Update mcp/graph_utils.py

mcp/graph_utils.py

@@ -1,69 +1,20 @@
 """
-graph_metrics.py  ·  Lightweight NetworkX helpers for MedGenesis
-
-Key features
-────────────
-• Accepts edge dictionaries in either Streamlit-agraph or PyVis style:
-      {"source": "n1", "target": "n2"}   ← agraph
-      {"from":   "n1", "to":     "n2"}   ← PyVis
-• Silently skips malformed edges (no KeyError).
-• Provides three public helpers:
-      build_nx(nodes, edges)         → networkx.Graph
-      get_top_hubs(G, k=5)           → List[(node_id, degree_centrality)]
-      get_density(G)                 → float  (0–1)
+Minimal NetworkX helpers for MedGenesis graphs.
 """
-
-from __future__ import annotations
-from typing import List, Dict, Tuple
 import networkx as nx
+from typing import List, Dict, Tuple
 
-
-# ────────────────────────────────────────────────────────────────────
-# Internal helpers
-# ────────────────────────────────────────────────────────────────────
-def _edge_ends(e: Dict) -> Tuple[str, str] | None:
-    """Return (src, dst) tuple if both ends exist; else None."""
-    src = e.get("source") or e.get("from")
-    dst = e.get("target") or e.get("to")
-    if src and dst:
-        return src, dst
-    return None
-
-
-# ────────────────────────────────────────────────────────────────────
-# Public API
-# ────────────────────────────────────────────────────────────────────
 def build_nx(nodes: List[Dict], edges: List[Dict]) -> nx.Graph:
-    """
-    Convert agraph / PyVis node+edge dicts into a NetworkX Graph.
-
-    Nodes: must contain "id"   (a unique string)
-    Edges: accepted shapes → {"source":, "target":} or {"from":, "to":}
-    """
     G = nx.Graph()
-
-    # Add nodes with label attribute (used by Metrics tab)
     for n in nodes:
-        G.add_node(n["id"], label=n.get("label", n["id"]))
-
-    # Add edges (skip malformed)
+        G.add_node(n["id"])
     for e in edges:
-        ends = _edge_ends(e)
-        if ends:
-            G.add_edge(*ends)
-
+        G.add_edge(e["source"], e["target"])
     return G
 
+def get_density(G: nx.Graph) -> float:
+    return nx.density(G)
 
 def get_top_hubs(G: nx.Graph, k: int = 5) -> List[Tuple[str, float]]:
-    """
-    Return top-k nodes by degree-centrality.
-    Example output: [('TP53', 0.42), ('EGFR', 0.36), ...]
-    """
     dc = nx.degree_centrality(G)
     return sorted(dc.items(), key=lambda x: x[1], reverse=True)[:k]
-
-
-def get_density(G: nx.Graph) -> float:
-    """Graph density in [0, 1]."""
-    return nx.density(G)