Movie Recommendation System

This project implements a movie recommendation system using Streamlit and cloud storage for data persistence. The system is content-based, using cosine similarity on movie tags to recommend similar movies.

Features

• Content-Based Recommendation: Recommends movies based on similarity in movie tags.
• Dynamic Poster Display: Displays movie posters for recommended movies.
• Data Persistence: Utilizes cloud storage (Google Drive and Dropbox) for storing large data files.
• Streamlit Interface: Provides an interactive interface for users to select a movie and view recommendations.

Setup

1. Installation

   • Clone the repository:

     git clone https://github.com/Rithish5513U/Movie-Recommendation-System.git
     cd <repository_name>
     

   • Install dependencies:

     pip install -r requirements.txt
     

2. Running the Application

   • Start the Streamlit application:

     streamlit run app.py
     

   • Open your browser and go to http://localhost:8501 to view the application.

3. Usage

   • Select a movie from the dropdown menu.
   • Click on the "Show Recommendation" button to display recommended movies and their posters.

Data Handling

• Movies Data: Initially downloaded from Google Drive and stored locally as movie_list.pkl.
• Similarity Data: Downloaded from Dropbox and stored locally as similarity.pkl.

Handling Data Download

• The system checks for local data files (movie_list.pkl and similarity.pkl).
• If not found locally, it downloads them from cloud storage.
• Handles errors and retries during download using requests and custom exception handling.

Credits

• Data Sources:
  • Movies data: Google Drive
  • Similarity data: Dropbox
• Libraries:
  • Streamlit
  • Pandas
  • Requests
  • NLTK
  • Scikit-learn
  • Pickle

Troubleshooting

• Downloading Large Files: If encountering issues with downloading large files from cloud storage platforms like Google Drive or Dropbox, ensure the file sharing settings allow public access.

Project Structure

• src/
  • app.py: Main application file.
  • components/: Contains the components of the application.
    • data_ingestion.py: Importing the data for the movies
    • data_transformation.py: Preprocess the data
    • model_trainer.py: Train the model using sklearn
  • utils.py: Utility functions.
  • exception.py: Custom exception handling.
  • logger.py: Logging configuration.
• Artifacts/
  • movie_list.pkl: Pickle file containing the list of movies.
  • similarity.pkl: Pickle file containing the similarity matrix.
• requirements.txt: List of required packages.
• setup.py: Used to initiate the setup for the project.

Contributing

Feel free to submit issues or pull requests for new features, bug fixes, or enhancements.

Acknowledgements

• Special thanks to the TMDb API for providing the movie data.
• Krish Naik for the original inspiration and datasets.

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Enjoy using the Movie Recommendation System! If you have any questions or feedback, feel free to reach out.