Update app.py

app.py

@@ -4,8 +4,8 @@
 import os, pathlib
 
 # ── Streamlit telemetry dir fix ───────────────────────────────────────
-os.environ["STREAMLIT_DATA_DIR"] = "/tmp/.streamlit"
-os.environ["XDG_STATE_HOME"] = "/tmp"
+os.environ["STREAMLIT_DATA_DIR"]               = "/tmp/.streamlit"
+os.environ["XDG_STATE_HOME"]                  = "/tmp"
 os.environ["STREAMLIT_BROWSER_GATHERUSAGESTATS"] = "false"
 pathlib.Path("/tmp/.streamlit").mkdir(parents=True, exist_ok=True)
 
@@ -18,10 +18,10 @@ from fpdf import FPDF
 from streamlit_agraph import agraph
 
 from mcp.orchestrator import orchestrate_search, answer_ai_question
-from mcp.workspace import get_workspace, save_query
+from mcp.workspace      import get_workspace, save_query
 from mcp.knowledge_graph import build_agraph
 from mcp.graph_metrics import build_nx, get_top_hubs, get_density
-from mcp.alerts import check_alerts
+from mcp.alerts         import check_alerts
 
 ROOT = Path(__file__).parent
 LOGO = ROOT / "assets" / "logo.png"
@@ -59,11 +59,9 @@ def _workspace_sidebar():
 def render_ui():
     st.set_page_config("MedGenesis AI", layout="wide")
 
-    # Initialize session-state
+    # Persist follow-up input
     if "followup_input" not in st.session_state:
         st.session_state.followup_input = ""
-    if "tab_index" not in st.session_state:
-        st.session_state.tab_index = 0
 
     _workspace_sidebar()
 
@@ -78,7 +76,6 @@ def render_ui():
     llm = st.radio("LLM engine", ["openai", "gemini"], horizontal=True)
     query = st.text_input("Enter biomedical question", placeholder="e.g. CRISPR glioblastoma therapy")
 
-    # Alert check
     if get_workspace():
         try:
             news = asyncio.run(check_alerts([w["query"] for w in get_workspace()]))
@@ -96,94 +93,86 @@ def render_ui():
             res = asyncio.run(orchestrate_search(query, llm=llm))
         st.success(f"Completed with **{res['llm_used'].title()}**")
         st.session_state.query_result = res
-        # Reset follow-up input
         st.session_state.followup_input = ""
-        st.session_state.tab_index = 0
     else:
         res = st.session_state.get("query_result", None)
 
     if res:
-        tabs_list = ["Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"]
-        tabs = st.tabs(tabs_list, index=st.session_state.tab_index)
-        
-        for idx, name in enumerate(tabs_list):
-            with tabs[idx]:
-                st.session_state.tab_index = idx
-                if name == "Results":
-                    for i, p in enumerate(res["papers"], 1):
-                        st.markdown(f"**{i}. [{p['title']}]({p['link']})**  *{p['authors']}*")
-                        st.write(p["summary"])
-                    col1, col2 = st.columns(2)
-                    with col1:
-                        st.download_button("CSV", pd.DataFrame(res["papers"]).to_csv(index=False),
-                                            "papers.csv", "text/csv")
-                    with col2:
-                        st.download_button("PDF", _pdf(res["papers"]), "papers.pdf", "application/pdf")
-                    if st.button("💾 Save"):
-                        save_query(query, res)
-                        st.success("Saved to workspace")
-                    st.subheader("UMLS concepts")
-                    for c in res["umls"]:
-                        if c.get("cui"):
-                            st.write(f"- **{c['name']}** ({c['cui']})")
-                    st.subheader("OpenFDA safety")
-                    for d in res["drug_safety"]:
-                        st.json(d)
-                    st.subheader("AI summary")
-                    st.info(res["ai_summary"])
-
-                elif name == "Genes":
-                    st.header("Gene / Variant signals")
-                    for g in res["genes"]:
-                        st.write(f"- **{g.get('name', g.get('geneid'))}** "
-                                 f"{g.get('description', '')}")
-                    if res["gene_disease"]:
-                        st.markdown("### DisGeNET links")
-                        st.json(res["gene_disease"][:15])
-                    if res["mesh_defs"]:
-                        st.markdown("### MeSH definitions")
-                        for d in res["mesh_defs"]:
-                            if d:
-                                st.write("-", d)
-
-                elif name == "Trials":
-                    st.header("Clinical trials")
-                    if not res["clinical_trials"]:
-                        st.info("No trials (rate-limited or none found).")
-                    for t in res["clinical_trials"]:
-                        st.markdown(f"**{t['NCTId'][0]}** – {t['BriefTitle'][0]}")
-                        st.write(f"Phase {t.get('Phase',[''])[0]} | "
-                                 f"Status {t['OverallStatus'][0]}")
-
-                elif name == "Graph":
-                    nodes, edges, cfg = build_agraph(res["papers"], res["umls"], res["drug_safety"])
-                    hl = st.text_input("Highlight node:", key="hl")
-                    if hl:
-                        pat = re.compile(re.escape(hl), re.I)
-                        for n in nodes:
-                            n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3"
-                    agraph(nodes, edges, cfg)
-
-                elif name == "Metrics":
-                    nodes, edges, _ = build_agraph(res["papers"], res["umls"], res["drug_safety"])
-                    G = build_nx([n.__dict__ for n in nodes], [e.__dict__ for e in edges])
-                    st.metric("Density", f"{get_density(G):.3f}")
-                    st.markdown("**Top hubs**")
-                    for nid, sc in get_top_hubs(G):
-                        lab = next((n.label for n in nodes if n.id == nid), nid)
-                        st.write(f"- {lab}  {sc:.3f}")
-
-                elif name == "Visuals":
-                    years = [p["published"] for p in res["papers"] if p.get("published")]
-                    if years:
-                        st.plotly_chart(px.histogram(years, nbins=12,
-                                                     title="Publication Year"))
-
-        # Follow-up Q-A persistently under tabs
+        tabs = st.tabs(["Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"])
+
+        with tabs[0]:  # Results
+            for i, p in enumerate(res["papers"], 1):
+                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  *{p['authors']}*")
+                st.write(p["summary"])
+            col1, col2 = st.columns(2)
+            with col1:
+                st.download_button("CSV", pd.DataFrame(res["papers"]).to_csv(index=False),
+                                   "papers.csv", "text/csv")
+            with col2:
+                st.download_button("PDF", _pdf(res["papers"]), "papers.pdf", "application/pdf")
+            if st.button("💾 Save"):
+                save_query(query, res)
+                st.success("Saved to workspace")
+            st.subheader("UMLS concepts")
+            for c in res["umls"]:
+                if c.get("cui"):
+                    st.write(f"- **{c['name']}** ({c['cui']})")
+            st.subheader("OpenFDA safety")
+            for d in res["drug_safety"]:
+                st.json(d)
+            st.subheader("AI summary")
+            st.info(res["ai_summary"])
+
+        with tabs[1]:  # Genes
+            st.header("Gene / Variant signals")
+            for g in res["genes"]:
+                st.write(f"- **{g.get('name', g.get('geneid'))}** {g.get('description', '')}")
+            if res["gene_disease"]:
+                st.markdown("### DisGeNET links")
+                st.json(res["gene_disease"][:15])
+            if res["mesh_defs"]:
+                st.markdown("### MeSH definitions")
+                for d in res["mesh_defs"]:
+                    if d:
+                        st.write("-", d)
+
+        with tabs[2]:  # Trials
+            st.header("Clinical trials")
+            if not res["clinical_trials"]:
+                st.info("No trials (rate-limited or none found).")
+            for t in res["clinical_trials"]:
+                st.markdown(f"**{t['NCTId'][0]}** – {t['BriefTitle'][0]}")
+                st.write(f"Phase {t.get('Phase',[''])[0]} | Status {t['OverallStatus'][0]}")
+
+        with tabs[3]:  # Graph
+            nodes, edges, cfg = build_agraph(res["papers"], res["umls"], res["drug_safety"])
+            hl = st.text_input("Highlight node:", key="hl")
+            if hl:
+                pat = re.compile(re.escape(hl), re.I)
+                for n in nodes:
+                    n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3"
+            agraph(nodes, edges, cfg)
+
+        with tabs[4]:  # Metrics
+            nodes, edges, _ = build_agraph(res["papers"], res["umls"], res["drug_safety"])
+            G = build_nx([n.__dict__ for n in nodes], [e.__dict__ for e in edges])
+            st.metric("Density", f"{get_density(G):.3f}")
+            st.markdown("**Top hubs**")
+            for nid, sc in get_top_hubs(G):
+                lab = next((n.label for n in nodes if n.id == nid), nid)
+                st.write(f"- {lab}  {sc:.3f}")
+
+        with tabs[5]:  # Visuals
+            years = [p["published"] for p in res["papers"] if p.get("published")]
+            if years:
+                st.plotly_chart(px.histogram(years, nbins=12, title="Publication Year"))
+
         st.markdown("---")
-        follow = st.text_input("Ask follow‑up question:",
-                               value=st.session_state.followup_input,
-                               key="followup_input")
+        follow = st.text_input(
+            "Ask follow‑up question:",
+            value=st.session_state.followup_input,
+            key="followup_input"
+        )
         if st.button("Ask AI"):
             st.session_state.followup_input = follow
             if follow.strip():