update description

main.py

@@ -6,8 +6,7 @@ from model import DocuGAN
 
 
 chk_path = "best_model.ckpt"
-model = DocuGAN(hidden_size=64, num_channel=1, latent_size=128, batch_size=128)
-# model = DocuGAN.load_from_checkpoint(chk_path, strict=False)
+model = DocuGAN.load_from_checkpoint(chk_path, strict=False)
 model.eval()
 transform = T.ToPILImage()
 
@@ -17,6 +16,7 @@ def fn(seed: int = 42):
     noise = torch.randn(1, 128, 1, 1)
     with torch.no_grad():
         pred = model(noise)
+        pred = pred.mul(0.5).add(0.5)
         img = transform(pred.squeeze(1))
     return img
 
@@ -29,8 +29,14 @@ gr.Interface(
     outputs='image',
     examples=[],
     enable_queue=True,
-    title="DocuGAN",
-    description="Select random seed and click on Submit to generate a new Document",
-    article="DocuGAN, Document Generator by ChainYo",
+    title="📄 DocuGAN - This document doesn't exist",
+    description="Select your seed and click on `Submit` to generate a new document",
+    article="""
+    The SN-GAN model has been trained on the `invoice` part of RVL-CDIP dataset, available [here](https://huggingface.co/datasets/ChainYo/rvl-cdip-invoice).
+
+    You can see the full implementation on the dedicated [Colab notebook](https://colab.research.google.com/drive/1u6Ct3KnNl7rcgla0268cp-XGTMmVUuJL?usp=sharing).
+
+    Made with ❤️ by [@ChainYo](-https://huggingface.co/ChainYo)
+    """,
     css=".panel { padding: 5px } .moflo-link { color: #999 }"
 ).launch()