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Added model selected option

PiiMaskingService.py

@@ -9,11 +9,12 @@ from presidio_analyzer.nlp_engine import (
     NlpEngine,
     NlpEngineProvider,
 )
+from presidio_analyzer.nlp_engine import TransformersNlpEngine, NerModelConfiguration
 
 
 class PiiMaskingService():
 
-    def analyze(self, text: str):
+    def analyze(self, text: str, model: str):
 
         entitiesToRecognize=['UK_NHS','EMAIL','AU_ABN','CRYPTO','ID','URL',
                              'AU_MEDICARE','IN_PAN','ORGANIZATION','IN_AADHAAR',
@@ -23,10 +24,12 @@ class PiiMaskingService():
                              'US_SSN','AU_TFN','US_PASSPORT','US_ITIN','NRP','AGE','GENERIC_PII'
                              ]
 
-        a, b= self.create_nlp_engine_with_flair("flair/ner-english-large")
-        print(a)
-        print(b)
-        analyzer = AnalyzerEngine()
+        if model == "HuggingFace/obi/deid_roberta_i2b2":
+            nlp_engine, registry= self.create_nlp_engine_with_transformers("obi/deid_roberta_i2b2")
+        elif model == "flair/ner-english-large":
+            nlp_engine, registry= self.create_nlp_engine_with_flair("flair/ner-english-large")
+
+        analyzer = AnalyzerEngine(nlp_engine=nlp_engine, registry=registry)
 
         results = analyzer.analyze(text=text, entities=entitiesToRecognize, language='en') 
         print("analyzer results:")
@@ -39,6 +42,7 @@ class PiiMaskingService():
             self,
             text: str,
             operator: str,
+            model: str
             # analyze_results: List[RecognizerResult],
         ):
         operator_config = None
@@ -48,7 +52,7 @@ class PiiMaskingService():
             operator_config = {
                 "type": "mask",
                 "masking_char": "*",
-                "chars_to_mask": 10,
+                "chars_to_mask": 15,
                 "from_end": False,
             }
         elif operator == "encrypt":
@@ -61,7 +65,7 @@ class PiiMaskingService():
             operator = "custom"
 
 
-        analyzer_result = self.analyze(text)
+        analyzer_result = self.analyze(text, model)
 
         engine = AnonymizerEngine()