Spotify Hyper & Opposite Playlist Generator

Description

The Spotify Hyper & Opposite Playlist Generator is a Python-based tool to generate hyper-focused and polar opposite playlists based on a user's Spotify "for you" playlist. The project uses the Spotify API to fetch relevant playlist and track data to analyze and generate new playlist recommendations.

Requirements

• Python 3.8+
• Streamlit
• Requests
• Pandas
• Numpy
• python-dotenv
• Pickle

Setup

1. Environment Variables: Before running the tool, ensure you have a .env file in the project root with the following variable:

   SPOTIFY_REFRESH_TOKEN=your_spotify_refresh_token
   

   Replace your_spotify_refresh_token with your actual Spotify refresh token.

2. Python Dependencies: Install the necessary python packages using pip:

   pip install requests pandas numpy python-dotenv streamlit
   

Usage

1. Fetching Spotify Data: Utilize the functions provided in playlist_functions.py to fetch and process Spotify data.

2. Streamlit App: The provided streamlit app is an example UI that uses the functions from playlist_functions.py. You can run the app with:

   streamlit run your_app_filename.py
   

   Replace your_app_filename.py with the name of your streamlit script if it's different.

3. Enter your Spotify username in the Streamlit app and discover your hyper and opposite playlists!

Features

1. Caching: Data is cached to reduce redundant API calls to Spotify, enhancing the efficiency of the application.

2. Modularity: The codebase is modular, allowing developers to easily expand or modify functionality.

3. Recommendation Engine: The core logic uses audio features to recommend hyper playlists and can be expanded for opposite playlist recommendations.

Limitations

• Ensure your Spotify refresh token is valid. If the token is invalid, API requests will fail.
• The rate limit for the Spotify API is handled, but excessive requests in a short time might still cause the tool to pause its operation temporarily.

Contributing

Feel free to fork this project, create a feature branch, and submit a pull request if you wish to contribute.

License

This project is licensed under the MIT License.

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