classify.py

from tensorflow.keras.preprocessing.image import load_img
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.applications.vgg16 import decode_predictions
from tensorflow.keras.applications.vgg16 import VGG16
#import keras.backend.tensorflow_backend as tb
#tb._SYMBOLIC_SCOPE.value = True

def predict(upload_img):
    model = VGG16()
    image = load_img(upload_img, target_size=(224, 224))
    # convert the image pixels to a numpy array
    image = img_to_array(image)
    # reshape data for the model
    image = image.reshape((1, image.shape[0], image.shape[1], image.shape[2]))
    # prepare the image for the VGG model
    image = preprocess_input(image)
    # predict the probability across all output classes
    yhat = model.predict(image)
    # convert the probabilities to class labels
    label = decode_predictions(yhat)
    # retrieve the most likely result, e.g. highest probability
    label = label[0][0]
    return label