diff --git a/infer.py b/infer.py
new file mode 100644
index 0000000..fc21d47
--- /dev/null
+++ b/infer.py
@@ -0,0 +1,38 @@
+from model import lowlight_enhance, load_images
+import tensorflow as tf
+import os
+
+
+def lowlight_test(input_file, lowlight_enhance):
+    test_low_data_name = [input_file]
+    test_low_data = []
+    test_high_data = []
+    for i in range(1):
+        print('fileload', test_low_data_name[i])
+        test_low_im = load_images(test_low_data_name[i])
+        print('fileload return', test_low_im)
+        test_low_data.append(test_low_im)
+
+    lowlight_enhance.test(test_low_data, test_high_data, test_low_data_name, save_dir='test_results', decom_flag=0)
+
+
+def main(input_file, use_gpu=False):
+    print('called main')
+    if use_gpu:
+        print("[*] GPU\n")
+        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_idx
+        gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_mem)
+        with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
+            model = lowlight_enhance(sess)
+            lowlight_test(input_file, model)
+    else:
+        print("[*] CPU\n")
+        with tf.Session() as sess:
+            model = lowlight_enhance(sess)
+            lowlight_test(input_file, model)
+
+
+#if __name__ == '__main__':
+#    main(input_file, use_gpu=False)
+
+        #tf.app.run()
\ No newline at end of file
diff --git a/model.py b/model.py
index ad784fb..a2fab36 100644
--- a/model.py
+++ b/model.py
@@ -10,9 +10,11 @@
 
 from utils import *
 
+
 def concat(layers):
     return tf.concat(layers, axis=3)
 
+
 def DecomNet(input_im, layer_num, channel=64, kernel_size=3):
     input_max = tf.reduce_max(input_im, axis=3, keepdims=True)
     input_im = concat([input_max, input_im])
@@ -27,6 +29,7 @@ def DecomNet(input_im, layer_num, channel=64, kernel_size=3):
 
     return R, L
 
+
 def RelightNet(input_L, input_R, channel=64, kernel_size=3):
     input_im = concat([input_R, input_L])
     with tf.variable_scope('RelightNet'):
@@ -49,6 +52,7 @@ def RelightNet(input_L, input_R, channel=64, kernel_size=3):
         output = tf.layers.conv2d(feature_fusion, 1, 3, padding='same', activation=None)
     return output
 
+
 class lowlight_enhance(object):
     def __init__(self, sess):
         self.sess = sess
@@ -194,7 +198,7 @@ def train(self, train_low_data, train_high_data, eval_low_data, batch_size, patc
                 iter_num += 1
 
             # evalutate the model and save a checkpoint file for it
-            if (epoch + 1) % eval_every_epoch == 0:
+            if (epoch + 1) % int(eval_every_epoch) == 0:
                 self.evaluate(epoch + 1, eval_low_data, sample_dir=sample_dir, train_phase=train_phase)
                 self.save(saver, iter_num, ckpt_dir, "RetinexNet-%s" % train_phase)
 
diff --git a/utils.py b/utils.py
index 6fc320c..c0e4fb4 100644
--- a/utils.py
+++ b/utils.py
@@ -1,5 +1,10 @@
 import numpy as np
 from PIL import Image
+import time
+
+# import storgae library for cloud
+from google.cloud import storage
+
 
 def data_augmentation(image, mode):
     if mode == 0:
@@ -30,10 +35,12 @@ def data_augmentation(image, mode):
         image = np.rot90(image, k=3)
         return np.flipud(image)
 
+
 def load_images(file):
     im = Image.open(file)
     return np.array(im, dtype="float32") / 255.0
 
+
 def save_images(filepath, result_1, result_2 = None):
     result_1 = np.squeeze(result_1)
     result_2 = np.squeeze(result_2)
@@ -45,3 +52,21 @@ def save_images(filepath, result_1, result_2 = None):
 
     im = Image.fromarray(np.clip(cat_image * 255.0, 0, 255.0).astype('uint8'))
     im.save(filepath, 'png')
+
+
+def get_epoch_time():   #epoch time get function
+    epoch_time = int(time.time())
+    return epoch_time
+
+
+def upload_blob(bucket_name, source_file_name, destination_blob_name):   #GCP Storage upload function
+    """Uploads a file to the bucket."""
+    storage_client = storage.Client()
+    bucket = storage_client.get_bucket(bucket_name)
+    blob = bucket.blob(destination_blob_name)
+    blob.upload_from_filename(source_file_name)
+    print('File {} uploaded to {}.'.format(
+        source_file_name, destination_blob_name))
+
+
+