Update app.py

app.py

@@ -15,9 +15,9 @@ transformer = transforms.Compose([
     #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225
     #])
 ])
-model=torch.jit.load('model1.pt',map_location=torch.device('cpu'))
+model=torch.jit.load('model.pt',map_location=torch.device('cpu'))
 #model=torch.jit.load('model1.pt')
-classes=['Minivan Images', 'Muscle Car Images', 'Sedan Images', 'Sports car Images']
+classes=['Minivan Car', 'Muscle Car ', 'Sedan Car', 'Sports Car', 'None of the Above class']
 
 def predict(inp):
   inp=transformer(inp).unsqueeze(0)
@@ -26,9 +26,9 @@ def predict(inp):
   with torch.no_grad():
   
     prediction =F.softmax(model(inp)[0], dim=0)
-    confidences = {classes[i]: float(prediction[i]) for i in range(4)}    
+    confidences = {classes[i]: float(prediction[i]) for i in range(5)}    
   return confidences
   
  
  # gr.Interface(fn=predict, inputs=gr.Image(type="pil"),outputs=gr.Label(num_top_classes=4),title='Image classification',interpretation='default').launch(debug='True')
-gr.Interface(predict, gr.inputs.Image(type="pil"),outputs='label').launch(debug='True')
+gr.Interface(predict, gr.inputs.Image(type="pil"),outputs='label',title='Image classification').launch(debug='True')