YouTube Video Uploader FastAPI Microservice

An automated video publishing service built with Python and FastAPI that publishes videos directly to YouTube via YouTube Data API v3.

Features

• FastAPI Microservice: Web API endpoint (POST /upload) for automated video ingestion.
• Resumable Uploads: Supports chunked, resumable uploads for large video files.
• OAuth2 Authentication: Auto-refreshing OAuth token management stored safely in token.json.
• Command-line Interface: CLI tool (python -m youtube_uploader.cli) for quick uploads from terminal.
• Mock/Dry-Run Mode: Test upload pipelines locally without Google API credentials or consuming quota.
• Extensible Architecture: Ready to integrate with AI metadata generator pipelines (titles, descriptions, thumbnails).

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Directory Structure

.
├── youtube_uploader/
│   ├── __init__.py
│   ├── app.py          # FastAPI application & endpoints
│   ├── auth.py         # Google OAuth2 login & token refresh
│   ├── cli.py          # Command-line interface
│   ├── config.py       # Pydantic Settings management
│   └── upload.py       # Core YouTube Data API chunked upload logic
├── tests/
│   ├── test_api.py     # FastAPI endpoint integration tests
│   └── test_upload.py  # Upload module unit tests
├── .env.example
├── .gitignore
├── plan.md
├── README.md
└── requirements.txt

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Setup Instructions

1. Create Virtual Environment & Install Dependencies

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

2. Configure Google Cloud OAuth Credentials (For Live Uploads)

1. Go to Google Cloud Console.
2. Create a Project and enable YouTube Data API v3.
3. Configure the OAuth consent screen (add scope: https://www.googleapis.com/auth/youtube.upload).
4. Go to Credentials -> Create Credentials -> OAuth 2.0 Client ID.
5. Select Desktop Application as Application Type.
6. Download the JSON file, rename it to credentials.json, and place it in the project root directory.

Note: You can test the entire pipeline in --mock mode without credentials.json.

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Quickstart Usage

Running the Streamlit Web UI

Run Streamlit on port 8502 so that port 8501 remains available for the initial Google OAuth browser login callback (http://localhost:8501/oauth2callback):

streamlit run app.py --server.port 8502

Once you authenticate with Google for the first time, your access token is saved in token.json and future uploads will authenticate automatically.

• API Documentation (Swagger UI): Open http://localhost:8000/docs
• Health Check: GET http://localhost:8000/health

Example curl Request to Upload Video:

curl -X POST "http://localhost:8000/upload" \
  -F "file=@my_video.mp4" \
  -F "title=My Automated Upload" \
  -F "description=Uploaded via YouTube Uploader API" \
  -F "privacy_status=private" \
  -F "tags=python,automation,fastapi" \
  -F "mock=true"

Example Response:

{
  "status": "uploaded",
  "mock": true,
  "video_id": "mock_vid_1770441900",
  "youtube_url": "https://youtu.be/mock_vid_1770441900",
  "title": "My Automated Upload",
  "privacy_status": "private",
  "category_id": "22",
  "tags": ["python", "automation", "fastapi"]
}

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Option 2: Using the Command Line Interface (CLI)

# Upload video in mock mode
python -m youtube_uploader.cli my_video.mp4 --title "CLI Upload" --privacy private --mock

# Live upload (requires credentials.json)
python -m youtube_uploader.cli my_video.mp4 --title "Live Upload" --privacy private

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Running Tests

Run unit tests and API integration tests using pytest:

pytest -v

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License

MIT License