Files

T

Stefan 002ed92bea Add mobile app support and opening training feature

This commit implements iOS/Android mobile app support using Capacitor
and adds a comprehensive opening training feature with LLM-powered
explanations.

## Mobile App Infrastructure

- Add Capacitor configuration for iOS/Android builds
- Create mobile build script that excludes API routes
- Update Next.js config for conditional static export
- Add layout components with generateStaticParams for static builds
- Generate 500+ static pages for offline mobile use

## Chess Engine Abstraction

- Create ChessEngine interface for pluggable implementations
- Add LocalEngine (GPL - uses stockfish.js in browser)
- Add RemoteEngine (proprietary - calls API server)
- Factory pattern selects engine based on environment
- Enables GPL compliance for web, proprietary for mobile

## Opening Training Feature

- Interactive opening repertoire training
- Move validation with engine-backed feedback
- LLM explanations using Gemini API
- Wikipedia integration for opening context
- Opening family grouping (e4, d4, c4, etc.)
- Session state management
- Real-time move feedback with evaluation

Components:
- OpeningSelector: Browse and select openings by family
- OpeningTrainer: Main training interface with chessboard
- MoveFeedback: Display move quality and LLM explanations
- WikipediaSummary: Show opening history and context
- ErrorBoundary: Graceful error handling

Services:
- openingLoader: Load and filter opening database
- engineService: Engine evaluation wrapper
- moveValidator: Validate moves against repertoire
- feedbackGenerator: Generate contextual feedback
- wikipediaService: Fetch and cache Wikipedia data
- sessionManager: Track training session state

## Wikipedia Integration

- Automatic Wikipedia article fetching for openings
- Client-side and server-side caching
- Sanitized summaries with proper formatting
- Link opening database to Wikipedia slugs
- API endpoints for on-demand fetching

## Docker Improvements

- Add entrypoint script for automatic data setup
- Fetch Wikipedia data on first container startup
- Generate opening move index automatically
- Remove generated data from git (public/openings/*.json, public/wikipedia/*.json)
- Add READMEs explaining data requirements
- Update .gitignore for generated files

## Dual Licensing Strategy

- Add LICENSING.md explaining dual licensing approach
- GPL-3.0 for web builds (includes Stockfish)
- Proprietary option for mobile builds (no GPL code)
- Single codebase, multiple licensing models
- Legal compliance documented

## API Endpoints

- POST /api/v1/llm/opening-explanation - Get LLM move explanations
- GET /api/v1/wikipedia/summary - Fetch Wikipedia summaries

## Type Updates

- Add openingTraining types
- Update Tutor component to use ChessEngine interface
- Add Gemini error handling types

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

2025-12-07 18:40:58 +01:00

.gitkeep

Add mobile app support and opening training feature

2025-12-07 18:40:58 +01:00

README.md

Add mobile app support and opening training feature

2025-12-07 18:40:58 +01:00

README.md

Opening Database

This directory contains the ECO (Encyclopedia of Chess Openings) database files.

Required Files

The following files are required for opening training:

ecoA.json - ECO codes A00-A99
ecoB.json - ECO codes B00-B99
ecoC.json - ECO codes C00-C99
ecoD.json - ECO codes D00-D99
ecoE.json - ECO codes E00-E99
moveIndex.json - Generated move sequence index

File Format

Each ECO file (ecoA-E.json) should be a JSON object mapping FEN positions to opening metadata:

{
  "fen_position": {
    "eco": "A00",
    "name": "Opening Name",
    "moves": "e4 e5 Nf3 Nc6",
    "wikipediaSlug": "opening-name" // optional
  }
}

Setup

Option 1: Docker (Automatic)

When running via Docker, these files should be provided as a volume mount:

docker run -v ./openings:/app/public/openings ghcr.io/stefan-kp/chess-tutor

Option 2: Local Development

Obtain ECO database files (ecoA-E.json)
Place them in this directory
Generate the move index:

npm run build:opening-index

This will create moveIndex.json from the ECO files.

Option 3: Generate from PGN

If you have a PGN database, you can extract ECO codes using chess tools like:

pgn-extract
Custom scripts

Notes

These files are not included in git (too large, ~4MB total)
Users must provide their own opening database
Wikipedia integration is optional (see public/wikipedia/README.md)
The move index is automatically generated during Docker startup