Update app.py

app.py

@@ -8,7 +8,7 @@ import types
 from huggingface_hub import hf_hub_url, cached_download
 
 #TOKEN = os.environ['TOKEN']
-with open(cached_download(hf_hub_url('CorvaeOboro/gen_item_ring', 'gen_item_ring_stylegan2ada_20230218.pkl')), 'rb') as f:
+with open(cached_download(hf_hub_url('CorvaeOboro/gen_item_potion', 'gen_item_potion_stylegan2ada_20230430.pkl')), 'rb') as f:
     G = pickle.load(f)['G_ema']# torch.nn.Module
 
 device = torch.device("cpu")
@@ -56,10 +56,10 @@ def infer(num_images, interpolate):
 with demo:
     gr.Markdown(
     """
-    # gen_item_ring
-    ![item_ring_process_single](https://raw.githubusercontent.com/CorvaeOboro/gen_item/master/docs/ring/item_ring_process_single.jpg?raw=true "item_ring_process_single")
+    # gen_item_potion
+    ![item_potion_process_single](https://raw.githubusercontent.com/CorvaeOboro/gen_item/master/docs/potion/item_potion_process_single.jpg?raw=true "item_potion_process_single")
 
-    creates ring item images from stylegan2ada model trained on synthetic dataset utilizing procgen and neural networks .
+    creates potion item images from stylegan2ada model trained on synthetic dataset utilizing procgen and neural networks .
     more information here :  [https://github.com/CorvaeOboro/gen_item](https://github.com/CorvaeOboro/gen_item).
     """)
     images_num = gr.inputs.Slider(default=6, label="Num Images", minimum=1, maximum=16, step=1)