Update app.py

app.py

@@ -28,22 +28,24 @@ def load_cnn():
   model.eval()
 
   return model
-
-def predict_sentiment(text, model, torch=False):
+def predict_sentiment(text, model, vocab, tokenizer):
     if torch == True:
-        processed_text = processor.transform(text)
-        with torch.no_grad():  # Ensure no gradients are computed
-            prediction = model(processed_text)  # Get raw model output
-            # Convert output to probabilities
-            probs = torch.softmax(prediction, dim=1)
-            # Get the predicted class
-            pred_class = torch.argmax(probs, dim=1)
-        return pred_class.item()  # Return the predicted class as a Python int
+        processor.transform(text)
+        tokens = tokenizer(text)
+        encoded = [vocab[token] for token in tokens]
+        input_tensor = torch.tensor(encoded).unsqueeze(0).to(device)
+        
+        with torch.no_grad():  # No gradient needed
+            model.eval()  # Evaluation mode
+            outputs = model(input_tensor)
+            probs = torch.softmax(outputs, dim=1)
+            pred_class = torch.argmax(probs, dim=1).item()
+        
+        return pred_class  # Return the predicted class index
     else:
         processor.transform(text)
         prediction = model.predict([text])
         return prediction
-        
 
 
 model_1 = load_model()