Savarkar-GPT

Savarkar-GPT is an AI-powered chatbot designed to answer questions about the ideology and works of Vinayak Damodar Savarkar. By leveraging Savarkar's books and writings, this project creates a knowledge base that allows users to explore his thoughts and philosophy interactively.

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🚀 Features

• AI-Powered Chatbot: Ask questions about Savarkar's ideology, writings, and philosophy.
• Knowledge Base: Built using Savarkar's books, processed into a searchable database.
• Streamlit UI: A user-friendly interface for interacting with the chatbot.
• Flask API: Backend API to handle queries and generate responses.

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📚 Project Structure

savarkar-gpt/
├── main.py               # Flask API backend
├── ui.py                 # Streamlit-based user interface
├── pdf_loader.py         # Utility to load and process PDF files
├── embedding_utils.py    # Embedding manager for ChromaDB
├── config.py             # Configuration file for models and paths
├── requirements.txt      # Python dependencies
└── README.md             # Project documentation

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🛠️ Installation and Setup

Follow these steps to set up and run the project:

1. Clone the Repository

git clone https://github.com/your-username/savarkar-gpt.git
cd savarkar-gpt

2. Install Dependencies

pip install -r requirements.txt

3. Set Up ChromaDB

Ensure ChromaDB is installed and configured. The project uses ChromaDB for managing the knowledge base.

4. Add Savarkar's Books

Place the PDF files of Savarkar's books in the project directory. Update the pdf_path in main.py to point to the correct file.

5. Run the Flask API

Start the backend API:

python main.py

The API will run at http://127.0.0.1:5000.

6. Run the Streamlit UI

Start the user interface:

streamlit run ui.py

The UI will be available at http://localhost:8501.

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🧠 How It Works

📖 PDF Processing

• pdf_loader.py extracts text from Savarkar's books and splits it into manageable chunks.

🏛️ Knowledge Base

• The text chunks are stored in a ChromaDB collection for efficient querying.

🔍 Query Handling

• The Flask API processes user queries, retrieves relevant text from the knowledge base, and generates responses.

💬 Streamlit UI

• A simple and interactive interface for users to ask questions and view responses.

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🤝 Contributing

Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request.

For major changes, please open an issue first to discuss what you would like to change.

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📜 License

This project is licensed under the MIT License. See LICENSE for more details.