Custom Generator in Keras: Making Predictions

Question

I implemented a dual input model using custom generator as in here: Create a mixed data generator (images,csv) in keras

import random
import pandas as pd
import numpy as np
from glob import glob
from keras.preprocessing import image as krs_image

# Create the arguments for image preprocessing
data_gen_args = dict(
    horizontal_flip=True,
    brightness_range=[0.5, 1.5],
    shear_range=10,
    channel_shift_range=50,
    rescale=1. / 255,
)

# Create an empty data generator
datagen = ImageDataGenerator()

# Read the image list and csv
image_file_list = glob(f'{images_dir}/{split}/**/*.JPG', recursive=True)
df = pd.read_csv(f'{csv_dir}/{split}.csv', index_col=csv_data[0])
random.shuffle(image_file_list)

def custom_generator(images_list, dataframe, batch_size):
    i = 0
    while True:
        batch = {'images': [], 'csv': [], 'labels': []}
        for b in range(batch_size):
            if i == len(images_list):
                i = 0
                random.shuffle(images_list)
            # Read image from list and convert to array
            image_path = images_list[i]
            image_name = os.path.basename(image_path).replace('.JPG', '')
            image = krs_image.load_img(image_path, target_size=(img_height, img_width))
            image = datagen.apply_transform(image, data_gen_args)
            image = krs_image.img_to_array(image)

            # Read data from csv using the name of current image
            csv_row = dataframe.loc[image_name, :]
            label = csv_row['class']
            csv_features = csv_row.drop(labels='class')

            batch['images'].append(image)
            batch['csv'].append(csv_features)
            batch['labels'].append(label)

            i += 1

        batch['images'] = np.array(batch['images'])
        batch['csv'] = np.array(batch['csv'])
        # Convert labels to categorical values
        batch['labels'] = np.eye(num_classes)[batch['labels']]

        yield [batch['images'], batch['csv']], batch['labels']

And fitted the model with:

history = model.fit(custom_generator(path_train, df_train, batch_size),
          steps_per_epoch= train_steps,
          epochs=epochs,
          verbose=1,
          #callbacks=callbacks,
          validation_data=custom_generator(path_valid, df_valid, batch_size),
          validation_steps=val_steps)

path_train & path_valid are the path to image folders train and validation data. df_train and df_valid are dataframes of metadata.

However, I am not sure how to make predictions on a model fitted with custom generator. Should I do as follows?

predictions = model.predict(custom_generator(test_path, df_test, batch_size=1), verbose =1) 

Any help is greatly appreciated.