Music Recommendation System

This repository contains a Music Recommendation System that integrates Spotify's API and Genius API to provide personalized music recommendations based on song lyrics and user playlists. The application is built using Streamlit for the front end and uses machine learning models for generating recommendations.

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Features

• Spotify Integration: Allows users to log in with their Spotify account, fetch their playlists, and recommend similar tracks.
• Lyrics Analysis: Scrapes and analyzes lyrics using Genius API for better recommendation accuracy.
• Custom Recommendations: Generates song recommendations using TF-IDF vectorization and cosine similarity on the lyrics.
• Hover Effects: Adds an interactive hover and zoom-in effect for song posters and names.
• Scalable Backend: Modular design to support additional features in the future.

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Technologies Used

Backend Technologies

• Python: Core programming language.
• Spotipy: Spotify API integration for fetching user playlists and track details.
• Genius API: For fetching song lyrics.
• pandas: Data manipulation and cleaning.
• nltk: Tokenization and stemming for natural language processing.
• scikit-learn: TF-IDF vectorization and cosine similarity calculations.
• langdetect: Detects non-English lyrics.

Frontend Technologies

• Streamlit: For creating an interactive web interface.
• HTML/CSS: Embedded in Streamlit for enhanced styling (hover effects, navbar, etc.).

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Setup Instructions

Follow these steps to set up and run the Music Recommendation System on your local machine:

1. Clone the Repository

$ git clone https://github.com/Om1513/MusicRecommendationSystem.git
$ cd music-recommendation-system

2. Install Dependencies

Ensure you have Python 3.8+ installed. Install the required dependencies:

$ pip install -r requirements.txt

3. Set Up Environment Variables

Create a .env file in the root directory with the following keys:

SPOTIPY_CLIENT_ID=<your-spotify-client-id>
SPOTIPY_CLIENT_SECRET=<your-spotify-client-secret>
SPOTIPY_REDIRECT_URI=<your-redirect-uri>
GENIUS_ACCESS_TOKEN=<your-genius-access-token>

To obtain these credentials:

• Spotify API: Create a Spotify Developer App.
• Genius API: Get an API Key.

4. Prepare the Dataset (Optional)

If you wish to use your own dataset, ensure it is a CSV file with the following structure:

song	artist	lyrics
Song Name	Artist Name	Song Lyrics

Place the file in the root directory and update the file path in the app.py script.

5. Run the Application

Run the Streamlit application:

$ streamlit run app.py

The application will be accessible in your browser at http://localhost:8501.

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How It Works

1. Spotify Login: Users log in with their Spotify credentials to fetch playlists.
2. Lyrics Scraping: The app retrieves song lyrics from Genius for playlist songs.
3. Preprocessing:
   • Removes non-English lyrics.
   • Tokenizes and stems lyrics using NLTK.
4. Recommendation Model:
   • Uses TF-IDF vectorization to convert lyrics into numerical vectors.
   • Calculates cosine similarity to find similar songs.
5. Interactive UI:
   • Displays recommended songs with hover effects for posters and names.

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Folder Structure

Music-Recommendation-System/
├── app.py                    # Main application script
├── requirements.txt          # Required dependencies
├── dataset.csv               # Initial dataset (optional)
├── similarity.pkl            # Precomputed similarity matrix
├── README.md                 # Project documentation
├── .env                      # Environment variables

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Customization

Using a New Dataset

Replace the playlist_songs_with_lyrics.csv file with your own dataset and update the file path in app.py. Run the following function to generate a new similarity matrix:

from app import generate_similarity_pickle

generate_similarity_pickle('your_dataset.csv', 'your_similarity.pkl')

Update the similarity.pkl file in your app.

Adding Features

You can enhance the app by adding:

• Genre-Based Recommendations: Use Spotify's track features like tempo, danceability, and energy.
• User Preferences: Save user preferences for personalized results.

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Known Issues

1. Rate Limits: Both Spotify and Genius APIs have rate limits. If you hit the limit, wait for some time before retrying.
2. Incomplete Data: Some songs may not have lyrics available on Genius, which might lead to exclusions.

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Contribution Guidelines

We welcome contributions! If you'd like to improve the project, please fork the repository and create a pull request.

Steps to Contribute:

1. Fork the repository.
2. Create a feature branch: git checkout -b feature-name.
3. Commit your changes: git commit -m "Added new feature".
4. Push to the branch: git push origin feature-name.
5. Open a pull request in the main repository.

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License

This project is licensed under the MIT License.

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Contact

If you have any questions or suggestions, feel free to reach out at:

• Email: [email protected]
• GitHub: https://github.com/Om1513