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main.py
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main.py
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# 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)