https://github.com/jarif87/tune-popularity-app
Flask web app to predict song popularity using CatBoost. Enter five song features for instant predictions. Modern, responsive UI, no CSRF for development.
https://github.com/jarif87/tune-popularity-app
catboost-classifier eda flask-application matplotlib-python music-classification python scikit-learn seaborn
Last synced: 4 months ago
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Flask web app to predict song popularity using CatBoost. Enter five song features for instant predictions. Modern, responsive UI, no CSRF for development.
- Host: GitHub
- URL: https://github.com/jarif87/tune-popularity-app
- Owner: jarif87
- Created: 2025-06-25T19:32:39.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-25T19:37:16.000Z (4 months ago)
- Last Synced: 2025-06-25T20:32:01.591Z (4 months ago)
- Topics: catboost-classifier, eda, flask-application, matplotlib-python, music-classification, python, scikit-learn, seaborn
- Language: Python
- Homepage:
- Size: 0 Bytes
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Song Popularity Prediction Web App
A Flask-based web application to predict song popularity using a CatBoost machine learning model. Users can input five song features—`artist_familiarity`, `artist_hotttnesss`, `song_hotttnesss`, `year`, and `start_of_fade_out`—to determine if a song is likely to be popular.
## Table of Contents
- [Features](#features)
- [Screenshots](#screenshots)
- [Installation](#installation)
- [Usage](#usage)
- [File Structure](#file-structure)
- [Dependencies](#dependencies)
- [Model Details](#model-details)
- [Contributing](#contributing)
- [License](#license)
## Features
- Input form for five song features with default values for ease of use.
- Modern, responsive UI with a clean design and gradient background.
- Real-time prediction of song popularity (popular or not popular).
- Error handling for invalid inputs.
- CSRF protection disabled for development (not recommended for production).
## Screenshots
### Home Page
## Installation
1. **Clone the repository**:
```
git clone https://github.com/your-username/song-popularity-prediction.git
cd song-popularity-prediction
```
2. **Set up a virtual environment**
```
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies:**
```
pip install -r requirements.txt
```
- Ensure the model file:
- Place the trained CatBoost model (cat_model.pkl) in the project root directory.
- The model must be trained on the features: artist_familiarity, artist_hotttnesss, song_hotttnesss, year, start_of_fade_out.
### Usage
- **Run the application:**
```
python app.py
```
### File Structure
```
song-popularity-prediction/
├── app.py # Main Flask application
├── cat_model.pkl # Trained CatBoost model (not included in repo)
├── templates/
│ └── index.html # HTML template for the web interface
├── static/
│ └── style.css # CSS for styling the web interface
├── images/
│ ├── image.png # Project banner image
│
│
│
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
### Dependencies
```
scikit-learn==1.2.2
numpy==1.26.4
catboost==1.2.8
flask
Flask-WTF
```
### Model Details
- Algorithm: CatBoost
- Features:
- artist_familiarity (0.0–1.0)
- artist_hotttnesss (0.0–1.0)
- song_hotttnesss (0.0–1.0)
- year (e.g., 1969–2001)
- start_of_fade_out (seconds)
- Output: Binary classification (0 = not popular, 1 = popular)
- Model File: cat_model.pkl (must be trained on the above features)
- Note: Ensure cat_model.pkl is compatible with the five input features. If you encounter a feature mismatch error, retrain the model using the same features.