ChessCommentator: Enhancing Chess Analysis with AI-Generated Commentaries

Introduction

Combining my love for chess with cutting-edge technology, I’m thrilled to introduce ChessCommentator. This Python Streamlit application enriches chess analysis by providing experimental commentaries on both newly entered games by Lichess API and pre-saved games. Join me as I share the journey of creating ChessCommentator, the technologies involved, and how they come together to enhance your experience with this timeless game.

Project Overview

ChessCommentator brings a new dimension to chess analysis, offering AI-generated commentaries on a variety of games sourced from both Lichess API and archived sources. The application features two distinct tabs, each leveraging specific technologies to deliver an engaging user experience.

Tab 1: AI-Powered Commentary Generation

The first tab enhances user engagement by generating AI-driven commentaries for given game IDs. Here’s how it operates:

• Streamlit App: Allows users to enter a game ID.
• Lichess API: Provides game data based on the entered ID.
• OpenAI API: Utilized to generate experimental commentaries based on the game data fetched from Lichess.

How It Works

1. Game Selection: Using the Streamlit app, users enter a game ID and an OpenAI token.
2. Data Fetching: The app retrieves game details from the Lichess API.
3. Commentary Generation: Using the retrieved data, the OpenAI API generates experimental commentaries.
4. Display: The generated commentaries are displayed through the Streamlit app and Chess library.

Tab 2: Presenting Pre-Saved Games

The second tab focuses on presenting and analyzing popular pre-saved chess games. Here’s a breakdown of how it works:

• Streamlit App: Interface for interacting with ChessCommentator.
• API Gateway: Manages requests and ensures smooth communication.
• Lambda App (Spring Boot): Processes requests and interacts with the database.
• DynamoDB: Stores and retrieves pre-saved game data efficiently.

How It Works

1. User Interaction: Users select a game through the Streamlit app.
2. Request Handling: The app sends the request to the API Gateway.
3. Processing: The API Gateway forwards the request to the Lambda app, which processes it.
4. Data Retrieval: The Lambda app retrieves the pre-saved game data from DynamoDB.
5. Display: Finally, the data is sent back through the API Gateway to the Streamlit app for display.

Conclusion

ChessCommentator is an experimental showcase of the synergy between technology and chess, offering enthusiasts new insights and perspectives through AI-driven analysis. Whether you’re exploring newly entered games or revisiting classics, ChessCommentator aims to provide experimental commentaries that enhance your chess experience.

You can check the repository from the link below:

GitHub - victoraysev/ChessCommentator: Chess game commentator using ChatGPT API and shows some of…

You can check the website from the link below:

https://chesscommentator.streamlit.app/