{"id":29089326,"url":"https://github.com/jarif87/tune-popularity-app","last_synced_at":"2026-04-30T03:34:43.414Z","repository":{"id":301230480,"uuid":"1008591346","full_name":"jarif87/tune-popularity-app","owner":"jarif87","description":"Flask web app to predict song popularity using CatBoost. Enter five song features for instant predictions. 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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.\n\n## Table of Contents\n- [Features](#features)\n- [Screenshots](#screenshots)\n- [Installation](#installation)\n- [Usage](#usage)\n- [File Structure](#file-structure)\n- [Dependencies](#dependencies)\n- [Model Details](#model-details)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Features\n- Input form for five song features with default values for ease of use.\n- Modern, responsive UI with a clean design and gradient background.\n- Real-time prediction of song popularity (popular or not popular).\n- Error handling for invalid inputs.\n- CSRF protection disabled for development (not recommended for production).\n\n## Screenshots\n\n### Home Page\n![Home Page](images/image.png)\n\n\n\n\n## Installation\n\n1. **Clone the repository**:\n   ```\n   git clone https://github.com/your-username/song-popularity-prediction.git\n   cd song-popularity-prediction\n   ```\n\n2. **Set up a virtual environment**\n```\npython -m venv venv\nsource venv/bin/activate  # On Windows: venv\\Scripts\\activate\n```\n\n3. **Install dependencies:**\n```\npip install -r requirements.txt\n```\n- Ensure the model file:\n    - Place the trained CatBoost model (cat_model.pkl) in the project root directory.\n\n    - The model must be trained on the features: artist_familiarity, artist_hotttnesss, song_hotttnesss, year, start_of_fade_out.\n\n### Usage\n\n- **Run the application:**\n\n```\npython app.py\n\n```\n\n### File Structure \n\n```\nsong-popularity-prediction/\n├── app.py                    # Main Flask application\n├── cat_model.pkl             # Trained CatBoost model (not included in repo)\n├── templates/\n│   └── index.html            # HTML template for the web interface\n├── static/\n│   └── style.css             # CSS for styling the web interface\n├── images/\n│   ├── image.png            # Project banner image\n│  \n│   \n│   \n├── requirements.txt          # Python dependencies\n└── README.md                 # Project documentation\n```\n### Dependencies\n```\nscikit-learn==1.2.2\nnumpy==1.26.4\ncatboost==1.2.8\nflask\nFlask-WTF\n```\n\n### Model Details\n- Algorithm: CatBoost\n\n- Features: \n    - artist_familiarity (0.0–1.0)\n\n    - artist_hotttnesss (0.0–1.0)\n\n    - song_hotttnesss (0.0–1.0)\n\n    - year (e.g., 1969–2001)\n\n    - start_of_fade_out (seconds)\n\n- Output: Binary classification (0 = not popular, 1 = popular)\n\n- Model File: cat_model.pkl (must be trained on the above features)\n\n- 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.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjarif87%2Ftune-popularity-app","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjarif87%2Ftune-popularity-app","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjarif87%2Ftune-popularity-app/lists"}