Create app.py

app.py

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+import os
+from fastapi import FastAPI, Query, HTTPException
+from typing import List, Dict, Optional
+import httpx
+import openai
+
+# ENVIRONMENT VARIABLES for API keys (set these securely in your deployment)
+ARXIV_API = "http://export.arxiv.org/api/query?search_query="
+PUBMED_API = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi"
+PUBMED_FETCH = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi"
+OPENFDA_ENDPOINT = "https://api.fda.gov/drug/event.json"
+UMLS_AUTH = "https://utslogin.nlm.nih.gov/cas/v1/api-key"
+UMLS_SEARCH = "https://uts-ws.nlm.nih.gov/rest/search/current"
+OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
+UMLS_API_KEY = os.environ.get("UMLS_KEY")
+PUB_KEY = os.environ.get("PUB_KEY")
+
+app = FastAPI(
+    title="Ultimate AI-Powered Research MCP Server",
+    description="Integrates ArXiv, PubMed, OpenFDA, UMLS, and OpenAI for world-class research.",
+    version="1.0.0",
+)
+
+# -- Utility Functions --
+
+async def fetch_arxiv(query: str) -> List[Dict]:
+    # Use httpx or requests to fetch arXiv results
+    # Parse and return relevant metadata (simplified for brevity)
+    url = f"{ARXIV_API}{query}&max_results=5"
+    async with httpx.AsyncClient() as client:
+        r = await client.get(url)
+        # TODO: Parse arXiv Atom XML and extract paper metadata
+    return []
+
+async def fetch_pubmed(query: str) -> List[Dict]:
+    # Use PubMed e-utilities for search and fetch
+    params = {"db": "pubmed", "term": query, "retmode": "json", "api_key": PUB_KEY, "retmax": 5}
+    async with httpx.AsyncClient() as client:
+        r = await client.get(PUBMED_API, params=params)
+        ids = r.json()["esearchresult"]["idlist"]
+        fetch_params = {"db": "pubmed", "id": ",".join(ids), "retmode": "xml", "api_key": PUB_KEY}
+        rf = await client.get(PUBMED_FETCH, params=fetch_params)
+        # TODO: Parse PubMed XML to get metadata
+    return []
+
+async def fetch_umls(term: str) -> Dict:
+    # Authenticate with UMLS and fetch CUIs for term
+    async with httpx.AsyncClient() as client:
+        auth = await client.post(UMLS_AUTH, data={"apikey": UMLS_API_KEY})
+        ticket = auth.text  # TODO: parse ticket
+        params = {"string": term, "ticket": ticket}
+        search = await client.get(UMLS_SEARCH, params=params)
+        # TODO: Extract CUIs and definitions
+    return {}
+
+async def fetch_openfda(drug: str) -> Dict:
+    params = {"search": f"patient.drug.medicinalproduct:{drug}", "limit": 3}
+    async with httpx.AsyncClient() as client:
+        r = await client.get(OPENFDA_ENDPOINT, params=params)
+        # TODO: Parse adverse event data
+    return {}
+
+async def ai_summarize(text: str) -> str:
+    openai.api_key = OPENAI_API_KEY
+    response = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": "You are an expert biomedical research assistant."},
+            {"role": "user", "content": f"Summarize and synthesize: {text}"}
+        ],
+        max_tokens=256
+    )
+    return response['choices'][0]['message']['content']
+
+# -- Endpoints --
+
+@app.post("/unified_search")
+async def unified_search(query: str):
+    arxiv = await fetch_arxiv(query)
+    pubmed = await fetch_pubmed(query)
+    all_papers = arxiv + pubmed
+    # TODO: Semantic ranking (e.g., use OpenAI embeddings for similarity)
+    # Extract keywords for UMLS/Drug enrichment
+    keywords = []  # TODO: implement keyword extraction (e.g., spaCy)
+    umls_results = [await fetch_umls(k) for k in keywords]
+    drug_data = [await fetch_openfda(k) for k in keywords]
+    summary = await ai_summarize(str(all_papers))
+    return {
+        "papers": all_papers,
+        "umls": umls_results,
+        "drug_safety": drug_data,
+        "ai_summary": summary,
+        "suggested_reading": [p.get('link') for p in all_papers[:3]]
+    }
+
+@app.get("/get_umls_info")
+async def get_umls_info(term: str):
+    result = await fetch_umls(term)
+    return result
+
+@app.get("/fetch_drug_safety")
+async def fetch_drug_safety(drug: str):
+    result = await fetch_openfda(drug)
+    return result
+
+@app.post("/ask_ai")
+async def ask_ai(question: str, context: Optional[str] = None):
+    openai.api_key = OPENAI_API_KEY
+    response = openai.ChatCompletion.create(
+        model="gpt-4o",
+        messages=[
+            {"role": "system", "content": "You are an advanced multi-domain biomedical research agent. Use information from ArXiv, PubMed, OpenFDA, and UMLS when answering."},
+            {"role": "user", "content": f"Question: {question}\nContext: {context or ''}"}
+        ],
+        max_tokens=512
+    )
+    return {"answer": response['choices'][0]['message']['content']}
+
+# -- Extensibility: Plug-and-play new tools via simple Python modules and endpoint registration. --