ARC AGI Challenge Solver

An interactive web application for solving ARC (Abstraction and Reasoning Corpus) AGI challenges with an enhanced grid editor, AI assistance, and comprehensive puzzle navigation.

Features

🎨 Grid Editor

• Interactive color palette - 10 ARC colors (0-9) with visual selection
• Click-to-paint grid cells with selected color
• Adjustable grid size - Resize answer grid from 1x1 to 30x30
• Copy from input - Start with the test input as template
• Clear grid - Reset all cells to black (color 0)

🧩 Puzzle Viewer

• Full dataset support - Load entire ARC AGI dataset
• Navigation controls - Previous/Next buttons and direct puzzle number input
• Training examples - View input/output pairs for pattern learning
• Test cases - Solve the test inputs with the grid editor

🤖 AI Assistant

• Context-aware chat - AI has access to current puzzle data
• Pattern analysis - Get insights about grid transformations
• Grid summaries - Automatic analysis of grid sizes and structure

📊 Answer Validation

• Submit answers - Check your solutions
• Visual feedback - Success/error messages for submissions

Quick Start

Prerequisites

• Python 3.8+
• pip package manager

Installation

1. Clone the repository:

git clone <repository-url>
cd ArcAGI

2. Run the application:

python run.py

This will:

• Create/activate a virtual environment
• Install all required dependencies
• Start the web server on port 8050
• Launch Microsoft Edge browser (or default browser)

Manual Setup (Optional)

If you prefer manual setup:

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Windows:
venv\Scripts\activate
# On Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run the application
python arc_app_enhanced.py

Then open http://localhost:8050 in your browser.

Usage Guide

Solving Puzzles

1. Navigate to a puzzle using the Previous/Next buttons or enter a puzzle number
2. Study the training examples to identify the transformation pattern
3. Use the grid editor to create your answer:
   • Select a color from the palette
   • Click grid cells to paint them
   • Resize the grid if needed
   • Use "Copy Input" to start with the test input
4. Submit your answer to check if it's correct
5. Ask the AI Assistant for help analyzing patterns

Keyboard Shortcuts

• Enter in chat input - Send message
• Enter in puzzle number input - Go to puzzle

Project Structure

ArcAGI/
├── run.py                  # Main launcher with venv management
├── arc_app_enhanced.py     # Enhanced FastAPI application
├── requirements.txt        # Python dependencies
├── data/
│   └── arc_agi/           # ARC puzzle datasets
├── logs/                   # Application logs
└── venv/                   # Virtual environment (auto-created)

Configuration

Change Port

Edit arc_app_enhanced.py and modify the port number:

uvicorn.run(app, host="0.0.0.0", port=8050)  # Change 8050 to desired port

Dataset Location

Puzzles are loaded from data/arc_agi/ directory. Place your ARC dataset JSON files there.

Development

Adding Custom Puzzles

Edit the load_arc_dataset() function in arc_app_enhanced.py to add custom puzzles:

puzzles.append({
    "id": "custom_001",
    "dataset": "custom",
    "train": [...],
    "test": [...]
})

Extending the AI Assistant

Modify the /api/chat endpoint to add more sophisticated pattern analysis or integrate with external AI services.

Troubleshooting

Port Already in Use

# Find process using port 8050
# On Windows:
netstat -ano | findstr :8050
# On Linux/Mac:
lsof -i :8050

# Kill the process or use a different port

Dependencies Not Installing

# Upgrade pip
python -m pip install --upgrade pip

# Clear pip cache
pip cache purge

# Reinstall requirements
pip install -r requirements.txt --no-cache-dir

Application Not Starting

Check the logs in the logs/ directory for detailed error messages:

tail -f logs/backend_*.log

Testing

Run the test suite:

python test_app.py

System Requirements

• OS: Windows, Linux, macOS, WSL
• Python: 3.8 or higher
• Browser: Modern web browser (Chrome, Firefox, Edge, Safari)
• RAM: 2GB minimum
• Disk Space: 1GB for virtual environment and datasets

Technologies Used

• Backend: FastAPI, Uvicorn
• Frontend: Vanilla JavaScript, HTML5, CSS3
• Data Format: JSON (ARC dataset format)
• Logging: Python logging module

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is for educational and research purposes. The ARC dataset is created by François Chollet.

Acknowledgments

• ARC Prize for the challenge dataset
• François Chollet for creating the ARC benchmark
• The ARC community for insights and strategies

Support

For issues or questions:

1. Check the troubleshooting section
2. Review the logs in logs/ directory
3. Open an issue on GitHub

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Current Version: 1.0.0
Last Updated: 2025