Below we will show you how to spin up a webpage for classifying images on-demand. Users will be able to provide the URL to an image, and the application will predict its contents.
Keras
Keras is a machine learning library for training deep neural networks that runs on top of TensorFlow, CNTK or Theano.
Flask
Flask is a web-application microframework for Python.
Code
from keras.applications.resnet50 import ResNet50
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input, decode_predictions
import numpy as np
from flask import Flask
from flask import request
from flask import jsonify
from flask import render_template
from flask_cors import CORS
import os
import urllib.request
import imghdr
import base64
app = Flask(__name__)
CORS(app)
model = ResNet50(weights='imagenet')
@app.route('/', methods=['GET'])
def base():
return '''<html>
<body>
<form id="img-prediction-form">
<label>Image URL:</label>
<input type="text" name="imgurl" />
<input type="submit" value="Submit" name="submit">
</form>
<table>
<thead>
<th>Name</th>
<th>Accuracy</th>
</thead>
<tbody id="prediction-results"></tbody>
</table>
<script
src="https://code.jquery.com/jquery-3.2.1.min.js"
integrity="sha256-hwg4gsxgFZhOsEEamdOYGBf13FyQuiTwlAQgxVSNgt4="
crossorigin="anonymous">
</script>
<script type="text/javascript">
$(document).on("submit","form",function(e) {
e.preventDefault();
$("#prediction-results").html("");
$.ajax({
url: "/predict",
method: "POST",
data: $("#img-prediction-form").serialize(),
success: function(result) {
$.each(result, function(x,y) {
$("#prediction-results").append("<tr><td>"+x+"</td><td>"+y+"</td></tr>");
});
}
});
});
</script>
</body>
</html>'''
@app.route('/predict', methods=['POST'])
def predit():
response = {}
if request.method != 'POST':
response['status'] = False
response['message'] = 'Invalid request'
else:
imgurl = request.form.get('imgurl')
imgpth = "/tmp/{}".format(base64.b64encode(imgurl.encode('utf-8')))
urllib.request.urlretrieve(imgurl, imgpth)
if not os.path.isfile(imgpth):
response['status'] = False
response['message'] = 'Could not obtain the specified image.'
else:
if imghdr.what(imgpth) is None:
response['status'] = False
response['message'] = 'Invalid image provided'
else:
img = image.load_img(imgpth, target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)
preds = model.predict(x)
for x in decode_predictions(preds, top=3)[0]:
response[x[1]] = format(x[2], "g")
return jsonify(response)
Execute
$ export FLASK_APP=filename.py $ python -m flask run --host=[IP_ADDRESS]
Replace [IP_ADDRESS] with your server's IP address, and replace filename.py with the filename of the above.
