feat(emotistream): Complete EmotiStream MVP with Real RL Engine

[proffesor-for-testing]

EmotiStream MVP - Complete Implementation

This PR delivers a fully functional EmotiStream MVP with real reinforcement learning, emotion analysis, and content recommendation capabilities.

Overview

EmotiStream is an emotion-aware content recommendation system that uses Q-learning to personalize recommendations based on users' emotional states and viewing feedback.

Key Features Implemented

🎯 Core RL Engine

• Q-Learning Policy Engine with configurable parameters:
  • Learning rate (α): 0.1
  • Discount factor (γ): 0.95
  • Epsilon-greedy exploration: 0.15 → 0.01 (decays with experience)
• 5×5×3 Emotional State Space: Discretized valence × arousal × stress
• Reward Calculator: 0.6 × directionAlignment + 0.4 × magnitude + proximityBonus

🧠 Emotion Analysis

• Gemini API Integration: Real NLP-based emotion detection
• Emotion Vector: 8-dimensional emotion representation
• Valence-Arousal-Stress Model: Continuous emotional state tracking

📺 Content Recommendation

• RecommendationEngine: Combines Q-values with content similarity
• ContentProfiler: Profiles content emotional impact
• VectorStore: Cosine similarity-based content matching
• 10 Seed Content Items: Demo content for immediate testing

🔐 Authentication

• JWT Token Auth: Secure stateless authentication
• bcrypt Password Hashing: Industry-standard password security
• User Persistence: FileStore-based user storage

💾 Persistence Layer

• FileStore: Debounced JSON file persistence (1s write delay)
• Q-Table Persistence: RL learning persists across server restarts
• User Data Persistence: User accounts survive restarts

API Endpoints

Endpoint	Method	Description
/api/v1/emotion/analyze	POST	Analyze text for emotional content (Gemini)
/api/v1/recommend	POST	Get personalized content recommendations
/api/v1/feedback	POST	Submit viewing feedback, update RL policy
/api/v1/auth/register	POST	Register new user account
/api/v1/auth/login	POST	Login and receive JWT token
/health	GET	Server health check

P0 Fixes Completed

1. /recommend endpoint - Now returns real recommendations from RecommendationEngine
2. /feedback endpoint - Now processes feedback through FeedbackProcessor + RLPolicyEngine
3. Q-table persistence - Learning persists to data/qtable.json
4. Gemini integration - Real emotion analysis via Google AI

Architecture

┌─────────────────┐     ┌──────────────────┐
│   API Routes    │────▶│ ServiceContainer │
└─────────────────┘     └──────────────────┘
                               │
        ┌──────────────────────┼──────────────────────┐
        ▼                      ▼                      ▼
┌───────────────┐    ┌─────────────────┐    ┌────────────────┐
│EmotionDetector│    │  RLPolicyEngine │    │RecommendEngine │
│  (Gemini API) │    │    (Q-Table)    │    │(VectorStore)   │
└───────────────┘    └─────────────────┘    └────────────────┘
                            │
                     ┌──────┴──────┐
                     ▼             ▼
              ┌──────────┐  ┌──────────────┐
              │ FileStore│  │EpsilonGreedy │
              │(Persist) │  │ (Exploration)│
              └──────────┘  └──────────────┘

Files Changed (24 files, +2,890 lines)

New Files

• src/services/index.ts - ServiceContainer singleton for DI
• src/api/routes/auth.ts - Authentication endpoints
• src/auth/jwt-service.ts - JWT token management
• src/auth/password-service.ts - bcrypt password hashing
• src/emotion/gemini-client.ts - Gemini API client
• src/persistence/file-store.ts - Debounced file persistence
• src/persistence/user-store.ts - User data persistence

Modified Files

• src/api/routes/recommend.ts - Wired to real RecommendationEngine
• src/api/routes/feedback.ts - Wired to real FeedbackProcessor + RLPolicyEngine
• src/api/routes/emotion.ts - Integrated Gemini API
• src/rl/q-table.ts - Added FileStore persistence
• src/api/index.ts - Added auth routes

Testing

# Start server
cd apps/emotistream && npm start

# Test recommendation
curl -X POST http://localhost:3000/api/v1/recommend \
  -H "Content-Type: application/json" \
  -d '{"userId":"test123","currentState":{"valence":-0.5,"arousal":0.7,"stressLevel":0.8},"desiredState":{"targetValence":0.5},"limit":3}'

# Test feedback (updates Q-table)
curl -X POST http://localhost:3000/api/v1/feedback \
  -H "Content-Type: application/json" \
  -d '{"userId":"test123","contentId":"calming-001","actualPostState":{"valence":0.3,"arousal":-0.1,"stressLevel":0.3},"watchDuration":25,"completed":true}'

Verified Results

• /recommend returns 3+ real recommendations with Q-values and similarity scores
• /feedback returns real reward (0.676), newQValue (0.068), explorationRate (0.149)
• Q-table persists to data/qtable.json and loads on restart
• Emotion analysis returns real Gemini-powered emotional state

---

🤖 Generated with Claude Code

[proffesor-for-testing]

Latest Update: Complete Frontend Implementation

Changes in this commit:

Frontend (emotistream-web - Next.js 15):

• ✅ Complete auth flow (login/register pages)
• ✅ Dashboard with emotion analysis and recommendations
• ✅ Progress page with real-time learning metrics
• ✅ Feedback modal for content rating
• ✅ Zustand state management with auth persistence
• ✅ API client with token refresh interceptor

Backend:

• ✅ Progress routes for learning analytics
• ✅ FeedbackStore singleton for data consistency
• ✅ Watch session tracking endpoints
• ✅ Enhanced feedback submission with persistence

Documentation:

• ✅ Supabase migration plan (Option B architecture) - /docs/SUPABASE_MIGRATION_PLAN.md
• ✅ API integration guide
• ✅ QA test reports and checklists

Next Steps:

1. Implement Supabase migration for production persistence
2. Deploy to Lovable + Supabase stack
3. Add E2E tests with Playwright

Note: Direct push to this repo was denied. Changes are synced via the fork at proffesor-for-testing/hackathon-tv5.

🤖 Generated with Claude Code