old_contents: import os, sys
  
  import tensorflow as tf
  
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  
  # change this as you see fit
  image_path = sys.argv[1]
  
  # Read in the image_data
  image_data = tf.gfile.FastGFile(image_path, 'rb').read()
  
  # Loads label file, strips off carriage return
  label_lines = [line.rstrip() for line
                     in tf.gfile.GFile("retrained_labels.txt")]
  
  # Unpersists graph from file
  with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      tf.import_graph_def(graph_def, name='')
  
  with tf.Session() as sess:
      # Feed the image_data as input to the graph and get first prediction
      softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
  
      predictions = sess.run(softmax_tensor, \
               {'DecodeJpeg/contents:0': image_data})
  
      # Sort to show labels of first prediction in order of confidence
      top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
  
      for node_id in top_k:
          human_string = label_lines[node_id]
          score = predictions[0][node_id]
          #print('%s (score = %.5f)' % (human_string, score))
          if human_string == "pepe":
              if score >= .9:
                  print("PEPE")
              else:
                  print("NOT PEPE")
  
new_contents: # command line tool for determining if passed in filepath is pepe or not
  
  import os, sys
  import tensorflow as tf
  
  os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
  
  # change this as you see fit
  image_path = sys.argv[1]
  
  # Read in the image_data
  image_data = tf.gfile.FastGFile(image_path, 'rb').read()
  
  # Loads label file, strips off carriage return
  label_lines = [line.rstrip() for line
                     in tf.gfile.GFile("retrained_labels.txt")]
  
  # Unpersists graph from file
  with tf.gfile.FastGFile("retrained_graph.pb", 'rb') as f:
      graph_def = tf.GraphDef()
      graph_def.ParseFromString(f.read())
      tf.import_graph_def(graph_def, name='')
  
  with tf.Session() as sess:
      # Feed the image_data as input to the graph and get first prediction
      softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
  
      predictions = sess.run(softmax_tensor, \
               {'DecodeJpeg/contents:0': image_data})
  
      # Sort to show labels of first prediction in order of confidence
      top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
  
      for node_id in top_k:
          human_string = label_lines[node_id]
          score = predictions[0][node_id]
          if human_string == "pepe":
              if score >= .9:
                  print("PEPE")
              else:
                  print("NOT PEPE")
  
subject: Add comment to explain what file does`