Update app.py

app.py

@@ -66,16 +66,16 @@ inference_model = LitResnet.load_from_checkpoint("cifar10_customresnet_20_epoch.
 
 def inference(input_img, see_misclassified,num_misclassified_imgs,see_gradcam,num_gradcam_imgs,transparency = 0.85, target_layer_number = -1,top_classes=3):
 
-    if see_misclassified: # show misclassified images
-      org_img   =  cv2.imread('/content/drive/MyDrive/AI/ERA_course/session12/example_images/img_eg_0.jpg')
-      input_img = org_img
+    # if see_misclassified: # show misclassified images
+    #   org_img   =  cv2.imread('/content/drive/MyDrive/AI/ERA_course/session12/example_images/img_eg_0.jpg')
+    #   input_img = org_img
 
-    elif num_gradcam_imgs > 0: # show gradcam on example images
-      org_img   =  cv2.imread('/content/drive/MyDrive/AI/ERA_course/session12/example_images/img_eg_0.jpg')
-      input_img = org_img
+    # elif num_gradcam_imgs > 0: # show gradcam on example images
+    #   org_img   =  cv2.imread('/content/drive/MyDrive/AI/ERA_course/session12/example_images/img_eg_0.jpg')
+    #   input_img = org_img
 
-    else: # nothing chosen - misclassified or gradcam
-      org_img = input_img
+    # else: # nothing chosen - misclassified or gradcam
+    #   org_img = input_img
 
     # model inference
     transform = transforms.ToTensor()
@@ -109,12 +109,14 @@ def inference(input_img, see_misclassified,num_misclassified_imgs,see_gradcam,nu
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
+examples = [["img_eg_0.jpg", False,0,False,0.5, -1,3], ["img_eg_1.jpg", False,0,False,0.5, -1,3],["img_eg_2.jpg", False,0,False,0.5, -1,3],["img_eg_3.jpg", False,0,False,0.5, -1,3],["img_eg_4.jpg", False,0,False,0.5, -1,3],["img_eg_5.jpg", False,0,False,0.5, -1,3],["img_eg_6.jpg", False,0,False,0.5, -1,3],["img_eg_7.jpg", False,0,False,0.5, -1,3],["img_eg_8.jpg", False,0,False,0.5, -1,3]]
 
 demo = gr.Interface(
     inference,
     inputs =  [gr.Image(shape=(32, 32), label="Input Image"), gr.Checkbox(label="Misclassified"),gr.Slider(0, 10, value = 0, step=1,label="Total Misclassified Images"),gr.Checkbox(label="Gradcam"),gr.Slider(0, 10, value = 0, step=1,label="Total GradCam Images"),gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-2, -1, value = -1, step=1, label="Which Layer?"), gr.Slider(1, 10, value=3, step=1, label="How many top classes?")],
     outputs = [gr.Label(), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
     title = title,
-    description = description,)
+    description = description,
+    examples = examples)
 
 demo.launch()