app.py

import streamlit as st

# ──────────────────────────────────────────────────────────────────────────────
# 1) Initialize DB (imports models under the hood, then create_all)
from models.db import init_db
init_db()

# ──────────────────────────────────────────────────────────────────────────────
# 2) First-run admin signup (before any queries to the user table)
from repositories.user_repo import UserRepo
from config.settings import settings

repo = UserRepo(settings.database_url)
if not repo.get_all_users():
    st.title("🚀 Welcome to Quantum Healthcare AI")
    st.warning("No users exist yet. Create the first admin account below:")
    new_user = st.text_input("Username")
    new_name = st.text_input("Full name")
    new_pw   = st.text_input("Password", type="password")
    if st.button("Create Admin User"):
        if new_user and new_name and new_pw:
            repo.add_user(new_user, new_name, new_pw)
            st.success(f"✅ Admin `{new_user}` created! Refresh the page to log in.")
        else:
            st.error("All fields are required.")
    st.stop()

# ──────────────────────────────────────────────────────────────────────────────
# 3) Authentication
from services.auth import authenticator, require_login
username = require_login()

# ──────────────────────────────────────────────────────────────────────────────
# 4) Core services & repositories
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

# ──────────────────────────────────────────────────────────────────────────────
# 5) Logging & metrics
from services.logger import logger
from services.metrics import CHAT_COUNT, OPTIMIZE_COUNT

# ──────────────────────────────────────────────────────────────────────────────
# 6) UI Layout
st.set_page_config(page_title="Quantum Healthcare AI", layout="wide")
st.image("assets/logo.png", width=64)
st.title(f"Hello, {username}!")

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

with tab1:
    query = st.text_area("Describe your symptoms or ask a clinical question:", height=100)

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

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

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

    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")