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import os |
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from typing import List, Optional |
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import dotenv |
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from azure.ai.textanalytics import TextAnalyticsClient |
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from azure.core.credentials import AzureKeyCredential |
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from presidio_analyzer import EntityRecognizer, RecognizerResult, AnalysisExplanation |
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from presidio_analyzer.nlp_engine import NlpArtifacts |
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class TextAnalyticsWrapper(EntityRecognizer): |
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from azure.ai.textanalytics._models import PiiEntityCategory |
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TA_SUPPORTED_ENTITIES = [r.value for r in PiiEntityCategory] |
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def __init__( |
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self, |
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supported_entities: Optional[List[str]] = None, |
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supported_language: str = "en", |
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ta_client: Optional[TextAnalyticsClient] = None, |
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ta_key: Optional[str] = None, |
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ta_endpoint: Optional[str] = None, |
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): |
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""" |
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Wrapper for the Azure Text Analytics client |
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:param ta_client: object of type TextAnalyticsClient |
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:param ta_key: Azure cognitive Services for Language key |
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:param ta_endpoint: Azure cognitive Services for Language endpoint |
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""" |
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if not supported_entities: |
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supported_entities = self.TA_SUPPORTED_ENTITIES |
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super().__init__( |
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supported_entities=supported_entities, |
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supported_language=supported_language, |
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name="Azure Text Analytics PII", |
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) |
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self.ta_key = ta_key |
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self.ta_endpoint = ta_endpoint |
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if not ta_client: |
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ta_client = self.__authenticate_client(ta_key, ta_endpoint) |
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self.ta_client = ta_client |
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@staticmethod |
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def __authenticate_client(key: str, endpoint: str): |
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ta_credential = AzureKeyCredential(key) |
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text_analytics_client = TextAnalyticsClient( |
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endpoint=endpoint, credential=ta_credential |
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) |
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return text_analytics_client |
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def analyze( |
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self, text: str, entities: List[str] = None, nlp_artifacts: NlpArtifacts = None |
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) -> List[RecognizerResult]: |
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if not entities: |
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entities = [] |
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response = self.ta_client.recognize_pii_entities( |
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[text], language=self.supported_language |
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) |
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results = [doc for doc in response if not doc.is_error] |
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recognizer_results = [] |
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for res in results: |
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for entity in res.entities: |
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if entity.category not in self.supported_entities: |
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continue |
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analysis_explanation = TextAnalyticsWrapper._build_explanation( |
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original_score=entity.confidence_score, |
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entity_type=entity.category, |
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) |
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recognizer_results.append( |
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RecognizerResult( |
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entity_type=entity.category, |
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start=entity.offset, |
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end=entity.offset + len(entity.text), |
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score=entity.confidence_score, |
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analysis_explanation=analysis_explanation, |
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) |
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) |
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return recognizer_results |
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@staticmethod |
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def _build_explanation( |
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original_score: float, entity_type: str |
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) -> AnalysisExplanation: |
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explanation = AnalysisExplanation( |
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recognizer=TextAnalyticsWrapper.__class__.__name__, |
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original_score=original_score, |
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textual_explanation=f"Identified as {entity_type} by Text Analytics", |
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) |
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return explanation |
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def load(self) -> None: |
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pass |
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if __name__ == "__main__": |
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import presidio_helpers |
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dotenv.load_dotenv() |
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text = """ |
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Here are a few example sentences we currently support: |
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Hello, my name is David Johnson and I live in Maine. |
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My credit card number is 4095-2609-9393-4932 and my crypto wallet id is 16Yeky6GMjeNkAiNcBY7ZhrLoMSgg1BoyZ. |
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On September 18 I visited microsoft.com and sent an email to test@presidio.site, from the IP 192.168.0.1. |
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My passport: 191280342 and my phone number: (212) 555-1234. |
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This is a valid International Bank Account Number: IL150120690000003111111 . Can you please check the status on bank account 954567876544? |
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Kate's social security number is 078-05-1126. Her driver license? it is 1234567A. |
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""" |
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analyzer = presidio_helpers.analyzer_engine( |
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model_path="Azure Text Analytics PII", |
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ta_key=os.environ["TA_KEY"], |
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ta_endpoint=os.environ["TA_ENDPOINT"], |
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) |
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analyzer.analyze(text=text, language="en") |
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