app.py

import streamlit as st
from transformers import pipeline
import requests
from PyPDF2 import PdfReader

# Load Hugging Face Question Answering model
qa_pipeline = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")

# Function to download and extract text from PDF
def extract_text_from_huggingface_pdf(url):
    response = requests.get(url)
    with open("document.pdf", "wb") as f:
        f.write(response.content)
    pdf_reader = PdfReader("document.pdf")
    text = ""
    for page in pdf_reader.pages:
        text += page.extract_text() + "\n"
    return text

# Hugging Face URL 
PDF_URL = "https://huggingface.co/spaces/SujathaL/AWS_Restart_Program_Chatbot/blob/main/AWS%20restart%20program%20information.docx.pdf"
pdf_text = extract_text_from_huggingface_pdf(PDF_URL)

# Streamlit UI
st.title("Chat with Your PDF")

# User Question Input
question = st.text_input("Ask a question about the PDF:")

if st.button("Get Answer") and question:
    response = qa_pipeline(question=question, context=pdf_text)
    st.write("Answer:", response['answer'])