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 img_to_array
from flask import request
from flask import jsonify
from flask import Flask
import tensorflow as tf

app = Flask(__name__)

def get_model():
    global model
    model = load_model("VGG16_cats_and_dogs.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...")

get_model()
global graph
graph = tf.get_default_graph()

@app.route("/predict", methods=['POST'])

def predict():
    with graph.as_default():
        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))
        prediction = model.predict(processed_image).tolist()
        response = { 'prediction': {'dog':prediction[0][0],'cat':prediction[0][1]}}
        return jsonify(response)

# def predict():
#     message = request.get_json(force=True)
#
#     image = Image.open(message['image'])
#     processed_image = preprocess_image(image, target_size=(224,224))
#     prediction = model.predict(processed_image).tolist()
#     response = { 'prediction': {'dog':prediction[0][0],'cat':prediction[0][1]}}
#     return jsonify(response)