Emotion-Based Music Recommendation System 🎵😊

This project integrates facial emotion classification with a music recommendation engine. Users upload an image, and the system predicts their emotion, recommending music tracks that align with their mood. The application includes a Django backend, a React frontend, and a Dockerized environment for easy deployment.

Features

• Emotion Classification: Detects faces using OpenCV and classifies emotions with a custom model.
• Music Recommendation: Suggests songs based on valence-arousal mapping, leveraging a curated Spotify music database.
• Interactive UI: A React-based frontend allows users to upload images and preview song recommendations.
• Rich Music Details: Includes song name, album cover, and 30-second audio previews.

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Components

Backend (Django)

• Hosts the ML model for emotion classification.
• Exposes API routes for processing uploaded images and returning music recommendations.
• Stores a music database populated using the Spotify API.

Frontend (React)

• Provides a user-friendly interface for uploading images.
• Displays recommended songs along with album artwork and audio previews.

ML Model

• Utilizes a Haar Cascade classifier for face detection.
• Maps classified emotions to valence-arousal (V-A) values for music selection.

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Running the Project with Docker Compose

1. Clone the repository:

   git clone [email protected]:paudelanil/MinorProject.git
   cd MinorProject
   

2. Start the services:

   docker-compose up --build
   

3. Access the application
   • FrontEnd: http://localhost:3000

Prerequisites

Docker and Docker Compose installed.

Future Enhancements

• Add multilingual support for emotions and recommendations.
• Expand the database with additional music genres and regional tracks.