app.py

import streamlit as st
import PyPDF2
import google.generativeai as genai

# Step 1: Set up Gemini API
GEMINI_API_KEY = "AIzaSyAJ9xJ83VSmJoyVXmplPw-HaDkBfro-jiM"  # Replace with your Gemini API key
genai.configure(api_key=GEMINI_API_KEY)

# Initialize the Gemini model
model = genai.GenerativeModel('gemini-pro')

# Step 2: Function to extract text from PDF
def extract_text_from_pdf(pdf_file):
    reader = PyPDF2.PdfReader(pdf_file)
    text = ""
    for page in reader.pages:
        text += page.extract_text()
    return text

# Step 3: Function to summarize text using Gemini
def summarize_text(text):
    prompt = f"Summarize the following research paper in 200 words:\n\n{text}"
    response = model.generate_content(prompt)
    return response.text

# Step 4: Function to answer user questions using Gemini
def answer_question(text, question):
    prompt = f"Based on the following research paper, answer the question:\n\n{text}\n\nQuestion: {question}"
    response = model.generate_content(prompt)
    return response.text

# Step 5: Streamlit App
def main():
    st.title("Haroon Research Paper Summarizer")
    st.write("Upload a research paper in PDF format, and I'll summarize it and answer your questions!")

    # File uploader
    uploaded_file = st.file_uploader("Upload a PDF file", type="pdf")

    if uploaded_file is not None:
        # Extract text from the uploaded PDF
        text = extract_text_from_pdf(uploaded_file)
        st.success("PDF uploaded and text extracted successfully!")

        # Summarize the text
        st.subheader("Summary of the Research Paper")
        summary = summarize_text(text)
        st.write(summary)

        # Interactive Q&A
        st.subheader("Ask Questions About the Research Paper")
        question = st.text_input("Type your question here:")
        if question:
            answer = answer_question(text, question)
            st.write("**Answer:**")
            st.write(answer)

if __name__ == "__main__":
    main()