Movie Recommendation System

A machine learning-powered movie recommendation system built with Python, leveraging vector embeddings (pgvector) on Supabase for scalable, fast, and personalized suggestions. Now featuring a "Watch" button for instant movie viewing!

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🖼️ UI Preview

🎬 Demo

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🚀 Features

• AI-Powered Recommendations: Personalized movie suggestions using vector similarity search.
• Watch Movies Directly: Instantly stream recommended movies with the new watch feature.
• Massive Movie Database: Millions of movies with metadata and embeddings.
• Blazing Fast: Uses pgvector for high-performance similarity queries.
• Modern Stack: Python backend, PostgreSQL (pgvector), Supabase, Streamlit/React frontend.

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🏗️ Tech Stack

• Backend: Python, Flask
• Database: PostgreSQL + pgvector (on Supabase)
• Data: TMDB API for movie metadata
• Frontend: Streamlit, Svelte, or React components
• Other: Docker (optional), Redis (optional for caching)

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⚡ Quick Start

1. Clone the Repository

git clone https://github.com/Talnz007/Movie_Recomendation_System.git
cd Movie_Recomendation_System

2. Setup Python Virtual Environment

python3 -m venv .venv
source .venv/bin/activate   # On Windows: .venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Copy and edit the .env.example file:

cp .env.example .env

Fill in your database and TMDB API credentials in .env.

5. Run the Application

python main.py

Or, if you have a frontend:

streamlit run app.py

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🔑 Environment Variables

Edit .env as follows:

DB_USER=your_db_user
DB_PASSWORD=your_db_password
DB_HOST=your_db_host
DB_PORT=6543
DB_NAME=your_db_name
DB_OPTIONS=-c statement_timeout=0

TMDB_API_KEY=your_tmdb_api_key

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🎬 How to Use

1. Start the app: Run the backend and frontend as above.
2. Search for a movie: Enter a movie you like.
3. Get recommendations: The system finds similar movies using vector similarity search.
4. Watch a movie: Click the "Watch" button to stream a recommended title.

OR JUST GO TO This Link

🧠 How It Works

• Embeddings: Each movie is represented as a vector (embedding) in a high-dimensional space.
• Vector Search: When you select a movie, the app finds other movies with similar embeddings using pgvector.
• Supabase: Provides scalable, cloud-hosted PostgreSQL with vector support.
• Integrated Watching: Watch workflow connects you to available streaming sources.

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📂 Project Structure

Movie_Recomendation_System/
├── api/                  # API backend code
├── frontend/             # Frontend UI code
├── database.py           # Database logic
├── main.py               # Main app logic
├── app.py                # App entry point for Streamlit
├── requirements.txt      # Python dependencies
├── .env.example          # Example environment config
├── .gitignore
└── README.md

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🛡️ Security & Best Practices

• Never commit your .env file (it’s in .gitignore).
• Update requirements.txt regularly for security.
• Check GitHub Security Alerts for vulnerabilities.

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🤝 Contributing

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

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📈 Future Plans

• Collaborative filtering and hybrid models
• User profiles and login
• Advanced analytics and rating system
• Improved streaming provider integration

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📄 License

This project is licensed under the MIT License.

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📬 Contact

Maintained by Talha Niazi

Project URL: https://github.com/Talnz007/Movie_Recomendation_System