[Feature Request]: Adding Emotion Based Music Player Project

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Feature Description

The emotion-based music player successfully integrates deep learning and computer vision techniques to create a personalized, emotion-driven music experience. By leveraging facial expression and hand gesture recognition through Mediapipe, combined with a pretrained deep learning model, the system can detect the user's emotional state in real-time. This allows for dynamic music recommendations that adapt to the user's mood, enhancing the listening experience.

The project demonstrates how artificial intelligence can transform user interaction with media, making it more intuitive, personalized, and engaging. With future improvements, such as more advanced emotion recognition and enhanced music recommendations, this system could revolutionize how users interact with digital content, making it more emotionally responsive and contextually aware.

The models and technologies used in the emotion-based music player project include:

Pretrained Keras Model (model.h5): A deep learning model, likely a Convolutional Neural Network (CNN), is loaded to predict emotions based on processed facial landmarks and hand movements.
Mediapipe Library: Mediapipe is used for extracting facial landmarks and hand landmarks, which serve as input features for emotion recognition. It captures key points from the user's face and hands for emotion detection.
Streamlit and WebRTC: Used for the web interface and real-time video streaming, capturing the users face for emotion recognition through a web camera.
The project leverages deep learning (Keras) and computer vision (Mediapipe) to detect emotions based on facial and hand landmark data, then uses the model to predict the emotion, which influences the music recommendation.

Use Case

This feature—emotion-based music recommendation—enhances the project by significantly improving user experience in the following ways:

Personalized Music Curation: By detecting the user’s emotions in real-time, the system provides highly tailored music selections that align with their current mood, making the listening experience more enjoyable and meaningful.

Automation and Convenience: Users no longer need to manually search for music to suit their mood. The system automatically curates playlists, saving time and reducing decision fatigue.

Adaptive and Dynamic Interaction: As emotions fluctuate throughout the day, the music player adapts instantly, ensuring that the music remains relevant and engaging.

Innovative and Engaging User Experience: Emotion-based music players offer a cutting-edge and interactive approach, differentiating the project from traditional music applications, thus increasing user satisfaction and retention.

Stress and Mood Management: By selecting mood-enhancing or calming music based on emotional states, the player could help users manage stress, improve focus, or enhance relaxation.