Update app.py

app.py

@@ -7,6 +7,8 @@ from fastapi.middleware.cors import CORSMiddleware
 from mcp.orchestrator import orchestrate_search, answer_ai_question
 from mcp.schemas import UnifiedSearchInput, UnifiedSearchResult
 from pathlib import Path
+import pandas as pd
+from fpdf import FPDF
 import asyncio
 
 ROOT_DIR = Path(__file__).resolve().parent
@@ -36,6 +38,23 @@ async def unified_search_endpoint(data: UnifiedSearchInput):
 async def ask_ai_endpoint(question: str, context: str = ""):
     return await answer_ai_question(question, context)
 
+# --- PDF Export Utility ---
+
+def generate_pdf(papers):
+    pdf = FPDF()
+    pdf.add_page()
+    pdf.set_font("Arial", size=12)
+    pdf.cell(200, 10, txt="MedGenesis AI - Search Results", ln=True, align='C')
+    pdf.ln(10)
+    for i, paper in enumerate(papers, 1):
+        pdf.set_font("Arial", style="B", size=12)
+        pdf.multi_cell(0, 10, f"{i}. {paper['title']}")
+        pdf.set_font("Arial", style="", size=10)
+        pdf.multi_cell(0, 8, f"Authors: {paper['authors']}\nLink: {paper['link']}\nSummary: {paper['summary']}\n")
+        pdf.ln(2)
+    pdf_out = pdf.output(dest='S').encode('latin-1')
+    return pdf_out
+
 # --- STREAMLIT UI ---
 
 def render_ui():
@@ -61,37 +80,60 @@ def render_ui():
     st.subheader("🔍 Unified Semantic Search")
     query = st.text_input("Enter your biomedical research question:", placeholder="e.g. New treatments for glioblastoma using CRISPR")
 
+    results = None
     if st.button("Run Search 🚀"):
         with st.spinner("Thinking... Gathering and analyzing data across 5 systems..."):
             results = asyncio.run(orchestrate_search(query))
             st.success("Search complete! 🎉")
 
-            # 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)
-
-            # 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}")
+    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)
+
+        # 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}")
 
     # Follow-up AI Q&A
     st.markdown("---")