Update app.py

app.py

@@ -2,6 +2,10 @@ import torch
 from PIL import Image
 from torchvision import transforms
 import gradio as gr
+import os
+
+# Download ImageNet labels
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
 
 model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True)
 # or any of these variants
@@ -11,6 +15,8 @@ model = torch.hub.load('PingoLH/Pytorch-HarDNet', 'hardnet68', pretrained=True)
 model.eval()
 torch.hub.download_url_to_file("https://github.com/pytorch/hub/raw/master/images/dog.jpg", "dog.jpg")
 
+
+
 # sample execution (requires torchvision)
 def inference(input_image):
     preprocess = transforms.Compose([
@@ -31,8 +37,7 @@ 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()]