SnookerApp - Practice & Performance Tracker

A cross-platform mobile application (iOS/Android) for snooker players focused on practice routines, performance tracking, and AI-powered coaching.

Features

1. Practice Routines Library

• 12+ categorized routines (Break Building, Basics, Safety Play, Straight Cueing)
• Difficulty tiers (Beginner, Intermediate, Advanced)
• Detailed setup instructions and scoring systems

2. Practice Session Tracker

• Create and manage practice sessions
• Track multiple routines per session
• Record scores and notes
• View progress statistics

3. Match Tracking

• Log matches against opponents
• Track frame scores and results
• View match history and win rates

4. AI Personal Coach

• Upload videos of your shots
• AI analysis of technique, alignment, and cue action
• Personalized feedback and recommendations

Tech Stack

• Frontend: React Native with Expo (TypeScript)
• Navigation: React Navigation v6
• State Management: Zustand with persistence
• Backend: Supabase (PostgreSQL, Auth, Storage)
• Offline Support: AsyncStorage with background sync

Getting Started

Prerequisites

• Node.js (v18+)
• npm or yarn
• iOS Simulator (Mac) or Android Emulator
• Expo Go app (for physical device testing)

Installation

1. Clone the repository:

git clone <repo-url>
cd SnookerApp/SnookerApp

2. Install dependencies:

npm install

3. Create environment file:

cp .env.example .env

4. Update .env with your Supabase credentials:

EXPO_PUBLIC_SUPABASE_URL=https://your-project.supabase.co
EXPO_PUBLIC_SUPABASE_ANON_KEY=your-anon-key

5. Start the development server:

npm start

6. Run on device:

• Press i for iOS simulator
• Press a for Android emulator
• Scan QR code with Expo Go app for physical device

Project Structure

SnookerApp/
+-- src/
�   +-- api/           # Supabase client and API calls
�   +-- components/    # Reusable UI components
�   +-- constants/     # App constants and theme
�   +-- hooks/         # Custom React hooks
�   +-- navigation/    # Navigation configuration
�   +-- screens/       # Screen components
�   +-- services/      # Business logic services
�   +-- store/         # Zustand state stores
�   +-- types/         # TypeScript types
�   +-- utils/         # Utility functions
+-- supabase/
�   +-- functions/     # Edge Functions
�   +-- migrations/    # Database migrations
+-- assets/            # Images and fonts

Database Setup

1. Create a new Supabase project at https://supabase.com

2. Run the migrations in the SQL Editor:

   • supabase/migrations/001_initial_schema.sql
   • supabase/migrations/002_seed_data.sql

3. Configure Storage buckets:

   • Create ai-videos bucket (private)
   • Create profile-images bucket (public)

4. Enable Row Level Security (RLS) policies (already included in migrations)

Development

Adding a New Routine

Edit src/constants/routines.ts and add a new routine object to DEFAULT_ROUTINES:

{
  id: 'rtn-new',
  category_id: 'cat-1',
  name: 'New Routine',
  description: 'Description here',
  difficulty: 'intermediate',
  setup_instructions: 'Setup steps...',
  scoring_type: 'points',
  max_score: 100,
  is_system_routine: true,
  created_at: new Date().toISOString(),
  updated_at: new Date().toISOString(),
}

Running Tests

npm test

Building for Production

# iOS
expo build:ios

# Android
expo build:android

AI Coach Architecture

The AI Coach feature uses a multi-stage pipeline:

1. Video Upload: User uploads video to Supabase Storage
2. Processing Trigger: Database trigger starts Edge Function
3. Video Analysis:
   • Extract keyframes from video
   • Run pose estimation (MediaPipe/OpenCV)
   • Analyze cue action metrics
4. Feedback Generation: LLM generates personalized feedback
5. Results Storage: Store analysis results in database
6. User Notification: Push notification when analysis is complete

Future AI Improvements

• Real-time pose estimation during practice
• Comparative analysis with professional players
• Personalized training plan generation
• Progress tracking with AI-generated insights

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/my-feature
3. Commit changes: git commit -am "Add my feature"
4. Push to branch: git push origin feature/my-feature
5. Submit a Pull Request

License

MIT License - see LICENSE file for details

Support

For support, email [email protected] or open an issue on GitHub.