Keras siamese model history is empty

Hello, i am making a siamese neural network with triplet loss using keras, and have encountered an ood problem. I tried saving my history twice: once in a callback (saved as a dictionary), and once after training with, using pickle (saved as a history object). In both instances, the code runs smoothly, but afterwards the saved files are only several hundred bytes and when I plot them with matplotlib, the plot is empty.

Here’s the callback I passed to

class MetricsCallback(tensorflow.keras.callbacks.Callback):
    def __init__(self, train_data, validation_data, embedding_model, folder_path, file_name):
        super(MetricsCallback, self).__init__()
        self.validation_data = validation_data
        self.train_data = train_data
        self.file_name = file_name
        self.folder_path = folder_path

    def calc_metrics(self ,dataset):
      #for  accuracy

        #for auc-roc and eer

        for triplet in dataset: #dataset is train_data or validation_data
          anchor, positive, negative = triplet #get the anchor, posotive and negative tensors from each triplet tuple
          a_emb=embedding_model(np.expand_dims(anchor, axis=0)) #get the embeddings for each
          p_emb=embedding_model(np.expand_dims(positive, axis=0))
          n_emb=embedding_model(np.expand_dims(negative, axis=0))

          dist_p = np.linalg.norm(a_emb - p_emb) #calculate distances
          dist_n = np.linalg.norm(a_emb - n_emb)
          #for triplet acc:
          y_true_acc. append(1) #the true label is always 1
          if dist_p +alpha<dist_n : #accurate

          #for auc-roc and eer:
          y_true.append(0) #for a-p
          y_true.append(1) #for a-n

          y_scores.append(1-dist_p)#if it's small like we want, it gets a high score
          y_scores.append(dist_n)# if it's big like we want, it gets a high score

        #compute acc:
        accuracy = accuracy_score(y_true_acc, y_pred)
        #compute auc-roc and eer:
        fpr, tpr, thresholds = roc_curve(y_true, y_scores)
        auc_roc = auc(fpr, tpr)
        eer = 1 - max(tpr - fpr)

        return accuracy, auc_roc, eer, fpr, tpr

    def on_epoch_end(self, epoch, logs=None):
        # Compute metrics on train data
        loss_train = logs.get('loss')
        accuracy_train , auc_roc_train, eer_train, fpr_train, tpr_train= self.calc_metrics(self.train_data)

        self.history['train_loss'] = loss_train
        self.history['train_accuracy'] = accuracy_train
        self.history['train_auc_roc'] = auc_roc_train
        self.history['train_eer'] = eer_train
        self.history['train_fpr'] = fpr_train
        self.history['train_tpr'] = tpr_train

        # Log the metrics
        print(f'Epoch {epoch + 1} - Train Loss: {loss_train}, Accuracy: {accuracy_train}, AUC-ROC: {auc_roc_train}, EER: {eer_train}')

        # Compute metrics on validation data
        loss_val = logs.get('val_loss')

        accuracy_val, auc_roc_val, eer_val, fpr_val, tpr_val= self.calc_metrics(self.validation_data)

        self.history['val_loss'] = loss_val
        self.history['val_accuracy'] = accuracy_val
        self.history['val_auc_roc'] = auc_roc_val
        self.history['val_eer'] = eer_val
        self.history['val_fpr'] = fpr_val
        self.history['val_tpr'] = tpr_val

        # Log the metrics
        print(f'Epoch {epoch + 1} - Validation Loss: {loss_val},  Accuracy: {accuracy_val}, AUC-ROC: {auc_roc_val}, EER: {eer_val}')

        # Save the metric history to Google Drive
        epoch_number = epoch + 1  # Epoch starts from 0, so we add 1 to get the actual epoch number
        file_name_with_epoch = f"{self.file_name}_epoch_{epoch_number}.pkl"
        file_path = os.path.join(self.folder_path, file_name_with_epoch)
        with open(file_path, 'wb') as file_pi:
            pickle.dump(self.history, file_pi)
        print('saved metrics')

Does anyone know why this is happening? Thanks.