Update mcp/knowledge_graph.py
Browse files- mcp/knowledge_graph.py +127 -49
mcp/knowledge_graph.py
CHANGED
@@ -1,62 +1,140 @@
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from typing import List, Tuple
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def
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"""
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def build_agraph(
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papers:
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umls:
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drug_safety:
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) -> Tuple[List[Node], List[Edge], Config]:
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cfg = Config(
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width="100%",
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collapsible=True,
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)
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return nodes, edges, cfg
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# mcp/knowledge_graph.py
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"""
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Build agraph-compatible nodes + edges for the MedGenesis UI.
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Robustness notes
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----------------
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* Accepts *any* iterable for ``papers``, ``umls``, ``drug_safety``.
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* Silently skips items that are **not** dictionaries or have missing keys.
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* Normalises drug-safety payloads that may arrive as dict **or** list.
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* Always casts labels to string β avoids ``None.lower()`` errors.
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"""
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from __future__ import annotations
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import re
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from typing import List, Tuple
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from streamlit_agraph import Node, Edge, Config
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# ββ helpers -----------------------------------------------------------------
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def _safe_str(x) -> str:
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"""Return UTF-8 string or empty string."""
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return str(x) if x is not None else ""
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def _uniquify(nodes: List[Node]) -> List[Node]:
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"""Remove duplicate node-ids (keep first)."""
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seen, out = set(), []
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for n in nodes:
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if n.id not in seen:
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out.append(n)
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seen.add(n.id)
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return out
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# ββ public builder ----------------------------------------------------------
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def build_agraph(
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papers: list,
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umls: list,
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drug_safety: list,
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) -> Tuple[List[Node], List[Edge], Config]:
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"""
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Parameters
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----------
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papers : List[dict]
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Must contain keys ``title``, ``summary``.
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umls : List[dict]
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Dicts with at least ``name`` and ``cui``.
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drug_safety : List[dict | list]
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OpenFDA records β could be one dict or list of dicts.
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Returns
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-------
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nodes, edges, cfg : tuple
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Ready for ``streamlit_agraph.agraph``.
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"""
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nodes: List[Node] = []
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edges: List[Edge] = []
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# ββ UMLS concepts -------------------------------------------------------
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for c in umls:
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if not isinstance(c, dict):
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continue
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cui = _safe_str(c.get("cui")).strip()
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name = _safe_str(c.get("name")).strip()
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if not (cui and name):
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continue
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nodes.append(
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Node(id=f"concept_{cui}", label=name, size=28, color="#00b894")
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)
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# ββ Drug safety --------------------------------------------------------
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drug_nodes: List[Tuple[str, str]] = []
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for idx, rec in enumerate(drug_safety):
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if not rec:
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continue
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recs = rec if isinstance(rec, list) else [rec]
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for j, r in enumerate(recs):
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if not isinstance(r, dict):
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continue
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dn = (
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r.get("drug_name")
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or r.get("patient", {}).get("drug")
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or r.get("medicinalproduct")
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)
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dn = _safe_str(dn).strip() or f"drug_{idx}_{j}"
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did = f"drug_{idx}_{j}"
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drug_nodes.append((did, dn))
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nodes.append(Node(id=did, label=dn, size=25, color="#d35400"))
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# ββ Papers & edges ------------------------------------------------------
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for p_idx, p in enumerate(papers):
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if not isinstance(p, dict):
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continue
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pid = f"paper_{p_idx}"
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title = _safe_str(p.get("title"))
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summary = _safe_str(p.get("summary"))
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nodes.append(
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Node(
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id=pid,
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label=f"P{p_idx + 1}",
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tooltip=title,
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size=16,
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color="#0984e3",
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)
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)
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text_blob = f"{title} {summary}".lower()
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# β concept edges
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for c in umls:
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if not isinstance(c, dict):
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continue
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name = _safe_str(c.get("name")).lower()
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cui = _safe_str(c.get("cui"))
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if name and cui and name in text_blob:
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edges.append(
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Edge(source=pid, target=f"concept_{cui}", label="mentions")
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)
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# β drug edges
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for did, dn in drug_nodes:
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if dn.lower() in text_blob:
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edges.append(Edge(source=pid, target=did, label="mentions"))
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# ββ deduplicate & config ------------------------------------------------
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nodes = _uniquify(nodes)
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cfg = Config(
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width="100%",
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height="600px",
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directed=False,
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nodeHighlightBehavior=True,
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highlightColor="#f1c40f",
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collapsible=True,
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node={"labelProperty": "label"},
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)
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return nodes, edges, cfg
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