videoinstruct 0.1.7

A tool that automatically generates step-by-step documentation from instructional videos

VideoInstruct

VideoInstruct is a tool that automatically generates step-by-step documentation from instructional videos. It uses AI to extract transcriptions, interpret video content, and create comprehensive markdown guides.

Quick Start

Using Docker (Recommended)

The fastest and simplest way to use VideoInstruct is through our Docker image. See DOCKER_USAGE.md for detailed instructions on:

• Installation and prerequisites
• Downloading the Docker file from Docker Hub.
• Running with local videos or YouTube URLs
• Configuration options
• Troubleshooting common issues

Using Python Package

# Install from PyPI
pip install videoinstruct

# Set up environment variables
export OPENAI_API_KEY=your_openai_key
export GEMINI_API_KEY=your_gemini_key
export DEEPSEEK_API_KEY=your_deepseek_key

# Use in your code
from videoinstruct import VideoInstructor
instructor = VideoInstructor(video_path="path/to/video.mp4")
documentation = instructor.generate_documentation()

Features

• Automatic video transcription extraction
• AI-powered video interpretation
• Step-by-step documentation generation
• Automated documentation quality evaluation with conversation memory
• Interactive Q&A workflow between AI agents
• User feedback integration for documentation refinement
• Configurable escalation to human users
• Screenshot generation and annotation
• PDF export capabilities
• Enhanced workflow visibility with real-time status updates
• Transparent model information display for each agent

Installation Options

1. Docker (Recommended): See DOCKER_USAGE.md
2. PyPI: pip install videoinstruct
3. Source:

   git clone https://github.com/PouriaRouzrokh/VideoInstruct.git
   cd VideoInstruct
   pip install -r requirements.txt
   

Project Structure

VideoInstruct/
├── data/                  # Place your video files here
├── examples/              # Example usage scripts
│   ├── example_usage.py   # Basic Python example
│   └── docker_example.py  # Docker-based example
├── output/                # Generated documentation output
├── scripts/               # Utility scripts
├── videoinstruct/         # Main package
│   ├── agents/           # AI agent modules
│   ├── prompts/          # System prompts for agents
│   ├── tools/            # Utility tools
│   ├── utils/            # Utility functions
│   ├── cli.py            # Command-line interface
│   ├── configs.py        # Configuration classes
│   └── videoinstructor.py # Main orchestration class
├── DOCKER_USAGE.md       # Docker setup and usage guide
└── README.md             # This file

Using as a Python Package

from videoinstruct import VideoInstructor, VideoInstructorConfig
from videoinstruct.agents import DocGeneratorConfig, VideoInterpreterConfig, DocEvaluatorConfig

# Configure the VideoInstructor
config = VideoInstructorConfig(
    doc_generator_config=DocGeneratorConfig(
        api_key=openai_api_key,
        model_provider="openai",
        model="o3-mini",
        temperature=0.7
    ),
    video_interpreter_config=VideoInterpreterConfig(
        api_key=gemini_api_key,
        model="gemini-2.0-flash"
    ),
    doc_evaluator_config=DocEvaluatorConfig(
        api_key=deepseek_api_key,
        model="deepseek-reasoner"
    )
)

# Initialize and run
instructor = VideoInstructor(
    video_path="path/to/video.mp4",
    config=config
)
documentation = instructor.generate_documentation()

Contributing

To contribute to VideoInstruct:

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Commit your changes: git commit -am 'Add some feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

Troubleshooting

• For Docker-related issues, see DOCKER_USAGE.md
• For Python package issues:
  • Make sure all dependencies are installed
  • Check your Python version (3.8+ required)
  • Verify your API keys and internet connection

License

MIT License