Semantic Book Recommender (website link)

The project includes multiple components, such as text data cleaning, vector search, text classification, sentiment analysis, and a Gradio-based web application.

Installation and Setup

Prerequisites

Ensure you have the following installed:

• Miniconda or Anaconda
• Git (optional, for cloning the repository)

Step 1: Clone the Repository

git clone https://github.com/your-repo/semantic-book-recommender.git
cd semantic-book-recommender

Step 2: Create and Activate the Conda Environment

This project provides an environment.yml file to set up dependencies.

conda env create -f environment.yml
conda activate books

Step 3: Download Necessary Data

Some components may require additional datasets or models to be downloaded. Follow the instructions within each Jupyter notebook to ensure you have all necessary files.

Step 4: Running Jupyter Notebooks

Launch Jupyter Notebook to explore and run each notebook:

jupyter notebook

Open the desired .ipynb file in your browser and execute the cells step by step.

This will start a local server, and a URL will be provided in the terminal to access the application.

Usage

• Enter a book-related query (e.g., "a book about a detective solving a mystery").
• Filter results by fiction/non-fiction using the classification model.
• Sort results by sentiment/tone (e.g., suspenseful, joyful, sad).
• Explore similar books based on vector search.

Contributing

Made with Free code camp

License

This project is open-source and available under the MIT License.