BookWise

Your Intelligent Book Recommendation System

Discover your next favorite read with the power of Large Language Models (LLMs). BookWise uses semantic search, sentiment analysis and zero-shot classification to recommend books tailored to your preferences.

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Table of Contents

1. Project Overview
2. Components
3. Installation
4. Usage
5. Technologies Used

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Project Overview

This project is a comprehensive guide to building a semantic book recommender system using LLMs. It covers the following key steps:

• Cleaning and preprocessing text data.
• Building a vector database for semantic search.
• Classifying books using zero-shot classification.
• Performing sentiment analysis to extract emotions from text.
• Creating a web application for users to interact with the recommender system.

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Components

The project is divided into five main components, each implemented in a separate Jupyter notebook or Python script:

1. Data Exploration and Cleaning

   • Notebook: data-exploration.ipynb
   • Cleans and preprocesses the text data for further analysis.

2. Semantic Search and Vector Database

   • Notebook: vector-search.ipynb
   • Builds a vector database to enable semantic search, allowing users to find books similar to a natural language query (e.g., "a book about a person seeking revenge").

3. Text Classification

   • Notebook: text-classification.ipynb
   • Uses zero-shot classification to classify books as "fiction" or "non-fiction," enabling users to filter books by genre.

4. Sentiment Analysis

   • Notebook: sentiment-analysis.ipynb
   • Performs sentiment analysis to extract emotions from text, allowing users to sort books by tone (e.g., suspenseful, joyful, or sad).

5. Web Application

   • File: gradio-dashboard.py
   • Creates a user-friendly web application using Gradio for book recommendations.

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Installation

To set up the project, follow these steps:

1. Clone the repository:

   git clone https://github.com/imad2702/BookWise.git
   cd BookWise
   

2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Create a .env file in the root directory and add your GoogleAPI key:

   GOOGLE_API_KEY=your_api_key_here
   

4. Download the dataset from Kaggle and place it in the appropriate directory. Follow the instructions in the repository for downloading the data.

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Usage

1. Running the Notebooks
   • Open the Jupyter notebooks in the order specified above to explore the data, build the vector database, classify books and perform sentiment analysis.
2. Running the Web Application
   • Execute the Gradio dashboard script to launch the web application:

   python gradio-dashboard.py

   • Access the application via the provided local URL.

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Technologies Used

1. Libraries and Frameworks:

   • pandas, matplotlib, seaborn for data manipulation and visualization.
   • langchain-community, langchain-chroma, transformers for LLM integration and semantic search.
   • gradio for building the web application.
   • python-dotenv for managing environment variables.

2. APIs: Google API for LLM functionalities.