text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  # */AIPND-revision/intropyproject-classify-pet-images/classify_images.py
  #                                                                             
  # PROGRAMMER: Michael Ohakwe
  # DATE CREATED: 4/8/2019                                
  # REVISED DATE: 4/11/2019
  # PURPOSE: Create a function classify_images that uses the classifier function 
  #          to create the classifier labels and then compares the classifier 
  #          labels to the pet image labels. This function inputs:
  #            -The Image Folder as image_dir within classify_images and function 
  #             and as in_arg.dir for function call within main. 
  #            -The results dictionary as results_dic within classify_images 
  #             function and results for the functin call within main.
  #            -The CNN model architecture as model within classify_images function
  #             and in_arg.arch for the function call within main. 
  #           This function uses the extend function to add items to the list 
  #           that's the 'value' of the results dictionary. You will be adding the
  #           classifier label as the item at index 1 of the list and the comparison 
  #           of the pet and classifier labels as the item at index 2 of the list.
  #
  ##
  # Imports classifier function for using CNN to classify images 
  from classifier import classifier 
  
  # TODO 3: Define classify_images function below, specifically replace the None
  #       below by the function definition of the classify_images function. 
  #       Notice that this function doesn't return anything because the 
  #       results_dic dictionary that is passed into the function is a mutable 
  #       data type so no return is needed.
  # 
  def classify_images(images_dir, results_dic, model):
      """
      Creates classifier labels with classifier function, compares pet labels to 
      the classifier labels, and adds the classifier label and the comparison of 
      the labels to the results dictionary using the extend function. Be sure to
      format the classifier labels so that they will match your pet image labels.
      The format will include putting the classifier labels in all lower case 
      letters and strip the leading and trailing whitespace characters from them.
      For example, the Classifier function returns = 'Maltese dog, Maltese terrier, Maltese' 
      so the classifier label = 'maltese dog, maltese terrier, maltese'.
      Recall that dog names from the classifier function can be a string of dog 
      names separated by commas when a particular breed of dog has multiple dog 
      names associated with that breed. For example, you will find pet images of
      a 'dalmatian'(pet label) and it will match to the classifier label 
      'dalmatian, coach dog, carriage dog' if the classifier function correctly 
      classified the pet images of dalmatians.
       PLEASE NOTE: This function uses the classifier() function defined in 
       classifier.py within this function. The proper use of this function is
       in test_classifier.py Please refer to this program prior to using the 
       classifier() function to classify images within this function 
       Parameters: 
        images_dir - The (full) path to the folder of images that are to be
                     classified by the classifier function (string)
        results_dic - Results Dictionary with 'key' as image filename and 'value'
                      as a List. Where the list will contain the following items: 
                    index 0 = pet image label (string)
                  --- where index 1 & index 2 are added by this function ---
                    NEW - index 1 = classifier label (string)
                    NEW - index 2 = 1/0 (int)  where 1 = match between pet image
                      and classifer labels and 0 = no match between labels
        model - Indicates which CNN model architecture will be used by the 
                classifier function to classify the pet images,
                values must be either: resnet alexnet vgg (string)
       Returns:
             None - results_dic is mutable data type so no return needed.         
      """
  
  
  #   Process each key(filename) and run the classifier function using the model passed
  #   into the function
      for key in results_dic:
  
  #     Initialize model label
        model_label = ''
  
  #     Sets path using key and image directory
        path = images_dir + '/' + key
  
  #     Uses classifier passing in the model name and the path
        model_label = classifier(path,model)
  
  #     Format model label similarly to pet image label
        model_label = model_label.lower()
        model_label = model_label.strip()
  
  #     Sets the pet image label from the results dictionary as truth
        truth = results_dic[key][0]
  
  #     If model label has a correct breed name
        if truth in model_label:
  
  #       Extend results dictionary to include model label and '1' 
          results_dic[key].extend([model_label,1])
        else:
  
  #       Extend results dictionary to include model label and '0' 
          results_dic[key].extend([model_label,0])
  