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