Update app.py

app.py

@@ -1,13 +1,15 @@
-# app.py  –  MedGenesis AI (CPU-only, dual-LLM)
-
+# app.py ── MedGenesis AI · dual-LLM · CPU-only
 import asyncio, re
 from pathlib import Path
-import streamlit as st, pandas as pd, plotly.express as px
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
 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.orchestrator    import orchestrate_search, answer_ai_question
+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
@@ -15,108 +17,165 @@ from mcp.alerts          import check_alerts
 ROOT  = Path(__file__).parent
 LOGO  = ROOT / "assets" / "logo.png"
 
-# ────────────────────────────────────────────────────────────────
+# ─────────────────────────── helpers ────────────────────────────
 def _pdf(papers):
-    pdf=FPDF(); pdf.add_page(); pdf.set_font("Arial",size=11)
-    for i,p in enumerate(papers,1):
-        pdf.multi_cell(0,7,f"{i}. {p['title']} – {p['authors']}\n{p['summary']}\n{p['link']}\n")
+    pdf = FPDF(); pdf.add_page(); pdf.set_font("Arial", size=11)
+    pdf.cell(200, 8, "MedGenesis AI – Results", ln=True, align="C"); pdf.ln(3)
+    for i, p in enumerate(papers, 1):
+        pdf.set_font("Arial", "B", 11); pdf.multi_cell(0, 7, f"{i}. {p['title']}")
+        pdf.set_font("Arial", "", 9)
+        pdf.multi_cell(0, 6, f"{p['authors']}\n{p['summary']}\n{p['link']}\n")
         pdf.ln(1)
     return pdf.output(dest="S").encode("latin-1")
 
-def _sidebar_workspace():
+def _workspace_sidebar():
     with st.sidebar:
         st.header("🗂️ Workspace")
-        for i,item in enumerate(get_workspace(),1):
+        for i, item in enumerate(get_workspace(), 1):
             with st.expander(f"{i}. {item['query']}"):
                 st.write(item["result"]["ai_summary"])
+                df = pd.DataFrame(item["result"]["papers"])
+                st.download_button("CSV", df.to_csv(index=False),
+                                   f"ws_{i}.csv", "text/csv")
+        if not get_workspace():
+            st.info("Run a search and press **Save** to fill your workspace.")
 
-# ────────────────────────────────────────────────────────────────
+# ─────────────────────────── UI ────────────────────────────────
 def render_ui():
     st.set_page_config("MedGenesis AI", layout="wide")
-    _sidebar_workspace()
+    _workspace_sidebar()
 
-    # ── Header
-    col1,col2 = st.columns([0.15,0.85])
+    # Logo & title
+    col1, col2 = st.columns([0.15, 0.85])
     with col1:
-        if LOGO.exists(): st.image(str(LOGO), width=100)
+        if LOGO.exists():
+            st.image(str(LOGO), width=105)
     with col2:
         st.markdown("## 🧬 **MedGenesis AI**")
-        st.caption("Multi-source biomedical assistant · OpenAI / Gemini LLMs")
+        st.caption("Multi-source biomedical assistant · OpenAI / Gemini")
 
-    llm = st.radio("Choose LLM Engine", ["openai","gemini"], horizontal=True)
-    query = st.text_input("Enter biomedical question…",
-                          placeholder="e.g. CRISPR glioblastoma therapy")
+    llm = st.radio("LLM Engine", ["openai", "gemini"], horizontal=True)
+    query = st.text_input("Enter biomedical question …",
+                          placeholder="e.g. CRISPR for glioblastoma")
 
-    # 🔔 Alert check
+    # 🔔 alert check
     if get_workspace():
         try:
-            news = asyncio.run(check_alerts([q["query"] for q in get_workspace()]))
+            news = asyncio.run(check_alerts([w["query"] for w in get_workspace()]))
             if news:
-                st.sidebar.subheader("🔔 New Papers")
-                for q, lnks in news.items(): st.sidebar.write(f"**{q}** – {len(lnks)} new")
-        except Exception: pass
-
-    # ── Run
+                with st.sidebar:
+                    st.subheader("🔔 New Papers")
+                    for q, links in news.items():
+                        st.write(f"**{q}** – {len(links)} new")
+        except Exception as e:
+            st.sidebar.warning(f"Alert check failed: {e}")
+
+    # Run
     if st.button("Run Search 🚀") and query:
-        with st.spinner("Gathering literature & biomedical data…"):
-            res = asyncio.run(orchestrate_search(query, llm))
-        st.success(f"Completed with **{res['llm_used'].title()}**")
+        with st.spinner("Gathering literature & databases …"):
+            res = asyncio.run(orchestrate_search(query, llm=llm))
+        st.success(f"Finished with **{res['llm_used'].title()}**")
+
+        tabs = st.tabs(["Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"])
 
-        # Tabs
-        tabs = st.tabs(["Results","Genes","Trials","Graph","Metrics","Visuals"])
-        # 1) Results
+        # ── Results
         with tabs[0]:
-            for i,p in enumerate(res["papers"],1):
-                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  – *{p['authors']}*")
-                st.write(p["summary"])
-            st.download_button("📥 CSV", pd.DataFrame(res["papers"]).to_csv(index=False),
-                               "papers.csv","text/csv")
-            st.download_button("📄 PDF", _pdf(res["papers"]), "papers.pdf","application/pdf")
+            for i, p in enumerate(res["papers"], 1):
+                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  *{p['authors']}*")
+                st.markdown(f"<span style='color:gray'>{p['summary']}</span>",
+                            unsafe_allow_html=True)
+
+            col_a, col_b = st.columns(2)
+            with col_a:
+                st.download_button("📥 CSV",
+                                   pd.DataFrame(res["papers"]).to_csv(index=False),
+                                   "papers.csv", "text/csv")
+            with col_b:
+                st.download_button("📄 PDF", _pdf(res["papers"]),
+                                   "papers.pdf", "application/pdf")
+
+            if st.button("💾 Save to Workspace"):
+                save_query(query, res)
+                st.success("Saved!")
+
+            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"])
-            if st.button("Save to Workspace"): save_query(query,res); st.success("Saved!")
 
-        # 2) Genes
+        # ── Genes
         with tabs[1]:
-            st.write("### Gene hits")
-            for g in res["genes"]: st.write("-", g.get("name",g.get("geneid")))
-
-        # 3) Trials
+            st.header("Gene 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)
+
+        # ── Trials
         with tabs[2]:
-            if res["clinical_trials"]:
-                for t in res["clinical_trials"]:
-                    st.write(f"**{t['NCTId'][0]}** – {t['BriefTitle'][0]}")
-            else:
+            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]}")
 
-        # 4) Graph
+        # ── Graph
         with tabs[3]:
-            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=nodes,edges=edges,config=cfg)
-
-        # 5) Metrics
+            nodes, edges, cfg = build_agraph(res["papers"],
+                                             res["umls"], res["drug_safety"])
+            highlight = st.text_input("Highlight node name:")
+            if highlight:
+                pat = re.compile(re.escape(highlight), re.I)
+                for n in nodes:
+                    n.color = "#f1c40f" if pat.search(n.label) else "#d3d3d3"
+            agraph(nodes=nodes, edges=edges, config=cfg)
+
+        # ── Metrics
         with tabs[4]:
-            G=build_nx([n.__dict__ for n in nodes],[e.__dict__ for e in edges])
-            st.metric("Density",f"{get_density(G):.3f}")
-            st.write("**Top hubs**")
-            for nid,sc in get_top_hubs(G): st.write(nid, f"{sc:.3f}")
-
-        # 6) Visuals
+            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 Hub Nodes")
+            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}")
+
+        # ── Visuals
         with tabs[5]:
-            yrs=[p["published"] for p in res["papers"] if p.get("published")]
-            if yrs: st.plotly_chart(px.histogram(yrs,title="Publication Year"))
+            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
-        q2=st.text_input("Ask follow-up:")
-        if st.button("Ask AI"): 
-            ans=asyncio.run(answer_ai_question(q2,context=query,llm=llm))
+        st.markdown("---")
+        follow = st.text_input("Ask follow-up question:")
+        if st.button("Ask AI"):
+            ans = asyncio.run(answer_ai_question(follow,
+                                                 context=query,
+                                                 llm=llm))
             st.write(ans["answer"])
+
     else:
         st.info("Enter a question and press **Run Search 🚀**")
 
-if __name__=="__main__":
+
+# ────────────────────────────────────────────────────────────────
+if __name__ == "__main__":
     render_ui()