Update app.py

app.py

@@ -1,6 +1,6 @@
 import torch
 import nltk
-#nltk.download('wordnet')
+nltk.download('wordnet')
 nltk.download('omw-1.4')
 from PIL import Image
 from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
@@ -29,7 +29,7 @@ with torch.no_grad():
     output = gan_model(noise_vector, class_vector, truncation)
 
 # If you have a GPU put back on CPU
-output = output.to('cpu')
+#output = output.to('cpu')
 
 # If you have a sixtel compatible terminal you can display the images in the terminal
 # (see https://github.com/saitoha/libsixel for details)