old_contents: import os, sys
  import tensorflow as tf
  from flask import Flask, jsonify, render_template, request
  from six.moves import urllib
  
  def label_image(url):
      # Read image from url
      req = urllib.request.Request(url)
      response = urllib.request.urlopen(req)
      image_data = response.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:
                      return "PEPE"
                  else:
                      return "NOT_PEPE"
  
  
  # HTTP API
  app = Flask(__name__)
  
  @app.route('/', methods=['POST'])
  def classify():
      return jsonify(status='OK', results=label_image(request.form['url']))
  
  @app.route('/', methods=['GET'])
  def main():
      return jsonify(status='OK', message='FEED ME IMAGES\nRead more: https://github.com/LindseyB/not-pepe')
  
  if __name__ == '__main__':
      port = int(os.environ.get('PORT', 5000))
      app.run(host='0.0.0.0', port=port)
  
new_contents: # Not-pepe as a service server
  
  import os, sys
  import tensorflow as tf
  from flask import Flask, jsonify, render_template, request
  from six.moves import urllib
  
  def label_image(url):
      # Read image from url
      req = urllib.request.Request(url)
      response = urllib.request.urlopen(req)
      image_data = response.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:
                      return "PEPE"
                  else:
                      return "NOT_PEPE"
  
  
  # HTTP API
  app = Flask(__name__)
  
  @app.route('/', methods=['POST'])
  def classify():
      return jsonify(status='OK', results=label_image(request.form['url']))
  
  @app.route('/', methods=['GET'])
  def main():
      return jsonify(status='OK', message='FEED ME IMAGES, Read more: https://github.com/LindseyB/not-pepe')
  
  if __name__ == '__main__':
      port = int(os.environ.get('PORT', 5000))
      app.run(host='0.0.0.0', port=port)
  
subject: Update server response on get