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# mcp/knowledge_graph.py
from streamlit_agraph import Node, Edge, Config
import re
def build_agraph(papers, umls, drug_safety):
"""
Build interactive agraph nodes and edges.
Handles drug_safety entries that may be dict or list.
"""
nodes, edges = [], []
# Add UMLS concept nodes
for c in umls:
cui = c.get("cui")
name = c.get("name", "")
if cui and name:
nid = f"concept_{cui}"
nodes.append(Node(id=nid, label=name, size=25, color="#00b894"))
# Add drug nodes, handling list or dict
drug_names = []
for i, dr in enumerate(drug_safety):
if not dr:
continue
# Normalize to single dict
recs = dr if isinstance(dr, list) else [dr]
for j, rec in enumerate(recs):
# Attempt to extract a drug name
dn = rec.get("drug_name") or rec.get("patient", {}).get("drug", "") or rec.get("medicinalproduct", "")
dn = dn or f"drug_{i}_{j}"
did = f"drug_{i}_{j}"
drug_names.append((did, dn))
nodes.append(Node(id=did, label=dn, size=25, color="#d35400"))
# Add paper nodes and link to concepts & drugs
for pi, p in enumerate(papers):
pid = f"paper_{pi}"
nodes.append(Node(id=pid, label=f"P{pi+1}", tooltip=p["title"], size=15, color="#0984e3"))
text = f"{p.get('title','')} {p.get('summary','')}".lower()
# Link to concepts
for c in umls:
cname = c.get("name", "")
cui = c.get("cui")
if cname and cui and cname.lower() in text:
edges.append(Edge(source=pid, target=f"concept_{cui}", label="mentions"))
# Link to drugs
for did, dn in drug_names:
if dn.lower() in text:
edges.append(Edge(source=pid, target=did, label="mentions"))
config = Config(
width="100%", height="600", directed=False,
nodeHighlightBehavior=True, highlightColor="#f1c40f",
collapsible=True,
node={"labelProperty": "label"}
)
return nodes, edges, config
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