Added files

app.py

@@ -100,8 +100,8 @@ def generate_prediction(input_image, num_classes=3, show_gradcam=True, transpare
         cam = GradCAM(model=model, target_layers=target_layers)
         cam_generated = cam(input_tensor=input_image, targets=None)
         cam_generated = cam_generated[0, :]
-        display_image = show_cam_on_image(np.array(orginal_img) / 255, cam_generated, use_rgb=True, image_weight=transparency)
-        
+        display_image = show_cam_on_image(orginal_img / 255, cam_generated, use_rgb=True, image_weight=transparency)
+
     else:
         display_image = orginal_img
 
@@ -121,8 +121,11 @@ def app_interface(
 ):
     """Function which provides the Gradio interface"""
     input_image = resize_image_pil(input_image, 32, 32)
+
+    input_image = np.array(input_image)
+    org_img = input_image
     # Get the prediction for the input image along with confidence and display_image
-    confidences, display_image = generate_prediction(input_image, num_classes, show_gradcam, transparency, layer_name)
+    confidences, display_image = generate_prediction(org_img, num_classes, show_gradcam, transparency, layer_name)
 
     if show_misclassified:
         misclassified_fig, misclassified_axs = plot_misclassified_images(