Update app.py

app.py

@@ -1,13 +1,11 @@
 import gradio as gr
 import keras
 from keras.models import load_model
-# from keras_contrib.layers.normalization.instancenormalization import InstanceNormalization
 from tensorflow_addons.layers import InstanceNormalization
 import matplotlib.pyplot as plt
 import numpy as np
 import tensorflow as tf
 
-
 cust = {'InstanceNormalization': InstanceNormalization}
 model=load_model('g-cycleGAN-photo2monet-500images-epoch10_30_30_30_30_30_1000images_30.h5',cust)
 
@@ -21,56 +19,29 @@ IMG_WIDTH = 256
 IMG_HEIGHT = 256
 
 def resize(image,height,width):
-  '''
-    Resizing the image
-  '''
   resized_image = tf.image.resize(image,[height,width],method = tf.image.ResizeMethod.NEAREST_NEIGHBOR)
   return resized_image
 
 def normalize(input_image):
-  # def normalize(real_image, input_image)
   input_image = (input_image/127.5) - 1
   return input_image
-#   real_image = (real_image/127.5) - 1
-#   return real_image,input_image
 
 def load(img_file):
-  '''
-    load the image. Since we need only the target image and a 
-    gray scale version of the same, we are going to load one 
-    and create the other from it
-  '''
   img = tf.io.read_file(img_file)
   img = tf.io.decode_jpeg(img)
-
-#   w = tf.shape(img)[1]
-#   w = w//2
-
-#   real_image = img[:,:w,:]
-  
   real_image = tf.cast(img,tf.float32)
 
   return real_image
 
 def load_image_test(image_file):
-  '''
-  We are not using random jitter here and thus creating
-  a gray scale image after resizing.
-  '''
   re = load(image_file)
   re = resize(re,IMG_HEIGHT,IMG_WIDTH)
-#   inp = tf.image.rgb_to_grayscale(re)
-#   re,inp = normalize(re,inp)
-#   inp = re
-#   re, inp = normalize(re,inp)
   re = normalize(re)
-#   return inp,re
   return re
 
 
 def show_preds_image(image_path):
     A = load_image_test(image_path)
-    # A = (A - 127.5) / 127.5
     A = np.expand_dims(A,axis=0)
     B = model(A)
     # B = np.squeeze(B,axis=0)