Update app.py

app.py

@@ -1,8 +1,11 @@
+import os
 import torch
 from PIL import Image
 from torchvision import transforms
 import gradio as gr
 
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
 model = torch.hub.load('huawei-noah/ghostnet', 'ghostnet_1x', pretrained=True)
 model.eval()
 # Download an example image from the pytorch website
@@ -27,8 +30,7 @@ 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()]