Movie Recommendation System

Description

This is a Flask-based web application that provides movie recommendations based on user input. It uses content-based filtering to recommend movies similar to the one selected by the user. The application retrieves movie posters dynamically from the TMDb API and displays them on the website.

Features

• Search for movies using a search bar.
• Display a list of movies with posters on the homepage.
• Clickable movie posters to view recommendations for similar movies.
• Dynamically fetch movie posters using the TMDb API.

Technologies Used

• Backend Framework: Flask
• Frontend: HTML, CSS (with Jinja2 templating)
• Database: CSV file (movie_dataset.csv)
• Machine Learning: scikit-learn (content-based filtering using cosine similarity)
• API Integration: TMDb API for fetching movie posters

Requirements

• Python 3.8 or higher
• Dependencies listed in requirements.txt

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/movie-recommendation-app.git
   cd movie-recommendation-app

2. Create a virtual environment (optional but recommended):

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

3. Install dependencies:

   pip install -r requirements.txt

4. Set up the TMDb API:

   • Sign up at TMDb and get an API key.
   • Replace the placeholder API key in app.py with your TMDb API key:

     TMDB_API_KEY = "your_tmdb_api_key"

5. Run the application:

   python app.py

   The application will be available at http://127.0.0.1:5000/.

Deployment

To deploy the app on platforms like Render, Heroku, or Vercel:

1. Ensure you have a requirements.txt file and, optionally, a runtime.txt file specifying the Python version.
2. Follow the platform-specific deployment instructions.

For example, to deploy on Render:

• Add a runtime.txt file with your desired Python version:

  python-3.11.11
  

• Push the code to a GitHub repository and link it to Render.

File Structure

project/
├── app.py                # Main Flask application
├── movie_dataset.csv      # Movie dataset used for recommendations
├── requirements.txt       # Python dependencies
├── runtime.txt            # Python version for deployment
├── static/                # Static files (CSS)
│   ├── style.css
├── templates/             # HTML templates
│   ├── main.html          # Homepage
│   ├── recommendations.html  # Recommendation page

Usage

1. Homepage:

   • Displays the first 50 movies from the dataset.
   • Use the search bar to find a specific movie.

2. Recommendations:

   • Click on a movie poster to see recommendations for similar movies.

Contributing

Contributions are welcome! Feel free to submit a pull request or open an issue for any bugs or feature requests.

License

This project is licensed under the MIT License.

Acknowledgements

• Flask for making web development simple and intuitive.
• TMDb for providing movie posters and metadata.
• scikit-learn for powering the recommendation algorithm.

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