An AI-powered file analysis tool that converts various document formats into clear, concise summaries using OpenAI.

• 📄 Supports multiple file formats (PDF, DOCX, PPTX, Images, etc.)
• 🤖 AI-powered document summarization
• 🖼️ Intelligent image analysis and text extraction
• 📊 Text extraction from various formats
• 📱 Mobile-friendly interface
• 🔄 Conversion history tracking
• 🔒 Secure file handling
• ⚡ Fast processing

• Python 3.8 or higher
• OpenAI API key
• Docker (optional, for containerized deployment)

1. Clone the repository:

git clone https://github.com/timtech4u/ai-file-analyzer.git
cd file-analyzer

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

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

3. Install dependencies:

pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the project root:

OPENAI_API_KEY=your-api-key-here
DEBUG=False
MAX_FILE_SIZE=10  # in MB

1. Start the Streamlit app:

streamlit run app.py

2. Visit http://localhost:8501 in your web browser.

The easiest way to run File Analyzer is using our pre-built Docker image:

docker pull ghcr.io/timtech4u/ai-file-analyzer:latest
docker run -p 8501:8501 -e OPENAI_API_KEY=your-api-key ghcr.io/timtech4u/ai-file-analyzer:latest

Available tags:

• latest: Latest stable release
• vX.Y.Z: Specific version releases
• main: Latest development build

If you prefer to build the image yourself:

1. Build the Docker image:

docker build -t file-analyzer .

2. Run the container:

docker run -p 8501:8501 -e OPENAI_API_KEY=your-api-key file-analyzer

The application processes files through several stages:

1. Upload: Files are temporarily stored and validated
2. Processing: Content is extracted based on file type
3. Analysis: AI generates summaries and extracts key information
4. Response: Results are displayed and stored in history

file-analyzer/
├── app.py              # Main application
├── requirements.txt    # Dependencies
├── Dockerfile         # Container configuration
├── .env              # Environment variables
└── tests/            # Test suite

pytest tests/

This project follows PEP 8 guidelines. Run linting with:

flake8 .

The application implements comprehensive error handling:

• File validation errors
• API connection issues
• Processing timeouts
• Size limit exceeded
• Unsupported file types

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

• Add tests for new features
• Update documentation
• Follow the existing code style
• Add type hints to new functions
• Include error handling

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

• Built with Streamlit
• Powered by OpenAI
• Uses MarkItDown for file conversion

For support, please open an issue in the GitHub repository.