AgenTeX

A sophisticated tool that uses AI agents to parse images containing mathematical content and convert them to LaTeX code, with additional features for mathematical analysis and step-by-step solutions.

📜 Overview

AgenTeX is an advanced tool that leverages OpenAI's powerful GPT-4o model to:

• Parse images containing mathematical equations
• Classify mathematical content by type and difficulty
• Generate LaTeX code for mathematical expressions
• Provide step-by-step solutions to mathematical problems

The application offers both a command-line interface and a user-friendly web interface built with Streamlit.

✨ Features

• Multi-modal input:

  • Upload images containing mathematical content
  • Provide URLs to images with equations
  • Enter mathematical text directly

• Comprehensive analysis:

  • Mathematical content parsing from images
  • Classification of mathematical type and difficulty level
  • Step-by-step problem solutions
  • High-quality LaTeX code generation optimized for KaTeX
  • LaTeX preview right in the web interface

• User-friendly interfaces:

  • Simple command-line interface
  • Intuitive Streamlit web application
  • Copy-to-clipboard functionality for generated LaTeX

🛠️ Architecture

The system is built using a multi-agent architecture with specialized AI agents:

1. Image Parser Agent: Extracts mathematical content from images using GPT-4o vision capabilities
2. Math Classifier Agent: Categorizes the mathematical content and assesses difficulty
3. Solution Generator Agent: Provides step-by-step solutions to problems
4. LaTeX Generator Agent: Converts mathematical expressions to LaTeX code optimized for KaTeX

🚀 Installation

Prerequisites

• Python 3.8 or higher
• OpenAI API key
• (Optional) AgentOps API key for tracking

Setup Steps

1. Clone the repository:

   git clone https://github.com/yourusername/AgenTeX.git
   cd AgenTeX

2. Create and activate a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Create a .env file in the project root directory with your API keys:

   OPENAI_API_KEY=your_openai_api_key
   AGENTOPS_API_KEY=your_agentops_api_key  # Optional
   

📊 Usage

Command Line Interface

Run the command line version to process mathematical images or text:

python main.py

The CLI will guide you through the process of providing an image URL or mathematical text input.

Web Interface

Run the web application for a more interactive experience:

streamlit run app.py

This will start a local web server and open the application in your default browser. From there you can:

1. Upload an image: Use the "Upload Image" tab to upload an image file containing mathematical content
2. Provide an image URL: Use the "Image URL" tab to enter a URL pointing to an image with mathematical content
3. Enter text directly: Use the "Text Input" tab to enter mathematical expressions or problems as text

The application will process your input and display:

• The parsed mathematical content
• Classification of the mathematical type and difficulty
• Step-by-step solution (if applicable)
• Generated LaTeX code with a preview
• Key concepts involved in the mathematical content

🧩 Dependencies

The project relies on the following key Python packages:

• openai: For accessing GPT-4o and vision models
• streamlit: For the web interface
• python-dotenv: For loading environment variables
• pydantic: For data validation and settings management
• agentops: For agent telemetry (optional)
• openai-agents: For the agent framework

🧪 Example Workflow

1. User uploads an image containing the quadratic formula
2. Image Parser Agent extracts the text "x = (-b ± √(b² - 4ac)) / 2a"
3. Math Classifier Agent identifies it as algebra (category) and easy (difficulty)
4. LaTeX Generator Agent converts it to x = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}
5. Solution Generator Agent provides step-by-step solution if applicable
6. The web interface displays the results with rendered LaTeX

🤝 Contributing

Contributions are welcome! Here's how you can contribute:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📝 License

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

🙏 Acknowledgments

• AgentOps for hosting this hackathon
• OpenAI for providing the GPT-4o model
• Streamlit for the web framework
• The open-source community for various libraries used in this project