Update app.py

app.py

@@ -2,8 +2,6 @@
 
 import os
 import streamlit as st
-from fastapi import FastAPI
-from fastapi.middleware.cors import CORSMiddleware
 import asyncio
 from pathlib import Path
 import pandas as pd
@@ -20,27 +18,15 @@ from streamlit_agraph import agraph, Node, Edge, Config
 ROOT = Path(__file__).parent
 LOGO = ROOT / "assets" / "logo.png"
 
-api = FastAPI(
-    title="MedGenesis MCP Server",
-    version="2.0.0",
-    description="MedGenesis AI – Unified Intelligence from PubMed, ArXiv, OpenFDA, UMLS, GPT-4o"
-)
-api.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
-
-@api.post("/unified_search", response_model=UnifiedSearchResult)
-async def unified_search_endpoint(data: UnifiedSearchInput):
-    return await orchestrate_search(data.query)
-
-@api.post("/ask_ai")
-async def ask_ai_endpoint(question: str, context: str = ""):
-    return await answer_ai_question(question, context)
-
 def generate_pdf(papers):
-    pdf = FPDF(); pdf.add_page(); pdf.set_font("Arial", size=12)
+    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)
     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, 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.ln(2)
@@ -49,21 +35,17 @@ def generate_pdf(papers):
 def render_ui():
     st.set_page_config(page_title="MedGenesis AI", layout="wide")
 
-    # Sidebar workspace
     with st.sidebar:
         st.header("🗂️ Workspace")
-        ws = get_workspace()
-        if ws:
-            for i, item in enumerate(ws, 1):
-                with st.expander(f"{i}. {item['query']}"):
-                    st.write("**AI Summary:**", item["result"]["ai_summary"])
-                    st.write("**Top Paper:**", item["result"]["papers"][0]["title"] if item["result"]["papers"] else "None")
-                    df = pd.DataFrame(item["result"]["papers"])
-                    st.download_button("📥 CSV", df.to_csv(index=False), f"query_{i}.csv", "text/csv")
-        else:
-            st.info("Run & save searches here.")
-
-    # Header
+        for i, item in enumerate(get_workspace(), 1):
+            with st.expander(f"{i}. {item['query']}"):
+                st.write("**AI Summary:**", item["result"]["ai_summary"])
+                st.write("**Top Paper:**", item["result"]["papers"][0]["title"] if item["result"]["papers"] else "None")
+                df = pd.DataFrame(item["result"]["papers"])
+                st.download_button("📥 CSV", df.to_csv(index=False), f"workspace_{i}.csv", "text/csv")
+        if not get_workspace():
+            st.info("Run and save searches here.")
+
     col1, col2 = st.columns([0.15, 0.85])
     with col1:
         if LOGO.exists():
@@ -73,85 +55,69 @@ def render_ui():
         st.caption("Built by Oluwafemi Idiakhoa • Hugging Face Spaces")
 
     st.markdown("---")
-    query = st.text_input("🔍 Enter biomedical research question:", placeholder="e.g. Glioblastoma CRISPR treatments")
+    query = st.text_input("🔍 Enter research question:", placeholder="e.g. CRISPR treatments for glioblastoma")
 
     results = None
     if st.button("Run Search 🚀"):
-        with st.spinner("Fetching & synthesizing..."):
+        with st.spinner("Analyzing..."):
             results = asyncio.run(orchestrate_search(query))
-            st.success("✅ Search complete")
+            st.success("Search complete")
 
     if results:
         tabs = st.tabs(["📝 Results", "🗺️ Knowledge Graph", "📊 Visualizations"])
 
-        # Tab 1: Results
         with tabs[0]:
-            st.header("📚 Top Papers")
+            st.header("📚 Papers")
             for i, p in enumerate(results["papers"], 1):
-                st.markdown(f"**{i}. [{p['title']}]({p['link']})**  \n*{p['authors']}* ({p['source']})")
+                st.markdown(f"**{i}. [{p['title']}]({p['link']})** *{p['authors']}*")
                 st.markdown(f"<div style='color:gray'>{p['summary']}</div>", 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("📥 Download CSV", df.to_csv(index=False), "results.csv", "text/csv")
+            st.download_button("📥 CSV", df.to_csv(index=False), "results.csv", "text/csv")
             pdf = generate_pdf(results["papers"])
-            st.download_button("📄 Download PDF", pdf, "results.pdf", "application/pdf")
-
+            st.download_button("📄 PDF", pdf, "results.pdf", "application/pdf")
             st.subheader("🧠 UMLS Concepts")
             for c in results["umls"]:
                 if c.get("cui"):
-                    st.markdown(f"- **{c['name']}** (CUI: `{c['cui']}`): {c.get('definition','No definition')}")
-
-            st.subheader("💊 Drug Safety (OpenFDA)")
+                    st.markdown(f"- **{c['name']}** (CUI:{c['cui']}): {c.get('definition','')}")
+            st.subheader("💊 Drug Safety")
             for d in results["drug_safety"]:
                 st.json(d)
-
             st.subheader("🤖 AI Summary")
             st.info(results["ai_summary"])
 
-        # Tab 2: Knowledge Graph with Search & Highlight
         with tabs[1]:
-            st.header("🗺️ Knowledge Graph Explorer")
-            search_term = st.text_input("🔎 Highlight nodes containing:", value="")
+            st.header("🔎 Knowledge Graph")
+            search_term = st.text_input("Highlight node containing text:", value="")
             try:
                 nodes, edges, config = build_agraph(results["papers"], results["umls"], results["drug_safety"])
-                # Highlight logic
-                if search_term.strip():
-                    pattern = re.compile(re.escape(search_term), re.IGNORECASE)
+                if search_term:
+                    pat = re.compile(re.escape(search_term), re.IGNORECASE)
                     for node in nodes:
-                        if pattern.search(node.label) or (hasattr(node, "tooltip") and pattern.search(getattr(node, "tooltip", ""))):
-                            node.color = "#f1c40f"
-                            node.size = max(node.size, 30)
+                        if pat.search(node.label) or (hasattr(node, "tooltip") and pat.search(getattr(node, "tooltip", ""))):
+                            node.color = "#f1c40f"; node.size = max(node.size, 30)
                         else:
                             node.color = "#ddd"
                 agraph(nodes=nodes, edges=edges, config=config)
             except Exception as e:
-                st.warning("Knowledge graph unavailable: " + str(e))
+                st.warning("Graph unavailable: " + str(e))
 
-        # Tab 3: Visualizations
         with tabs[2]:
-            pub_years = [p["published"] for p in results["papers"] if p.get("published")]
-            if pub_years:
-                fig = px.histogram(pub_years, nbins=10, title="Publication Year Distribution")
-                st.plotly_chart(fig)
+            years = [p["published"] for p in results["papers"] if p.get("published")]
+            if years:
+                st.plotly_chart(px.histogram(years, title="Pub Year"))
 
-    # Follow-up Q&A
     st.markdown("---")
-    st.subheader("💬 Ask Follow-up AI Question")
-    follow_up = st.text_input("Based on above:", placeholder="e.g. Most promising therapy?")
+    st.subheader("💬 Ask AI a Follow-up")
+    fq = st.text_input("Ask based on above:", "")
     if st.button("Ask AI"):
         with st.spinner("Thinking..."):
-            ai_ans = asyncio.run(answer_ai_question(follow_up, context=query))
-            st.write(ai_ans.get("answer", ai_ans))
+            ans = asyncio.run(answer_ai_question(fq, context=query))
+            st.write(ans.get("answer", ans))
 
     st.markdown("---")
-    st.caption("✨ Built by Oluwafemi Idiakhoa • Powered by FastAPI, Streamlit, OpenAI, UMLS, OpenFDA, NCBI")
+    st.caption("✨ Built by Oluwafemi Idiakhoa • Powered by Streamlit & AI")
 
 if __name__ == "__main__":
-    import sys
-    if "runserver" in sys.argv:
-        import uvicorn; uvicorn.run(api, host="0.0.0.0", port=7860)
-    else:
-        render_ui()
+    render_ui()