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")

@@ -1,12 +1,86 @@
+# command line tool for determining if passed in filepath is pepe or not%0A%0A
 import os, s
@@ -82,17 +82,16 @@
 os, sys%0A
-%0A
 import t
@@ -1155,68 +1155,8 @@
 id%5D%0A
-        #print('%25s (score = %25.5f)' %25 (human_string, score))%0A
     

Add comment to explain what file does`