Classy Books Web: AI-Powered Book Recommendation System

A modern book recommendation system that combines traditional Machine Learning with Large Language Models to provide personalized classic literature recommendations.

🚀 Features

• Dual Recommendation Approaches:
  • Questionnaire-based recommendations using SVM
  • Natural language chat interface using BERT embeddings
• Interactive Web Interface
• Vector-based Book Matching
• SQLite Database with Vector Store

🛠️ Tech Stack

• Backend: Python, Flask
• ML/AI:
  • SVM for questionnaire processing
  • BERT embeddings for similarity matching
  • Ollama (llama3.2:3b) for chat interface
• Database: SQLite with FAISS vector store
• Frontend: HTML, CSS, JavaScript

📋 Prerequisites

• Python 3.8+
• Ollama installed and running
• SQLite3

🔧 Installation

1. Clone the repository

git clone https://github.com/yourusername/classy_books_web.git
cd classy_books_web

2. Create and activate virtual environment

python -m venv venv
source venv/bin/activate  # Linux/Mac
venv\Scripts\activate     # Windows

3. Install dependencies

pip install -r requirements.txt

4. Start Ollama service

ollama run llama3.2:3b

5. Run the application

python app_CCB.py

💡 Usage

1. Questionnaire-Based Recommendations:

   • Navigate to /form
   • Answer 13 questions about your preferences
   • Get personalized book recommendations

2. Chat Interface:

   • Click the chat icon or go to /chat
   • Describe your interests or ask for recommendations
   • Get AI-powered suggestions with similarity scores

3. Database Portal:

   • Access /db_portal for database exploration
   • Execute custom queries
   • View book information and vectors

🏗️ Project Structure

classy_books_web/
├── app_CCB.py              # Main Flask application
├── ML_algorithm_CCB.py     # SVM implementation
├── nlp_components/         # NLP and chat functionality
├── static/                 # CSS, JS, and assets
├── templates/             # HTML templates
└── my_database.db         # SQLite database

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

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

👤 Contact

Shubham Dalvi - @ShubhamDalvi1999

🙏 Acknowledgments

• BERT model for embeddings
• Ollama for LLM capabilities
• FAISS for vector similarity search
• Flask for web framework

🔍 Troubleshooting

If you encounter any issues with the LangChain packages, you can check your installed versions:

pip show langchain-community
pip show langchain-ollama

Make sure these versions are compatible with your Python environment.