Project Info

MUSES

Inspiration

Our team wanted to address the Education Track Challenge presented by Zoom— in particular addressing music education. As avid lovers and musicians, all 3 of us in the team wanted to join in the process of gamifying education by using AI algorithms to aid learning an instrument or vocal singing.

What it does

Our app will first display a tailored feed of music recommendations. They may choose to like, save, or scroll past each piece of music. The app will process data from likes, saves, and retention in a cosine similarity algorithm to determine musical repertoire recommendations for the user to practice and play.

How we built it

Our team used Flutter for frontend development and SQL relational database for backend development. With Flutter, we ran both an IOS and Android simulator to see the UI / UX design element changes in real time. The SQL database was used to store all the songs and musical pieces in our mobile app, while OpenAI API was used to generate vector embeddings for our recommendation engine’s cosine similarity search. Hence, our tech stack included Dart in addition to algorithms in Python for data ingestion and C++ for app development.

Challenges we ran into

We didn’t know how to create a recommendation system to match users with songs they might like, for which we want into challenges while generating and storing vector embeddings. In addition to navigating vector databases, our team struggled with merging the git commits and push-pull requests as working in a team in a hackathon was a new experience for all of us.

Accomplishments we're proud of

The queue shuffler for songs on the home page of the mobile app is very enjoyable, as our AI LLM powered recommendation algorithm presents these songs to our users based on their favorite genres, previous liked and saved songs. Users have the ability to both save and like the songs recommended by our queue shuffler. The app also collects data from these interactions to fuel the recommendation algorithm.

What we learned

All of us in the team learned how to code in Flutter and FlutterFlow for frontend mobile app development, which has been a very enjoyable and learning experience for us. From possessing the added flexibility to drag and drop design elements to our OS simulator to learning how to create complex screens with embedded media links, learning to code in Flutter has been the highlight of Treehacks for us. Additionally, we were able to utilize the new IDE developed by Codeium, Windsurf, whose Claude Sonnet-powered copilot has helped us brainstorm and plan complex algorithms.

What's next

We have already started creating customer surveys for fellow hackers to gather user testimonials and interest. Based on the vast majority of our users listening to music through Spotify and YouTube Music, we plan to integrate Spotify song links into our mobile app’s homepage in addition to the YouTube Music integration. In the future after the hackathon ends, we plan to iterate MUSES and pivot if necessary to reach a bigger audience and spread the joy of learning music.

Muses - Music Learning Platform

Muses is a modern, interactive music learning platform that helps users discover and learn music through an engaging interface. Named after the Greek muse of music, Euterpe combines powerful music recommendation algorithms with an intuitive learning experience.

Features

🎵 Interactive music discovery feed
💖 Like and save favorite tracks
🎯 Personalized music recommendations
👤 User profiles with learning history
📱 Responsive mobile-first design
🔄 Continuous playback queue

Tech Stack

Backend (Python)

Flask web framework
SQLAlchemy ORM
PostgreSQL database
RESTful API architecture
YouTube API integration

Frontend (Flutter/Dart)

Flutter framework for cross-platform development
Provider for state management
HTTP package for API communication
SharedPreferences for local storage
Custom UI components

Project Structure

euterpe/
├── backend/
│   ├── app/
│   │   ├── models/        # Database models
│   │   ├── routes/        # API endpoints
│   │   └── utils/         # Helper functions
│   ├── config.py          # Configuration settings
│   ├── requirements.txt   # Python dependencies
│   ├── seed_database.py   # Database seeding script
│   └── server.py         # Main server file
│
└── frontend/
    └── lib/
        ├── components/    # Reusable UI components
        ├── providers/     # State management
        ├── screens/       # App screens
        ├── services/      # API services
        └── main.dart     # Entry point

Getting Started

Backend Setup

Create a Python virtual environment:

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

Install dependencies:

cd backend
pip install -r requirements.txt

Set up the database:
```
python seed_database.py
```
Start the server:
```
python server.py
```

Frontend Setup

Install Flutter dependencies:
```
cd frontend
flutter pub get
```
Run the app:
```
flutter run
```

API Endpoints

POST /auth/register - Register new user
POST /auth/login - User login
POST /interactions/like - Like a track
POST /interactions/unlike - Unlike a track
GET /recommendations - Get personalized recommendations
GET /saved-songs - Get user's saved songs

Contributing

Create your feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add some amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

License

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

Acknowledgments

Flutter team for the amazing cross-platform framework
Flask team for the lightweight WSGI web application framework
All contributors who have helped shape this project

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only

CIn code
C++In code
DartIn code
FlaskIn code
HTMLIn code
KotlinIn code
PythonIn code
SwiftIn code
OpenAIClaimed
SQLClaimed

8 of 10 appear in the indexed code. 2 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

No AI coding agent signals were found in this repository.

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

167 KB

Source files

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.