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🎶 Melody Mart

Feel the Beat, Live the Moment

Melody Mart is a cross-platform music application that empowers users to upload, manage, and play their favorite music with ease. Built using Flutter for the frontend, FastAPI for the backend, and PostgreSQL for data storage, it combines modern technologies to deliver a responsive and scalable experience.


🚀 Features

  • 🔐 User Authentication – Secure signup and login functionality.
  • 📂 Upload Music – Upload songs easily using Cloudinary integration.
  • 🎵 Play Songs – Seamless audio playback powered by just_audio.
  • ❤️ Favorites – Mark and manage your favorite tracks.
  • 🌐 Cross-Platform Support – Available on Android, iOS, and Web via Flutter.
  • 📦 Offline Support (Future) – Planned support for offline playback and advanced features.

🎯 Project Aim

To develop a cross-platform music app with:

  • Intuitive UI & smooth performance
  • Personalized music management
  • Integration of modern tools for full-stack development

🛠️ Tech Stack

Frontend

  • Framework: Flutter
  • Architecture: MVVM (Model-View-ViewModel)
  • State Management: Riverpod
  • Packages Used:
    • State: fpdart, flutter_riverpod, riverpod_annotation
    • Storage: shared_preferences, hive, isar_flutter_libs
    • File Handling: file_picker, path_provider
    • Audio: just_audio, just_audio_background, audio_waveforms
    • UI: dotted_border, flex_color_picker

Backend

  • Framework: FastAPI (Python)
  • Features:
    • REST API for auth, uploads, and song metadata
    • Asynchronous and scalable endpoints
    • Cloudinary integration for file storage

Database

  • Database: PostgreSQL
  • Interface: pgAdmin

📐 Architecture

  • MVVM Structure for frontend modularity
  • Cloudinary for media storage
  • Secure API handling with FastAPI
  • Relational Storage using PostgreSQL for song metadata and user data

📌 Outcome

Melody Mart showcases the synergy between Flutter and FastAPI in delivering a clean, robust, and modular music management platform. The project lays a solid foundation for future upgrades like:

  • Offline playback
  • AI-powered recommendations
  • Enhanced user insights

📚 References


👥 Contributors

  • Yash Shrivastava

📄 License

This project is for educational purposes only. Licensing information can be added here if made open-source.

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