Lum App

Table of Contents

1. Introduction
2. Features
3. Screenshots
   • Login & Register
   • Home Page
   • Uploading Image
   • Suggested Playlists
   • User Playlist Suggestion
   • About Lum
   • Contact Form
4. Future Scope
5. Installation
6. Contributing
7. License
8. Contact

Introduction

Lum is an innovative music app that elevates your mood by curating personalized playlists based on your emotions. Using advanced emotion detection powered by machine learning and the Google Cloud Vision API, Lum tunes into your feelings and recommends music that resonates with your current state.

Features

• Emotion detection through camera input.
• Personalized playlist suggestions based on detected emotions.
• Integration with Spotify for a vast music library.
• Simple and user-friendly interface.

Screenshots

Login & Register:

Home Page:

Uploading Image:

Suggested Playlsts:

User Playlist Suggestion:

About Lum:

Contact Form:

Future Scope

As Lum continues to evolve, we plan to implement several advancements and features to enhance the user experience and the app's capabilities. Some of these future enhancements include:

1. Database-Driven Playlist Management: Implementing a database to store and manage playlists will allow for more dynamic and varied playlist suggestions. Users can add their favorite playlists, and Lum will learn over time to suggest music that aligns more closely with individual preferences.

2. Advanced Real-Time Face Recognition: Upgrading the emotion detection system with more advanced real-time face recognition software. This will improve the accuracy of emotion detection and allow Lum to respond more sensitively to subtle emotional changes, providing an even more personalized music experience.

3. Integration with Multiple Music Services: Expanding the range of music services integrated with Lum, beyond Spotify, to include other major platforms like Apple Music, Amazon Music, and YouTube Music. This will give users access to a wider range of music and enhance the app's versatility.

4. User Profile and Learning Algorithm: Developing a user profile system where Lum learns from past interactions to refine and personalize playlist suggestions over time. This feature would take into account user feedback, frequently played tracks, and preferred genres.

5. Community Features: Creating a community aspect where users can share playlists, discover music based on other users with similar tastes, and contribute to a collaborative music discovery experience.

6. Mobile Application: Developing a mobile app version of Lum to provide users with the convenience of accessing their emotion-based music playlists on the go.

7. Enhanced Privacy and Security: As Lum evolves, maintaining user privacy and data security will be a top priority, especially with the integration of more personal data and advanced recognition technologies.

By continually innovating and integrating these features, Lum aims to redefine the music listening experience, making it more responsive, personal, and immersive.

Installation

To set up Lum on your local machine, follow these steps:

1. Clone the repository: git clone https://github.com/reyanshgupta/Lum.git

2. Install the required dependencies: pip install -r requirements.txt

3. To run Lum, execute the following command in the project directory: flask run

4. Open your web browser and navigate to http://localhost:5000 to start using Lum.

Contributing

Contributions to Lum are welcome! Here's how you can contribute:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature-branch).
5. Create a new Pull Request.

Please make sure to update tests as appropriate.

License

MIT

Contact

For any queries or feedback, please contact Reyansh Gupta.