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Last active Feb 13, 2017

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Flask code for Fashion Recognizer
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os.path
import re
import sys
import tarfile
import numpy as np
from six.moves import urllib
import tensorflow as tf
from flask import Flask, jsonify
from flask import make_response
from flask import request, render_template
from flask_bootstrap import Bootstrap
from werkzeug import secure_filename
app = Flask(__name__)
bootstrap = Bootstrap(app)
def index():
return render_template("upload.html")
# import default command line flags from TensorFlow
# define directory that the model is stored in (default is the current directory)
'model_dir', '/home/ubuntu/flaskapp',
"""Path to output_graph.pb, """
"""output_labels.txt""")'num_top_predictions', 3,
"""Display this many predictions.""")
# Classificaiton endpoint
@app.route("/classify", methods=["POST"])
def classify():
print("Model loaded")
node_lookup = NodeLookup()
print("Node lookup loaded")
predictions = dict(run_inference_on_image(
return jsonify(predictions=predictions)
def not_found(error):
return make_response(jsonify({'error': 'Not found'}), 404)
# The following code performs the recognition, and is derived from the examples
# provided in the Tensorflow package
# ==============================================================================
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Simple image classification with Inception.
Run image classification with Inception trained on ImageNet 2012 Challenge data
This program creates a graph from a saved GraphDef protocol buffer,
and runs inference on an input JPEG image. It outputs human readable
strings of the top 5 predictions along with their probabilities.
Change the --image_file argument to any jpg image to compute a
classification of that image.
Please see the tutorial and website for a detailed description of how
to use this script to perform image recognition.
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self, label_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
FLAGS.model_dir, 'output_labels.txt')
self.node_lookup = self.load(label_lookup_path)
def load(self, label_lookup_path):
"""Loads a human readable English name for each softmax node.
label_lookup_path: string UID to integer node ID.
dict from integer node ID to human-readable string.
node_id_to_name = {}
label_file = open(label_lookup_path)
i = 0
# labels are ordered from 0 to N in the lookup file
for line in label_file:
node_id_to_name[i] = line.strip()
i = i + 1
return node_id_to_name
# return the friendly name for the given node_id
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]
def create_graph():
"""Creates a graph from saved GraphDef file and returns a saver."""
# Creates graph from saved graph_def.pb.
with tf.gfile.FastGFile(os.path.join(
FLAGS.model_dir, 'output_graph.pb'), 'rb') as f:
graph_def = tf.GraphDef()
_ = tf.import_graph_def(graph_def, name='')
# sess = None
# node_lookup = None
def run_inference_on_image(image_data):
"""Runs inference on an image.
image_data: Image data.
sess = tf.Session()
print("Tensorflow session ready")
node_lookup = NodeLookup()
print("Node lookup loaded")
# Runs the softmax tensor by feeding the image_data as input to the graph.
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions =, {'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
# sort the predictions
top_k = predictions.argsort()[-FLAGS.num_top_predictions:][::-1]
# map to the friendly names and return the tuples
return [(node_lookup.id_to_string(node_id), float(predictions[node_id])) for node_id in top_k]
if __name__ == '__main__':
print("Model loaded")
node_lookup = NodeLookup()
print("Node lookup loaded")
sess = tf.Session()
print("Tensorflow session ready")
print("Launching web application...")
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