
| problems = json.loads(json_str) | |
| # Ensure all eight keys are present with default values | |
| for key in [ | |
| "stress_management", | |
| "low_therapy", | |
| "balanced_weight", | |
| "restless_night", | |
| "lack_of_motivation", | |
| "gut_health", | |
| "anxiety", | |
| "burnout" | |
| ]: | |
| problems.setdefault(key, 0.0) | |
| return {k: float(v) for k, v in problems.items()} | |
| except Exception as e: | |
| logger.error(f"Error parsing problem percentages from LLM: {e}") | |
| # Return default values for all eight themes in case of an error | |
| return { | |
| "stress_management": 0.0, | |
| "low_therapy": 0.0, | |
| "balanced_weight": 0.0, | |
| "restless_night": 0.0, | |
| "lack_of_motivation": 0.0, | |
| "gut_health": 0.0, | |
| "anxiety": 0.0, | |
| "burnout": 0.0 | |
| } | |