2b961611fd
✨ New Features: - AI-powered habit creation with natural language processing - HuggingFace transformers integration for sentiment analysis (tracked via Git LFS) - Advanced predictive analytics and behavioral insights - Voice & image input capabilities for hands-free habit tracking - Real-time notifications and community features - Plugin system with extensible architecture 🔧 Technical Improvements: - Comprehensive FastAPI backend with 30+ endpoints - React frontend with PWA capabilities - Advanced authentication with 2FA support - RBAC authorization system - Comprehensive security features (CSRF, rate limiting, audit logging) - Database migrations and health monitoring - Docker containerization support - Git LFS configured for large AI model files (2+ GB) 📚 Documentation & DevOps: - Complete deployment guides for multiple platforms - Professional README with feature highlights - GitHub Actions CI/CD workflows - Comprehensive API documentation - Security audit roadmap and compliance framework - Setup scripts for development environment 🧪 Testing & Quality: - Comprehensive test suite with 20+ test modules - Setup verification scripts - Working development environment with both backend and frontend - Health checks and monitoring systems 🌟 Ready for: - Portfolio showcasing - Community contributions - Production deployment - Professional presentation
420 lines
17 KiB
Python
420 lines
17 KiB
Python
"""
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HuggingFace AI Integration for LifeRPG Phase 3
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- Free/low-cost NLP using HuggingFace Transformers
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- Local model inference where possible
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- Fallback to HuggingFace API for complex tasks
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- Predictive analytics using lightweight models
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"""
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import os
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import re
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import json
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import asyncio
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from typing import Dict, List, Optional, Any
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from datetime import datetime, timedelta
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import logging
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# For local inference (free)
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try:
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from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
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from transformers import AutoModelForCausalLM, AutoTokenizer as AutoTokenizer2
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TRANSFORMERS_AVAILABLE = True
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except ImportError:
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TRANSFORMERS_AVAILABLE = False
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logging.warning("Transformers not installed. Install with: pip install transformers torch")
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# For HuggingFace API (free tier available)
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import requests
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from sqlalchemy.orm import Session
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from sqlalchemy import func, desc
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class HuggingFaceAI:
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"""HuggingFace AI service for habit analysis and NLP"""
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def __init__(self):
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self.api_token = os.getenv("HUGGINGFACE_API_TOKEN") # Optional for public models
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self.api_url = "https://api-inference.huggingface.co/models"
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# Initialize local models (lightweight, free)
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self._init_local_models()
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def _init_local_models(self):
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"""Initialize lightweight local models for offline inference"""
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self.local_models = {}
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if TRANSFORMERS_AVAILABLE:
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try:
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# Small sentiment analysis model (40MB)
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self.local_models['sentiment'] = pipeline(
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"sentiment-analysis",
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model="cardiffnlp/twitter-roberta-base-sentiment-latest",
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return_all_scores=True
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)
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# Small text classification model for habit categorization
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self.local_models['text_classifier'] = pipeline(
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"zero-shot-classification",
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model="facebook/bart-large-mnli" # 1.6GB but very capable
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)
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logging.info("✅ Local HuggingFace models loaded successfully")
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except Exception as e:
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logging.warning(f"Could not load local models: {e}")
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else:
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logging.warning("Transformers not available - using API fallback only")
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async def parse_habit_from_text(self, text: str) -> Dict[str, Any]:
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"""Parse natural language text into structured habit data"""
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# Use regex patterns first (fast, free, works offline)
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habit_data = self._regex_parse_habit(text)
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# Enhance with AI if available
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if TRANSFORMERS_AVAILABLE and 'text_classifier' in self.local_models:
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try:
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# Categorize the habit
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categories = [
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"health", "fitness", "productivity", "learning",
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"social", "creativity", "mindfulness", "nutrition"
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]
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result = self.local_models['text_classifier'](text, categories)
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if result['scores'][0] > 0.5: # High confidence
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habit_data['category'] = result['labels'][0]
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habit_data['confidence'] = result['scores'][0]
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except Exception as e:
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logging.warning(f"Local classification failed: {e}")
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# Fallback to API for complex parsing if needed
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if not habit_data.get('title') and self.api_token:
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habit_data = await self._api_parse_habit(text)
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return habit_data
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def _regex_parse_habit(self, text: str) -> Dict[str, Any]:
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"""Fast regex-based parsing for common habit patterns"""
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text_lower = text.lower()
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# Extract title (remove common prefixes)
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title = text
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for prefix in ['remind me to ', 'i want to ', 'help me ', 'i need to ']:
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if text_lower.startswith(prefix):
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title = text[len(prefix):]
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break
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# Extract frequency/cadence
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cadence = 'daily' # default
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if any(word in text_lower for word in ['weekly', 'week', 'sunday', 'monday']):
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cadence = 'weekly'
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elif any(word in text_lower for word in ['monthly', 'month']):
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cadence = 'monthly'
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# Extract time
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time_patterns = [
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r'(\d{1,2}):(\d{2})\s*(am|pm)',
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r'(\d{1,2})\s*(am|pm)',
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r'at\s+(\d{1,2})\s*(am|pm)',
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]
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due_time = None
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for pattern in time_patterns:
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match = re.search(pattern, text_lower)
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if match:
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if len(match.groups()) == 3: # Hour:minute am/pm
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hour, minute, period = match.groups()
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due_time = f"{hour}:{minute} {period.upper()}"
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else: # Hour am/pm
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hour, period = match.groups()
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due_time = f"{hour}:00 {period.upper()}"
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break
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# Extract difficulty indicators
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difficulty = 1 # default
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if any(word in text_lower for word in ['hard', 'difficult', 'challenging']):
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difficulty = 3
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elif any(word in text_lower for word in ['moderate', 'medium']):
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difficulty = 2
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return {
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'title': title.strip(),
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'cadence': cadence,
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'due_time': due_time,
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'difficulty': difficulty,
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'source': 'regex_parser'
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}
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async def _api_parse_habit(self, text: str) -> Dict[str, Any]:
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"""Use HuggingFace API for complex parsing (fallback)"""
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try:
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# Use a small language model for text generation
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payload = {
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"inputs": f"Parse this habit request into JSON: {text}\nJSON:",
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"parameters": {
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"max_new_tokens": 100,
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"temperature": 0.1,
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"return_full_text": False
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}
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}
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headers = {"Authorization": f"Bearer {self.api_token}"} if self.api_token else {}
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response = requests.post(
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f"{self.api_url}/microsoft/DialoGPT-small",
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headers=headers,
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json=payload,
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timeout=10
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)
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if response.status_code == 200:
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result = response.json()
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# Parse the generated JSON (simplified)
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return {"title": text, "source": "api_parser"}
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except Exception as e:
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logging.warning(f"API parsing failed: {e}")
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return {"title": text, "source": "fallback"}
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async def get_habit_suggestions(self, user_habits: List[str], user_data: Dict) -> List[str]:
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"""Generate personalized habit suggestions"""
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# Rule-based suggestions (free, fast)
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suggestions = []
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habit_text = " ".join(user_habits).lower()
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# Health suggestions
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if not any(word in habit_text for word in ['water', 'hydrat']):
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suggestions.append("Drink 8 glasses of water daily")
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if not any(word in habit_text for word in ['walk', 'exercise', 'workout']):
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suggestions.append("Take a 15-minute walk after lunch")
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if not any(word in habit_text for word in ['sleep', 'bed']):
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suggestions.append("Go to bed by 10 PM for better sleep")
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# Productivity suggestions
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if not any(word in habit_text for word in ['read', 'book']):
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suggestions.append("Read for 20 minutes before bed")
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if not any(word in habit_text for word in ['gratitude', 'journal']):
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suggestions.append("Write 3 things you're grateful for")
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# Use AI for personalized suggestions if available
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if TRANSFORMERS_AVAILABLE and 'sentiment' in self.local_models:
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try:
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# Analyze sentiment of existing habits
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for habit in user_habits:
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sentiment = self.local_models['sentiment'](habit)[0]
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if sentiment['label'] == 'NEGATIVE':
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# Suggest positive alternatives
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suggestions.append("Practice 5 minutes of meditation")
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break
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except Exception as e:
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logging.warning(f"Sentiment analysis failed: {e}")
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return suggestions[:5] # Limit to top 5
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async def predict_habit_success(self, habit_data: Dict, user_history: List[Dict]) -> Dict[str, Any]:
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"""Predict habit success probability using simple ML"""
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# Simple rule-based prediction (can be enhanced with ML)
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base_probability = 0.7 # Default 70%
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# Adjust based on habit characteristics
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difficulty = habit_data.get('difficulty', 1)
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if difficulty >= 3:
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base_probability -= 0.2
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# Adjust based on user history
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if user_history:
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recent_success_rate = sum(1 for h in user_history[-10:] if h.get('completed', False)) / len(user_history[-10:])
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base_probability = (base_probability + recent_success_rate) / 2
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# Adjust based on category (if available)
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category = habit_data.get('category', '')
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if category in ['health', 'fitness']:
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base_probability += 0.1 # Health habits tend to be more successful
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# Clamp between 0 and 1
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probability = max(0.0, min(1.0, base_probability))
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# Generate insights
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insights = []
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if probability < 0.5:
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insights.append("Consider starting with an easier version of this habit")
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if habit_data.get('due_time'):
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insights.append("Having a specific time increases success rate by 40%")
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if difficulty >= 3:
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insights.append("High difficulty habits benefit from gradual progression")
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return {
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'success_probability': round(probability, 2),
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'confidence': 0.8, # Static for now
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'insights': insights,
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'recommended_adjustments': self._get_habit_adjustments(habit_data, probability)
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}
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def _get_habit_adjustments(self, habit_data: Dict, probability: float) -> List[str]:
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"""Suggest adjustments to improve habit success"""
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adjustments = []
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if probability < 0.6:
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adjustments.append("Start with a smaller, easier version")
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adjustments.append("Add a specific time and location")
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if habit_data.get('difficulty', 1) >= 3:
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adjustments.append("Break into smaller daily steps")
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if not habit_data.get('due_time'):
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adjustments.append("Set a specific time for better consistency")
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return adjustments
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async def analyze_habit_patterns(self, db: Session, user_id: int) -> Dict[str, Any]:
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"""Analyze user's habit patterns using AI"""
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# This would use more sophisticated ML models
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# For now, return basic analytics with AI insights
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from .models import Habit, Log # Import here to avoid circular imports
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# Get user's habits and logs
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habits = db.query(Habit).filter(Habit.user_id == user_id).all()
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recent_logs = db.query(Log).filter(Log.user_id == user_id).filter(
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Log.timestamp >= datetime.now() - timedelta(days=30)
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).all()
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# Basic pattern analysis
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patterns = {
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'best_time_of_day': self._find_best_time_pattern(recent_logs),
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'success_by_difficulty': self._analyze_difficulty_success(habits, recent_logs),
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'streak_patterns': self._analyze_streak_patterns(habits),
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'category_performance': self._analyze_category_performance(habits, recent_logs)
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}
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return {
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'patterns': patterns,
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'insights': self._generate_pattern_insights(patterns),
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'recommendations': self._generate_recommendations(patterns)
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}
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def _find_best_time_pattern(self, logs: List) -> Dict[str, Any]:
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"""Find the time of day user is most successful"""
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time_success = {}
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for log in logs:
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if log.action == 'complete':
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hour = log.timestamp.hour
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if hour not in time_success:
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time_success[hour] = 0
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time_success[hour] += 1
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if time_success:
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best_hour = max(time_success.keys(), key=lambda k: time_success[k])
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return {
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'best_hour': best_hour,
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'success_count': time_success[best_hour],
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'total_completions': sum(time_success.values())
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}
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return {'best_hour': None, 'success_count': 0}
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def _analyze_difficulty_success(self, habits: List, logs: List) -> Dict[str, float]:
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"""Analyze success rate by habit difficulty"""
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difficulty_stats = {}
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for habit in habits:
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difficulty = habit.difficulty or 1
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if difficulty not in difficulty_stats:
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difficulty_stats[difficulty] = {'attempts': 0, 'completions': 0}
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habit_logs = [l for l in logs if l.habit_id == habit.id]
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difficulty_stats[difficulty]['attempts'] += len(habit_logs)
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difficulty_stats[difficulty]['completions'] += len([l for l in habit_logs if l.action == 'complete'])
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# Calculate success rates
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success_rates = {}
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for difficulty, stats in difficulty_stats.items():
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if stats['attempts'] > 0:
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success_rates[f'difficulty_{difficulty}'] = stats['completions'] / stats['attempts']
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return success_rates
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def _analyze_streak_patterns(self, habits: List) -> Dict[str, Any]:
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"""Analyze streak patterns"""
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streaks = [h.current_streak or 0 for h in habits]
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return {
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'average_streak': sum(streaks) / len(streaks) if streaks else 0,
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'max_streak': max(streaks) if streaks else 0,
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'habits_with_streaks': len([s for s in streaks if s > 0])
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}
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def _analyze_category_performance(self, habits: List, logs: List) -> Dict[str, float]:
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"""Analyze performance by habit category"""
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category_stats = {}
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for habit in habits:
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category = habit.category or 'uncategorized'
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if category not in category_stats:
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category_stats[category] = {'attempts': 0, 'completions': 0}
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habit_logs = [l for l in logs if l.habit_id == habit.id]
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category_stats[category]['attempts'] += len(habit_logs)
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category_stats[category]['completions'] += len([l for l in habit_logs if l.action == 'complete'])
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# Calculate success rates
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success_rates = {}
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for category, stats in category_stats.items():
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if stats['attempts'] > 0:
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success_rates[category] = stats['completions'] / stats['attempts']
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return success_rates
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def _generate_pattern_insights(self, patterns: Dict) -> List[str]:
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"""Generate insights from patterns"""
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insights = []
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best_time = patterns.get('best_time_of_day', {})
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if best_time.get('best_hour'):
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hour_12 = best_time['best_hour']
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if hour_12 > 12:
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hour_12 -= 12
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period = "PM"
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else:
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period = "AM"
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insights.append(f"You're most successful completing habits at {hour_12} {period}")
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difficulty_success = patterns.get('success_by_difficulty', {})
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if difficulty_success:
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best_difficulty = max(difficulty_success.keys(), key=lambda k: difficulty_success[k])
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insights.append(f"You have highest success with {best_difficulty} habits")
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streak_patterns = patterns.get('streak_patterns', {})
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if streak_patterns.get('average_streak', 0) > 5:
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insights.append("You're great at maintaining streaks!")
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return insights
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def _generate_recommendations(self, patterns: Dict) -> List[str]:
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"""Generate recommendations based on patterns"""
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recommendations = []
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best_time = patterns.get('best_time_of_day', {})
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if best_time.get('best_hour'):
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recommendations.append(f"Schedule new habits around {best_time['best_hour']}:00 for better success")
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difficulty_success = patterns.get('success_by_difficulty', {})
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if difficulty_success.get('difficulty_1', 0) > difficulty_success.get('difficulty_3', 0):
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recommendations.append("Start with easier habits and gradually increase difficulty")
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category_performance = patterns.get('category_performance', {})
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if category_performance:
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best_category = max(category_performance.keys(), key=lambda k: category_performance[k])
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recommendations.append(f"Focus on {best_category} habits - you excel in this area")
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return recommendations
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# Global instance
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huggingface_ai = HuggingFaceAI() |