"""
PII detector service (step 2)  --  Presidio Analyzer behind a tiny HTTP API.

CONTRACT: it returns typed FINDINGS (entity type + offsets + the original text),
it does NOT redact. Fusion owns the mint/splice/vault-write. This is the whole
"detection != redaction" point from the design discussion.

Demo scope: tuned to catch the scripted query's entities (a Chinese name + a
Taiwan ROC national ID). NOT production-grade zh-TW recall -- see README caveats.
"""
from fastapi import FastAPI
from pydantic import BaseModel
from presidio_analyzer import AnalyzerEngine, PatternRecognizer, Pattern
from presidio_analyzer.nlp_engine import NlpEngineProvider

app = FastAPI(title="hncb-deid-detector")


# spaCy zh model gives us Chinese PERSON detection. See Dockerfile.
_provider = NlpEngineProvider(nlp_configuration={
    "nlp_engine_name": "spacy",
    "models": [{"lang_code": "zh", "model_name": "zh_core_web_lg"}],
})
analyzer = AnalyzerEngine(nlp_engine=_provider.create_engine(), supported_languages=["zh"])

# Custom recognizer: Taiwan ROC national ID  (1 letter + [1|2] + 8 digits).
roc_id = PatternRecognizer(
    supported_entity="TW_ROC_ID",
    supported_language="zh",
    patterns=[Pattern(name="roc_id", regex=r"\b[A-Z][12]\d{8}\b", score=0.85)],
    context=["身分證", "身份證", "統一編號", "ID"],
)
analyzer.registry.add_recognizer(roc_id)


class AnalyzeRequest(BaseModel):
    text: str
    language: str = "zh"


@app.get("/health")
def health():
    return {"status": "ok"}


@app.post("/analyze")
def analyze(req: AnalyzeRequest):
    results = analyzer.analyze(text=req.text, language=req.language)
    # Return typed findings incl. the original substring. Fusion tokenizes from this.
    return [
        {
            "entity_type": r.entity_type,
            "start": r.start,
            "end": r.end,
            "score": round(r.score, 3),
            "text": req.text[r.start:r.end],
        }
        for r in results
    ]