app2.py

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"
# print 'loading model'
# #model = load_model('dundun.h5', compile=False)
# #model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# print 'model loaded'
# 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)]
#     print result
#     return 'bye'


@app.route('/', methods=['GET', 'POST'])
def start():
    if request.method == 'POST':
        print 'hiii'
        strng = request.values
        print strng
        imageInstring = strng['image']
        print imageInstring
        imgdata = base64.b64decode(imageInstring)
        print imgdata
        # with open("image.jpeg", "wb") as fh:
        #     fh.write(imgdata)
        # print 'image written'

        # pred = prediction()
        # print "prediction is ", pred
        print "sending chair"

        return 'chair'
    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)