import?numpy?as?np
import?tensorflow?as?tf
from?flask?import?Flask,?jsonify,?render_template,?request

from?mnist?import?model

x?=?tf.placeholder("float",?[None,?784])
sess?=?tf.Session()

with?tf.variable_scope("regression"):
????y1,?variables?=?model.regression(x)
saver?=?tf.train.Saver(variables)
#saver.restore(sess,?"mnist/data/regression.ckpt")
module_file?=??tf.train.latest_checkpoint('mnist/data/regression.ckpt')
with?tf.Session().as_default()?as?sess:
???sess.run(tf.global_variables_initializer())
???if?module_file?is?not?None:
??????saver.restore(sess,?module_file)

with?tf.variable_scope("convolutional"):
????keep_prob?=?tf.placeholder("float")
????y2,?variables?=?model.convolutional(x,?keep_prob)
saver?=?tf.train.Saver(variables)
#saver.restore(sess,?"mnist/data/convalutional.ckpt")
module_file?=??tf.train.latest_checkpoint('mnist/data/convolutional.ckpt')
with?tf.Session().as_default()?as?sess:
???sess.run(tf.global_variables_initializer())
???if?module_file?is?not?None:
??????saver.restore(sess,?module_file)

def?regression(input):
????return?sess.run(y1,?feed_dict={x:?input}).flatten().tolist()
def?convolutional(input):
????return?sess.run(y2,?feed_dict={x:?input,?keep_prob:1.0}).flatten().tolist()

app?=?Flask(__name__)

@app.route('/api/mnist',?methods=['post'])
def?mnist():
????input?=?((255?-?np.array(request.json,?dtype=np.uint8))?/?255.0).reshape(1,?784)
????output1?=?regression(input)
????output2?=?convolutional(input)
????return?jsonify(results?=?[output1,?output2])


@app.route('/')
def?main():
????return?render_template('index.html')
if?__name__?==?'__main__':
????app.debug?=?True
????app.run(host='0.0.0.0',?port=0000)