emotion-based-music-Recommendation-System-

Emotion-Based Music Recommender WebApp

Welcome to our project on emotion-based music recommendation! This project utilizes various technologies including Mediapipe, Keras, OpenCV, and Streamlit to create a web application where users can capture their webcam feed and receive music recommendations based on their detected emotions.

Overview

In this project, we have developed a web application that leverages computer vision and machine learning techniques to detect facial expressions in real-time using the user’s webcam feed. Based on the detected emotions, the application recommends music tracks that correspond to the user’s current mood.

Technologies Used

• Mediapipe: Utilized for facial landmark detection and facial expression recognition.
• Keras: Employed for training the emotion recognition model.
• OpenCV: Used for image processing tasks such as reading webcam feed and displaying the results.
• Streamlit: Framework for building interactive web applications with Python.
• Streamlit-WebRTC: Module for capturing webcam feed directly within the browser.

Video Tutorial

To assist with understanding the code and the process of creating the web application, we have provided a detailed video tutorial. You can watch the tutorial here.

Getting Started

To run the application locally, follow these steps:

1. Clone the repository.
2. Install the necessary dependencies using pip install -r requirements.txt.
3. Run the Streamlit application with streamlit run app.py.
4. Open the provided URL in your web browser to access the web application.

Code Structure

• app.py: Main Python script containing the Streamlit application code.
• model.py: Script for defining and training the emotion recognition model using Keras.
• utils.py: Utility functions for image processing and interfacing with Mediapipe and OpenCV.
• requirements.txt: List of Python dependencies required to run the application.

Contributing

Contributions to the project are welcome! If you find any issues or have suggestions for improvements, please feel free to open an issue or create a pull request.