Automates the upload workflow for Google Meet lecture recordings to SafaBrura (WordPress) and Captivate.fm podcast.

• Watch Google Drive folder for new lecture recordings
• Upload video to AWS S3
• Convert video to MP3 using FFmpeg
• Upload audio to Captivate.fm as podcast episode
• Create WordPress draft post with embedded video and podcast player

• Python 3.11+
• FFmpeg installed and available in PATH
• Google Cloud service account with Drive API access
• AWS S3 bucket
• WordPress site with application password
• Captivate.fm account

1. Create and activate virtual environment:

python -m venv venv
# Windows
venv\Scripts\activate
# Linux/Mac
source venv/bin/activate

2. Install dependencies:

pip install -r requirements.txt

3. Copy .env.example to .env and fill in your credentials:

cp .env.example .env

4. Set up Google Drive service account:
   • Create a service account in Google Cloud Console
   • Enable Google Drive API
   • Download the JSON key file
   • Share your Drive folder with the service account email

python -m src.main --check

python -m src.main --dry-run

python -m src.main

python -m src.main --file-id YOUR_FILE_ID --filename "Lecture Title - 2024-01-15.mp4"

python -m src.main -v

1. Check Google Drive - Find new video files in the specified folder
2. Upload to S3 - Upload video and get public URL
3. Convert to MP3 - Extract audio using FFmpeg
4. Upload to Captivate.fm - Create podcast episode
5. Create WordPress Draft - Create post with video and audio embeds
6. Cleanup - Delete local temp files

All configuration is done via environment variables (see .env.example):

pytest tests/ -v

safabrura-automation/
├── src/
│   ├── main.py              # CLI entry point
│   ├── orchestrator.py      # LangGraph workflow
│   ├── services/
│   │   ├── google_drive.py  # Google Drive API
│   │   ├── s3_upload.py     # AWS S3 uploads
│   │   ├── ffmpeg_converter.py  # Video to MP3
│   │   ├── captivate.py     # Captivate.fm API
│   │   └── wordpress.py     # WordPress REST API
│   ├── models/
│   │   └── lecture.py       # Data models
│   └── config/
│       └── settings.py      # Environment config
├── tests/
│   └── test_services.py
├── .env.example
├── .gitignore
├── requirements.txt
└── README.md