pii.py

from pprint import pprint
import json

from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
from presidio_analyzer.nlp_engine import NlpEngineProvider, NlpArtifacts
from presidio_analyzer import PatternRecognizer
from presidio_analyzer import Pattern, PatternRecognizer
from presidio_analyzer.predefined_recognizers import SpacyRecognizer
from presidio_analyzer.predefined_recognizers import IbanRecognizer, EmailRecognizer, IpRecognizer,\
EmailRecognizer, PhoneRecognizer, UrlRecognizer, DateRecognizer

from presidio_anonymizer import AnonymizerEngine
from presidio_anonymizer.entities import OperatorConfig

import logging
from typing import Optional, List, Tuple, Set
from presidio_analyzer import (
    RecognizerResult,
    EntityRecognizer,
    AnalysisExplanation,
)

from flair.data import Sentence
from flair.models import SequenceTagger

### Creating FlairRecognizer class for NER(names, location)

class FlairRecognizer(EntityRecognizer):
   
    ENTITIES = [
        "LOCATION",
        "PERSON",
        "ORGANIZATION",
        # "MISCELLANEOUS"   # - There are no direct correlation with Presidio entities.
    ]

    DEFAULT_EXPLANATION = "Identified as {} by Flair's Named Entity Recognition"

    CHECK_LABEL_GROUPS = [
        ({"LOCATION"}, {"LOC", "LOCATION"}),
        ({"PERSON"}, {"PER", "PERSON"}),
        ({"ORGANIZATION"}, {"ORG"}),
        # ({"MISCELLANEOUS"}, {"MISC"}), # Probably not PII
    ]

    MODEL_LANGUAGES = {
        #"en": "flair/ner-english-large",
        #"es": "flair/ner-spanish-large",
        "de": "flair/ner-german-large",
        #"nl": "flair/ner-dutch-large",
    }

    PRESIDIO_EQUIVALENCES = {
        "PER": "PERSON",
        "LOC": "LOCATION",
        "ORG": "ORGANIZATION",
        # 'MISC': 'MISCELLANEOUS'   # - Probably not PII
    }

    def __init__(
        self,
        supported_language: str = "de",
        supported_entities: Optional[List[str]] = None,
        check_label_groups: Optional[Tuple[Set, Set]] = None,
        model: SequenceTagger = None,
    ):
        self.check_label_groups = (
            check_label_groups if check_label_groups else self.CHECK_LABEL_GROUPS
        )

        supported_entities = supported_entities if supported_entities else self.ENTITIES
        self.model = (
            model
            if model
            else SequenceTagger.load(self.MODEL_LANGUAGES.get(supported_language))
        )

        super().__init__(
            supported_entities=supported_entities,
            supported_language=supported_language,
            name="Flair Analytics",
        )
        print("Flair class initialized")

    def load(self) -> None:
        """Load the model, not used. Model is loaded during initialization."""
        pass

    def get_supported_entities(self) -> List[str]:
        """
        Return supported entities by this model.

        :return: List of the supported entities.
        """
        return self.supported_entities

    # Class to use Flair with Presidio as an external recognizer.
    def analyze(
        self, text: str, entities: List[str], nlp_artifacts: NlpArtifacts = None
    ) -> List[RecognizerResult]:
        """
        Analyze text using Text Analytics.

        :param text: The text for analysis.
        :param entities: Not working properly for this recognizer.
        :param nlp_artifacts: Not used by this recognizer.
        :param language: Text language. Supported languages in MODEL_LANGUAGES
        :return: The list of Presidio RecognizerResult constructed from the recognized
            Flair detections.
        """

        results = []

        sentences = Sentence(text)
        self.model.predict(sentences)

        # If there are no specific list of entities, we will look for all of it.
        if not entities:
            entities = self.supported_entities

        for entity in entities:
            if entity not in self.supported_entities:
                continue

            for ent in sentences.get_spans("ner"):
                if not self.__check_label(
                    entity, ent.labels[0].value, self.check_label_groups
                ):
                    continue
                textual_explanation = self.DEFAULT_EXPLANATION.format(
                    ent.labels[0].value
                )
                explanation = self.build_flair_explanation(
                    round(ent.score, 2), textual_explanation
                )
                flair_result = self._convert_to_recognizer_result(ent, explanation)

                results.append(flair_result)

        return results

    def _convert_to_recognizer_result(self, entity, explanation) -> RecognizerResult:

        entity_type = self.PRESIDIO_EQUIVALENCES.get(entity.tag, entity.tag)
        flair_score = round(entity.score, 2)

        flair_results = RecognizerResult(
            entity_type=entity_type,
            start=entity.start_position,
            end=entity.end_position,
            score=flair_score,
            analysis_explanation=explanation,
        )

        return flair_results

    def build_flair_explanation(
        self, original_score: float, explanation: str
    ) -> AnalysisExplanation:
        """
        Create explanation for why this result was detected.

        :param original_score: Score given by this recognizer
        :param explanation: Explanation string
        :return:
        """
        explanation = AnalysisExplanation(
            recognizer=self.__class__.__name__,
            original_score=original_score,
            textual_explanation=explanation,
        )
        return explanation

    @staticmethod
    def __check_label(
        entity: str, label: str, check_label_groups: Tuple[Set, Set]
    ) -> bool:
        return any(
            [entity in egrp and label in lgrp for egrp, lgrp in check_label_groups]
        )
    

class PII_IDENTIFIER:
    def __init__(self):

        configuration = {
            "nlp_engine_name": "spacy",
            "models": [
                {"lang_code": "de", "model_name": "de_core_news_sm"}
            ],
        }

        # Create NLP engine based on configuration
        provider = NlpEngineProvider(nlp_configuration=configuration)
        nlp_engine = provider.create_engine()

        ## Creating regex for PatternRecognizers - SWIFT, vehicle number, zipcode, ssn
        swift_regex = r"\b[A-Z]{4}DE[A-Z0-9]{2}(?:[A-Z0-9]{3})?"
        vehicle_number_with_hyphen_regex = r"\b[A-ZÄÖÜ]{1,3}-[A-ZÄÖÜ]{1,2}-[0-9]{1,4}" 
        vehicle_number_without_hyphen_regex = r"\b[A-ZÄÖÜ]{1,3}[A-ZÄÖÜ]{1,2}[0-9]{1,4}"
        german_zipcode_regex = r"\b((?:0[1-46-9]\d{3})|(?:[1-357-9]\d{4})|(?:[4][0-24-9]\d{3})|(?:[6][013-9]\d{3}))\b(?![\d/])"
        german_ssn_regex = r"\b\d{2}\s?\d{6}\s?[A-Z]\s?\d{3}\b"
        # Creating Presidio pattern object
        vehicle_numbers_pattern1 = Pattern(name="vehicle_pattern", regex=vehicle_number_without_hyphen_regex, score=1)
        vehicle_numbers_pattern2 = Pattern(name="vehicle_pattern", regex=vehicle_number_with_hyphen_regex, score=1)
        swift_pattern = Pattern(name="bank_swift_pattern", regex=swift_regex, score=1)
        germanzipcode_pattern = Pattern(name="german_zip_pattern",regex=german_zipcode_regex, score=1)
        german_ssn_pattern = Pattern(name="german_ssn_pattern",regex=german_ssn_regex, score=1)

        # Define the recognizer
        swift_recognizer = PatternRecognizer(supported_entity="SWIFT", supported_language="de",patterns=[swift_pattern])
        vehicle_number_recognizer = PatternRecognizer(supported_entity="VEHICLE_NUMBER", supported_language="de",patterns=[vehicle_numbers_pattern1,vehicle_numbers_pattern2])
        germanzip_recognizer = PatternRecognizer(supported_entity="GERMAN_ZIP", supported_language="de",patterns=[germanzipcode_pattern])
        germanssn_recognizer = PatternRecognizer(supported_entity="GERMAN_SSN", supported_language="de",patterns=[german_ssn_pattern])

        ## Lading flair entity model for person, location ID
        print("Loading flair")
        flair_recognizer = FlairRecognizer(supported_language="de")
        print("Flair loaded")

        registry = RecognizerRegistry(supported_languages=["de"])
        #registry.load_predefined_recognizers()
        #registry.add_recognizer(SpacyRecognizer(supported_language="de"))
        #registry.add_recognizer(SpacyRecognizer(supported_language="en"))

        registry.remove_recognizer("SpacyRecognizer")
        registry.add_recognizer(flair_recognizer)

        registry.add_recognizer(swift_recognizer)
        registry.add_recognizer(vehicle_number_recognizer)
        registry.add_recognizer(germanzip_recognizer)
        registry.add_recognizer(germanssn_recognizer)

        ## Adding predefined recognizers
        registry.add_recognizer(IbanRecognizer(supported_language="de"))
        registry.add_recognizer(DateRecognizer(supported_language="de"))
        registry.add_recognizer(EmailRecognizer(supported_language="de"))
        registry.add_recognizer(IpRecognizer(supported_language="de"))
        registry.add_recognizer(PhoneRecognizer(supported_language="de"))
        registry.add_recognizer(UrlRecognizer(supported_language="de"))
        registry.add_recognizer(PhoneRecognizer(supported_language="de"))
        print("Recognizer registry loaded")

        self.analyzer = AnalyzerEngine(registry=registry, nlp_engine=nlp_engine, supported_languages=["de"])

        #print(f"Type of recognizers ::\n {self.analyzer.registry.recognizers}")
        print("PII initialized")

        self.anonymizer = AnonymizerEngine()
    
    def identify(self, text):
        results_de = self.analyzer.analyze(
            text,
            language='de'
        )

        anonymized_results = self.anonymize(results_de, text)
        entities = []

        for result in results_de:
            result_dict = result.to_dict()
            temp_entity = {
                "start":result_dict['start'],
                "end":result_dict['end'],
                "entity_type":result_dict['entity_type'],
                "score":result_dict['score'],
                "word":text[result_dict['start']:result_dict['end']]
            }
            entities.append(temp_entity)
        
        return {"entities":entities, "text":text, "anonymized_text":anonymized_results['text']}
    
    def anonymize(self, entities, text):
        anonymized_results = self.anonymizer.anonymize(
            text=text,
            analyzer_results=entities,
            #operators={"DEFAULT": OperatorConfig("replace", {"new_value": "<ANONYMIZED>"})},
        )

        return json.loads(anonymized_results.to_json())
    
    def remove_overlapping_entities(entities):

        return