Update app.py

app.py

@@ -108,69 +108,78 @@ def render_ui():
         with st.spinner("Thinking... Gathering and analyzing data across 5 systems..."):
             results = asyncio.run(orchestrate_search(query))
             st.success("Search complete! 🎉")
-            save_query(query, results)  # Save to workspace
 
     if results:
-        # Papers
-        st.markdown("### 📚 Most Relevant Papers")
-        for i, paper in enumerate(results["papers"], 1):
-            st.markdown(f"**{i}. [{paper['title']}]({paper['link']})**  \n*{paper['authors']}* ({paper['source']})")
-            st.markdown(f"<div style='font-size: 0.9em; color: gray'>{paper['summary']}</div>", unsafe_allow_html=True)
-
-        # --- INTERACTIVE VISUALIZATION: Publication Year Histogram ---
-        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)
-
-        # --- INTERACTIVE KNOWLEDGE GRAPH ---
-        st.markdown("### 🗺️ Knowledge Graph Explorer")
-        kg_html_path = build_knowledge_graph(results["papers"], results["umls"], results["drug_safety"])
-        with open(kg_html_path, 'r', encoding='utf-8') as f:
-            kg_html = f.read()
-        components.html(kg_html, height=600)
-
-        # Export as CSV
-        if results["papers"]:
-            df = pd.DataFrame(results["papers"])
-            csv = df.to_csv(index=False)
-            st.download_button(
-                label="📥 Download results as CSV",
-                data=csv,
-                file_name="medgenesis_results.csv",
-                mime="text/csv",
-            )
-
-        # Export as PDF
-        if results["papers"]:
-            pdf_bytes = generate_pdf(results["papers"])
-            st.download_button(
-                label="📄 Download results as PDF",
-                data=pdf_bytes,
-                file_name="medgenesis_results.pdf",
-                mime="application/pdf",
-            )
-
-        # UMLS Concepts
-        st.markdown("### 🧠 Biomedical Concept Enrichment (UMLS)")
-        for concept in results["umls"]:
-            if concept["cui"]:
-                st.markdown(f"🔹 **{concept['name']}** (CUI: `{concept['cui']}`): {concept['definition'] or 'No definition available'}")
-
-        # Drug Safety
-        st.markdown("### 💊 Drug Safety Insights (OpenFDA)")
-        for drug_report in results["drug_safety"]:
-            if drug_report:
-                st.json(drug_report)
-
-        # AI Summary
-        st.markdown("### 🤖 AI-Powered Summary")
-        st.info(results["ai_summary"])
-
-        # Suggested Reading
-        st.markdown("### 📖 Suggested Links")
-        for link in results["suggested_reading"]:
-            st.write(f"- {link}")
+        tabs = st.tabs(["📝 Results", "🗺️ Knowledge Graph", "📊 Visualizations"])
+
+        # --- TAB 1: Results ---
+        with tabs[0]:
+            for i, paper in enumerate(results["papers"], 1):
+                st.markdown(f"**{i}. [{paper['title']}]({paper['link']})**  \n*{paper['authors']}* ({paper['source']})")
+                st.markdown(f"<div style='font-size: 0.9em; color: gray'>{paper['summary']}</div>", unsafe_allow_html=True)
+
+            # Save to workspace (user-initiated, for clarity)
+            if st.button("Save this search to Workspace"):
+                save_query(query, results)
+                st.success("Saved to your workspace!")
+
+            # Export as CSV
+            if results["papers"]:
+                df = pd.DataFrame(results["papers"])
+                csv = df.to_csv(index=False)
+                st.download_button(
+                    label="📥 Download results as CSV",
+                    data=csv,
+                    file_name="medgenesis_results.csv",
+                    mime="text/csv",
+                )
+
+            # Export as PDF
+            if results["papers"]:
+                pdf_bytes = generate_pdf(results["papers"])
+                st.download_button(
+                    label="📄 Download results as PDF",
+                    data=pdf_bytes,
+                    file_name="medgenesis_results.pdf",
+                    mime="application/pdf",
+                )
+
+            # UMLS Concepts
+            st.markdown("### 🧠 Biomedical Concept Enrichment (UMLS)")
+            for concept in results["umls"]:
+                if concept["cui"]:
+                    st.markdown(f"🔹 **{concept['name']}** (CUI: `{concept['cui']}`): {concept['definition'] or 'No definition available'}")
+
+            # Drug Safety
+            st.markdown("### 💊 Drug Safety Insights (OpenFDA)")
+            for drug_report in results["drug_safety"]:
+                if drug_report:
+                    st.json(drug_report)
+
+            # AI Summary
+            st.markdown("### 🤖 AI-Powered Summary")
+            st.info(results["ai_summary"])
+
+            # Suggested Reading
+            st.markdown("### 📖 Suggested Links")
+            for link in results["suggested_reading"]:
+                st.write(f"- {link}")
+
+        # --- TAB 2: Knowledge Graph ---
+        with tabs[1]:
+            st.markdown("#### Explore Connections")
+            kg_html_path = build_knowledge_graph(results["papers"], results["umls"], results["drug_safety"])
+            with open(kg_html_path, 'r', encoding='utf-8') as f:
+                kg_html = f.read()
+            components.html(kg_html, height=600)
+
+        # --- 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)
+            # Placeholder for more charts
 
     # Follow-up AI Q&A
     st.markdown("---")