A semantic book recommendation system that uses natural language processing and machine learning to provide personalized book recommendations based on user preferences and book descriptions.

• Semantic search for books using vector embeddings
• Emotion-based book categorization
• Interactive Gradio dashboard for easy book discovery
• Data exploration and analysis tools
• Text classification for book categorization

• gradio-dashboard.py: Interactive web interface for book recommendations
• text-classification.ipynb: Notebook for training and evaluating text classification models
• vector-search.ipynb: Implementation of semantic search using vector embeddings
• data-exploration.ipynb: Data analysis and visualization tools
• books_with_emotions.csv: Dataset with emotion-based book categorizations
• books_with_categories.csv: Dataset with category-based book classifications
• books_cleaned.csv: Cleaned and processed book dataset

1. Clone the repository:

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

2. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

3. Install required dependencies:

pip install -r requirements.txt

4. Set up environment variables:
   • Copy .env.example to .env
   • Fill in the required environment variables

To start the interactive book recommendation dashboard:

python gradio-dashboard.py

Open the Jupyter notebooks to explore the data and models:

• data-exploration.ipynb for data analysis
• text-classification.ipynb for model training
• vector-search.ipynb for semantic search implementation

The project uses several datasets sourced from Kaggle:

• Original dataset: Kaggle Book Dataset

Note: The original dataset needs to be downloaded from Kaggle and processed using the provided notebooks to generate the cleaned and categorized versions.

Contributions are welcome! Please feel free to submit a Pull Request.

This project is licensed under the MIT License - see the LICENSE file for details.