problem in testing accuracy after using saved trained model

Question

I run code for training and testing dataset with many methods and it gives good and high accuracy for testing data, I saved the trained models with p=0 when I use the saved files of the trained models to run the testing data again with p=1 I got very bad accuracies there is a problem in saving the models I don't how and what is the problem and how to fix it. I need to save training models to get thier testing accuracies when I need without make training again

def model_fitting(model, model_name, p):
history_path = '../input/' + model_name+'/model_history.npy'
#history_path = model_name+'_history.npy'
#print(model_path)
print(history_path)
reduce_lr = ReduceLROnPlateau(monitor='val_accuracy',factor=0.09,patience=2)

if ((not os.path.exists(model_name+'.h5')) or (not os.path.exists(history_path))):
    history = model.fit(train_data_generator, epochs=epoch_num, validation_data=validation_data_generator, callbacks=[reduce_lr])
    history = history.history
    model.save(model_name+'.h5')
    np.save(history_path,history)

else:
#Enter '0' if you want to train a new model,
#'1' to use a pretrained model and 
#'2' to train a pretraind model
    if (p==0):
        history = model.fit(train_data_generator, epochs=epoch_num, validation_data=validation_data_generator, callbacks=[reduce_lr])
        history = history.history
        np.save(history_path,history)
        model.save(model_name+'.h5')
    elif (p==1):
        model = tensorflow.keras.models.load_model(model_name+'.h5')
        history=np.load(history_path,allow_pickle='TRUE').item()
    elif (p==2):
        model = tensorflow.keras.models.load_model(model_name+'.h5')
        history = model.fit(train_data_generator, epochs=epoch_num, validation_data=validation_data_generator, callbacks=[reduce_lr])
        history = history.history
        np.save(history_path,history)
        model.save(model_name+'.h5')
return history