Flask server to be used on top of TensorFlow Serving server

app.py

import base64
import json
from io import BytesIO

import numpy as np
import requests
from flask import Flask, request, jsonify
from keras.applications import inception_v3
from keras.preprocessing import image

# from flask_cors import CORS

app = Flask(__name__)


# Uncomment this line if you are making a Cross domain request
# CORS(app)

# Testing URL
@app.route('/hello/', methods=['GET', 'POST'])
def hello_world():
    return 'Hello, World!'


@app.route('/imageclassifier/predict/', methods=['POST'])
def image_classifier():
    # Decoding and pre-processing base64 image
    img = image.img_to_array(image.load_img(BytesIO(base64.b64decode(request.form['b64'])),
                                            target_size=(224, 224))) / 255.

    # this line is added because of a bug in tf_serving(1.10.0-dev)
    img = img.astype('float16')

    # Creating payload for TensorFlow serving request
    payload = {
        "instances": [{'input_image': img.tolist()}]
    }

    # Making POST request
    r = requests.post('http://localhost:9000/v1/models/ImageClassifier:predict', json=payload)

    # Decoding results from TensorFlow Serving server
    pred = json.loads(r.content.decode('utf-8'))

    # Returning JSON response to the frontend
    return jsonify(inception_v3.decode_predictions(np.array(pred['predictions']))[0])