Gemini PDF Chatbot

Gemini PDF Chatbot is a Streamlit-based application that allows users to chat with a conversational AI model trained on PDF documents. The chatbot extracts information from uploaded PDF files and answers user questions based on the provided context. https://gmultichat.streamlit.app/

https://github.com/kaifcoder/gemini_multipdf_chat/assets/57701861/f6a841af-a92d-4e54-a4fd-4a52117e17f6

Features

• PDF Upload: Users can upload multiple PDF files.
• Text Extraction: Extracts text from uploaded PDF files.
• Conversational AI: Uses the Gemini conversational AI model to answer user questions.
• Chat Interface: Provides a chat interface to interact with the chatbot.

Getting Started

If you have docker installed, you can run the application using the following command:

• Obtain a Google API key and set it in the .env file.

  GOOGLE_API_KEY=your_api_key_here

docker compose up --build

Your application will be available at http://localhost:8501.

Deploying your application to the cloud

First, build your image, e.g.: docker build -t myapp .. If your cloud uses a different CPU architecture than your development machine (e.g., you are on a Mac M1 and your cloud provider is amd64), you'll want to build the image for that platform, e.g.: docker build --platform=linux/amd64 -t myapp ..

Then, push it to your registry, e.g. docker push myregistry.com/myapp.

Consult Docker's getting started docs for more detail on building and pushing.

References

• Docker's Python guide

Local Development

Follow these instructions to set up and run this project on your local machine.

Note: This project requires Python 3.10 or higher.

1. Clone the Repository:

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

2. Install Dependencies:

   pip install -r requirements.txt

3. Set up Google API Key:

   • Obtain a Google API key and set it in the .env file.

   GOOGLE_API_KEY=your_api_key_here

4. Run the Application:

   streamlit run main.py

5. Upload PDFs:

   • Use the sidebar to upload PDF files.
   • Click on "Submit & Process" to extract text and generate embeddings.

6. Chat Interface:

   • Chat with the AI in the main interface.

Project Structure

• app.py: Main application script.
• .env: file which will contain your environment variable.
• requirements.txt: Python packages required for working of the app.
• README.md: Project documentation.

Dependencies

• PyPDF2
• langchain
• Streamlit
• google.generativeai
• dotenv

Acknowledgments

• Google Gemini: For providing the underlying language model.
• Streamlit: For the user interface framework.