/Book-Recommendation-System

NLP Project

Primary LanguageJupyter Notebook

Book Recommendation Agent

This project is a Book Recommendation Agent that leverages Streamlit and BERT-based NLP techniques to recommend books based on user input. Users can specify a genre and receive tailored book recommendations based on data from a Goodreads dataset.

Features

  • Data Loading: Efficiently loads and displays data from a CSV file.
  • Top 100 Books Selection: Filters and ranks the top 100 books in the specified genre based on average ratings.
  • Top 10 Books Selection: Further refines the list to the top 10 books from the top 100.
  • BERT-based Recommendation: Utilizes the SentenceTransformer model to recommend the best book from the top 10 using cosine similarity.
  • User Interaction: Provides an interactive and user-friendly interface via Streamlit, enabling users to enter genres and receive real-time recommendations.
  • Deployment: The application is deployed using ngrok for easy access and testing without complex setup.

Repository Contents

  • app.py: Main application file containing the Streamlit app and recommendation logic.
  • goodreads_data.csv: Dataset containing book information from Goodreads.
  • requirements.txt: List of required Python packages.
  • README.md: This file, providing an overview of the project.
  • Book_Recommendation_System.pdf: Documentation explaining why this model and approach were used.
  • FinalBookRecc.ipynb: Jupyter notebook with the code used to create the app.py file.
  • Demo Video.mp4: A demo video showing the functionality of the application.

Installation

  1. Clone the repository:

    git clone https://github.com/yourusername/book-recommendation-agent.git
cd book-recommendation-agent

  2. Install the required packages:

    pip install -r requirements.txt

  3. Ensure you have the goodreads_data.csv file in the same directory as app.py.

Usage

  1. Set your ngrok authentication token:

    from pyngrok import ngrok
ngrok.set_auth_token('YOUR_NGROK_AUTH_TOKEN')

  2. Run the Streamlit app:

    streamlit run app.py

  3. Once the app is running, ngrok will provide a public URL. Use this URL to access the Streamlit app.

Example

  • Input: Enter a genre such as "Science Fiction" or "Fantasy".
  • Output: The app displays the top 100 books in the specified genre, then narrows it down to the top 10, and finally recommends the best book using BERT-based similarity.

Contributing

Feel free to contribute to this project by submitting a pull request or opening an issue on GitHub.

Acknowledgments

  • Streamlit
  • Sentence Transformers
  • Goodreads
  • ngrok

Make sure to replace the placeholder YOUR_NGROK_AUTH_TOKEN with your actual ngrok authentication token and update the repository URL and any other project-specific details.

Book-Recommendation-System