text: import os.path
  import numpy as np
  import keras
  from keras.models import load_model
  
  from skimage.io import imread
  from skimage.transform import resize
  
  # Path to saved classifier
  my_path = os.path.abspath(os.path.dirname(__file__))
  cat_dog_classifier = os.path.join(my_path, "../static/cat_dog_classifier/cat_dog_classifier_v1.h5")
  
  
  ALLOWED_EXTENSIONS = set(['jpg'])
  def allowed_file(filename):
      """Only .jpg files allowed"""
      return '.' in filename and \
             filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
  
  def image_classification(image):
      """Apply image classifier"""
      # clear Tensor session to avoid error
      keras.backend.clear_session()
      # load saved model
      image_classifier = load_model(cat_dog_classifier)
      # prepare labels
      class_labels = {0: 'Cat', 1: 'Dog'}
      # read photo & transform it into array
      img = imread(image)
      img = resize(img, (128, 128))
      img = np.expand_dims(img, axis=0)
      if np.max(img) > 1:
          img = img / 255.0
      # predict class
      prediction = image_classifier.predict_classes(img)
      percent_values = prediction.tolist()
      # for website display
      guess = class_labels[prediction[0][0]]
      return guess
  
  
  
  