Update app.py

app.py

@@ -12,6 +12,7 @@ models = {}
 model = AutoModel.from_pretrained(f'ZarahShibli/tmp_trainer',return_dict=False)
 
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
+print("device",device)
 MAX_LEN = 200
 
 tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', return_tensors='pt')
@@ -45,9 +46,9 @@ def predict(comment_sentence):
     with torch.no_grad():
         outputs = model(ids, mask, token_type_ids)
 
-    outputs = outputs.to('cpu')
-    print(outputs)
-    fin_outputs.extend(torch.sigmoid(outputs).detach().numpy().tolist())
+    
+        print(outputs)
+        fin_outputs.extend(torch.sigmoid(outputs).detach().numpy().tolist())
     print(fin_outputs)
     # Convert the outputs to boolean values based on the threshold
     outputs_boolean = np.array(fin_outputs) >= 0.25