Update app.py

app.py

@@ -245,7 +245,7 @@ def upsample(filters, size, apply_dropout=False):
   return result
 
 def Generator(tpu=False):
-  inputs = tf.keras.layers.Input(shape=[256, 256, 1])
+  inputs = tf.keras.layers.Input(shape=[256, 256, 3])
 
   down_stack = [
     downsample(64, 4, apply_batchnorm=False),  # (batch_size, 128, 128, 64)
@@ -327,7 +327,7 @@ def ssim_psnr(pre,target):
 def Discriminator():
   initializer = tf.random_normal_initializer(0., 0.02)
 
-  inp = tf.keras.layers.Input(shape=[256, 256, 1], name='input_image') # lr
+  inp = tf.keras.layers.Input(shape=[256, 256, 3], name='input_image') # lr
   tar = tf.keras.layers.Input(shape=[256, 256, 3], name='target_image') # hr
 
   x = tf.keras.layers.concatenate([inp, tar])  # (batch_size, 256, 256, channels*2)
@@ -464,7 +464,6 @@ def result(Input,Choice):
       size0 = Input.shape[0]
       size1 = Input.shape[1]
       Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
-      Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
       Input = np.array(Input).reshape(1,256,256,3)
       prediction = pre_trained_2(Input,training=True)
       Input = prediction[0]