PDF Summarization & QA Web App

📌 Overview

This project is a FastAPI and Streamlit-based web application that allows users to:

• Summarize PDF documents using an LLM-powered summarization model.
• Ask questions about the content of a PDF and receive relevant answers.

🚀 Features

• Upload a PDF document (one-time upload for both summarization and QA).
• Generate different summaries every time you run summarization.
• Perform detailed summarization for more insightful results.
• Ask questions related to the PDF and get precise answers.
• Uses LangChain, Hugging Face embeddings, and FAISS for retrieval.
• Frontend built with Streamlit for a smooth user experience.

---

🛠️ Tech Stack

• Backend: FastAPI, LangChain, Groq API, FAISS, Hugging Face embeddings
• Frontend: Streamlit
• PDF Processing: PyPDFLoader

---

🏗️ Installation & Setup

Clone the Repository

git clone https://github.com/renaldiangsar/PDF-Summarizer-QA.git
cd PDF-Summarizer-QA

Create a Virtual Environment & Install Dependencies

# open command prompt and run
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install -r requirements.txt

Run the Backend (FastAPI)

# open command prompt and run
uvicorn serve:app --reload # or just
# python serve.py

The FastAPI server will start at http://127.0.0.1:8000

Run the Frontend (Streamlit)

# open command prompt and run
streamlit run client.py

The Streamlit app will open in your browser at http://localhost:8501

Don't forget to give your api in .env file

• open .env file an set your groq and huggingface api

---

🔄 Workflow (How it Works?)

1. User uploads a PDF (file is stored temporarily).
2. User selects:
   • "Summarize" → Calls FastAPI /summarize/ endpoint to generate a summary.
   • "Ask a Question" → Calls /ask/ endpoint with the query to get a response.
3. FastAPI processes the request using:
   • LangChain for text processing
   • FAISS for document retrieval (for QA)
   • Groq / Hugging Face models for LLM responses
4. Response is displayed on the Streamlit UI.

---

🛠️ Customization & Improvements

• Modify the summarization prompt in serve.py to change summary length/detail. Because shorter summarization will run faster.
• Adjust the chunk size in RecursiveCharacterTextSplitter for better retrieval.
• Use a different LLM model (e.g., GPT-4, LLaMA, or local models) for customization.
• If you want to do a lot of use, you can use paid Openai API.

---

📝 Future Enhancements

• Add multilingual support for summarization & QA.
• Implement document summarization history.
• Support multiple PDFs at once.
• Looking better option to PDF processing, because PyPDFLoader not give a optimal results for unclean/irregular pdfs

---

Visual

---

This is my first project in github, there are still many shortcomings. I hope i can do better in my next project. 🎉