Update app.py

app.py

@@ -36,7 +36,9 @@ tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
 modelSentiment = BertForTargetSentiment()  # Use the model class defined earlier
 
 # Load the trained model weights (adjust the path if needed)
-modelSentiment.load_state_dict(torch.load('bert_target_sentiment_model.pth'))
+# modelSentiment.load_state_dict(torch.load('bert_target_sentiment_model.pth'))
+modelSentiment.load_state_dict(torch.load('bert_target_sentiment_model.pth', map_location=torch.device('cpu')))
+
 modelSentiment.eval()  # Set the model to evaluation mode
 
 # Sentiment labels