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Chess Tutor AI :Level up your chess skills with AI-powered insights!

Chess tutor AI made for the Ctrl+Shift+Intelligence Hackathon hosted by COPS IIT(BHU) Varanasi.

Chess Tutor AI

šŸ† A Multi-Page Chess Tutor AI Powered by DeepSeek-R1-LLaMA-70B, Stockfish, and Streamlit


šŸ“Œ Overview

Chess Tutor AI is an interactive and intelligent chess tutor designed to help users improve their chess skills. It leverages DeepSeek-R1-LLaMA-70B for advanced reasoning, the Groq API for processing, and the Stockfish API for best move evaluation. The project is built using Streamlit, providing a seamless and engaging web interface.

šŸš€ Features

1ļøāƒ£ Chess Bot

  • šŸ“Œ Best Move Analysis: Provides the best move along with an in-depth explanation when given an FEN string.
  • šŸ“Œ Opening Theory: Offers insights into chess openings, including their pros, cons, and major variations.
  • šŸ“Œ Strategic Insights: Explains why a move is optimal and suggests alternative approaches.

2ļøāƒ£ Chess Puzzles

  • šŸ“Œ Difficulty Levels: Puzzles categorized into Easy, Medium, and Hard, selected at random.
  • šŸ“Œ Interactive UI: Buttons for Get Hint, Submit Move, and Next Puzzle.
  • šŸ“Œ Board Visualization: Implemented using SVG images for clarity and better understanding.

3ļøāƒ£ Human vs. AI Mode

  • šŸ“Œ Adjustable AI Strength: Modify Stockfish depth to customize difficulty.
  • šŸ“Œ AI Move Automation: The AI automatically responds to each human move.
  • šŸ“Œ Move Assessment: Detailed breakdown in a three-column layout:
    • AI Move Explanation 🧠
    • User Move Evaluation šŸ“Š
    • Best Possible Continuation ā™Ÿļø

šŸ› ļø Technologies Used

  • DeepSeek-R1-LLaMA-70B - For chess reasoning and explanations
  • Groq API - For AI model inference
  • Stockfish API - For move evaluation and analysis
  • Streamlit - For building the interactive web UI
  • SVG Rendering - For board visualization

šŸ Installation & Setup

  1. Clone the Repository

    git clone https://github.com/adityaamehra/Chess-Tutor.git
    cd Chess-Tutor
  2. Install Dependencies

    pip install -r requirements.txt
  3. Run the Application

    streamlit run main.py

šŸ¤ Contributions

Contributions are welcome! Feel free to open issues and submit pull requests.

šŸ“œ License

This project is licensed under the MIT License.


šŸŽÆ Level up your chess skills with AI-powered insights! šŸ†