Update app.py

app.py

@@ -4,13 +4,11 @@ import torch.nn as nn
 import gradio as gr
 from PIL import Image
 import torchvision.transforms as transforms
-
 norm_layer = nn.InstanceNorm2d
 
 class ResidualBlock(nn.Module):
     def __init__(self, in_features):
         super(ResidualBlock, self).__init__()
-
         conv_block = [  nn.ReflectionPad2d(1),
                         nn.Conv2d(in_features, in_features, 3),
                         norm_layer(in_features),
@@ -19,9 +17,7 @@ class ResidualBlock(nn.Module):
                         nn.Conv2d(in_features, in_features, 3),
                         norm_layer(in_features)
                         ]
-
         self.conv_block = nn.Sequential(*conv_block)
-
     def forward(self, x):
         return x + self.conv_block(x)
 
@@ -29,15 +25,11 @@ class ResidualBlock(nn.Module):
 class Generator(nn.Module):
     def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
         super(Generator, self).__init__()
-
-        # Initial convolution block
         model0 = [   nn.ReflectionPad2d(3),
                     nn.Conv2d(input_nc, 64, 7),
                     norm_layer(64),
                     nn.ReLU(inplace=True) ]
         self.model0 = nn.Sequential(*model0)
-
-        # Downsampling
         model1 = []
         in_features = 64
         out_features = in_features*2
@@ -48,14 +40,10 @@ class Generator(nn.Module):
             in_features = out_features
             out_features = in_features*2
         self.model1 = nn.Sequential(*model1)
-
         model2 = []
-        # Residual blocks
         for _ in range(n_residual_blocks):
             model2 += [ResidualBlock(in_features)]
         self.model2 = nn.Sequential(*model2)
-
-        # Upsampling
         model3 = []
         out_features = in_features//2
         for _ in range(2):
@@ -65,13 +53,10 @@ class Generator(nn.Module):
             in_features = out_features
             out_features = in_features//2
         self.model3 = nn.Sequential(*model3)
-
-        # Output layer
         model4 = [  nn.ReflectionPad2d(3),
                         nn.Conv2d(64, output_nc, 7)]
         if sigmoid:
             model4 += [nn.Sigmoid()]
-
         self.model4 = nn.Sequential(*model4)
 
     def forward(self, x, cond=None):
@@ -80,7 +65,6 @@ class Generator(nn.Module):
         out = self.model2(out)
         out = self.model3(out)
         out = self.model4(out)
-
         return out
 
 model1 = Generator(3, 1, 3)
@@ -107,8 +91,8 @@ def predict(input_img, ver):
     drawing = transforms.ToPILImage()(drawing)
     return drawing
 
-title="Image to Line Drawings - Complex and Simple Portraits and Landscapes"
-description="Image to Line Drawing  L4xKDhK7zqHtYRYptIOJ--N5LZB (1).mp4 "
+title="Art Style Line Drawings - Complex and Simple Portraits and Landscapes"
+description="Art Style Line Drawings  🦀🦁🦂🦃🦄🦅🦆🦇🦈🦉🦊🦋🦌🦍🦎🦏 🦐🦑🦒🦓🦔🦕🦖🦗🦘🦙🦚🦛🦜🦝🦞🦟🦠🦡🦢🦣🦤🦥🦦🦧🦨🦩🦪🦫🦬🦭🦮"
 # article = "<p style='text-align: center'></p>"
 examples=[
 ['Achilles-art-scale-6_00x-gigapixel.png', 'Simple Lines'],