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This project deploys a book recommendation system using Flask API, requiring Flask, pandas, scikit-learn, numpy, and joblib. It consists of a Jupyter Notebook for model building, an app.py file for APIs, and HTML/CSS templates for user interface. Users input preferences on the homepage and receive book recommendations.

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Nishant2018/Book_Recommendation

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Deploying Book Recommendation System using Flask

This project demonstrates the deployment of a Book Recommendation System using Flask API.

Prerequisites

Ensure that you have the following packages installed:

  • Flask==2.0.0
  • pandas==2.2.1
  • scikit-learn==0.24.2
  • numpy==1.24.3
  • joblib==1.0.1

Project Structure

This project consists of the following parts:

  1. book_recommendation.ipynb: Contains the code for building the book recommendation model.
  2. app.py: Flask application containing APIs to receive user input and provide book recommendations.
  3. templates: Folder containing HTML templates:
    • index.html: Allows users to input preferences and receive book recommendations.
  4. static: Folder containing CSS stylesheets:
    • style.css: Provides styling for the HTML templates.

Running the Project

  1. Navigate to the project directory.
  2. Create the book recommendation model by running the command:
    python book_recommendation.ipynb
    
    This will generate a serialized version of the model named book_recommendation_model.pkl.
  3. Run the Flask application by executing:
    python app.py
    
    The Flask app will start running on port 5000 by default.
  4. Open your web browser and navigate to:
    • http://127.0.0.1:5000/ or http://localhost:5000/ to access the homepage.
    • http://127.0.0.1:5000/predict to view the output of the book recommendation system.

Usage

  1. On the homepage, enter your preferences for book recommendations.
  2. Click on the "Recommend Books" button.
  3. You will receive a list of recommended books based on your preferences.

Note

Ensure that you have a stable internet connection to access the book recommendations.

About

This project deploys a book recommendation system using Flask API, requiring Flask, pandas, scikit-learn, numpy, and joblib. It consists of a Jupyter Notebook for model building, an app.py file for APIs, and HTML/CSS templates for user interface. Users input preferences on the homepage and receive book recommendations.

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