app.py

import streamlit as st
import requests
from transformers import pipeline, BertTokenizer

# Function to generate answers using the BERT model
def generate_answers(questions, paper_link):
    # Download the research paper
    response = requests.get(paper_link)
    paper_text = response.text

    # Initialize the BERT tokenizer
    tokenizer = BertTokenizer.from_pretrained("bert-large-uncased-whole-word-masking-finetuned-squad")

    # Initialize the question-answering pipeline
    model = pipeline("question-answering", model="bert-large-uncased-whole-word-masking-finetuned-squad")

    # Generate answers for each question
    answers = []
    for question in questions.split(","):
        inputs = tokenizer(question.strip(), paper_text, return_tensors="pt")
        answer = model(**inputs)
        answers.append(answer['answer'])

    return '\n\n'.join(answers)

# Streamlit app
st.title("Research Paper Question Answering")

questions = st.text_input("Enter comma-separated questions:")
paper_link = st.text_input("Enter the link to the research paper (Arxiv link):")

if st.button("Generate Answers"):
    if not (questions and paper_link):
        st.warning("Please provide both questions and the paper link.")
    else:
        with st.spinner("Generating answers..."):
            answers = generate_answers(questions, paper_link)
            st.success("Answers generated successfully!")
            st.text_area("Generated Answers", answers)