Update app.py

app.py

@@ -2,9 +2,13 @@ import torch
 from PIL import Image
 from torchvision import transforms
 import gradio as gr
+import os
+
 model = torch.hub.load('XingangPan/IBN-Net', 'resnet50_ibn_a', pretrained=True)
 model.eval()
 
+# Download ImageNet labels
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
 # Download an example image from the pytorch website
 torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
 
@@ -28,8 +32,7 @@ def inference(input_image):
         output = model(input_batch)
     # The output has unnormalized scores. To get probabilities, you can run a softmax on it.
     probabilities = torch.nn.functional.softmax(output[0], dim=0)
-    # Download ImageNet labels
-    !wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt
+
     # Read the categories
     with open("imagenet_classes.txt", "r") as f:
         categories = [s.strip() for s in f.readlines()]