Update app.py

app.py

@@ -7,9 +7,21 @@ from fastapi.middleware.cors import CORSMiddleware
 from mcp.orchestrator import orchestrate_search, answer_ai_question
 from mcp.schemas import UnifiedSearchInput, UnifiedSearchResult
 
-# Initialize FastAPI app for API users
-api = FastAPI(title="MCP Research Server", version="2.0")
-api.add_middleware(CORSMiddleware, allow_origins=["*"], allow_credentials=True, allow_methods=["*"], allow_headers=["*"])
+# --- FASTAPI BACKEND ---
+
+api = FastAPI(
+    title="Ultimate Research MCP Server",
+    version="2.0.0",
+    description="AI-powered unified biomedical search using ArXiv, PubMed, OpenFDA, UMLS, and OpenAI."
+)
+
+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):
@@ -19,46 +31,79 @@ async def unified_search_endpoint(data: UnifiedSearchInput):
 async def ask_ai_endpoint(question: str, context: str = ""):
     return await answer_ai_question(question, context)
 
-# Streamlit UI for Hugging Face Space
+# --- STREAMLIT UI ---
+
 def render_ui():
-    st.set_page_config(page_title="Ultimate Research Assistant", page_icon=":microscope:", layout="wide")
-    st.image("assets/logo.png", width=100)
-    st.title("🔬 Next-Gen AI-Powered Biomedical Research Assistant")
-    st.markdown(
-        """
-        *Combine the power of ArXiv, PubMed, UMLS, OpenFDA, and OpenAI.  
-        Get instant, unified, semantically-ranked answers—plus drug safety, concept enrichment, and expert Q&A!*
-        """
-    )
+    st.set_page_config(page_title="Neo Cures AI", layout="wide")
 
-    query = st.text_input("Enter your research question or topic:", value="What are the latest treatments for Alzheimer's disease?")
-    if st.button("Run Unified Search 🚀"):
-        with st.spinner("Retrieving and synthesizing knowledge..."):
+    # Header with logo
+    col1, col2 = st.columns([0.15, 0.85])
+    with col1:
+        st.image("assets/logo.png", width=100)
+    with col2:
+        st.markdown("""
+            ## 🧬 **Neo Cures AI** – Advanced Biomedical Research Assistant  
+            *Powered by GPT-4o, PubMed, ArXiv, OpenFDA, and UMLS*
+        """)
+        st.caption("Built by Oluwafemi Idiakhoa | Hugging Face Spaces")
+
+    st.markdown("---")
+
+    # Unified Semantic Search
+    st.subheader("🔍 Unified Semantic Search")
+    query = st.text_input("Enter your biomedical research question:", placeholder="e.g. New treatments for glioblastoma using CRISPR")
+
+    if st.button("Run Search 🚀"):
+        with st.spinner("Thinking... Gathering and analyzing data across 5 systems..."):
             results = orchestrate_search(query)
-            st.success("Here are the results!")
-            for i, paper in enumerate(results['papers'], 1):
-                st.markdown(f"**{i}. [{paper['title']}]({paper['link']})**  \n*{paper['authors']}*")
-                st.write(paper['summary'])
-            st.subheader("UMLS Concept Enrichment")
-            for c in results['umls']:
-                st.write(f"**{c['name']}** (CUI: {c['cui']}): {c['definition']}")
-            st.subheader("Drug & Safety Insights")
-            for d in results['drug_safety']:
-                st.write(d)
-            st.subheader("AI-Generated Synthesis")
-            st.info(results['ai_summary'])
-            st.markdown("#### 📚 Suggested Reading")
-            for link in results['suggested_reading']:
+            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}")
 
+    # Follow-up AI Q&A
     st.markdown("---")
-    st.subheader("🤖 Ask a follow-up (AI Q&A):")
-    follow_up = st.text_input("Type your question here:")
+    st.subheader("💬 Ask AI a Follow-up Question")
+    follow_up = st.text_input("What do you want to ask based on the above?", placeholder="e.g. What's the most promising therapy?")
     if st.button("Ask AI"):
-        with st.spinner("AI is thinking..."):
-            answer = answer_ai_question(follow_up, context=query)
-            st.success("AI says:")
-            st.write(answer['answer'])
+        with st.spinner("Analyzing and responding..."):
+            ai_answer = answer_ai_question(follow_up, context=query)
+            st.success("AI's Response:")
+            st.write(ai_answer["answer"])
+
+    # Footer
+    st.markdown("---")
+    st.markdown(
+        "<div style='text-align: center; font-size: 0.9em;'>"
+        "✨ Built with ❤️ by <strong>Oluwafemi Idiakhoa</strong> • Powered by FastAPI, Streamlit, Hugging Face, OpenAI, UMLS, OpenFDA, and NCBI</div>",
+        unsafe_allow_html=True
+    )
+
+# --- MAIN ENTRY ---
 
 if __name__ == "__main__":
     import sys