Rizal Setya Perdana rizalespe

## resnetPredict.html
<body>
  <input type='file' onchange="resnetPredict(this);" />
</body>

<script>
function resnetPredict(input) {
        if (input.files && input.files[0]) {
            $('#images_show').show();
            var reader = new FileReader();
            reader.onload = function (e) {

## ResNetPredict.py
# importing all required package library
from keras.applications import ResNet50
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
import numpy as np


# A class for image prediction based on ResNet pre-trained model
class ResNetPredict:
    def __init__(self):

## run_flask_service.py
# importing all required library
import flask
import io
from flask_cors import CORS
from PIL import Image
from ResNetPredict import ResNetPredict

app = flask.Flask(__name__)

# Cross Origin Resource Sharing (CORS), making the URL accessible from cross-origin AJAX
	<body>
	<input type='file' onchange="resnetPredict(this);" />
	</body>

	<script>
	function resnetPredict(input) {
	if (input.files && input.files[0]) {
	$('#images_show').show();
	var reader = new FileReader();
	reader.onload = function (e) {
	# importing all required package library
	from keras.applications import ResNet50
	from keras.preprocessing.image import img_to_array
	from keras.applications import imagenet_utils
	import numpy as np


	# A class for image prediction based on ResNet pre-trained model
	class ResNetPredict:
	def __init__(self):
	# importing all required library
	import flask
	import io
	from flask_cors import CORS
	from PIL import Image
	from ResNetPredict import ResNetPredict

	app = flask.Flask(__name__)

	# Cross Origin Resource Sharing (CORS), making the URL accessible from cross-origin AJAX