Update app.py

app.py

@@ -1,148 +1,164 @@
-# app.py
+# app.py  •  MedGenesis AI – CPU-only powerhouse
 
-import asyncio, os, re
+import asyncio, os, re, httpx
 from pathlib import Path
-
 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
 
 ROOT = Path(__file__).parent
 LOGO = ROOT / "assets" / "logo.png"
 
-# ------------------------- helpers -------------------------
-def generate_pdf(papers):
+# ---------- utilities ----------
+def gen_pdf(papers):
     pdf = FPDF(); pdf.add_page(); pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, "MedGenesis AI - Search Results", ln=True, align="C")
-    pdf.ln(10)
+    pdf.cell(200, 10, "MedGenesis AI – Results", ln=True, align="C"); pdf.ln(10)
     for i, p in enumerate(papers, 1):
-        pdf.set_font("Arial", "B", 12)
-        pdf.multi_cell(0, 10, f"{i}. {p['title']}")
-        pdf.set_font("Arial", "", 10)
-        pdf.multi_cell(0, 8,
-            f"Authors: {p['authors']}\nLink: {p['link']}\nSummary: {p['summary']}\n")
+        pdf.set_font("Arial", "B", 12); pdf.multi_cell(0, 10, f"{i}. {p['title']}")
+        pdf.set_font("Arial", "", 9)
+        pdf.multi_cell(0, 7, f"Authors: {p['authors']}\n{p['summary']}\n{p['link']}\n")
         pdf.ln(2)
     return pdf.output(dest="S").encode("latin-1")
 
-# ------------------------- UI -------------------------
+# ---------- UI ----------
 def render_ui():
     st.set_page_config(page_title="MedGenesis AI", layout="wide")
 
-    # ----- Sidebar ----------
+    # 🔔 Alert check (non-blocking)
+    saved_qs = [w["query"] for w in get_workspace()]
+    if saved_qs:
+        try:
+            news = asyncio.run(check_alerts(saved_qs))
+            if news:
+                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.error(f"Alert check error: {e}")
+
+    # Workspace sidebar
     with st.sidebar:
         st.header("🗂️ Workspace")
-        for i, it in enumerate(get_workspace(), 1):
-            with st.expander(f"{i}. {it['query']}"):
-                st.write("**AI Summary:**", it["result"]["ai_summary"])
-                df = pd.DataFrame(it["result"]["papers"])
-                st.download_button("CSV", df.to_csv(index=False), f"ws_{i}.csv", "text/csv")
+        for i, itm in enumerate(get_workspace(), 1):
+            with st.expander(f"{i}. {itm['query']}"):
+                st.write("AI summary:", itm["result"]["ai_summary"])
+                st.download_button(
+                    "CSV", pd.DataFrame(itm["result"]["papers"]).to_csv(index=False),
+                    f"ws_{i}.csv", "text/csv"
+                )
         if not get_workspace():
-            st.info("Run and save searches to populate workspace.")
+            st.info("No saved queries.")
 
-    # ----- Header ----------
+    # Header
     col1, col2 = st.columns([0.15, 0.85])
     with col1:
         if LOGO.exists(): st.image(str(LOGO), width=100)
     with col2:
-        st.markdown("## 🧬 MedGenesis AI")
-        st.write("*Unified PubMed, ArXiv, OpenFDA, UMLS, NCBI, DisGeNET, ClinicalTrials & GPT-4o*")
+        st.markdown("## 🧬 **MedGenesis AI**")
+        st.caption("PubMed • ArXiv • OpenFDA • UMLS • NCBI • DisGeNET • ClinicalTrials • GPT-4o")
 
     st.markdown("---")
-    query = st.text_input("🔍 Biomedical research question:", placeholder="e.g. CRISPR glioblastoma")
+    query = st.text_input("🔍 Ask a biomedical research question:",
+                          placeholder="e.g. CRISPR glioblastoma treatment")
 
-    # ------------- Search -------------
     if st.button("Run Search 🚀") and query:
-        with st.spinner("Collecting literature & biomedical data…"):
-            results = asyncio.run(orchestrate_search(query))
-        st.success("Completed!")
+        with st.spinner("Crunching literature & biomedical databases…"):
+            res = asyncio.run(orchestrate_search(query))
+        st.success("Done!")
 
-        # -------- Tabs ---------
         tabs = st.tabs([
-            "📝 Results", "🧬 Genes & Variants", "💊 Clinical Trials",
-            "🗺️ Graph", "📊 Visuals"
+            "Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"
         ])
 
-        # -- Results ----------
+        # --- Results ---
         with tabs[0]:
             st.header("📚 Top Papers")
-            for i, p in enumerate(results["papers"], 1):
-                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  \n*{p['authors']}* ({p['source']})")
+            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)
-
-            if st.button("Save to Workspace"):
-                save_query(query, results); st.success("Saved!")
-
-            df = pd.DataFrame(results["papers"])
-            st.download_button("CSV", df.to_csv(index=False), "results.csv", "text/csv")
-            st.download_button("PDF", generate_pdf(results["papers"]), "results.pdf", "application/pdf")
-
-            st.subheader("🧠 UMLS Concepts")
-            for c in results["umls"]:
+            if st.button("Save Query"):
+                save_query(query, res); st.success("Saved to workspace")
+            csv = pd.DataFrame(res["papers"]).to_csv(index=False)
+            st.download_button("CSV", csv, "papers.csv", "text/csv")
+            st.download_button("PDF", gen_pdf(res["papers"]), "papers.pdf", "application/pdf")
+
+            st.subheader("🧠 Key UMLS Concepts")
+            for c in res["umls"]:
                 if c.get("cui"):
-                    st.markdown(f"- **{c['name']}** (`{c['cui']}`)")
+                    st.write(f"- **{c['name']}** ({c['cui']})")
 
             st.subheader("💊 Drug Safety (OpenFDA)")
-            for d in results["drug_safety"]:
-                st.json(d)
+            for d in res["drug_safety"]: st.json(d)
 
-            st.subheader("🤖 AI Summary")
-            st.info(results["ai_summary"])
+            st.subheader("🤖 AI Synthesis")
+            st.info(res["ai_summary"])
 
-        # -- Genes & Variants ----------
+        # --- Genes / Variants ---
         with tabs[1]:
-            st.header("🧬 Gene Associations (NCBI / DisGeNET)")
-            for g in results["genes"]:
+            st.header("🧬 Gene & Variant Signals")
+            for g in res["genes"]:
                 st.write(f"- **{g.get('name', g.get('geneid'))}** – {g.get('description','')}")
-            if results["gene_disease"]:
-                st.markdown("#### DisGeNET Disease → Gene links")
-                st.json(results["gene_disease"][:15])
-            if results["mesh_definitions"]:
-                st.markdown("#### MeSH Definitions")
-                for d in results["mesh_definitions"]:
-                    if d: st.write(f"- {d}")
-
-        # -- Clinical Trials ----------
+            if res["gene_disease"]:
+                st.write("### DisGeNET Links")
+                st.json(res["gene_disease"][:15])
+            if res["mesh_defs"]:
+                st.write("### MeSH Definitions")
+                for d in res["mesh_defs"]: st.write("-", d)
+
+        # --- Clinical Trials ---
         with tabs[2]:
             st.header("💊 Registered Clinical Trials")
-            for t in results["clinical_trials"]:
+            if not res["clinical_trials"]:
+                st.info("No trials (API rate-limited or none found).")
+            for t in res["clinical_trials"]:
                 st.markdown(f"**{t['NCTId'][0]}** – {t['BriefTitle'][0]}")
-                st.write(f"Condition: {', '.join(t['Condition'])} | Phase: {t.get('Phase',[None])[0]} | Status: {t['OverallStatus'][0]}")
+                st.write(f"Phase: {t.get('Phase', [''])[0]} | Status: {t['OverallStatus'][0]}")
 
-        # -- Graph ----------
+        # --- Knowledge Graph ---
         with tabs[3]:
-            search_term = st.text_input("Highlight node containing:", key="graphsearch")
-            try:
-                nodes, edges, cfg = build_agraph(results["papers"], results["umls"], results["drug_safety"])
-                if search_term:
-                    pat = re.compile(re.escape(search_term), re.I)
-                    for n in nodes:
-                        if pat.search(n.label): n.color, n.size = "#f1c40f", max(n.size, 30)
-                        else: n.color = "#ddd"
-                agraph(nodes=nodes, edges=edges, config=cfg)
-            except Exception as e:
-                st.error(f"Graph error: {e}")
-
-        # -- Visualizations ----------
+            st.header("🗺️ Knowledge Graph")
+            nodes, edges, cfg = build_agraph(res["papers"], res["umls"], res["drug_safety"])
+            highlight = st.text_input("Highlight nodes:", key="hl")
+            if highlight:
+                pat = re.compile(re.escape(highlight), re.I)
+                for n in nodes:
+                    if pat.search(n.label): n.color, n.size = "#f1c40f", 30
+                    else: n.color = "#d3d3d3"
+            agraph(nodes=nodes, edges=edges, config=cfg)
+
+        # --- Metrics ---
         with tabs[4]:
-            yrs = [p["published"] for p in results["papers"] if p.get("published")]
+            st.header("📈 Graph Metrics")
+            import networkx as nx
+            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("#### 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, nbins=10, title="Publication Year"))
 
-        # -- Follow-up Q&A ----------
+        # --- Follow-up Q&A ---
         st.markdown("---")
-        q = st.text_input("Ask follow-up:", key="follow")
+        q = st.text_input("Ask follow-up question:")
         if st.button("Ask AI"):
-            ans = asyncio.run(answer_ai_question(q, context=query))
-            st.write(ans["answer"])
-
+            st.write(asyncio.run(answer_ai_question(q, context=query))["answer"])
     else:
         st.info("Enter a question and press **Run Search 🚀**")
 
-# ------------- Run -------------
+# Run
 if __name__ == "__main__":
     render_ui()