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import streamlit as st
from transformers import pipeline
import fitz # PyMuPDF
import tempfile
import os
# Load the QA model
qa_model = pipeline("question-answering", "timpal0l/mdeberta-v3-base-squad2")
# Function to extract text from a PDF file
def extract_text_from_pdf(uploaded_file):
temp_file = None
try:
# Save the uploaded PDF as a temporary file
temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
temp_file.write(uploaded_file.read())
# Open the temporary PDF file and extract text
doc = fitz.open(temp_file.name)
text = ""
for page_num in range(doc.page_count):
page = doc[page_num]
text += page.get_text()
doc.close()
return text
except Exception as e:
st.error(f"Error extracting text from PDF: {str(e)}")
return None
finally:
# Remove the temporary file
if temp_file:
temp_file.close()
# Uncomment the line below if you want to delete the temporary file after use
# os.remove(temp_file.name)
# Streamlit app
def main():
st.title("PDF Question Answering App")
# Upload PDF file through Streamlit
uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
if uploaded_file is not None:
# Read the PDF file and extract text
pdf_text = extract_text_from_pdf(uploaded_file)
if pdf_text is not None:
# Display the extracted text
st.subheader("Extracted Text from PDF")
st.text(pdf_text)
# Input for user question
question = st.text_input("Ask a question about the PDF:")
# Button to trigger question answering
if st.button("Get Answer"):
if question:
# Use the QA model to get the answer
answer = qa_model(question=question, context=pdf_text)
st.subheader("Answer:")
st.write(answer["answer"])
else:
st.warning("Please enter a question.")
if __name__ == "__main__":
main()
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