Update app.py

app.py

@@ -1,98 +1,98 @@
-import streamlit as st 
-import spacy
-from spacy import displacy
-from transformers import AutoTokenizer, AutoModelForTokenClassification
-from transformers import pipeline
-import pandas as pd
-
-
-st.title('Named Entity Recognizer')
-
-st.write('Named Entity Recognition (NER) is like a smart highlighter that scans through text and highlights important words, such as people’s names, places, companies, and dates. It’s a tool used in language processing that helps computers understand and organize chunks of text by picking out these key terms and saying, “Hey, this word is a location, and this one is a person’s name.” By doing this, NER helps turn messy, unstructured text into neat, organized data that’s easier for machines to understand and work with. It’s like teaching computers to read a map of words, so they can help us find the information we need much faster.')
-
-st.write('')
-
-with st.form(key='form_named_entity_recognition'):
-
-    input_from_user = st.text_area('enter your input')
-
-    model_options = st.selectbox('choose a model', ('Choose a model', 'Spacy\'s en_core_web_sm model', 'dslim/bert-base-NER model'))
-
-    submit_button = st.form_submit_button('Submit')
-
-
-if submit_button:
-
-    if input_from_user == '':
-
-        st.error('empty form submitted')
-    
-    else:
-
-        if model_options == 'Choose a model':
-
-            st.error('Please choose a model for named entity recognition')
-    
-        else:
-
-            st.subheader('Result Analysis')
-
-            if model_options == 'Choose a model':
-
-                st.error('Please choose a model for Named Entity Recognition')
-
-            elif model_options == 'Spacy\'s en_core_web_sm model':
-
-                st.write('Model Used for Named Entity Recognition:')
-                st.success(model_options)
-
-                spacy_model = spacy.load('en_core_web_sm')
-
-                res = spacy_model(input_from_user)
-
-                st.write(f'Analysis of the detected entities from the text ==>')
-                st.markdown(f'**{input_from_user}**')
-                
-                entities = [{'Entity': entity.text, 'Label of the Entity': entity.label_, 'Description of the Label': spacy.explain(entity.label_)} for entity in res.ents]
-
-                df = pd.DataFrame(entities)
-
-                st.table(df)
-
-                st.write('Entites marked in the input text:')
-                st.markdown(displacy.render(res, style='ent'), unsafe_allow_html=True)
-
-            elif model_options == 'dslim/bert-base-NER model':
-
-                st.write('Model Used for Named Entity Recognition:')
-                st.success(model_options)
-                
-                tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
-                model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
-
-                bert_ner_model = pipeline('ner', model=model, tokenizer=tokenizer)
-
-                res = bert_ner_model(input_from_user)
-
-                abbreviations = {
-                    "O": "Outside of a named entity",
-                    "B-MISC": "Beginning of a miscellaneous entity right after another miscellaneous entity",
-                    "I-MISC": "Miscellaneous entity",
-                    "B-PER": "Beginning of a person’s name right after another person’s name",
-                    "I-PER": "Person’s name",
-                    "B-ORG": "Beginning of an organization right after another organization",
-                    "I-ORG": "Organization",
-                    "B-LOC": "Beginning of a location right after another location",
-                    "I-LOC": "Location"
-                }
-
-                st.write(f'Analysis of the detected entities from the text ==>')
-                st.markdown(f'**{input_from_user}**')
-                
-                entities = [{'Entity': input_from_user[entity['start']:entity['end']], 'Label of the Entity': entity['entity'], 'Description of the Label': abbreviations.get(entity['entity'])} for entity in res]
-
-                df = pd.DataFrame(entities)
-
-                st.table(df)
-    
-
+import streamlit as st 
+import spacy
+from spacy import displacy
+from transformers import AutoTokenizer, AutoModelForTokenClassification
+from transformers import pipeline
+import pandas as pd
+
+
+st.title('Named Entity Recognizer')
+
+st.write('Named Entity Recognition (NER) is like a smart highlighter that scans through text and highlights important words, such as people’s names, places, companies, and dates.')
+
+st.write('')
+
+with st.form(key='form_named_entity_recognition'):
+
+    input_from_user = st.text_area('enter your input')
+
+    model_options = st.selectbox('choose a model', ('Choose a model', 'Spacy\'s en_core_web_sm model', 'dslim/bert-base-NER model'))
+
+    submit_button = st.form_submit_button('Submit')
+
+
+if submit_button:
+
+    if input_from_user == '':
+
+        st.error('empty form submitted')
+    
+    else:
+
+        if model_options == 'Choose a model':
+
+            st.error('Please choose a model for named entity recognition')
+    
+        else:
+
+            st.subheader('Result Analysis')
+
+            if model_options == 'Choose a model':
+
+                st.error('Please choose a model for Named Entity Recognition')
+
+            elif model_options == 'Spacy\'s en_core_web_sm model':
+
+                st.write('Model Used for Named Entity Recognition:')
+                st.success(model_options)
+
+                spacy_model = spacy.load('en_core_web_sm')
+
+                res = spacy_model(input_from_user)
+
+                st.write(f'Analysis of the detected entities from the text ==>')
+                st.markdown(f'**{input_from_user}**')
+                
+                entities = [{'Entity': entity.text, 'Label of the Entity': entity.label_, 'Description of the Label': spacy.explain(entity.label_)} for entity in res.ents]
+
+                df = pd.DataFrame(entities)
+
+                st.table(df)
+
+                st.write('Entites marked in the input text:')
+                st.markdown(displacy.render(res, style='ent'), unsafe_allow_html=True)
+
+            elif model_options == 'dslim/bert-base-NER model':
+
+                st.write('Model Used for Named Entity Recognition:')
+                st.success(model_options)
+                
+                tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
+                model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
+
+                bert_ner_model = pipeline('ner', model=model, tokenizer=tokenizer)
+
+                res = bert_ner_model(input_from_user)
+
+                abbreviations = {
+                    "O": "Outside of a named entity",
+                    "B-MISC": "Beginning of a miscellaneous entity right after another miscellaneous entity",
+                    "I-MISC": "Miscellaneous entity",
+                    "B-PER": "Beginning of a person’s name right after another person’s name",
+                    "I-PER": "Person’s name",
+                    "B-ORG": "Beginning of an organization right after another organization",
+                    "I-ORG": "Organization",
+                    "B-LOC": "Beginning of a location right after another location",
+                    "I-LOC": "Location"
+                }
+
+                st.write(f'Analysis of the detected entities from the text ==>')
+                st.markdown(f'**{input_from_user}**')
+                
+                entities = [{'Entity': input_from_user[entity['start']:entity['end']], 'Label of the Entity': entity['entity'], 'Description of the Label': abbreviations.get(entity['entity'])} for entity in res]
+
+                df = pd.DataFrame(entities)
+
+                st.table(df)
+    
+