🎵 Discovering Your Soul Through Music

This project is a music recommendation and personality prediction system. It leverages objective Spotify audio features (e.g., energy, danceability, valence) and K-Means clustering to map musical preferences to personality traits.

🚀 Features

• User Account Management: Secure registration, login, and account deletion functionality with password hashing (SHA-256).
• Music Search Engine: Filter and search for songs based on keywords (title, artist), genres, and release year ranges. Includes direct links to YouTube search results.
• Personality Prediction System:
  • Uses K-Means clustering to present a diverse, sampled set of songs for users to select their preferences.
  • Calculates the average of 7 key audio features (energy, danceability, positiveness, speechiness, liveness, acousticness, instrumentalness) from selected songs.
  • Maps these averages to predefined personality types and generates match percentages.
• Data Visualization: Interactive Radar charts and Bar charts built with Plotly to visualize user audio feature profiles and personality match scores.
• Soul Matching (Recommendation): Recommends compatible personality types and curates shared "Soulmate Playlists" based on user personality and chosen social scenarios (e.g., studying, relaxing, exercising).

🛠️ Tech Stack

• Backend & Data Processing: Python, pandas, scikit-learn (K-Means clustering)
• Frontend UI: Streamlit, Plotly (Interactive Charts)
• Database: MySQL (Relational Database Management System), SQLAlchemy (ORM/Connection), mysql-connector-python

🗄️ Database Schema

The system utilizes a 9-table normalized relational database schema (exported as music_db_export.aql). Key tables include:

• User Management: Users
• Music Data: Songs, Genres, Song_Genres
• Personality & Logic: Personality_Types, Personality_Result, Personality_Match
• User Interactions: User_Selected_Songs, Recommended_Songs

⚙️ Installation & Setup

Follow these steps to run the application locally.

1. Prerequisites

• Python 3.8+
• MySQL Server running locally or remotely

2. Clone the Repository

git clone <your-repository-url>
cd <your-repository-directory>

3. Install Dependencies

Install the required Python packages:

pip install -r requirements.txt

(Ensure your requirements.txt includes: streamlit, pandas, scikit-learn, sqlalchemy, mysql-connector-python, plotly)

4. Database Setup

1. Create a MySQL database named music_db_new.

2. Import the provided database dump:

   mysql -u root -p music_db_new < music_db_export.aql

3. Configure Database Connection: Open app.py (or your main script file) and locate the get_engine() function. Update the connection string with your MySQL credentials:

   # Update username, password, host, and database name
   return create_engine("mysql+mysqlconnector://root:YOUR_PASSWORD@localhost/music_db_new") 

5. Run the Application

Start the Streamlit server:

streamlit run app.py

The application should now be running, typically accessible at http://localhost:8501.