update app.py

app.py

@@ -10,7 +10,7 @@ yolo_model.overrides['max_det'] = 10
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 yolo_model.to(device)
 
-print(os.path.dirname(__file__))
+!wget -q https://github.com/gradio-app/gradio/raw/main/demo/blocks_inputs/lion.jpg
 
 def seal_detection(
     image,
@@ -59,6 +59,7 @@ with gr.Blocks() as demo:
                 In this project, I leveraged transfer learning from the <a href="https://github.com/ultralytics/ultralytics" target="_blank">YOLOv8</a> model to customize an object detection model specifically for the hand seals. Developed by the Ultralytics team, YOLOv8 is the latest addition to the YOLO family and offers high performance while being easy to train and use.
             """
             )
+        
     with gr.Row():
         with gr.Column():
             # inputs = [
@@ -73,19 +74,15 @@ with gr.Blocks() as demo:
             # flag = gr.Button("Flag")
 
     with gr.Row():
-        # gr.Examples(
-        #     examples=[os.path.join(os.path.dirname(__file__), "/assets/Naruto_Hand_Seals_by_Megan.gif")],
-        #     inputs=image,
-        #     outputs=outputs,
-        #     fn=seal_detection,
-        #     run_on_click=True,
-        #     preprocess=False,
-        #     postprocess=False,
-        #     cache_examples=True,
-        # )
         gr.Examples(
-            [0.9],
-            inputs=slider,
+            examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
+            inputs=image,
+            outputs=outputs,
+            fn=seal_detection,
+            run_on_click=True,
+            # preprocess=False,
+            # postprocess=False,
+            cache_examples=True,
         )
 
     gr.Markdown(