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

# Function to perform question-answering
def question_answering(question, pdf_path):
    pdf_reader = PdfReader(pdf_path)
    pdf_text_with_pages = []

    for page_num, pdf_page in enumerate(pdf_reader.pages, start=1):
        pdf_text = pdf_page.extract_text()
        pdf_text_with_pages.append((page_num, pdf_text))

    pdf_text = "\n".join([text for _, text in pdf_text_with_pages])

    # Perform question-answering using Hugging Face's Transformers
    question_answerer = pipeline("question-answering", model="distilbert-base-cased-distilled-squad", tokenizer="distilbert-base-cased-distilled-squad")
    answer = question_answerer(question=question, context=pdf_text)

    return answer

def main():
    st.title("Question Answering on a PDF File")

    uploaded_file = st.file_uploader("Upload a PDF file:", type=["pdf"])
    question = st.text_input("Ask your question:")
    
    if st.button("Answer") and uploaded_file is not None:
        pdf_path = os.path.join(os.getcwd(), uploaded_file.name)
        with open(pdf_path, "wb") as f:
            f.write(uploaded_file.read())

        answer = question_answering(question, pdf_path)

        # Delete the uploaded file after processing
        os.remove(pdf_path)

        st.write(f"Question: '{question}'")
        st.write("Answer:", answer['answer'])
        st.write("Score:", answer['score'])

if __name__ == "__main__":
    main()