app.py

import streamlit as st
import fitz  # PyMuPDF for PDF extraction
import google.generativeai as genai
from io import BytesIO

# Configure Gemini API
genai.configure(api_key="AIzaSyCiFKbaF-HQsRaKT7_c1HlgzPE25TxwUr4")

# Function to extract text from PDF using BytesIO
def extract_text_from_pdf(pdf_file):
    # Read the file into memory as a byte stream
    file_bytes = BytesIO(pdf_file.read())
    
    # Open the byte stream with fitz (PyMuPDF)
    doc = fitz.open(stream=file_bytes)
    text = ""
    for page in doc:
        text += page.get_text("text") + "\n"
    return text

# Function to summarize the research paper
def summarize_paper(text):
    model = genai.GenerativeModel("gemini-pro")
    response = model.generate_content(f"Summarize this research paper:\n{text}")
    return response.text

# Function to answer user questions based on the paper
def answer_question(question, paper_text):
    model = genai.GenerativeModel("gemini-pro")
    response = model.generate_content(f"Based on this research paper:\n{paper_text}\nAnswer this question: {question}")
    return response.text

# Streamlit UI
st.title("AI-Powered Research Paper Summarizer")
st.write("Upload your research paper (PDF) and ask questions related to it.")

# Upload PDF
uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf"])

if uploaded_file is not None:
    # Extract and summarize the paper
    paper_text = extract_text_from_pdf(uploaded_file)
    summary = summarize_paper(paper_text)
    
    # Display the summary
    st.subheader("Summary of the Research Paper")
    st.write(summary)
    
    # Question input
    st.subheader("Ask a Question About the Paper")
    user_question = st.text_input("Your question:")
    
    if user_question:
        answer = answer_question(user_question, paper_text)
        st.write(f"**Answer:** {answer}")