Update app.py

app.py

@@ -8,22 +8,29 @@ tokenizer = BertTokenizer.from_pretrained('rpratap2102/The_Misfits')
 nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
 
 c_labels = {
-    'Negative': 'This does not look good for the Market',
-    'Positive': 'This seems to be good news for the market',
-    'Neutral': "This is normal in the market"
+    'Negative': {'text': 'This does not look good for the Market', 'emoji': '😞'},
+    'Positive': {'text': 'This seems to be good news for the market', 'emoji': '😃'},
+    'Neutral': {'text': "This is normal in the market", 'emoji': '😐'}
 }
 
 def predict_sentiment(text):
     result = nlp([text])[0]
     sentiment_label = result['label']
-    return c_labels[sentiment_label]
-
-
+    confidence_score = result['score']
+    
+    label_text = c_labels[sentiment_label]['text']
+    emoji = c_labels[sentiment_label]['emoji']
+    
+    output_text = f"{label_text} ({sentiment_label}) with a confidence score of {confidence_score:.2f}"
+    
+    return f"{emoji} {output_text}"
 
 iface = gr.Interface(
     fn=predict_sentiment,
     inputs="text",
     outputs="text",
+    live=True,
+    capture_session=True
 )
 
-iface.launch()
+iface.launch()