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 t in texts:
+    doc = model(t)
+    entities = [(ent.text, ent.label_) for ent in doc.ents]
+    result.append({'Text':t,'Entity':entities})
+result_df = pd.DataFrame(result)
+# Entities visualization
+from spacy import displacy
+
+for t in texts:
+    doc = model(t)
+    displacy.render(doc,style='ent')
+# Count Entities
+from collections import Counter
+all_entities = [ent for ents in result_df['Entity'] for ent in ents]
+labels = [label for text,label in all_entities]
+Counter(labels).most_common()
+# Extract entities from input
+def ext_ent(sentence):
+    doc = model(sentence)
+    output = ''
+    for ent in doc.ents:
+        output += f"{ent.text} - {ent.label_}\n"
+    return output
+import gradio as gr
+
+demo = gr.Interface(fn=ext_ent,inputs='text',outputs='text',title="Extract Entities")
+demo.launch()