Hi @Ashley,
If you are using softmax activation function at the last classification dense layer,  model.predict(images) is going to return the prediction scores for all classes/labels.
You can get the prediction scores by model.predict(image)[0], and then get the index of the maximum score by using np.argmax() or tf.math.argmax().
Here is a typical example from loading an image to getting its predicted label
image = tf.keras.preprocessing.image.load_img(image_path, target_size=(224,224))
image = tf.keras.preprocessing.image.img_to_array(image)
image = image/255.0
image = tf.expand_dims(image, 0) 

# predict the image

pred = model.predict(image)

# Get the label/class

im_class = tf.argmax(pred[0], axis=-1) #either tf.math.argmax() or tf.argmax will work

If you are using a specific pre-trained model from Keras directly, you can refer to some examples from the documentation on how to get the class using
decode_predictions()
Hope that helps!