app.py

import streamlit as st
import os
import json
from utils.ingestion import DocumentProcessor
from utils.llm import LLMProcessor
from utils.qa import QAEngine

st.set_page_config(page_title="AI-Powered Document QA", layout="wide")
st.title("📄 AI-Powered Document QA")

# Initialize processors
document_processor = DocumentProcessor()
llm_processor = LLMProcessor()
qa_engine = QAEngine()

# Ensure temp directory exists
os.makedirs("temp", exist_ok=True)

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

if uploaded_file:
    pdf_path = os.path.join("temp", uploaded_file.name)

    with open(pdf_path, "wb") as f:
        f.write(uploaded_file.read())

    st.sidebar.success("✅ File uploaded successfully!")

    with st.spinner("🔄 Processing document..."):
        document_processor.process_document(pdf_path)

    st.sidebar.success("✅ Document processed successfully!")

    # Query input
    question = st.text_input("Ask a question from the document:", placeholder="What are the key insights?")

    if st.button("🔍 Search & Answer"):
        if question:
            with st.spinner("🧠 Searching for relevant context..."):
                answer = qa_engine.query(question)

            st.subheader("📝 Answer:")
            st.write(answer.content)
        else:
            st.warning("⚠️ Please enter a question.")

st.markdown("---")
st.caption("🤖 Powered by ChromaDB + Groq LLM | Built with ❤️ using Streamlit")