Yavuz Kömeçoğlu yavuzKomecoglu

## split-train-test-val.py
import argparse
import errno
import os
import random
import shutil


def createFolder(path):
    try:
        os.makedirs(path)

## ai-image-recognition-web-index-3.html
$('#upload-file-btn').click(function () {
    $('.loader').show();
    $("#results").html('');
    var form_data = new FormData($('#upload-file')[0]);
    $.ajax({
        type: 'POST',
        url: '/predict',
        data: form_data,
        contentType: false,
        cache: false,

## ai-image-recognition-web-index-2.html
<form id="upload-file" method="post" enctype="multipart/form-data">
    <div class="form-group">
    <label for="InputFile">Resim Dosyası:</label>
    <input name="image" type="file" class="form-control-file" id="InputFile" aria-describedby="fileHelp">
    <small id="fileHelp" class="form-text text-muted">Tanımak istediğiniz resmi bilgisayarınızdan seçiniz</small>
    </div>
    <button id="upload-file-btn" type="button" class="btn btn-primary">Gönder</button>
</form>

## ai-image-recognition-web-5.py
if __name__ == "__main__":
    print("START FLASK")

    port = int(os.environ.get('PORT', 5000))
    app.run(host='0.0.0.0', port=port)

## ai-image-recognition-web-4.py
@app.route("/predict", methods=["POST"])
def predict():
    # initialize the data dictionary that will be returned from the
    # view
    data = {"success": False}
    # ensure an image was properly uploaded to our endpoint
    if request.method == "POST" and request.files['image']:
        imagefile = request.files["image"].read()
        image = Image.open(io.BytesIO(imagefile))
        # preprocess the image and prepare it for classification

## ai-image-recognition-web-3.py
model = ResNet50(weights="imagenet")

## ai-image-recognition-web-2.py
from keras.applications import ResNet50
from keras.preprocessing.image import img_to_array
from keras.applications import imagenet_utils
from PIL import Image
import numpy as np
from flask import Flask, render_template, request, jsonify, send_from_directory
import io
import werkzeug
import datetime
import os

## ai-image-recognition-web-index.html
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css">
<title>Merhaba Flask! Html demosu.</title>
</head>
<body>
  <div class="container">

## ai-image-recognition-web-1.py
from flask import Flask
app = Flask(__name__)
@app.route("/")
def main():
    return "Merhaba Flask ;)"
if __name__ == "__main__":
    app.run()
	import argparse
	import errno
	import os
	import random
	import shutil


	def createFolder(path):
	try:
	os.makedirs(path)
	$('#upload-file-btn').click(function () {
	$('.loader').show();
	$("#results").html('');
	var form_data = new FormData($('#upload-file')[0]);
	$.ajax({
	type: 'POST',
	url: '/predict',
	data: form_data,
	contentType: false,
	cache: false,
	<form id="upload-file" method="post" enctype="multipart/form-data">
	<div class="form-group">
	<label for="InputFile">Resim Dosyası:</label>
	<input name="image" type="file" class="form-control-file" id="InputFile" aria-describedby="fileHelp">
	<small id="fileHelp" class="form-text text-muted">Tanımak istediğiniz resmi bilgisayarınızdan seçiniz</small>
	</div>
	<button id="upload-file-btn" type="button" class="btn btn-primary">Gönder</button>
	</form>
	if __name__ == "__main__":
	print("START FLASK")

	port = int(os.environ.get('PORT', 5000))
	app.run(host='0.0.0.0', port=port)
	@app.route("/predict", methods=["POST"])
	def predict():
	# initialize the data dictionary that will be returned from the
	# view
	data = {"success": False}
	# ensure an image was properly uploaded to our endpoint
	if request.method == "POST" and request.files['image']:
	imagefile = request.files["image"].read()
	image = Image.open(io.BytesIO(imagefile))
	# preprocess the image and prepare it for classification
	from keras.applications import ResNet50
	from keras.preprocessing.image import img_to_array
	from keras.applications import imagenet_utils
	from PIL import Image
	import numpy as np
	from flask import Flask, render_template, request, jsonify, send_from_directory
	import io
	import werkzeug
	import datetime
	import os
	<!doctype html>
	<html lang="en">
	<head>
	<meta charset="utf-8">
	<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
	<link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.0.0/css/bootstrap.min.css">
	<title>Merhaba Flask! Html demosu.</title>
	</head>
	<body>
	<div class="container">
	from flask import Flask
	app = Flask(__name__)
	@app.route("/")
	def main():
	return "Merhaba Flask ;)"
	if __name__ == "__main__":
	app.run()