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| from typing import Tuple | |
| import logging | |
| import spacy | |
| from presidio_analyzer import RecognizerRegistry | |
| from presidio_analyzer.nlp_engine import NlpEngine, NlpEngineProvider | |
| logger = logging.getLogger("presidio-streamlit") | |
| def create_nlp_engine_with_spacy( | |
| model_path: str, | |
| ) -> Tuple[NlpEngine, RecognizerRegistry]: | |
| """ | |
| Instantiate an NlpEngine with a spaCy model | |
| :param model_path: spaCy model path. | |
| """ | |
| registry = RecognizerRegistry() | |
| registry.load_predefined_recognizers() | |
| if not spacy.util.is_package(model_path): | |
| spacy.cli.download(model_path) | |
| nlp_configuration = { | |
| "nlp_engine_name": "spacy", | |
| "models": [{"lang_code": "en", "model_name": model_path}], | |
| } | |
| nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() | |
| return nlp_engine, registry | |
| def create_nlp_engine_with_transformers( | |
| model_path: str, | |
| ) -> Tuple[NlpEngine, RecognizerRegistry]: | |
| """ | |
| Instantiate an NlpEngine with a TransformersRecognizer and a small spaCy model. | |
| The TransformersRecognizer would return results from Transformers models, the spaCy model | |
| would return NlpArtifacts such as POS and lemmas. | |
| :param model_path: HuggingFace model path. | |
| """ | |
| from transformers_rec import ( | |
| STANFORD_COFIGURATION, | |
| BERT_DEID_CONFIGURATION, | |
| TransformersRecognizer, | |
| ) | |
| registry = RecognizerRegistry() | |
| registry.load_predefined_recognizers() | |
| if not spacy.util.is_package("en_core_web_sm"): | |
| spacy.cli.download("en_core_web_sm") | |
| # Using a small spaCy model + a HF NER model | |
| transformers_recognizer = TransformersRecognizer(model_path=model_path) | |
| if model_path == "StanfordAIMI/stanford-deidentifier-base": | |
| transformers_recognizer.load_transformer(**STANFORD_COFIGURATION) | |
| elif model_path == "obi/deid_roberta_i2b2": | |
| transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION) | |
| else: | |
| print(f"Warning: Model has no configuration, loading default.") | |
| transformers_recognizer.load_transformer(**BERT_DEID_CONFIGURATION) | |
| # Use small spaCy model, no need for both spacy and HF models | |
| # The transformers model is used here as a recognizer, not as an NlpEngine | |
| nlp_configuration = { | |
| "nlp_engine_name": "spacy", | |
| "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], | |
| } | |
| registry.add_recognizer(transformers_recognizer) | |
| registry.remove_recognizer("SpacyRecognizer") | |
| nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() | |
| return nlp_engine, registry | |
| def create_nlp_engine_with_flair( | |
| model_path: str, | |
| ) -> Tuple[NlpEngine, RecognizerRegistry]: | |
| """ | |
| Instantiate an NlpEngine with a FlairRecognizer and a small spaCy model. | |
| The FlairRecognizer would return results from Flair models, the spaCy model | |
| would return NlpArtifacts such as POS and lemmas. | |
| :param model_path: Flair model path. | |
| """ | |
| from flair_recognizer import FlairRecognizer | |
| registry = RecognizerRegistry() | |
| registry.load_predefined_recognizers() | |
| if not spacy.util.is_package("en_core_web_sm"): | |
| spacy.cli.download("en_core_web_sm") | |
| # Using a small spaCy model + a Flair NER model | |
| flair_recognizer = FlairRecognizer(model_path=model_path) | |
| nlp_configuration = { | |
| "nlp_engine_name": "spacy", | |
| "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], | |
| } | |
| registry.add_recognizer(flair_recognizer) | |
| registry.remove_recognizer("SpacyRecognizer") | |
| nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() | |
| return nlp_engine, registry | |
| def create_nlp_engine_with_azure_text_analytics(ta_key: str, ta_endpoint: str): | |
| """ | |
| Instantiate an NlpEngine with a TextAnalyticsWrapper and a small spaCy model. | |
| The TextAnalyticsWrapper would return results from calling Azure Text Analytics PII, the spaCy model | |
| would return NlpArtifacts such as POS and lemmas. | |
| :param ta_key: Azure Text Analytics key. | |
| :param ta_endpoint: Azure Text Analytics endpoint. | |
| """ | |
| from text_analytics_wrapper import TextAnalyticsWrapper | |
| if not ta_key or not ta_endpoint: | |
| raise RuntimeError("Please fill in the Text Analytics endpoint details") | |
| registry = RecognizerRegistry() | |
| registry.load_predefined_recognizers() | |
| ta_recognizer = TextAnalyticsWrapper(ta_endpoint=ta_endpoint, ta_key=ta_key) | |
| nlp_configuration = { | |
| "nlp_engine_name": "spacy", | |
| "models": [{"lang_code": "en", "model_name": "en_core_web_sm"}], | |
| } | |
| nlp_engine = NlpEngineProvider(nlp_configuration=nlp_configuration).create_engine() | |
| registry.add_recognizer(ta_recognizer) | |
| registry.remove_recognizer("SpacyRecognizer") | |
| return nlp_engine, registry | |