JournalClubAssistant: Academic Paper to Presentation Converter

JournalClubAssistant is an AI-powered tool that automatically converts academic papers (PDF) into structured PowerPoint presentations while maintaining technical accuracy and logical flow. It leverages OpenAI's GPT-4 for content analysis and generates comprehensive presentations with proper figure integration.

Features

• Automatic extraction of paper structure and content
• Comprehensive figure analysis and integration
• Maintains technical accuracy and paper's logical flow
• Rich content summarization with evidence and implications
• Automatic PowerPoint generation with proper formatting
• Support for multi-panel figures and technical details

Installation

1. Clone the repository:

git clone https://github.com/yourusername/JournalClubAssistant.git
cd pdf2ppt

2. Create and activate a virtual environment:

python -m venv pdf2ppt
source pdf2ppt/bin/activate  # On Windows: pdf2ppt\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the project root directory with the following content:

# OpenAI API Configuration
OPENAI_API_KEY=your_api_key_here

# Output Configuration (optional)
OUTPUT_DIR=output
FIGURE_DIR=output/figures

# Logging Configuration (optional)
LOG_LEVEL=INFO

Example project structure with .env:

pdf2ppt/
├── .env                    # Environment variables file
├── src/
│   ├── __init__.py
│   ├── content_analyzer.py
│   ├── document_processor.py
│   ├── figure_extractor.py
│   └── presentation_generator.py
├── output/
│   └── figures/
├── main.py
├── requirements.txt
└── README.md

Usage

Run the script with your PDF file:

python main.py

When prompted, enter the path to your PDF file or a URL. The script will:

1. Extract figures from the PDF
2. Analyze the paper's content and structure
3. Generate a comprehensive PowerPoint presentation
4. Save the presentation in the output directory

Project Structure

pdf2ppt/
├── src/
│   ├── __init__.py
│   ├── content_analyzer.py
│   ├── document_processor.py
│   ├── figure_extractor.py
│   └── presentation_generator.py
├── output/
│   └── figures/
├── main.py
├── requirements.txt
└── README.md

Requirements

• Python 3.8+
• OpenAI API key
• Dependencies listed in requirements.txt:
  • PyMuPDF
  • opencv-python-headless
  • python-pptx
  • openai
  • python-dotenv
  • Pillow
  • numpy

Limitations

• Requires OpenAI API access and credits
• Processing time depends on paper length
• Figure extraction quality depends on PDF format
• May require manual verification for complex technical content

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

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

License

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

Acknowledgments

• OpenAI for GPT-4 API
• PyMuPDF for PDF processing
• python-pptx for presentation generation
• All contributors and users of this project

Citation

If you use this tool in your research, please cite:

@software{JournalClubAssistant2024,
  author = {Your Name},
  title = {JournalClubAssistant: Academic Paper to Presentation Converter},
  year = {2024},
  url = {https://github.com/yourusername/pdf2ppt}
}