🎵 Music Recommender

A web application that predicts music genres, extracts audio features, and recommends similar songs based on machine learning.

🚀 Features

• Welcome Page: Modern animated landing page (Urban Beatzs) with quick navigation
• Contact Form: Users can send feedback or queries (saved to contacts.json)
• About, Genre Analysis, Audio Features Pages: Informative pages for users
• Genre Prediction: Analyzes audio files to predict music genre using a TensorFlow/Keras model
• Audio Feature Extraction: Extracts energy, danceability, loudness, and more
• Song Recommendations: Suggests similar songs based on genre and audio features
• YouTube Integration: Downloads songs directly from YouTube for analysis
• Audio Playback: Built-in player for uploaded/downloaded songs
• Genre Fun Facts: Displays a random fun fact, tip, and icon for each predicted genre
• Modern UI: Animated visualizer, responsive design, and toast notifications

🧠 Machine Learning Model

The application uses a trained TensorFlow model that can classify songs into 10 genres:

• Blues
• Classical
• Country
• Disco
• Hip-Hop
• Jazz
• Metal
• Pop
• Reggae
• Rock

🛠️ Technical Stack

• Backend: Python, Flask
• Machine Learning: TensorFlow, Keras
• Audio Processing: librosa, soundfile
• Data Analysis: pandas, scikit-learn
• Frontend: HTML, CSS, JavaScript
• External Tools: yt-dlp for YouTube downloads

📁 Project Structure

MusicRecommender/
├── app.py                 # Main Flask application
├── extract_features.py    # Audio feature extraction
├── recommend.py           # Song recommendation system
├── requirements.txt       # Python dependencies
├── data/                  # Dataset for recommendations
├── model/                 # Trained ML model
├── static/                # CSS, JS, images, videos
│   ├── style.css          # Main styles
│   ├── main.js            # Main JS logic
│   ├── navbar.js, navbar.css
│   └── ...
├── templates/             # HTML templates
│   ├── welcome.html       # Animated landing page
│   ├── index.html         # Main page with player and song list
│   ├── result.html        # Results page with predictions
│   ├── about.html         # About page
│   ├── contact.html       # Contact form
│   ├── genre-analysis.html# Genre info
│   ├── audio-features.html# Audio features info
│   └── 404.html, 500.html # Error pages
├── uploads/               # Uploaded and downloaded audio files
├── utils/                 # Helper functions
│   ├── file_utils.py      # File handling utilities
│   └── model_utils.py     # Model prediction utilities
├── genre_facts.json       # Fun facts, tips, and icons for each genre
├── contacts.json          # Stores contact form submissions
└── ...

🌐 Main Routes & Endpoints

• / : Welcome page (Urban Beatzs)
• /index : Main app (upload, play, analyze, download)
• /about : About the project
• /contact : Contact form
• /genre-analysis : Genre info
• /audio-features : Audio features info
• /download : POST endpoint for YouTube song download
• /uploads/<filename> : Serve uploaded/downloaded audio

🚀 Getting Started

Prerequisites

• Python 3.8+ to Python 3.10
• pip
• yt-dlp (for YouTube downloads)
• 4GB RAM minimum
• 2GB free disk space
• Internet connection for YouTube downloads

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/MusicRecommender.git
   cd MusicRecommender

2. Create and activate a virtual environment:

   python -m venv env
   .\env\Scripts\Activate.ps1

3. Install dependencies:

   pip install -r requirements.txt
   pip install yt-dlp

4. Run the app:

   python app.py

5. Open your browser at http://127.0.0.1:5000

🎥 Demo

[Add screenshots or GIF of your application in action]

⚠️ Error Handling

• Invalid audio file formats
• Failed YouTube downloads
• Network connectivity issues
• File permission errors
• Memory limitations
• Custom 404 and 500 error pages

🔧 Troubleshooting

• YouTube Download Fails: Update yt-dlp, check internet, try a different URL
• Audio Analysis Error: Use supported formats (MP3, WAV), check file integrity, ensure enough memory
• Model Prediction Issues: Clear browser cache, restart app, check model files in /model

👥 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit changes (git commit -m 'Add AmazingFeature')
4. Push to branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📊 Web Interface

Main Page

• Music player with controls and animated visualizer
• List of uploaded/downloaded songs (with search)
• YouTube search and download
• Song analysis button

Results Page

• Predicted genre with confidence scores
• Extracted audio features (energy, danceability, loudness, etc.)
• Song recommendations
• Random fun fact, tip, and icon for the genre

Welcome, About, Contact, Genre/Audio Info Pages

• Modern animated welcome page
• About page with project and tech info
• Contact form (saves to contacts.json)
• Genre and audio feature info pages

🎤 Genre Fun Facts

• Results page displays a random fun fact, tip, and icon for each predicted genre, loaded from genre_facts.json
• Each genre has multiple unique facts/tips
• Backend randomly selects a fact for the predicted genre and passes it to the UI

Customizing Genre Facts

• Edit genre_facts.json to add or update facts, tips, or icons for any genre

🖌️ Modern UI Enhancements

• Animated confidence bars, genre icons, and a visually engaging, rhythm-inspired layout
• Responsive design for desktop and mobile
• Toast notifications for user feedback

🛣️ Ready for React (Optional)

• Backend is API-ready and can be connected to a React frontend in the future

🧩 How It Works

1. Upload or Download: Add songs by uploading files or downloading from YouTube
2. Select and Play: Choose a song to play in the built-in player
3. Analyze: Click "Analyze Song" to process the selected song
4. View Results: See the predicted genre, audio features, recommendations, and fun facts

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgements

• librosa for audio processing

• TensorFlow for machine learning

• Flask for web framework

• yt-dlp for YouTube downloads

• Disclaimer: This project was developed independently by the authors as a part of an academic course project. No institutional resources were used, and no intellectual property agreement was signed with the university. The authors retain full ownership under the MIT License.

• © 2025 Urban Beatzs Project Authors. All rights reserved.