import tensorflow as tf
from keras_tuner import HyperParameters
from src.models import MakeHyperModel
from src.preprocessing import get_data_augmentation
from src.config import IMAGE_SIZE

data_augmentation = get_data_augmentation()

img = tf.keras.preprocessing.image.load_img(
    "examples/cat2.jpg", target_size=IMAGE_SIZE
)
img_array = tf.keras.preprocessing.image.img_to_array(img)
img_array = tf.expand_dims(img_array, 0)

latest = tf.train.latest_checkpoint('./tuner_model/cat-vs-dog/trial_0484d8d758a5ef7b91ca97d334ba7870/checkpoints/epoch_0')
hypermodel = MakeHyperModel(input_shape=IMAGE_SIZE + (3,), num_classes=2, data_augmentation=data_augmentation)
model = hypermodel.build(hp=HyperParameters())
model.load_weights(latest).expect_partial()


predictions = model.predict(img_array)
score = predictions[0]
print(
    "This image is %.2f percent cat and %.2f percent dog."
    % (100 * (1 - score), 100 * score)
)