Update app.py

app.py

@@ -20,47 +20,47 @@ def get_analyzer():
     supported_entities = ["CREDIT_CARD","DATE_TIME","EMAIL_ADDRESS","IBAN_CODE","IP_ADDRESS","NRP","LOCATION","PERSON","PHONE_NUMBER","URL","US_BANK_NUMBER","US_DRIVER_LICENSE","US_PASSPORT","US_SSN","US_ITIN"]
     
     # using presidio default recognizer rules
-    # analyzer = AnalyzerEngine()
+    analyzer = AnalyzerEngine()
     
-    #uncomment below to add spacy predefined engines instead of default engine
-    config = {
-        'nlp_engine_name': 'spacy',
-        'models': [
-            {
-                'lang_code': 'en',
-                'model_name': 'en_core_web_sm'
-            },
+    # #uncomment below to add spacy predefined engines instead of default engine
+    # config = {
+    #     'nlp_engine_name': 'spacy',
+    #     'models': [
+    #         {
+    #             'lang_code': 'en',
+    #             'model_name': 'en_core_web_sm'
+    #         },
            
-        ],
-        'ner_model_configuration': {
-            'labels_to_ignore': ['O'],
-            'model_to_presidio_entity_mapping': {
-                'PER': 'PERSON',
-                'LOC': 'LOCATION',
-                'DATE': 'DATE_TIME',
-                'GPE': 'LOCATION',
-              'PERSON': 'PERSON',
-              'TIME': 'DATE_TIME',
-            },
-            # 'low_confidence_score_multiplier': 0.4,
-            # 'low_score_entity_names': ['ID', 'ORG']
-        }
-    }
-
-    # Initialize the NLP engine with the recognizer registry
-    provider = NlpEngineProvider(nlp_configuration=config)
-    nlp_engine = provider.create_engine()
+    #     ],
+    #     'ner_model_configuration': {
+    #         'labels_to_ignore': ['O'],
+    #         'model_to_presidio_entity_mapping': {
+    #             'PER': 'PERSON',
+    #             'LOC': 'LOCATION',
+    #             'DATE': 'DATE_TIME',
+    #             'GPE': 'LOCATION',
+    #           'PERSON': 'PERSON',
+    #           'TIME': 'DATE_TIME',
+    #         },
+    #         # 'low_confidence_score_multiplier': 0.4,
+    #         # 'low_score_entity_names': ['ID', 'ORG']
+    #     }
+    # }
+
+    # # Initialize the NLP engine with the recognizer registry
+    # provider = NlpEngineProvider(nlp_configuration=config)
+    # nlp_engine = provider.create_engine()
     
-    # Create the recognizer registry
-    registry = RecognizerRegistry()
-    registry.load_predefined_recognizers()
+    # # Create the recognizer registry
+    # registry = RecognizerRegistry()
+    # registry.load_predefined_recognizers()
     
-    # Pass the created NLP engine and supported_languages to the AnalyzerEngine
-    analyzer = AnalyzerEngine(
-        nlp_engine=nlp_engine,
-        supported_languages = "en",
-        registry=registry
-    )
+    # # Pass the created NLP engine and supported_languages to the AnalyzerEngine
+    # analyzer = AnalyzerEngine(
+    #     nlp_engine=nlp_engine,
+    #     supported_languages = "en",
+    #     registry=registry
+    # )
     
     # below mis useful when model to presidio mapping are same.
     # Load spaCy model with transformers
@@ -106,20 +106,21 @@ doc = nlp(input_text)
 # # style="dep" indicates dependencies should be generated.
 # dep_svg = displacy.render(doc, style=”dep, jupyter=False)
 # st.image(dep_svg, width=400, use_column_width=’never’)
-
-# Add a section header:
-st.header("Entity visualizer")
-# Take the text from the input field and render the entity html.
-# Note that style="ent" indicates entities.
-ent_html = displacy.render(doc, style="ent", jupyter=False)
-# Display the entity visualization in the browser:
-st.markdown(ent_html, unsafe_allow_html=True)
-
-st.divider()
-
-# Add a section header:
-st.header("Entity Anonymizer")
-# Display the entity visualization in the browser:
-st.markdown(anonym, unsafe_allow_html=True)
+col1, col2 = st.columns(2)
+
+with col1:
+    # Add a section header:
+    st.header("Entity visualizer")
+    # Take the text from the input field and render the entity html.
+    # Note that style="ent" indicates entities.
+    ent_html = displacy.render(doc, style="ent", jupyter=False)
+    # Display the entity visualization in the browser:
+    st.markdown(ent_html, unsafe_allow_html=True)
+
+with col2:
+    # Add a section header:
+    st.header("Entity Anonymizer")
+    # Display the entity visualization in the browser:
+    st.markdown(anonym, unsafe_allow_html=True)