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PDF-LLM Chatbot

A web-based application that allows users to upload PDF files, process their content, and interact with the document using a chatbot interface. The application supports text extraction, table processing, and optional summarization of long texts using deepseek r1.

Features

  • PDF Upload: Upload PDF files for processing.
  • Text and Table Extraction: Extract text and tables from PDFs.
  • Chat Interface: Ask questions based on the extracted content.
  • Optional Summarization: Summarize lengthy documents for easier understanding using deepseek r1's deep reasoning.

Prerequisites

Before running this project, ensure you have the following installed:

  • Python (3.8 or higher)
  • pip (Python package installer)
  • node (npm)

Installation

Follow these steps to set up and run the application locally:

  1. Clone the Repository:

     git clone https://github.com/your-username/pdf-chatbot.git
 cd pdf-chatbot

  2. Set Up a Virtual Environment:

     python -m venv venv
 source venv/bin/activate (For Linux/MacOS)
 venv\Scripts\activate (For Windows)

  3. Install Dependencies:

     pip install -r requirements.txt

  4. Environment Variables:

    Create a .env file in the project root and configure the following variables:

    i. Google Drive API Keys:

    • Sign up for Google Cloud Console and create a project
    • Enable the Google Drive API
    • Download the JSON credentials file and place it in the root directory of the application.

    ii. DeepSeek API Key

    • Sign up for DeepSeek and generate an API key

    • Add the key to your .env file:

         DEEPSEEK_API_KEY=your-deepseek-api-key

  5. Frontend Initialization:

    i. Navigate to the frontend directory:

        cd frontend

    ii. Install the dependencies:

        npm install

    iii. update .env with the local url for the initialized backend:

        NEXT_PUBLIC_BASE_URL=backend_local_url i.e: http://127.0.0.1:5000

Running the Application

i. Start the Flask server by running the following:

python app.py

The app will be available at http://127.0.0.1:5000/

ii. Start the react app:

npm run dev

Usage

  1. Upload PDF: Use the "Upload PDF" button to upload a file.

  2. Ask Questions: Click next, then enter your query in the chat interface text area and send. Optionally, you can enable the Summarization toggle for a concise summary using deepseek r1's deep reasoning capability.

  3. Responses: The chatbot will provide an answer based on the extracted text and tables from the uploaded PDF.

Testing

To test uploads to your google drive folder before integration, enter the folder ID in test_google_drive.py, and the path to a pdf you want to test with as defined in the file. After that, run:

python test_google_drive.py

To test querying deepseek r1, enter your deepseek api key in the .env file. After that, run:

python test_r1.py

Directory Structure

  • app.py: Main application logic.
  • templates/index.html: Frontend template.
  • utils/: Helper modules for Google Drive and API integration.
  • frontend: Friendly UI made with nextjs and typescript.

Key Libraries

  • Flask: Web framework.
  • PyMuPDF: PDF text extraction.
  • Camelot: PDF table extraction.
  • Hugging Face Transformers: Summarization pipeline.
  • NextJS: For user friendly UI and chat interface.

Known Limitations

Summarization might be slower for large texts when enabled.

License

This project is licensed under the MIT License.