The PDF Summarizer Chatbot is a user-friendly application that allows you to upload PDF documents and receive concise summaries generated using advanced Large Language Models (LLMs). This project leverages the power of Natural Language Processing (NLP) to extract meaningful insights from textual data, making document analysis faster and more efficient.
This project can be a starting point for beginners who want to learn about LLMs. I use Replicate, which provides free cloud API services with open-source models like LLaMa2. The open-source Python framework Streamlit is used to deploy the model into an interactive web app. Overall, this project was made as simple as possible to help your understanding of the implementation of the LLMs project.
- PDF Parsing: Extract text from PDF files using PyPDF2.
- AI-Powered Summarization: Summaries are generated using the Llama 2 model, renowned for its state-of-the-art performance in NLP tasks.
- Interactive User Interface: Built with Streamlit, providing an intuitive platform for users to upload files and receive outputs.
- Themes: Support light and dark themes for user convenience.
- API Integration: Utilizes the Replicate API for seamless communication with the LLM backend.
- Clone the repository
git clone https://github.com/0xichikawa/PDF-summarizer-chatbot-using-LLaMa2 cd PDF-summarizer-chatbot-using-LLaMa2 - Install dependencies
pip install -r requirements.txt
- Set Up Replicate API Key
Obtain your Replicate API key from replicate.com and add it to
secrets.tomlfile in the.streamlitfolder:REPLICATE_API_TOKEN = "INSERT_YOUR_REPLICATE_API_TOKEN_HERE" - Run the application
streamlit run app.py
- Multi-language support for summarization.
- Enhanced text extraction with OCR for scanned PDFs.
- Options for customized summary lengths and formats.
- Meta AI for Llama 2.
- Replicate for their API services.
- Streamlit and PyPDF2 for simplifying the development process.
- Data Professor (https://github.com/dataprofessor) for tutorials and project inspiration