Update app.py

app.py

@@ -96,34 +96,35 @@ def analyze(text):
 
     # Tokenize
     inputs = tokenizer(text, padding=True, truncation=True, max_length=256, return_tensors='pt')
+
+    # Get the subjectivity model outputs
+    outputs_base = model_without_sentiment(**inputs)
+    logits_base = outputs_base.get('logits')
+    # Calculate probabilities using softmax
+    prob_base = torch.nn.functional.softmax(logits_base, dim=1)[0]
     
     # Get the sentiment values
     positive = sentiment_values['positive']
     neutral = sentiment_values['neutral']
     negative = sentiment_values['negative']
+
     # Convert sentiment values to tensors
     inputs['positive'] = torch.tensor(positive).unsqueeze(0)
     inputs['neutral'] = torch.tensor(neutral).unsqueeze(0)
     inputs['negative'] = torch.tensor(negative).unsqueeze(0)
 
     # Get the sentiment model outputs
-    outputs1 = model_with_sentiment(**inputs)
-    logits1 = outputs1.get('logits')
+    outputs_sentiment = model_with_sentiment(**inputs)
+    logits_sentiment = outputs_sentiment.get('logits')
 
     # Calculate probabilities using softmax
-    p1 = torch.nn.functional.softmax(logits1, dim=1)[0]
-
-    # Get the subjectivity model outputs
-    outputs2 = model_without_sentiment(**inputs)
-    logits2 = outputs2.get('logits')
-    # Calculate probabilities using softmax
-    p2 = torch.nn.functional.softmax(logits2, dim=1)[0]
+    prob_sentiment = torch.nn.functional.softmax(logits_sentiment, dim=1)[0]
 
     # Format the output
     return {
         'Positive': f"{positive:.2%}", 'Neutral': f"{neutral:.2%}", 'Negative': f"{negative:.2%}",
-        'Sent-Subj OBJ': f"{p1[0]:.2%}", 'Sent-Subj SUBJ': f"{p1[1]:.2%}",
-        'TextOnly OBJ': f"{p2[0]:.2%}", 'TextOnly SUBJ': f"{p2[1]:.2%}"
+        'Sent-Subj OBJ': f"{prob_sentiment[0]:.2%}", 'Sent-Subj SUBJ': f"{prob_sentiment[1]:.2%}",
+        'TextOnly OBJ': f"{prob_base[0]:.2%}", 'TextOnly SUBJ': f"{prob_base[1]:.2%}"
     }
 
 # Update the Gradio interface