Jingle Music App

Jingle is a dynamic music streaming app that integrates a modern MVC architecture with personalized recommendations and a user-friendly interface. Built with JSP for the frontend, Java servlets as the controller, and a MySQL database for the backend, Jingle offers an engaging and tailored music experience for its users.

Features

1. User Authentication & Personalization

• Login/Logout: Secure login with validated credentials. Users can safely log out of their accounts.
• Account Management: New users can register with a unique email. Duplicate credentials trigger alerts.
• Personalized Greeting: Custom greetings based on the time of day.

2. Music Browsing & Interaction

• Search: Search functionality to easily find songs and albums.
• Recently Played & Trending: Displays the last 10 songs played and top 5 trending tracks.
• Song Interaction: Includes play/pause, next/previous song controls, loop, mute, and playlist management.

3. User Menu

• Profile Picture: Displays the user's profile picture, with a default option if none is set.
• User Information: Shows user details from the database (currently read-only).

4. Song Card

• A dynamically updating card showing the currently playing song with detailed information, song controls, and progress indicators.

Architecture Overview

Jingle follows the Model-View-Controller (MVC) design pattern:

• Frontend: Built with JSP, HTML, CSS for dynamic rendering and styling, and JavaScript for handling interactions.
• Controller: Java Servlets act as the controller, managing the app flow and interacting with the backend.
• Backend: A MySQL database stores user and music data. The server runs on Apache Tomcat, handling requests and responses.

Recommendation System

Jingle provides music recommendations through multiple methods:

1. Popularity-Based Recommendations: Tracks the top 5 most played songs using SQL queries.
2. Collaborative Filtering: Uses user behavior to recommend songs. If users with similar interests listen to different tracks, the app suggests those tracks to the user.
   • Implementation: Uses Flask and Python, employing matrix factorization and alternating least squares to generate suggestions.

Setup and Deployment

Requirements:

• Java 11+
• Apache Tomcat 9+
• MySQL Workbench
• Flask (for Recommendation System)

Steps:

1. Clone the repository.
2. Set up MySQL and import the necessary tables.
3. Deploy the app on Apache Tomcat.
4. Run the Flask server for recommendations.

Future Enhancements

• Add user profile update functionality.
• Improve content-based recommendation accuracy.