MelodyMind: Hybrid Music Recommendation System 🎧

Welcome to the MelodyMind project repository!
This repository showcases my work in building music recommendation systems, evolving from a real-time API-based recommender to a sophisticated hybrid model.

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🚀 Project 1: Last.fm API-Based Music Recommender (Live Demo)

👉 Live Demo

This application is your personal guide to discovering new music, powered by the extensive Last.fm API. It offers intelligent and diverse recommendations through an intuitive Streamlit interface.

✨ Project Overview

MelodyMind isn't just another music recommender; it's a hybrid system that intelligently blends various data points from Last.fm to unearth unique music tailored to your taste. Whether you provide an artist, a specific track, or both, MelodyMind digs deep to find your next favorite song. Its hybrid approach ensures you get a broad spectrum of suggestions, moving beyond simple direct similarities to explore connected artists and genres.

🌟 Key Features

• Intelligent Input Handling: Smartly processes your artist and track inputs, with built-in auto-correction for common typos, ensuring you always find what you're looking for.
• Diverse Recommendation Strategies:
  • Track-Focused: Discover songs similar to a given track, expanding your playlist effortlessly.
  • Artist-Centric: Explore the top hits from your favorite artists or discover new tracks from artists you'll love.
  • Contextual Hybridization: When direct matches are scarce, the system intelligently broadens its search to include top tracks from similar artists and songs associated with relevant genres, providing richer recommendations.
• Comprehensive Insights: Dive deeper into the music with detailed information about recommended artists and tracks, including their top tags and concise summaries.
• Sleek User Interface: Enjoy a smooth and responsive experience thanks to the interactive Streamlit web application.
• Instant Access: Direct links to Last.fm enable you to listen to recommended tracks immediately.

💻 Technology Stack

• Python: The robust foundation for all logic and data processing.
• Streamlit: For creating a beautiful, interactive, and easy-to-use web application.
• Requests: Handles all HTTP communications with the Last.fm API.
• Last.fm API: The rich data source powering the music recommendations.

⚙️ Getting Started (Last.fm API App)

To run this version of MelodyMind on your local machine, follow these simple steps!

Prerequisites

• Python 3.12 (highly recommended for best compatibility)
• Git
• A Last.fm API Key – essential for accessing music data.

Local Installation

1. Clone the Repository:

   git clone [https://github.com/indranil143/Hybrid-Music-Recommendation-System.git](https://github.com/indranil143/Hybrid-Music-Recommendation-System.git)
   cd Hybrid-Music-Recommendation-System

2. Create a Virtual Environment (Highly Recommended):

   python -m venv venv
   # On Windows:
   .\venv\Scripts\activate
   # On macOS/Linux:
   source venv/bin/activate

3. Install Dependencies: Ensure your requirements.txt file is present in the current directory.

   pip install -r requirements.txt

4. Set Your Last.fm API Key: The application needs your Last.fm API Key to function. Set it as an environment variable:
   • For Windows (Command Prompt):

     set LASTFM_API_KEY="YOUR_ACTUAL_LASTFM_API_KEY"

   • For Windows (PowerShell):

     $env:LASTFM_API_KEY="YOUR_ACTUAL_LASTFM_API_KEY"

   • For macOS/Linux (Bash/Zsh):

     export LASTFM_API_KEY="YOUR_ACTUAL_LASTFM_API_KEY"

   • Remember to replace YOUR_ACTUAL_LASTFM_API_KEY with the key you obtained from Last.fm. For persistent local development, consider using a .env file with the python-dotenv package.
5. Run the Streamlit App:

   streamlit run music_app_api_only.py

   Your web browser should automatically open the MelodyMind application.

☁️ Deployment to Streamlit Community Cloud

Deploying MelodyMind to Streamlit Community Cloud is straightforward:

1. GitHub Repository: Your project must be hosted on a public GitHub repository.
2. requirements.txt: Ensure this file is located in the same directory as music_app_api_only.py. It should accurately list all project dependencies and avoid platform-specific packages (like pywin32) or problematic +cpu PyTorch/Torchaudio versions, as these can cause deployment issues.
3. API Key as Secret: Securely store your LASTFM_API_KEY in the Streamlit Community Cloud dashboard's secrets. Go to your app settings, then "Advanced settings," and finally "Secrets," adding the following:

   LASTFM_API_KEY="YOUR_ACTUAL_LASTFM_API_KEY_HERE"

🎮 How to Use

• Simply enter an artist name, a track name, or both in the sidebar on the left.
• Adjust the "Number of Recommendations" slider to control the quantity of suggestions.
• Then click "Get Recommendations."
• Explore the results under the "🎵 Recommendations" tab, and dive into deeper context with the "📊 Insights" tab.

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🎶 Project 2: Hybrid Music Recommendation System (LightFM)

This project implements a Hybrid Music Recommendation System combining Content-Based Filtering and Collaborative Filtering using the LightFM library. It provides personalized song recommendations by analyzing audio features and simulating user interactions (artist-track associations).

✨ Features

• Hybrid Approach: Combines Content-Based and Collaborative Filtering.
• Content-Based Filtering: Recommends songs based on audio features (danceability, tempo, energy, etc.) using cosine similarity.
• Collaborative Filtering: Utilizes the LightFM library to learn user (artist) and item (track) embeddings from interaction data.
• Simulated User Interactions: Demonstrates collaborative filtering using artist-track associations as implicit feedback.
• Streamlit Web App: Provides an interactive web interface to get recommendations.
• Modular Design: Separated data processing and deployment (music_app.py).

💡 Concepts Explained

• Content-Based Filtering: Imagine recommending songs that sound similar to what you already like. This method analyzes the characteristics (features) of items (songs) and suggests others with similar characteristics.
• Collaborative Filtering: This method looks at the behavior of many users. If User A and User B like similar songs, and User A likes a song that User B hasn't heard, Collaborative Filtering might recommend that song to User B. It finds patterns in user-item interactions.
• Hybrid System: Combines both methods to recommend new, niche songs (Content-Based) while leveraging user patterns (Collaborative Filtering) for enhanced accuracy.

Dataset

Uses the Spotify Dataset 1921-2020, 160k+ Tracks. Source: https://www.kaggle.com/datasets/fcpercival/160k-spotify-songs-sorted

⚙️ Getting Started (LightFM Hybrid App)

Running the Notebook

Execute the cells in Hybrid_Music_Recommendation_System.ipynb using Kaggle or Google Colab. This trains the model and saves music_recommender_components.pkl.

Running the Streamlit App

1. Ensure music_recommender_components.pkl is in the root directory.
2. Activate your virtual environment (from the previous setup steps, or create a new one).
3. Run the app:

   streamlit run music_app.py

   The app will open in your browser.

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🤝 Contributing

Feel free to contribute to this project by opening issues or submitting pull requests!

📄 License

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