Tuner is a powerful tool that elevates your Spotify experience by curating personalized playlists with a seamless emotional flow. Using unsupervised machine learning, it clusters songs based on their emotional essence. Tuner then constructs a weighted distance matrix, calculating the emotional distances between tracks. This matrix is built using a supervised learning model that continuously adapts, adjusting weights to understand how audio features relate to emotion. This allows it to minimize emotional drag between consecutive tracks. Whether you're a music enthusiast, playlist curator, or someone who appreciates harmonious listening, Tuner unlocks the full potential of your Spotify playlists.

Before running the project, create a Supabase account at https://supabase.com and set up a new project. Copy the necessary environment variables from the Supabase project settings and paste them into a .env file in your project directory. Refer to the .env.example file for the required environment variables. Additionally, ensure you're using a virtual environment for your project.

Clone the project

  git clone https://github.com/dassaswat/tuner-backend.git

Go to the project directory

  cd tuner-backend

Install dependencies

  pip3 install -r requirements.txt

Run database migrations

  alembic upgrade head

Start the server

  uvicorn main:app --reload