add title, example

app.py

@@ -3,6 +3,7 @@ import torch
 import torch.nn as nn
 import gradio as gr
 from PIL import Image
+import torchvision.transforms as transforms
 
 norm_layer = nn.InstanceNorm2d
 
@@ -86,13 +87,24 @@ model = Generator(3, 1, 3)
 model.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
 model.eval()
 
+transforms_r = [transforms.Resize(int(opt.size), Image.BICUBIC),
+                   transforms.ToTensor()]
+transforms.Compose([transforms.Resize(int(opt.size), Image.BICUBIC), transforms.ToTensor()])
+
 def predict(input_img):
     input_img = Image.open(input_img)
     ratio = 256.0 / input_img.size[1]
     input_img = input_img.resize((int(ratio*input_img.size[0]),256), Image.ANTIALIAS)
+    
+
     return input_img
 
+title="informative-drawings"
+description="Gradio Demo for line drawing generation. "
+article = "<p style='text-align: center'><a href='https://film-net.github.io/' target='_blank'>FILM: Frame Interpolation for Large Motion</a> | <a href='https://github.com/google-research/frame-interpolation' target='_blank'>Github Repo</a></p>"
+examples=[['cat.png']]
+
 
-iface = gr.Interface(predict, gr.inputs.Image(type='filepath'), "image")
+iface = gr.Interface(predict, gr.inputs.Image(type='filepath'), "image", title=title,description=description,article=article,examples=examples)
 
 iface.launch()