"""Layer 2 — NER-based PII detection using Presidio + spaCy.

Detects PERSON, LOCATION, ORGANIZATION entities.
The spaCy model is loaded lazily on first access and cached.
"""

from __future__ import annotations

import logging
from typing import TYPE_CHECKING

from layers.regex_layer import DetectedEntity

if TYPE_CHECKING:
    from presidio_analyzer import AnalyzerEngine  # noqa: F401 — type hint only

logger = logging.getLogger(__name__)

# Presidio entity type → internal type mapping
_ENTITY_TYPE_MAP = {
    "PERSON": "PERSON",
    "LOCATION": "LOCATION",
    "ORGANIZATION": "ORGANIZATION",
}

# Presidio entities to request
_PRESIDIO_ENTITIES = list(_ENTITY_TYPE_MAP.keys())


class NERLayer:
    """Detect named entities using Presidio backed by spaCy fr_core_news_lg."""

    def __init__(self, fr_model: str = "fr_core_news_lg", en_model: str = "en_core_web_sm") -> None:
        self._fr_model = fr_model
        self._en_model = en_model
        self._analyzer: "AnalyzerEngine | None" = None

    @property
    def analyzer(self) -> "AnalyzerEngine":
        if self._analyzer is None:
            self._analyzer = self._build_analyzer()
        return self._analyzer

    @property
    def is_loaded(self) -> bool:
        return self._analyzer is not None

    def warm_up(self) -> None:
        """Force model loading — call at service startup to avoid cold-start latency."""
        _ = self.analyzer
        logger.info("NER model loaded: %s", self._fr_model)

    def detect(self, text: str, confidence_threshold: float = 0.85) -> list[DetectedEntity]:
        """Return NER entities found in *text* above *confidence_threshold*."""
        try:
            results = self.analyzer.analyze(
                text=text,
                entities=_PRESIDIO_ENTITIES,
                language="fr",
                score_threshold=confidence_threshold,
            )
        except Exception:
            # Fall back to English if French analysis fails
            try:
                results = self.analyzer.analyze(
                    text=text,
                    entities=_PRESIDIO_ENTITIES,
                    language="en",
                    score_threshold=confidence_threshold,
                )
            except Exception:
                logger.exception("NER analysis failed")
                return []

        entities = []
        for r in results:
            internal_type = _ENTITY_TYPE_MAP.get(r.entity_type)
            if internal_type is None:
                continue
            entities.append(
                DetectedEntity(
                    entity_type=internal_type,
                    original_value=text[r.start : r.end],
                    start=r.start,
                    end=r.end,
                    confidence=r.score,
                    detection_layer="ner",
                )
            )
        return entities

    # -----------------------------------------------------------------------
    # Internal
    # -----------------------------------------------------------------------

    def _build_analyzer(self) -> "AnalyzerEngine":
        from presidio_analyzer import AnalyzerEngine
        from presidio_analyzer.nlp_engine import NerModelConfiguration, SpacyNlpEngine

        # The default NerModelConfiguration incorrectly places 'ORG' and 'ORGANIZATION'
        # in labels_to_ignore, which prevents ORGANIZATION entities from being returned.
        # We build a custom configuration that keeps those labels.
        default_cfg = NerModelConfiguration()
        custom_labels_to_ignore = default_cfg.labels_to_ignore - {"ORG", "ORGANIZATION"}

        ner_config = NerModelConfiguration(
            model_to_presidio_entity_mapping=default_cfg.model_to_presidio_entity_mapping,
            low_score_entity_names=default_cfg.low_score_entity_names,
            labels_to_ignore=custom_labels_to_ignore,
        )

        nlp_engine = SpacyNlpEngine(
            models=[
                {"lang_code": "fr", "model_name": self._fr_model},
                {"lang_code": "en", "model_name": self._en_model},
            ],
            ner_model_configuration=ner_config,
        )
        return AnalyzerEngine(nlp_engine=nlp_engine, supported_languages=["fr", "en"])