Update app.py

app.py

@@ -36,7 +36,7 @@ os.system("mv shape_predictor_68_face_landmarks.dat models/dlibshape_predictor_6
 
 device = 'cpu' 
 
-os.system("gdown https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK")
+os.system("gdown https://drive.google.com/uc?id=1X6AvjLltUAYZrarLdrdGav5gsRkqJYOE")
 os.system("mv stylegan2-ffhq-config-f.pt models/stylegan2-ffhq-config-f.pt")
 
 
@@ -93,16 +93,12 @@ def inference(img):
         generator.eval()
     
         original_my_sample = original_generator(my_w, input_is_latent=True)
-        my_sample = generator(my_w, input_is_latent=True)
-    
-    return 
-   
-    my_output = torch.cat([style_image, face, my_sample], 0)   
-
-    return my_output
+        my_sample = generator(my_w, input_is_latent=True)   
+    npimage = my_sample[0].permute(1, 2, 0).detach().numpy()
+    return npimage
   
 title = "AnimeGANv2"
 description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below."
 article = "<p style='text-align: center'><a href='https://github.com/bryandlee/animegan2-pytorch' target='_blank'>Github Repo Pytorch</a></p> <center><img src='https://visitor-badge.glitch.me/badge?page_id=akhaliq_animegan' alt='visitor badge'></center> <p style='text-align: center'>samples from repo: <img src='https://user-images.githubusercontent.com/26464535/129888683-98bb6283-7bb8-4d1a-a04a-e795f5858dcf.gif' alt='animation'/> <img src='https://user-images.githubusercontent.com/26464535/137619176-59620b59-4e20-4d98-9559-a424f86b7f24.jpg' alt='animation'/><img src='https://user-images.githubusercontent.com/26464535/127134790-93595da2-4f8b-4aca-a9d7-98699c5e6914.jpg' alt='animation'/></p>"
 
-gr.Interface(inference, [gr.inputs.Image(type="file")], gr.outputs.Image(type="pil"),title=title,description=description,article=article,enable_queue=True,allow_flagging=False).launch()
+gr.Interface(inference, [gr.inputs.Image(type="file")], gr.outputs.Image(type="numpy"),title=title,description=description,article=article,enable_queue=True,allow_flagging=False).launch()