simple.py

import os
from flask import Flask, render_template, request
import tensorflow as tf, sys
from PIL import Image
app = Flask(__name__)
APP_ROOT = os.path.dirname(os.path.abspath(__file__))
@app.route("/")
def index():
    return render_template("upload.html")
@app.route("/upload", methods=['POST'])
def upload():
    target = os.path.join(APP_ROOT,'images/')
    print(target)
    if not os.path.isdir(target):
        os.mkdir(target)
    for file in request.files.getlist("file"):
        print(file)
        filename = file.filename
        destination = "/".join([target, filename])
        print(destination)
        file.save(destination)
    img=destination
    image_data = tf.gfile.FastGFile(img, 'rb').read()
    label_lines = [line.rstrip() for line
                       in tf.gfile.GFile("retrained_labels.txt")]
    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:
        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        predictions = sess.run(softmax_tensor, \
                 {'DecodeJpeg/contents:0': image_data})
        top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
        name=[]
        scores=[]
        for node_id in top_k:
            human_string = label_lines[node_id]
            score = predictions[0][node_id]
            score=score*100
            if (score>20):
                print('%s (score = %.5f)' % (human_string, score))
                name.append(human_string)
                scores.append(score)
   
    return render_template("complete.html",name=name,scores=scores)
if __name__ == "__main__":
    app.run(port=4555, debug=True)