new_contents: import os
  from tqdm import tqdm
  import base64
  
  def generate_training_data():
      d = []
      labels = []
  
      for root, dirs, files in os.walk("images"):
          for image in files:
              if image.endswith(".jpg") or image.endswith(".png"):
  
                  # get file paths
                  d.append(os.path.join(root, image))
  
                  # turn filenames into labels for the images
                  label = os.path.splitext(image)[0] # remove path elements from label
                  l = ''.join([c for c in label if not c.isdigit()]) # remove numbers from label
                  labels.append(l)
  
      # put images and labels into a single list to pass into the Custom Collections API
      all_data = [list(x) for x in zip(d, labels)]
      return all_data
  
  def test_model():
      print "Test results for CONTEMPORARY category:"
      print collection.predict("test_images/contemporary-test.jpg")
      print collection.predict("test_images/contemporary-test2.jpg")
      print collection.predict("test_images/contemporary-test3.jpg")
      print "******"
      print "Test results for INDUSTRIAL category:"
      print collection.predict("test_images/industrial-test.jpg")
      print collection.predict("test_images/industrial-test2.jpg")
      print collection.predict("test_images/industrial-test3.jpg")
      print "******"
      print "Test results for MINIMALISM category:"
      print collection.predict("test_images/minimalism-test.jpg")
      print collection.predict("test_images/minimalism-test2.jpg")
      print collection.predict("test_images/minimalism-test3.jpg")
  
  if __name__ == "__main__":
      # TODO
  
subject: Create main.py for interior design classifier post