Update app.py

app.py

@@ -1,3 +1,4 @@
+import os
 import torch
 import gradio as gr
 from PIL import Image
@@ -12,6 +13,9 @@ model = torch.hub.load('pytorch/vision:v0.9.0', 'densenet121', pretrained=True)
 # model = torch.hub.load('pytorch/vision:v0.9.0', 'densenet161', pretrained=True)
 model.eval()
 
+# Download ImageNet labels
+os.system("wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt")
+
 def inference(input_image):
 
     preprocess = transforms.Compose([
@@ -33,8 +37,7 @@ def inference(input_image):
     # Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes
     # 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()]