Update app.py

app.py

@@ -1,6 +1,6 @@
-# app.py  •  MedGenesis AI – CPU-only powerhouse
+# app.py — MedGenesis AI (CPU edition)
 
-import asyncio, os, re, httpx
+import asyncio, re
 from pathlib import Path
 import streamlit as st
 import pandas as pd
@@ -8,8 +8,8 @@ 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
@@ -17,46 +17,51 @@ from mcp.alerts          import check_alerts
 ROOT = Path(__file__).parent
 LOGO = ROOT / "assets" / "logo.png"
 
-# ---------- utilities ----------
-def gen_pdf(papers):
+# ---------------------------------------------------------------------
+def pdf_from_papers(papers):
     pdf = FPDF(); pdf.add_page(); pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, "MedGenesis AI – Results", ln=True, align="C"); pdf.ln(10)
+    pdf.cell(200, 10, "MedGenesis AI — Results", ln=True, align="C"); pdf.ln(8)
     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", "B", 12)
+        pdf.multi_cell(0, 8, 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.multi_cell(0, 6, f"{p['authors']}\n{p['summary']}\n{p['link']}\n")
         pdf.ln(2)
     return pdf.output(dest="S").encode("latin-1")
 
-# ---------- UI ----------
+# ---------------------------------------------------------------------
+def sidebar_workspace():
+    with st.sidebar:
+        st.header("🗂️ Workspace")
+        ws = get_workspace()
+        if not ws:
+            st.info("Run a search and click **Save** to build your workspace.")
+            return
+        for i, item in enumerate(ws, 1):
+            with st.expander(f"{i}. {item['query']}"):
+                st.write("**AI Summary**:", item["result"]["ai_summary"])
+                df = pd.DataFrame(item["result"]["papers"])
+                st.download_button("📥 CSV", df.to_csv(index=False),
+                                   f"workspace_{i}.csv", "text/csv")
+
+# ---------------------------------------------------------------------
 def render_ui():
     st.set_page_config(page_title="MedGenesis AI", layout="wide")
 
-    # 🔔 Alert check (non-blocking)
-    saved_qs = [w["query"] for w in get_workspace()]
-    if saved_qs:
+    # 🔔 quick alert check
+    saved_q = [q["query"] for q in get_workspace()]
+    if saved_q:
         try:
-            news = asyncio.run(check_alerts(saved_qs))
-            if news:
+            alerts = asyncio.run(check_alerts(saved_q))
+            if alerts:
                 with st.sidebar:
                     st.subheader("🔔 New Papers")
-                    for q, links in news.items():
+                    for q, links in alerts.items():
                         st.write(f"**{q}** – {len(links)} new")
         except Exception as e:
-            st.sidebar.error(f"Alert check error: {e}")
+            st.sidebar.warning(f"Alert check failed: {e}")
 
-    # Workspace sidebar
-    with st.sidebar:
-        st.header("🗂️ Workspace")
-        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("No saved queries.")
+    sidebar_workspace()
 
     # Header
     col1, col2 = st.columns([0.15, 0.85])
@@ -64,81 +69,88 @@ def render_ui():
         if LOGO.exists(): st.image(str(LOGO), width=100)
     with col2:
         st.markdown("## 🧬 **MedGenesis AI**")
-        st.caption("PubMed • ArXiv • OpenFDA • UMLS • NCBI • DisGeNET • ClinicalTrials • GPT-4o")
+        st.caption("PubMed·ArXiv·OpenFDA·UMLS·NCBI·DisGeNET·ClinicalTrials·GPT-4o")
 
     st.markdown("---")
-    query = st.text_input("🔍 Ask a biomedical research question:",
-                          placeholder="e.g. CRISPR glioblastoma treatment")
+    query = st.text_input("🔍 Ask your biomedical question:",
+                          placeholder="e.g. CRISPR for glioblastoma")
 
+    # -----------------------------------------------------------------
     if st.button("Run Search 🚀") and query:
-        with st.spinner("Crunching literature & biomedical databases…"):
+        with st.spinner("Synthesizing multi-source biomedical intel…"):
             res = asyncio.run(orchestrate_search(query))
-        st.success("Done!")
+        st.success("Ready!")
 
         tabs = st.tabs([
             "Results", "Genes", "Trials", "Graph", "Metrics", "Visuals"
         ])
 
-        # --- Results ---
+        # ----------- RESULTS -----------------
         with tabs[0]:
-            st.header("📚 Top Papers")
+            st.header("📚 Literature")
             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 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")
+                st.markdown(f"**{i}. [{p['title']}]({p['link']})** &nbsp;*{p['authors']}*")
+                st.markdown(f"<span style='color:gray'>{p['summary']}</span>",
+                            unsafe_allow_html=True)
+
+            colA, colB = st.columns(2)
+            with colA:
+                if st.button("💾 Save to Workspace"):
+                    save_query(query, res); st.success("Saved!")
+            with colB:
+                st.download_button("📥 CSV", pd.DataFrame(res["papers"]).to_csv(index=False),
+                                   "papers.csv", "text/csv")
+
+            st.download_button("📄 PDF", pdf_from_papers(res["papers"]),
+                               "papers.pdf", "application/pdf")
+
+            st.subheader("🧠 UMLS")
             for c in res["umls"]:
-                if c.get("cui"):
-                    st.write(f"- **{c['name']}** ({c['cui']})")
+                if c.get("cui"): st.write(f"- **{c['name']}** ({c['cui']})")
 
-            st.subheader("💊 Drug Safety (OpenFDA)")
+            st.subheader("💊 OpenFDA Safety")
             for d in res["drug_safety"]: st.json(d)
 
-            st.subheader("🤖 AI Synthesis")
+            st.subheader("🤖 AI Summary")
             st.info(res["ai_summary"])
 
-        # --- Genes / Variants ---
+        # ----------- GENES & VARIANTS --------
         with tabs[1]:
-            st.header("🧬 Gene & Variant Signals")
+            st.header("🧬 Gene Signals")
             for g in res["genes"]:
                 st.write(f"- **{g.get('name', g.get('geneid'))}** – {g.get('description','')}")
             if res["gene_disease"]:
-                st.write("### DisGeNET Links")
-                st.json(res["gene_disease"][:15])
+                st.markdown("### DisGeNET Links"); st.json(res["gene_disease"][:15])
             if res["mesh_defs"]:
-                st.write("### MeSH Definitions")
+                st.markdown("### MeSH Definitions")
                 for d in res["mesh_defs"]: st.write("-", d)
 
-        # --- Clinical Trials ---
+        # ----------- TRIALS ------------------
         with tabs[2]:
-            st.header("💊 Registered Clinical Trials")
+            st.header("💊 Clinical Trials")
             if not res["clinical_trials"]:
-                st.info("No trials (API rate-limited or none found).")
+                st.info("No trials retrieved (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]}")
+                st.write(f"Phase: {t.get('Phase',[''])[0]} | Status: {t['OverallStatus'][0]}")
 
-        # --- Knowledge Graph ---
+        # ----------- GRAPH -------------------
         with tabs[3]:
             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)
+            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:
                     if pat.search(n.label): n.color, n.size = "#f1c40f", 30
                     else: n.color = "#d3d3d3"
             agraph(nodes=nodes, edges=edges, config=cfg)
 
-        # --- Metrics ---
+        # ----------- METRICS -----------------
         with tabs[4]:
             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")
@@ -146,19 +158,21 @@ def render_ui():
                 lab = next((n.label for n in nodes if n.id == nid), nid)
                 st.write(f"- **{lab}** – {sc:.3f}")
 
-        # --- Visuals ---
+        # ----------- 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"))
+            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 ---
+        # -------- Follow-up AI ---------------
         st.markdown("---")
-        q = st.text_input("Ask follow-up question:")
+        follow = st.text_input("Ask follow-up question:")
         if st.button("Ask AI"):
-            st.write(asyncio.run(answer_ai_question(q, context=query))["answer"])
+            st.write(asyncio.run(answer_ai_question(follow, context=query))["answer"])
+
     else:
         st.info("Enter a question and press **Run Search 🚀**")
 
-# Run
+
+# ---------------------------------------------------------------------
 if __name__ == "__main__":
     render_ui()