Movie Recommender Engine

A content-based Movie Recommender System built using Python. This project leverages natural language processing and machine learning techniques to recommend movies similar to a selected movie. The core functionalities are implemented in Preprocessing.py, while app.py handles the deployment using Streamlit, providing an interactive web interface.

Demo

mre-demo-video.mp4

Table of Contents

• Features
• Demo
• Installation
• Usage
• Project Structure
• Requirements
• API Setup
• Contributing
• License
• Acknowledgements

Features

• Content-Based Recommendations: Recommends movies based on content similarity using cosine similarity.
• Interactive Web Interface: Users can select a movie and receive top 5 similar movie recommendations with posters.
• Movie Posters: Fetches and displays movie posters using The Movie Database (TMDB) API.
• Deployment Ready: Easily deployable using Streamlit for web applications.

Installation

1. Clone the Repository

   git clone https://github.com/nnilayy/Movie-Recommender-Engine.git
   cd Movie-Recommender-Engine

2. Create a Virtual Environment (Optional but Recommended)

   uv venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install Requirements

   pip install -r requirements.txt

4. Set Up TMDB API Key

   • Obtain an API key from TMDB.

   • Option 1 (Development): Create a .env file in the root directory and add:

     TMDB_API_KEY=your_api_key_here

     Ensure that python-dotenv is installed.

   • Option 2 (Production/Streamlit Sharing): Add the API key to Streamlit secrets.

     # In .streamlit/secrets.toml
     TMDB_API_KEY = "your_api_key_here"

Usage

1. Data Preprocessing

   Run the Preprocessing.py script to process the dataset and generate necessary pickle files for the recommendation engine.

   python Preprocessing.py

   This script will generate movie_dict.pkl and similarity.pkl inside the model_files directory.

2. Run the App

   streamlit run app.py

3. Interact with the App

   • Access the app at http://localhost:8501 in your web browser.
   • Select a movie from the dropdown menu.
   • View the top 5 recommended movies along with their posters.

Project Structure

Movie-Recommender-Engine/
── app.py
── Preprocessing.py
── requirements.txt
── README.md
── model_files/
│   ├── movie_dict.pkl
│   ── similarity.pkl
── .env

• app.py: The Streamlit app handling user interactions and displaying recommendations.
• Preprocessing.py: Script for preprocessing data and building the recommendation model.
• requirements.txt: Lists all Python dependencies.
• model_files/: Directory containing serialized model files.
• .env: File containing environment variables (not included in the repository).

API Setup

To fetch movie posters, the app uses TMDB API. Follow these steps to set up:

1. Register on TMDB

   • Sign up at TMDB to create an account.
   • Navigate to API section and apply for an API key.

2. Add API Key to the Project

   • As mentioned in the Installation section, add your API key using one of the options.

3. Modify app.py (If Necessary)

   • Ensure that tmdb_api_key is correctly set in app.py.

     # If using .env file
     from dotenv import load_dotenv
     load_dotenv()
     tmdb_api_key = os.getenv('TMDB_API_KEY')

     # If using Streamlit secrets
     tmdb_api_key = st.secrets["TMDB_API_KEY"]

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements.

1. Fork the Repository

2. Create a Feature Branch

   git checkout -b feature/YourFeature

3. Commit Changes

   git commit -m "Add your message"

4. Push to the Branch

   git push origin feature/YourFeature

5. Open a Pull Request

License

This project is licensed under the MIT License.

Acknowledgements

• Datasets: The dataset used for this project is sourced from TMDB 5000 Movie Dataset.
• Inspiration: This project is inspired by the Movie Recommendation systems commonly seen on platforms like Netflix and Amazon Prime.
• Libraries and Tools:
  • Pandas
  • NumPy
  • Scikit-Learn
  • Streamlit
  • NLTK

Contact

• Author: Nilay Kumar Bhatnagar
• Email: [email protected]