60 lines
1.9 KiB
Python
60 lines
1.9 KiB
Python
"""
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PII detector service (step 2) -- Presidio Analyzer behind a tiny HTTP API.
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CONTRACT: it returns typed FINDINGS (entity type + offsets + the original text),
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it does NOT redact. Fusion owns the mint/splice/vault-write. This is the whole
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"detection != redaction" point from the design discussion.
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Demo scope: tuned to catch the scripted query's entities (a Chinese name + a
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Taiwan ROC national ID). NOT production-grade zh-TW recall -- see README caveats.
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"""
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from fastapi import FastAPI
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from pydantic import BaseModel
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from presidio_analyzer import AnalyzerEngine, PatternRecognizer, Pattern
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from presidio_analyzer.nlp_engine import NlpEngineProvider
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app = FastAPI(title="hncb-deid-detector")
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# spaCy zh model gives us Chinese PERSON detection. See Dockerfile.
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_provider = NlpEngineProvider(nlp_configuration={
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"nlp_engine_name": "spacy",
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"models": [{"lang_code": "zh", "model_name": "zh_core_web_lg"}],
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})
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analyzer = AnalyzerEngine(nlp_engine=_provider.create_engine(), supported_languages=["zh"])
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# Custom recognizer: Taiwan ROC national ID (1 letter + [1|2] + 8 digits).
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roc_id = PatternRecognizer(
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supported_entity="TW_ROC_ID",
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supported_language="zh",
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patterns=[Pattern(name="roc_id", regex=r"\b[A-Z][12]\d{8}\b", score=0.85)],
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context=["身分證", "身份證", "統一編號", "ID"],
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)
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analyzer.registry.add_recognizer(roc_id)
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class AnalyzeRequest(BaseModel):
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text: str
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language: str = "zh"
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@app.get("/health")
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def health():
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return {"status": "ok"}
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@app.post("/analyze")
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def analyze(req: AnalyzeRequest):
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results = analyzer.analyze(text=req.text, language=req.language)
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# Return typed findings incl. the original substring. Fusion tokenizes from this.
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return [
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{
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"entity_type": r.entity_type,
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"start": r.start,
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"end": r.end,
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"score": round(r.score, 3),
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"text": req.text[r.start:r.end],
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}
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for r in results
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]
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