# mcp/nlp.py

#!/usr/bin/env python3
"""MedGenesis – spaCy helper for lightweight keyword extraction.

Features
~~~~~~~~
* Lazy‑loads **`en_core_web_sm`** at first call; cached thereafter.
* If model missing, raises actionable RuntimeError — Dockerfile must
  install via `python -m spacy download en_core_web_sm` (already in Dockerfile).
* `extract_keywords` returns **unique named‑entity strings** (>2 chars)
  stripped of whitespace, preserving original casing.
* Adds fallback to simple noun‑chunk extraction when no entities found –
  helps very short abstracts.
"""
from __future__ import annotations

import spacy
from functools import lru_cache
from typing import List


# ---------------------------------------------------------------------
# Model loader (cached)
# ---------------------------------------------------------------------

@lru_cache(maxsize=1)
def _load_model():
    try:
        return spacy.load("en_core_web_sm")
    except OSError as e:
        raise RuntimeError(
            "spaCy model 'en_core_web_sm' is not installed. Add\n"
            "    RUN python -m spacy download en_core_web_sm\n"
            "to your Dockerfile build stage."
        ) from e


# ---------------------------------------------------------------------
# Public API
# ---------------------------------------------------------------------

def extract_keywords(text: str, *, min_len: int = 3) -> List[str]:
    """Return de‑duplicated entity keywords (fallback noun chunks)."""
    nlp = _load_model()
    doc = nlp(text)

    ents = {ent.text.strip() for ent in doc.ents if len(ent.text.strip()) >= min_len}
    if ents:
        return list(ents)

    # Fallback: noun chunks if spaCy found no entities (rare for tiny texts)
    chunks = {chunk.text.strip() for chunk in doc.noun_chunks if len(chunk.text.strip()) >= min_len}
    return list(chunks)