Update app.py

app.py

@@ -121,9 +121,7 @@ def iterative_least_likely_fgsm_cifar10(image, model, epsilon, alpha, niter, nb_
     # adv_x = adv_x * 0.5 + 0.5
     return adv_x.numpy()
 
-def fn(dataset, attack):
-    epsilon=15/255
-    alpha=1/255
+def fn(dataset, attack, epsilon, alpha):
     niter = int(min(4 + epsilon*255, 1.25 * epsilon * 255))
     nb_classes = 10
     classes = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"]
@@ -186,7 +184,9 @@ demo = gr.Interface(
     description="A random image from the chosen dataset will be perturbated with the chosen attack type and both the original image and the perturbated image will be displayed.",
     inputs=[
         gr.Radio(choices=["MNIST", "CIFAR10"], label="Pick a dataset"),
-        gr.Radio(choices=["FGSM", "I-FGSM", "I-LL-FGSM"], label="Pick an attack")
+        gr.Radio(choices=["FGSM", "I-FGSM", "I-LL-FGSM"], label="Pick an attack"),
+        gr.Number(value=15, label="Epsilon"),
+        gr.Number(value=1, label="Alpha")
         ],
     outputs=[
         gr.Image(label="Original Image").style(height=256,width=256),