app.py

import streamlit as st

# 1) Ensure DB tables exist
from models.db import init_db
init_db()

# 2) Auth
from services.auth import authenticator, require_login

# 3) Core AI + Clinical NLP + Quantum
from agent.gemini_agent import chat_with_gemini
from clinical_nlp.umls_bioportal import lookup_umls, lookup_bioportal
from quantum.optimizer import optimize_treatment
from repositories.chat_repo import ChatRepo

# 4) Monitoring & Logging
from services.logger import logger
from services.metrics import CHAT_COUNT, OPTIMIZE_COUNT

# ——— UI ———
username = require_login()
st.set_page_config(page_title="Quantum Health AI", layout="wide")
st.image("assets/logo.png", width=64)
st.title(f"Welcome, {username}!")

tab1, tab2 = st.tabs(["🩺 Consult", "📊 Reports"])

with tab1:
    query = st.text_area("Describe symptoms or enter a medical question:", height=100)

    if st.button("Ask Gemini"):
        CHAT_COUNT.inc()
        with st.spinner("Consulting AI..."):
            response = chat_with_gemini(username, query)
            logger.info(f"[Chat] user={username} prompt={query}")
        st.markdown(f"**AI Response:** {response}")
        ChatRepo().save(user=username, prompt=query, response=response)

        with st.expander("🔎 UMLS Concept Lookup"):
            umls_res = lookup_umls(query)
            st.write(umls_res or "No UMLS concepts found.")

        with st.expander("🔬 BioPortal Concept Lookup"):
            bio_res = lookup_bioportal(query)
            st.write(bio_res or "No BioPortal matches found.")

    if st.button("Quantum Optimize Care Plan"):
        OPTIMIZE_COUNT.inc()
        with st.spinner("Running quantum-inspired optimizer..."):
            plan = optimize_treatment(query)
            logger.info(f"[Optimize] user={username} plan={plan}")
        st.markdown("### 🧬 Optimized Treatment Plan")
        st.json(plan)

with tab2:
    st.header("Generate PDF Report of Recent Chats")
    if st.button("Download Last 5 Chats"):
        recent = ChatRepo().get_recent(user=username, limit=5)
        from services.pdf_report import generate_pdf
        pdf_path = generate_pdf({"Recent Chats": recent})
        with open(pdf_path, "rb") as f:
            st.download_button("Download PDF", f, file_name=pdf_path)

st.markdown("---")
st.caption("Powered by Gemini LLM • UMLS/BioPortal • Quantum-inspired optimization")