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# app.py
import os
import streamlit as st
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
from mcp.orchestrator import orchestrate_search, answer_ai_question
from mcp.schemas import UnifiedSearchInput, UnifiedSearchResult
# --- 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):
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)
# --- STREAMLIT UI ---
def render_ui():
st.set_page_config(page_title="Neo Cures AI", layout="wide")
# 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("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 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("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
if "runserver" in sys.argv:
import uvicorn
uvicorn.run(api, host="0.0.0.0", port=7860)
else:
render_ui()
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