Update app.py

app.py

@@ -2,15 +2,24 @@ from transformers import BertTokenizer, BertForSequenceClassification
 from transformers import pipeline
 import gradio as gr
 
+finbert = BertForSequenceClassification.from_pretrained('rpratap2102/The_Misfits', num_labels=3)
+tokenizer = BertTokenizer.from_pretrained('rpratap2102/The_Misfits')
+
+nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
+
+from transformers import BertTokenizer, BertForSequenceClassification
+from transformers import pipeline
+
+
 finbert = BertForSequenceClassification.from_pretrained('rpratap2102/The_Misfits', num_labels=3)
 tokenizer = BertTokenizer.from_pretrained('rpratap2102/The_Misfits')
 
 nlp = pipeline("sentiment-analysis", model=finbert, tokenizer=tokenizer)
 
 c_labels = {
-    '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': '😐'}
+    '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):
@@ -20,10 +29,8 @@ def predict_sentiment(text):
     
     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}"
+        
+    return f"{label_text} {emoji} (Model Predicted it as {sentiment_label} with a confidence score of {confidence_score:.2f})"
 
 iface = gr.Interface(
     fn=predict_sentiment,