"""NER tests with a fake GLiNER predictor (no torch/model download)."""

from __future__ import annotations

from fastapi.testclient import TestClient

from extractors.app import app
from extractors.features import ner


class FakePredictor:
    """Mimics GLiNER.predict_entities with canned spans."""

    def __init__(self, ents):
        self._ents = ents

    def predict_entities(self, text, labels, threshold=0.5):
        return self._ents


_SAMPLE = [
    {"text": "Klaus Iohannis", "label": "persoană", "start": 0, "end": 14, "score": 0.97},
    {"text": "Guvernul României", "label": "instituție publică", "start": 20,
     "end": 37, "score": 0.91},
]


def test_ner_parses_and_sorts():
    res = ner.analyze("Klaus Iohannis și Guvernul României.", model=FakePredictor(_SAMPLE))
    assert res.ok is True
    assert res.results["count"] == 2
    assert res.results["entities"][0]["text"] == "Klaus Iohannis"
    assert res.results["by_label"]["persoană"] == 1
    assert "persoană×1" in res.evidence[0]


def test_ner_empty_entities():
    res = ner.analyze("text neutru", model=FakePredictor([]))
    assert res.ok is True
    assert res.results["count"] == 0
    assert "No entities" in res.evidence[0]


def test_ner_endpoint_503_without_ml_extra():
    # gliner/torch are not installed in the fast (dev) environment.
    client = TestClient(app)
    r = client.post("/v1/ner", json={"text": "Klaus Iohannis"})
    assert r.status_code == 503


def test_ner_endpoint_with_monkeypatched_model(monkeypatch):
    monkeypatch.setattr(ner, "get_model", lambda: FakePredictor(_SAMPLE))
    client = TestClient(app)
    r = client.post("/v1/ner", json={"text": "Klaus Iohannis și Guvernul României."})
    assert r.status_code == 200
    body = r.json()
    assert body["results"]["count"] == 2
    assert body["results"]["entities"][1]["label"] == "instituție publică"