Skip to main content

Project Overview

� What is Peack's Platform?​

Peack's platform represents a modern, AI-powered application ecosystem that showcases cutting-edge technologies and best practices in software development. Our platform demonstrates advanced machine learning integration, real-time collaboration features, and scalable architecture patterns.

🏗️ System Architecture​

Core Components​

graph TB
    A[Frontend Applications] --> B[Database Layer]
    A --> C[ML & AI Services]
    C --> B
    C --> D[External APIs]
    A --> E[Authentication & Security]
    B --> E

Technology Stack​

Frontend Applications​

  • Framework: Next.js 15 with React 19
  • Styling: Tailwind CSS 4, Framer Motion
  • UI Components: Radix UI primitives
  • Language: TypeScript
  • State Management: React Context + Custom hooks

Backend Services​

  • Database: Supabase (PostgreSQL)
  • Authentication: Supabase Auth
  • Real-time: Supabase Realtime
  • File Storage: Supabase Storage

ML Service​

  • Framework: Python FastAPI
  • ML Library: LightFM, scikit-learn
  • Data Processing: Pandas, NumPy
  • API: RESTful endpoints

External APIs​

  • Movie Data: The Movie Database (TMDB)
  • Deployment: [To be configured]

🎯 Key Features​

For Users​

  • Swipe Interface: Tinder-like movie discovery
  • Personal Profiles: Track preferences and history
  • Group Sessions: Collaborative movie selection
  • Smart Recommendations: AI-powered suggestions
  • Real-time Sync: Live updates across devices

For Developers​

  • Modular Architecture: Microservices approach
  • API-First Design: Clear service boundaries
  • Type Safety: Full TypeScript implementation
  • Modern Tooling: Latest framework versions
  • Comprehensive Docs: This documentation platform

🧠 Machine Learning Pipeline​

Recommendation Algorithm​

Our 8-factor enhanced algorithm considers:

  1. User Preferences: Historical likes/dislikes
  2. Genre Similarity: Genre overlap analysis
  3. Keyword Matching: Plot and theme keywords
  4. Director Similarity: Director preference patterns
  5. Cast Similarity: Actor preference patterns
  6. Rating Patterns: Rating behavior analysis
  7. Temporal Factors: Release year preferences
  8. Social Signals: Group consensus data

Data Flow​

  1. Data Collection: User interactions via DAGGH frontend
  2. Processing: Real-time data sync to Supabase
  3. ML Pipeline: FastAPI service processes preferences
  4. Recommendations: Generated recommendations returned to frontend
  5. Feedback Loop: User actions improve future recommendations

🔄 Development Workflow​

Local Development​

  1. Setup: Clone repositories and install dependencies
  2. Database: Configure Supabase local development
  3. Services: Start ML service and frontend concurrently
  4. Testing: Run comprehensive test suites
  5. Documentation: Update docs as needed

Production Deployment​

  1. Frontend: Deployed to [Platform TBD]
  2. ML Service: Containerized deployment
  3. Database: Supabase cloud instance
  4. Monitoring: [Monitoring solution TBD]

📊 Project Status​

✅ Completed Features​

  • Core DAGGH Frontend: Swipe interface, user profiles, preferences
  • Supabase Integration: Database, auth, real-time sync
  • ML Service Foundation: FastAPI service with 8-factor algorithm
  • TMDB Integration: Movie metadata caching and sync
  • Documentation Platform: Migration to Docusaurus complete

🔄 In Progress​

  • Group Features: Multi-user session management
  • Enhanced Recommendations: Algorithm refinements
  • Production Deployment: Infrastructure setup
  • Performance Optimization: Frontend and ML service optimization

📋 Planned Features​

  • Mobile App: React Native implementation
  • Advanced Analytics: User behavior insights
  • Social Features: Friend connections and sharing
  • Content Expansion: TV shows and streaming integration

🤝 Team Structure​

Development Areas​

  • Frontend Team: DAGGH interface and user experience
  • Backend Team: ML service and API development
  • DevOps Team: Infrastructure and deployment
  • Documentation Team: Content creation and maintenance

Collaboration​

  • Code Reviews: All changes reviewed before merge
  • Documentation: Updated with all feature changes
  • Testing: Comprehensive test coverage required
  • Communication: Regular sync meetings and async updates

🎯 Success Metrics​

User Experience​

  • Recommendation Accuracy: >80% user satisfaction
  • Response Time: <2s for recommendations
  • User Engagement: Daily active user growth
  • Feature Adoption: Group session usage rates

Technical Performance​

  • System Uptime: >99.9% availability
  • API Performance: <200ms average response time
  • Code Quality: >90% test coverage
  • Documentation: 100% feature coverage

🔗 Quick Links​

Ready to contribute? Check out our Getting Started Guide to begin your journey with MLContext!