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app.py
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app.py
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from flask import Flask, request
import numpy as np
import os
import pickle
import base64
from keras.models import load_model
from keras.preprocessing import image as image_utils
app = Flask(__name__)
print "hellooo"
model = load_model('dundun.h5')
label_list_path = 'datasets/cifar-10-batches-py/batches.meta'
keras_dir = os.path.expanduser(os.path.join('~', '.keras'))
datadir_base = os.path.expanduser(keras_dir)
if not os.access(datadir_base, os.W_OK):
datadir_base = os.path.join('/tmp', '.keras')
label_list_path = os.path.join(datadir_base, label_list_path)
with open(label_list_path, mode='rb') as f:
labels = pickle.load(f)
def prediction():
# building the path
# testing for a single image
test_image = image_utils.load_img('image.jpeg', target_size=(64, 64))
test_image = image_utils.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis=0)
result = model.predict_on_batch(test_image)
# print(result)
predicted_label = labels['label_names'][np.argmax(result)]
return predicted_label
@app.route('/', methods=['GET', 'POST'])
def start():
if request.method == 'POST':
strng = request.values
imageInstring = strng['image']
imgdata = base64.b64decode(imageInstring)
with open("image.jpeg", "wb") as fh:
fh.write(imgdata)
pred = prediction()
return pred
else:
return "<h1>use post method</h1>"
@app.route('/hello/<username>')
def hello(username):
return '<h1>u want soluchan %s ?</h1>' % username
if __name__ == '__main__':
app.run(port=8080, use_reloader=True)