Spaces:
Sleeping
Sleeping
File size: 1,282 Bytes
342f78f c3f2c28 342f78f c3f2c28 342f78f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 |
# app.py
import streamlit as st
import pdfplumber
from transformers import pipeline
# Load QA model pipeline
qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
st.title("📄 PDF Question Answering using LLM")
uploaded_pdf = st.file_uploader("Upload a PDF", type=["pdf"])
if uploaded_pdf:
# Extract text from PDF
with pdfplumber.open(uploaded_pdf) as pdf:
extracted_text = ""
for page in pdf.pages:
extracted_text += page.extract_text() + "\n"
# Store extracted text in session
st.session_state['document_text'] = extracted_text
st.success("✅ PDF uploaded and text extracted successfully!")
# Display preview
with st.expander("📄 Preview Extracted Text"):
st.write(extracted_text[:1500] + "...")
# Ask questions about the PDF
if 'document_text' in st.session_state:
st.subheader("💬 Ask a question based on the uploaded PDF")
user_question = st.text_input("Enter your question")
if user_question:
with st.spinner("Generating answer..."):
result = qa_pipeline({
"question": user_question,
"context": st.session_state['document_text']
})
st.markdown(f"**Answer:** {result['answer']}")
|