app.py

import streamlit as st
import google.generativeai as genai
from utils.extract_text import extract_text_from_pdf
from utils.rag_qa_engine import rag_query
from config import GEMINI_API_KEY
import os

# Configure Gemini API
genai.configure(api_key=GEMINI_API_KEY)

st.set_page_config(page_title="📄 RAG with Gemini", layout="wide")

st.title("📄 RAG (Retrieval-Augmented Generation) with Gemini + Streamlit")

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

if uploaded_file:
    file_path = os.path.join("data/uploads", uploaded_file.name)
    with open(file_path, "wb") as f:
        f.write(uploaded_file.getbuffer())
    
    st.success("✅ File uploaded successfully!")

    if st.button("Extract & Build Index"):
        text = extract_text_from_pdf(file_path)
        st.session_state["docs"] = text.split("\n")
        st.success("✅ Text extracted and indexed!")

if "docs" in st.session_state:
    user_query = st.text_input("Ask a question from the document:")
    if user_query:
        answer = rag_query(user_query, st.session_state["docs"])
        st.write("### 🤖 Answer:")
        st.write(answer)