🎵 Melodify - AI-Enhanced Indian Classical Music Recommendation System

Melodify is a Flask-based web application that recommends Indian classical and film songs to users based on their detected emotion (via facial expression) and current weather (via geolocation). The system uses AI for face emotion detection and hybrid recommendation logic powered by user feedback and content similarity.

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🚀 Features

• Emotion Detection using DeepFace

• Weather Detection via OpenWeatherMap API

• Hybrid Song Recommendation based on:

  • Emotion + Weather match
  • User feedback (likes)
  • Cosine similarity of singer-composer combinations

• Like/Dislike Feedback System to personalize future recommendations

• Avoids repeating recently recommended songs

• Similarity Matrix Visualization (optional)

• User Registration & Login with hashed passwords

• Backend in Flask, frontend via HTML templates

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🧠 How It Works

1. User logs in or creates an account.

2. Face image is captured and analyzed via DeepFace to detect emotion.

3. Weather is detected using the user's geolocation.

4. A song is recommended from a curated dataset, based on:

   • Matching EMOTION + WEATHER
   • Prior liked songs (cosine similarity on artist data)
   • Fallback to random if needed

5. User can like/dislike the song, improving future recommendations.

6. Feedback is stored in SQLite and used to adjust future suggestions.

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📁 Dataset Structure

The dataset (Dataset.csv) must include the following columns:

• EMOTION
• WEATHER
• LINK (YouTube or audio URL)
• RAGA
• SINGER
• COMPOSER

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🛠️ Tech Stack

• Frontend: HTML, Bootstrap (in templates/)

• Backend: Python, Flask

• AI Libraries:

  • DeepFace for facial emotion recognition
  • scikit-learn for TF-IDF and cosine similarity

• Database: SQLite (users.db)

• Data Visualization: Seaborn + Matplotlib (for similarity matrix)

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🔧 Setup Instructions

1. Clone the Repo

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

2. Install Dependencies

pip install -r requirements.txt

(Ensure deepface, opencv-python, flask, requests, scikit-learn, matplotlib, seaborn, etc. are included in requirements.txt.)

3. Add Dataset

Place Dataset.csv in the root directory with appropriate columns.

4. Configure Weather API

Replace the OpenWeatherMap API key in get_weather() function with your own:

API_KEY = "your_openweathermap_api_key"

5. Run the App

python app.py

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🔐 User Auth

• Passwords are securely hashed using werkzeug.security.
• SQLite stores users and feedback in users.db.
• Table structures are auto-created on app run (init_db() function).

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🎯 Future Enhancements

• 🎤 Voice-based emotion recognition
• 📱 Mobile-friendly UI
• 🎶 Spotify/Youtube integration for song playback
• 💾 Admin dashboard for adding/removing songs
• 🤖 Reinforcement learning for smarter personalization

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🤝 Contributing

Feel free to fork and submit pull requests or suggestions!

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📷 Screenshots

🎵 Song Recommendation Result

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📄 License

This project is open-source and free to use for educational purposes.

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