import numpy as np
import keras.preprocessing.image as image_utils
from keras.applications.imagenet_utils import decode_predictions, preprocess_input
# You can import any other model if you would like to :)
from keras.applications.resnet50 import ResNet50

IMG_SIZE = (224, 224)

# Loading the model only once
CLASSIFICATION_MODEL = ResNet50()


def get_top_label(img_path):
    """Given an image path, return the top detected label using a ResNet50 model. """
    image = image_utils.load_img(img_path, target_size=IMG_SIZE)
    image = image_utils.img_to_array(image)
    image = np.expand_dims(image, axis=0)
    image = preprocess_input(image)
    preds = CLASSIFICATION_MODEL.predict(image)
    top_label = decode_predictions(preds)[0][0][1]
    return top_label