spotify-multiverse is a full-stack application that helps users discover new songs based on a playlist they already love. Through mathematical analysis, the project extracts the essential elements of the user's favorite songs, enabling the search for other tracks that will also be appealing.

This app was developed as a Final Project in Scientific Computing and Data Analysis by Hugo Folloni. The motivation behind the project stems from a personal love for music and the desire to find new songs that match the vibe of existing favorite playlists. The application leverages the Spotify API to analyze and recommend music based on user preferences.

The project employs concepts from linear algebra, such as vector and matrix operations, to analyze and compare songs. Each song is represented as a vector with specific attributes like danceability, energy, and loudness. By creating a matrix from a playlist of songs, Principal Component Analysis (PCA) is used to identify patterns and extract the most important components. This allows the application to generate a base vector that represents the user's musical taste and compare it with other songs in the Spotify database using Euclidean distance.

1. Clone the Repository:

   git clone https://github.com/yourusername/spotify-multiverse.git
   cd spotify-multiverse

2. Install Dependencies:

   cd server 
   pip install -r requirements.txt
   cd ../website
   npm install

3. Set Up Environment Variables:

   Create a .env file in the server directory.

   SPOTIFY_CLIENT = 
   SPOTIFY_SECRET = 
   
   DB_DATABASE =
   DB_HOST =
   DB_USER = 
   DB_PASSWORD =
   DB_PORT = 
   
   API_KEY = 
   

4. Run the backend:

   cd server 
   app.py

5. Start frontend:

   cd ../website
   npm install

• Frontend: JavaScript, React
• Backend: Python, Flask
• Database: PostgreSQL
• Data Analysis: NumPy, Scikit-learn
• API: Spotify API

Errors may occur due to issues with the Spotify API.