Added examples

app.py

@@ -39,7 +39,7 @@ def resize_image_pil(image, new_width, new_height):
 
     return resized
 
-def inference(input_img, transparency = 0.5, is_grad_cam=True, target_layer_number = -1, top_predictions=3):
+def inference(input_img, is_grad_cam=True, transparency = 0.5, target_layer_number = -1, top_predictions=3):
     input_img = resize_image_pil(input_img, 32, 32)
     
     input_img = np.array(input_img)
@@ -73,13 +73,22 @@ def inference(input_img, transparency = 0.5, is_grad_cam=True, target_layer_numb
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
-examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
+examples = [["cat.jpg", True, 0.5, -1, 3], 
+            ["dog.jpg", True, 0.5, -1, 3], 
+            ["bird.jpg", True, 0.5, -1, 3], 
+            ["car.jpg", True, 0.5, -1, 3], 
+            ["deer.jpg", True, 0.5, -1, 3], 
+            ["frog.jpg", True, 0.5, -1, 3], 
+            ["horse.jpg", True, 0.5, -1, 3], 
+            ["plane.jpg", True, 0.5, -1, 3], 
+            ["ship.jpg", True, 0.5, -1, 3], 
+            ["truck.jpg", True, 0.5, -1, 3]]
 demo = gr.Interface(
     inference, 
     inputs = [
         gr.Image(width=256, height=256, label="Input Image"),
-        gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), 
         gr.Checkbox(label="Show GradCAM"),
+        gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), 
         gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
         gr.Slider(2, 10, value=3, step=1, label="Number of Top Classes")
         ],