Chat with Your PDF: A Streamlit and PyPDF2 Guide

Wouldn't it be amazing if you could upload a PDF and then ask questions about its content? With the combination of Python, Streamlit, and PyPDF2, this becomes possible. This guide will walk you through creating a web app where you can chat with your PDF.

Step 1: Setting Up Your Environment

Before starting, ensure you have Python installed. Then, install the required libraries using pip:

bash

pip install streamlit PyPDF2         

Step 2: Basic Streamlit App

Before integrating the PDF features, let's create a basic Streamlit app:

python

import streamlit as st def main(): st.title("Chat with Your PDF") st.write("Upload a PDF and start chatting!") if __name__ == '__main__': main()         

Save this code to a file, say app.py, and run it using:

bash

streamlit run app.py         

You should see a simple Streamlit page.

Step 3: Uploading the PDF

Now, let's add a feature to upload a PDF:

python

import streamlit as st from PyPDF2 import PdfReader def extract_text_from_pdf(pdf): pdf_reader = PdfReader(pdf) return ''.join(page.extract_text() for page in pdf_reader.pages) def main(): st.title("Chat with Your PDF") pdf = st.file_uploader("Upload your PDF", type="pdf") if pdf: text = extract_text_from_pdf(pdf) st.write("PDF Content Extracted!") # For testing, let's display the extracted text: # st.write(text) if __name__ == '__main__': main()         

Step 4: Asking Questions

Now, instead of displaying the entire PDF content, we will allow users to ask questions:

python

import streamlit as st from PyPDF2 import PdfReader def extract_text_from_pdf(pdf): pdf_reader = PdfReader(pdf) return ''.join(page.extract_text() for page in pdf_reader.pages) def main(): st.title("Chat with Your PDF") pdf = st.file_uploader("Upload your PDF", type="pdf") if pdf: text = extract_text_from_pdf(pdf) question = st.text_input("Ask a question about the PDF:") if question: # Placeholder for answer fetching st.write("Answer will go here!") st.download_button("Open PDF", pdf.getvalue(), "document.pdf", mime="application/pdf") if __name__ == '__main__': main()         

For now, the answer placeholder is static. In a full-fledged implementation, you could use NLP models to extract answers from the extracted text.

Step 5: Displaying the PDF

Streamlit does not natively render PDFs inside the app. As a workaround, we provide a button for users to download or view the PDF in a browser:

python

st.download_button("Open PDF", pdf.getvalue(), "document.pdf", mime="application/pdf")         

This guide provided a basic introduction to creating a Streamlit app where users can upload a PDF, ask questions about its content, and view the PDF in a browser. Advanced features can include integrating powerful NLP models, optimizing PDF text extraction, and more!

Note: The above guide is a basic introduction, and there are numerous ways you can enhance this app, such as by using advanced NLP models for question-answering, adding caching to improve performance, beautifying the UI using Streamlit's various widgets, and so on.

If you want extra features, don't hesitate to reach out to me...