https://github.com/cactusquill/ai-docgen
📸 ScreenDoc: Automate professional documentation generation from screen recordings using AI.
https://github.com/cactusquill/ai-docgen
ai documentation gemini llm python
Last synced: over 1 year ago
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📸 ScreenDoc: Automate professional documentation generation from screen recordings using AI.
- Host: GitHub
- URL: https://github.com/cactusquill/ai-docgen
- Owner: CactusQuill
- License: mit
- Created: 2024-12-29T19:27:40.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2025-03-16T11:35:14.000Z (over 1 year ago)
- Last Synced: 2025-03-16T12:28:30.977Z (over 1 year ago)
- Topics: ai, documentation, gemini, llm, python
- Language: Python
- Homepage:
- Size: 114 MB
- Stars: 3
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: Readme.md
- License: LICENSE
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README
# AI-DocGen 📸 - Automated Process Documentation Tool
AI-DocGen is a powerful Python-based tool that automatically generates professional documentation from screen recordings. It uses AI to detect steps, analyze screenshots, and create detailed documentation in multiple formats.
## 🌟 Features
- **Automated Screen Recording**: Capture your process with high-quality screen recording
- **Smart Step Detection**: Automatically identifies distinct steps in your workflow
- **AI-Powered Descriptions**: Generates detailed descriptions for each step using Google's Gemini Vision API
- **Multiple Output Formats**: Generate documentation in PDF, HTML, or Markdown
- **Interactive UI**: User-friendly Streamlit interface for easy recording and configuration
- **Customizable Sensitivity**: Adjust step detection parameters to match your needs
- **Professional Output**: Clean, well-formatted documentation with images and descriptions
## 🚀 Quick Start
### Prerequisites
- Python 3.8 or higher
- FFmpeg (for video processing)
- Google Cloud account with Gemini Vision API access
### Installation
1. Clone the repository:
```bash
git clone https://github.com/CactusQuill/AI-DocGen.git
cd AI-DocGen
```
2. Create and activate a virtual environment:
```bash
python -m venv venv
# Windows
.\venv\Scripts\activate
# Linux/Mac
source venv/bin/activate
```
3. Install dependencies:
```bash
pip install -r requirements.txt
```
4. Install FFmpeg:
- Windows: Download from [FFmpeg website](https://ffmpeg.org/download.html) and add to PATH
- Linux: `sudo apt-get install ffmpeg`
- Mac: `brew install ffmpeg`
5. Set up environment variables:
- Create a `.env` file in the project root
- Add your Gemini API key:
```
GEMINI_API_KEY=your_api_key_here
MODEL_NAME=gemini-vision-1.5
```
### Usage
1. Start the application:
```bash
streamlit run app.py
```
2. In the web interface:
- Click "Start Recording" to begin capturing your screen
- Perform the process you want to document
- Click "Stop Recording" when finished
- Adjust step detection settings if needed
- Generate documentation in your preferred format
## 🛠️ Configuration
### Step Detection Settings
- **Similarity Threshold** (0.0-1.0): Controls how different two frames need to be to be considered separate steps
- **Minimum Time Between Steps** (seconds): Minimum time that must pass between detected steps
### Output Formats
1. **PDF**
- Professional layout with table of contents
- Embedded images and formatted text
- Page numbers and headers
2. **HTML**
- Responsive design
- Interactive table of contents
- Print-friendly styling
3. **Markdown**
- GitHub-compatible format
- Easy to version control
- Convertible to other formats
## 🤝 Contributing
We welcome contributions! Please follow these steps:
1. Fork the repository
2. Create a 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
### Development Setup
1. Install development dependencies:
```bash
pip install -r requirements-dev.txt
```
2. Run tests:
```bash
pytest
```
3. Check code style:
```bash
flake8
black .
```
## 📝 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
## 🙏 Acknowledgments
- Google Gemini Vision API for AI-powered image analysis
- Streamlit for the amazing web interface framework
- FFmpeg for video processing capabilities
- All our contributors and users
## 📚 Documentation
For detailed documentation, visit our [Wiki](https://github.com/CactusQuill/AI-DocGen/wiki).
## 🐛 Troubleshooting
### Common Issues
1. **Video Playback Issues**
- Ensure FFmpeg is properly installed
- Check video codec compatibility
- Try converting the video manually using FFmpeg
2. **Step Detection Problems**
- Adjust similarity threshold
- Increase minimum time between steps
- Check if screen changes are significant enough
3. **API Issues**
- Verify API key is correct
- Check API quota and limits
- Ensure internet connectivity
### Getting Help
- Open an [Issue](https://github.com/CactusQuill/AI-DocGen/issues)
- Check existing [Discussions](https://github.com/CactusQuill/AI-DocGen/discussions)
- Read our [FAQ](https://github.com/CactusQuill/AI-DocGen/wiki/FAQ)
## 🔄 Updates
Stay updated with new releases:
- Watch this repository
- Follow our [Release Notes](https://github.com/CactusQuill/AI-DocGen/releases)
## 📊 Project Status
AI-DocGen is under active development. We're working on:
- [ ] Multi-monitor support
- [ ] Custom documentation templates
- [ ] Cloud storage integration
- [ ] Batch processing
- [ ] API endpoint for programmatic access
## 💡 Feature Requests
Have an idea? We'd love to hear it!
1. Check existing [Feature Requests](https://github.com/CactusQuill/AI-DocGen/labels/enhancement)
2. Open a new [Discussion](https://github.com/CactusQuill/AI-DocGen/discussions/new)