K-Recs - A Song Recommender App

Description

K-Recs is a song recommender application that allows users to input a song title and receive recommendations based on the song's characteristics. The app utilizes a dataset of songs with various features and integrates with the Spotify API to fetch additional song details.

Features

• Input a song title to receive recommendations.
• Displays song details including title, artist, album, and album cover.
• Provides recommendations based on genres, artist, and release year.
• User-friendly interface built with Streamlit.

Technologies Used

• Python
• Streamlit
• Pandas
• Spotipy (for Spotify API integration)
• dotenv (for environment variable management)

Installation

Prerequisites

• Python 3.7 or higher
• pip (Python package installer)

Steps

1. Clone the repository:

   git clone https://github.com/bruhu/spotify-song-recommender.git
   cd spotify-song-recommender

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install the required packages:

   pip install -r requirements.txt

4. Set up your Spotify API credentials:

   • Create a .env file in the root directory of the project.
   • Add your Spotify API credentials:

     SPOTIFY_CLIENT_ID=your_client_id
     SPOTIFY_CLIENT_SECRET=your_client_secret
     

Usage

1. Run the Streamlit app:

   streamlit run app/recommender_app.py

2. Open your web browser and go to http://localhost:8501.

3. Enter a song title in the input field and click "Submit" to receive recommendations.

Contributing

Contributions are welcome! If you have suggestions for improvements or new features, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

• Thanks to the Spotify API for providing access to song data.
• Thanks to the Streamlit community for creating an easy-to-use framework for building web apps.