This project aims to refactor a monolithic video processing toolkit into a modular, backend-driven framework. It allows users to seamlessly execute video processing tasks on different environments, from local machines to remote CI/CD pipelines.

1. Clone the repository:

   git clone https://github.com/your-username/vucar.git
   cd vucar

2. Install dependencies (using pip):

   pip install -e .

   Or using poetry (if you set it up):

   poetry install
   poetry shell

To run the video processing toolkit, use the vucar command followed by the run subcommand and the path to your video file.

vucar run <path_to_video_file> [OPTIONS]

• --backend, -b: Specify the backend to use. Options: local (default), github.
• --verbose, -v: Show raw output for debugging.

Run locally:

vucar run my_video.mp4 --backend local

Run via GitHub Actions:

vucar run my_video.mp4 --backend github

Create a config.toml file in the project root with your GitHub repository details and GPG key information:

[user]
repo = "your-username/your-repo"
workflow_file = "your_workflow.yml"
default_branch = "main"
action_gpg_recipient = "github-action-gpg-key-id"
user_gpg_recipient = "your-gpg-key-id"

Create a presets.toml file in the project root for FFmpeg command presets:

[preset_name_1]
name = "Preset 1 Description"
command = "ffmpeg -i {input} -c:v libx264 -crf 23 {output}"

[preset_name_2]
name = "Preset 2 Description"
command = "ffmpeg -i {input} -c:v libvpx-vp9 -crf 30 {output}"

vucar/
├── __main__.py
├── cli.py
├── config.py
├── pyproject.toml
├── README.md
├── requirements.txt
├── vucar blueprint.md
├── backends/
│   ├── __init__.py
│   ├── base.py
│   ├── github.py
│   └── local.py
├── core/
│   ├── __init__.py
│   ├── ffmpeg.py
│   ├── security.py
│   └── video.py
└── ui/
    ├── __init__.py
    └── prompts.py