predict_app.py

import base64
import numpy as np
import io
from PIL import Image
import keras
from keras import backend as k
from keras.models import Sequential, load_model
from keras.preprocessing.image import ImageDataGenerator, img_to_array
from keras.layers import Dense, Conv2D, MaxPool2D, Flatten
from flask import request
from flask import jsonify
from flask import Flask
from keras.optimizers import Adam
from keras.applications import VGG16
from keras.applications.vgg16 import VGG16
from keras.backend import set_session
import tensorflow as tf
from flask_cors import CORS



app = Flask(__name__)
CORS(app)

def get_model():

     with graph.as_default():
         set_session(sess)
         global model
         vgg_16_model=VGG16()
         model=Sequential()
         for layer in vgg_16_model.layers[:-1]:
             model.add(layer)
         for layer in model.layers:
             layer.trainable=False
         model.add(Dense(1, activation='sigmoid'))
         model.load_weights('silicon_valley_model.h5')

         model._make_predict_function()

         print(" * Model loaded!")




def preprocess_image(image, target_size):
    if image.mode != 'RGB':
        image = image.convert('RGB')
    image = image.resize(target_size)
    image = img_to_array (image)
    image = np.expand_dims(image, axis=0)

    return image



print(' * Loading Keras model.. ')
global graph
global sess
sess=tf.Session()
graph = tf.get_default_graph()
get_model()

@app.route('/predict', methods=['POST'])
def predict():
    message = request.get_json(force=True)
    encoded = message['image']
    decoded = base64.b64decode(encoded)
    image = Image.open(io.BytesIO(decoded))
    processed_image = preprocess_image(image, target_size=(224, 224))
    with graph.as_default():
        set_session(sess)
        prediction = model.predict(processed_image)
        val=float(np.squeeze(prediction[0][0]))
        #print(1-val)
        response = {
             'prediction' : {
                  'hot_dog': val,
                  'not_hot_dog': 1 - val
                        }
                }

    return jsonify(response)