Update app.py

app.py

@@ -1,60 +1,50 @@
 import streamlit as st
-from agent.gemini_agent import run_gemini_chat
-from agent.clinical_nlp import umls_concept_lookup, bioportal_concept_lookup
-from quantum.optimizer import optimize_treatment
-
-st.set_page_config(page_title="Quantum Healthcare AI", layout="wide")
-
-st.markdown("""
-    <div style='display:flex;align-items:center'>
-        <img src='https://cdn-icons-png.flaticon.com/512/891/891419.png' width='56' style='margin-right:15px;'/>
-        <h1 style='display:inline'>Quantum-AI Healthcare Assistant</h1>
-    </div>
-    """, unsafe_allow_html=True)
-
-st.write("**Ask anything about symptoms, conditions, diagnosis, or optimal care. Get instant LLM answers, clinical concept mapping, and even quantum-inspired optimization.**")
-
-query = st.text_area("Describe symptoms, a clinical problem, or a medical question:", height=70)
-
-cols = st.columns([1,1])
-with cols[0]:
-    get_ai = st.button("Get AI & Clinical Insight")
-with cols[1]:
-    run_quantum = st.button("Quantum Optimize Care Plan")
-
-if get_ai and query.strip():
-    with st.spinner("Gemini AI is reviewing your query..."):
-        ai_response = run_gemini_chat(query)
-    st.markdown(f"**Gemini Agent:**\n\n{ai_response}")
-
-    st.markdown("---")
-    with st.expander("🔎 UMLS Concept Mapping"):
-        concepts = umls_concept_lookup(query)
-        if concepts:
-            for c in concepts:
-                if 'error' in c:
-                    st.error(c['error'])
-                else:
-                    st.write(f"**{c['name']}**  |  ID: `{c['ui']}`  |  Source: *{c['rootSource']}*")
-        else:
-            st.info("No UMLS concepts found.")
-
-    with st.expander("🔬 BioPortal Ontology Lookup"):
-        concepts = bioportal_concept_lookup(query)
-        if concepts:
-            for c in concepts:
-                if 'error' in c:
-                    st.error(c['error'])
-                else:
-                    st.write(f"**{c['prefLabel']}**  |  [Ontology]({c['ontology']})  |  [Concept ID]({c['id']})")
-        else:
-            st.info("No BioPortal concepts found.")
-
-if run_quantum and query.strip():
-    with st.spinner("Quantum optimizer working..."):
-        plan = optimize_treatment(query)
-    st.markdown("### 🧬 Optimized Care Plan")
-    st.json(plan)
-
-st.markdown("---")
-st.caption("Powered by Gemini LLM, UMLS, BioPortal, and quantum-inspired algorithms. For research use only. No patient data is stored.")
+from services.auth import authenticator, require_login
+from services.logger import logger
+from services.metrics import CHAT_COUNT, OPTIMIZE_COUNT
+from agent.gemini_agent import chat_with_gemini
+from clinical_nlp.umls_bioportal import lookup_umls, lookup_bioportal
+from quantum.bf_dcqo import optimize_hubo
+from services.pdf_report import generate_pdf
+from repositories.chat_repo import ChatRepo
+
+# Initialize DB
+from models.db import init_db
+init_db()
+
+# 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("Enter clinical query or symptoms:")
+    if st.button("Ask Gemini"):
+        CHAT_COUNT.inc()
+        with st.spinner("Consulting AI..."):
+            response = chat_with_gemini(username, query)
+        st.markdown(f"**AI**: {response}")
+        # save chat
+        ChatRepo().save(username, query, response)
+        # clinical NLP
+        with st.expander("UMLS Results"):
+            st.write(lookup_umls(query))
+        with st.expander("BioPortal Results"):
+            st.write(lookup_bioportal(query))
+
+    if st.button("Quantum Optimize"):
+        OPTIMIZE_COUNT.inc()
+        with st.spinner("Running quantum optimizer..."):
+            result = optimize_hubo({"query":query})
+        st.json(result)
+
+with tab2:
+    if st.button("Generate PDF Report"):
+        # gather last 5 messages
+        pdf_data = {"Last Chats": ChatRepo().get_recent(username, limit=5)}
+        fname = generate_pdf(pdf_data)
+        st.success("Report Generated")
+        st.download_button("Download Report", data=open(fname,"rb"), file_name=fname)