🏆 Gambitron - Chess AI Bot

A sophisticated chess AI engine built with modern web technologies and advanced algorithms

🎯 Overview

Gambitron is a cutting-edge chess AI application that combines modern web development with sophisticated artificial intelligence algorithms. Built with React/TypeScript frontend and Python backend, it delivers a professional chess experience with real-time gameplay, advanced AI decision-making, and responsive design.

✨ Key Features

🧠 Advanced AI Engine

Minimax Algorithm with Alpha-Beta Pruning for optimal move selection
Piece-Square Tables for positional evaluation (Pawn, Knight, Bishop, Rook, Queen)
Material Evaluation with sophisticated piece value calculations
Center Control and Pawn Advancement bonuses
Bishop Pair and Rook Mobility evaluation
King Safety assessment for endgame scenarios

🎮 Rich Gameplay Experience

Real-time Chess Board with drag-and-drop piece movement
Move Validation ensuring only legal moves are allowed
Pawn Promotion with interactive piece selection dialog
Game State Persistence with localStorage integration
Move Highlighting showing valid moves for selected pieces
Game History tracking and replay capabilities

⏱️ Professional Timing System

Dual Timers for both player and AI
Configurable Time Controls (1-30 minutes)
Visual Timer Indicators with active/inactive states
Time Persistence across browser sessions
Real-time Updates with smooth countdown animations

📱 Responsive Design

Desktop Layout with sidebar game information
Mobile Layout optimized for touch devices
Adaptive UI that works on all screen sizes
Modern Dark Theme with professional styling
Social Media Integration with GitHub, LinkedIn, and LeetCode links

🔧 Developer Features

Admin Panel for FEN position loading and testing
Error Handling with retry mechanisms
API Integration with AWS Lambda backend
CORS Support for cross-origin requests
TypeScript for type safety and better development experience

🏗️ Architecture

Frontend Architecture

frontend/
├── src/
│   ├── components/
│   │   ├── board/
│   │   │   ├── Board.tsx          # Main game logic and state management
│   │   │   └── Board.css          # Board-specific styles
│   │   ├── tile/
│   │   │   ├── tile.tsx           # Individual chess square component
│   │   │   └── tile.css           # Tile styling
│   │   ├── ChessBoard.tsx         # Chess board rendering
│   │   ├── ChessPiece.tsx         # Piece rendering and interactions
│   │   ├── DesktopLayout.tsx      # Desktop-specific layout
│   │   ├── MobileLayout.tsx       # Mobile-responsive layout
│   │   ├── GameDialogs.tsx        # Modal dialogs (promotion, start, endgame)
│   │   ├── GameInfo.tsx           # Game information display
│   │   ├── Timer.tsx              # Timer component
│   │   ├── MobileTimer.tsx        # Mobile timer variant
│   │   └── SocialLinks.tsx        # Social media links
│   ├── App.tsx                    # Main application component
│   └── main.tsx                   # Application entry point
├── public/
│   ├── pieces/                    # Chess piece SVG assets
│   └── readme/                    # Documentation assets
└── package.json                   # Dependencies and scripts

Backend Architecture

lambda-backend/
├── backend.py                     # FastAPI application with chess logic
├── lambda.py                      # AWS Lambda handler
├── lambda-layer/
│   └── python/
│       └── chess/                 # Python chess library
│           ├── __init__.py
│           ├── engine.py          # Chess engine implementation
│           ├── gaviota.py         # Endgame tablebase support
│           ├── pgn.py             # PGN format support
│           ├── polyglot.py        # Opening book support
│           ├── svg.py             # SVG rendering
│           ├── syzygy.py          # Syzygy tablebase
│           └── variant.py         # Chess variant support
└── requirements.txt               # Python dependencies

🧮 AI Algorithm Deep Dive

Minimax with Alpha-Beta Pruning

The core AI engine uses the minimax algorithm enhanced with alpha-beta pruning for optimal performance:

def minimax(board_state: chess.Board, depth: int, alpha: float, beta: float, is_maximizing: bool) -> float:
    if depth == 0 or board_state.is_game_over():
        return evaluate_board_state(board_state)
    
    if is_maximizing:
        max_eval = float('-inf')
        for move in board_state.legal_moves:
            board_state.push(move)
            move_eval = minimax(board_state, depth - 1, alpha, beta, False)
            board_state.pop()
            max_eval = max(max_eval, move_eval)
            alpha = max(alpha, move_eval)
            if beta <= alpha:
                break  # Alpha-beta pruning
        return max_eval
    else:
        # Minimizing player logic...

Evaluation Function

The AI evaluates positions using multiple sophisticated criteria:

Material Values: Pawn (100), Knight (320), Bishop (330), Rook (500), Queen (900), King (20000)
Piece-Square Tables: Positional bonuses for each piece type
Center Control: Bonus for controlling central squares
Pawn Advancement: Rewards for advanced pawns
Bishop Pair: Bonus for having both bishops
Rook Mobility: Evaluation based on rook movement options
King Safety: Endgame king positioning

Move Ordering

The AI optimizes move selection by:

Capture Moves First: Prioritizes moves that capture pieces
Material Value Sorting: Orders captures by piece value
Alpha-Beta Pruning: Eliminates branches that won't affect the final decision

🚀 Getting Started

Prerequisites

Node.js (v14 or later)
Python 3.x
Git

Frontend Setup

Clone the repository:

git clone https://github.com/Cyrus-Krispin/gambitron
cd gambitron/frontend

Install dependencies:
```
npm install
```
Start development server:
```
npm run dev
```
Open your browser to http://localhost:5173

Backend Setup

Navigate to backend directory:
```
cd lambda-backend
```
Create virtual environment:
```
python -m venv venv
```

Activate virtual environment:

# Windows
venv\Scripts\activate

# macOS/Linux
source venv/bin/activate

Install dependencies:
```
pip install -r requirements.txt
```
Run the backend:
```
fastapi dev backend.py
```

🎮 How to Play

Start a New Game: Click "New Game" and select your preferred time control
Make Moves: Click on a piece, then click on a valid destination square
Pawn Promotion: When a pawn reaches the end, choose your promotion piece
Timer Management: Watch the countdown timers for both you and the AI
Game Over: The game automatically detects checkmate, stalemate, or timeouts

🔧 Technical Implementation

State Management

The application uses React hooks for state management:

useState for local component state
useEffect for side effects and API calls
useMemo for performance optimization
Custom hooks for game logic encapsulation

API Integration

The frontend communicates with the backend via REST API:

POST /move: Get AI's next move
POST /evaluate: Evaluate current board position
Error Handling: Comprehensive error management with retry mechanisms

Performance Optimizations

Move Generation: Efficient legal move generation using chess.js
Alpha-Beta Pruning: Reduces search tree size by up to 50%
Move Ordering: Prioritizes promising moves for better pruning
Memoization: Caches expensive calculations
Lazy Loading: Loads components only when needed

🛠️ Development

Available Scripts

# Frontend
npm run dev          # Start development server
npm run build        # Build for production
npm run preview      # Preview production build
npm run lint         # Run ESLint

# Backend
fastapi dev backend.py    # Start development server
python lambda.py          # Test Lambda function locally

Code Structure

TypeScript: Full type safety throughout the application
ESLint: Code quality and consistency enforcement
Tailwind CSS: Utility-first CSS framework
Material-UI: Component library for dialogs and UI elements

🌐 Deployment

Frontend (Vercel)

Automatic Deployment: Connected to GitHub repository
Preview Deployments: Automatic previews for pull requests
Custom Domain: Available at gambitron.vercel.app

Backend (AWS Lambda)

Serverless Architecture: Scales automatically with demand
Lambda Layers: Chess library packaged as a layer
API Gateway: RESTful API endpoint configuration

📊 Performance Metrics

AI Response Time: < 2 seconds for most positions
Search Depth: Configurable depth (typically 3-4 plies)
Memory Usage: Optimized for Lambda constraints
Frontend Bundle: < 1MB gzipped
Mobile Performance: 60fps animations on modern devices

🤝 Contributing

We welcome contributions! Please see our contributing guidelines:

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

Chess.js - JavaScript chess library
Python-chess - Python chess library
React - Frontend framework
FastAPI - Backend framework
AWS Lambda - Serverless computing platform
Vercel - Frontend deployment platform

Ready to challenge Gambitron? Play Now!

Name		Name	Last commit message	Last commit date
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frontend		frontend
lambda-backend		lambda-backend
README.md		README.md

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