fixed loadind model as ordered dict

api/Neural_Network2.py

@@ -18,6 +18,7 @@
 from torch.utils.data import Dataset, DataLoader
 from torch.utils.data.dataset import random_split
 from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
+from torchtext.vocab import build_vocab_from_iterator
 
 tqdm.pandas()
 
@@ -332,7 +333,7 @@ def create_and_train_rnn_model(df, name, epochs = 10, batch_size = 32, learning_
  # Preparação dos DataLoader
  train_loader = DataLoader(train_dataset, batch_size=batch_size, collate_fn=collate)
  valid_loader = DataLoader(valid_dataset, batch_size=batch_size, collate_fn=collate)
- test_loader = DataLoader(test_dataset, batch_size=batch_size, collate_fn=collate)
+ #test_loader = DataLoader(test_dataset, batch_size=batch_size, collate_fn=collate)
 
  # Inicialização do modelo
  model = RNNClassifier(
@@ -391,7 +392,8 @@ def create_and_train_rnn_model(df, name, epochs = 10, batch_size = 32, learning_
  if not canceled:
  model_path = os.path.join('api', 'models', name)
  os.makedirs(os.path.dirname(model_path), exist_ok=True)
- torch.save(model.state_dict(), model_path)
+ #torch.save(model.state_dict(), model_path)
+ torch.save(model, model_path)
 
  # Atualizar e salvar o estado de treinamento final
  training_progress = {