import numpy as np
import tensorflow as tf


def image_clasification(image_path, model):
    # loaded_model = keras.models.load_model("my_model")
    class_names = ['door', 'refrigerator', 'shelf']

    img = tf.keras.utils.load_img(
        image_path, target_size=(180, 180)
    )
    img_array = tf.keras.utils.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0)  # Create a batch

    predictions = model.predict(img_array)
    score = tf.nn.softmax(predictions[0])
    # print(
    #     "This image most likely belongs to {} with a {:.2f} percent confidence."
    #         .format(class_names[np.argmax(score)], 100 * np.max(score))
    # )
    return class_names[np.argmax(score)]