djai 0.3.0

Utilities for working with Django-based AI integrations.

djai

Tools for DJ ideation powered by AI/ML. The package ships with a command-line interface that helps you analyse your Spotify likes so you can feed curated metadata into downstream machine-learning workflows.

Command Line Interface

Ensure you have a Spotify access token with the user-library-read scope. You can either pass it as a flag or export it as an environment variable:

export SPOTIFY_API_TOKEN="your-spotify-token"
djai --max-items 100 > liked_tracks.json

Key flags:

• --token: Provide the Spotify token directly (defaults to SPOTIFY_API_TOKEN).
• --limit: Batch size per API call (max 50; defaults to 50).
• --max-items: Optional cap on total tracks to fetch.

The CLI calls Spotify's /v1/me/tracks endpoint, caches responses, downloads MP3 previews via yt-dlp (requires ffmpeg), falling back to a YouTube search when Spotify doesn't expose a preview URL, and prints the total number of liked tracks retrieved.

.env Support

Store secrets in a .env file to keep them out of your shell history:

SPOTIFY_CLIENT_ID="your-client-id"
SPOTIFY_CLIENT_SECRET="your-client-secret"
SPOTIFY_API_TOKEN="your-spotify-token"

The CLI automatically loads .env from the current working directory (or parent directories) using python-dotenv. If an explicit API token is missing but client credentials are configured, djai launches a one-time Authorization Code flow using a temporary localhost listener to obtain fresh access_token and refresh_token values before fetching tracks. The resulting tokens are cached in .djai_session (ignored by git) so subsequent runs in the same directory reuse them. Downloaded previews are stored under .djai_cache/audio, and API responses are cached under .djai_cache for up to 30 days. Note that analysing a user's liked tracks still requires a token granted with the user-library-read scope.

Development

python -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -e ".[dev]"
ruff check .
pytest

Continuous Integration

GitHub Actions runs linting (ruff) and tests (pytest) on pushes and pull requests targeting main via .github/workflows/ci.yml.

Publishing to PyPI

The manual Publish workflow in .github/workflows/deploy.yml builds and uploads the package. Before triggering it, bump the version in pyproject.toml and ensure a PyPI token is stored as PYPI_API_TOKEN.