Create app.py

app.py

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+# load dataset
+import pandas as pd 
+df = pd.read_csv('sentiment_data.csv')
+texts = df['text'].astype(str)
+# Load NER model
+import spacy
+model = spacy.load('en_core_web_lg')
+# Extract entities
+result = []
+for i in texts:
+    doc = model(i)
+    entities = [(ent.text,ent.label_) for ent in doc.ents]
+    result.append({'Text':i,'Entity':entities})
+result_df = pd.DataFrame(result)
+result_df.to_csv('Ben.csv',index=0)
+# Entity visualization
+from spacy import displacy
+for t in texts[:5]:
+    doc = model(t)
+    displacy.render(doc,style='ent')
+from collections import Counter
+all_entites = [ent for ents in result_df['Entity'] for ent in ents]
+print(all_entites)
+labels = [label for text, label in all_entites]
+Counter(labels).most_common(1)
+def ext_ent(sentence):
+    doc = model(sentence)
+    output = ''
+    for i in doc.ents:
+        output += f'{i.text} - {i.label_}\n'
+    return output
+import gradio as gr
+
+demo = gr.Interface(fn=ext_ent,inputs='text',outputs='text',title='Extract Entities')
+demo.launch()