Update app.py

app.py

@@ -280,7 +280,7 @@ with gr.Blocks() as demo_precomputed:
 
     gr.Markdown(
         """
-        ## Step 1: Abstract Symbols
+        ## Step 1: Neural Symbol Abstraction
         We first abstract the necessary visual elements from the provided diving video. This includes the platform, splash, and the pose estimation of the diver.
         """
     )
@@ -294,8 +294,8 @@ with gr.Blocks() as demo_precomputed:
                     <img src='file/platform.png' height='90'>
                 </td>
                 <td>
-                    The location of the platform, especially the position of its edge facing the pool, is crucial to determine when the diver leaves the platform, thus starting their dive.  
-                    The platform location is also important to assess how close the diver comes to its edge, which is relevant to scoring.
+                    The location of the platform is crucial to determine when the diver leaves the platform, thus starting their dive.  
+                    It is also important to assess how close the diver comes to its edge, which is relevant to scoring.
                 </td>
                 <td>
                     Pose Estimation of Diver
@@ -303,8 +303,7 @@ with gr.Blocks() as demo_precomputed:
                 </td>
                 <td>
                     The pose of the diver in the sequence of video frames is critical to understanding and assessing the dive.  
-                    We obtain 2D pose data with locations of various body parts, including the head, thorax, pelvis, shoulders, elbows, wrists, hips, knees, and ankles. 
-                    With this, we can recognize sub-actions being performed by the diver, such as a somersault, a twist, or an entry, and also assess the quality of that sub-action.
+                    We obtain 2D pose data with locations of various body parts to recognize sub-actions being performed by the diver, such as a somersault, a twist, or an entry, and also assess the quality of that sub-action.
                 </td>
                 <td>
                     Splash
@@ -325,15 +324,13 @@ with gr.Blocks() as demo_precomputed:
         2. Hit the **Abstract Symbols** button.
         """
     )
-    
-    examples = gr.Examples(examples = [['01_10.mp4'], ['01_11.mp4'], ['01_16.mp4'], ['01_33.mp4'], ['01_76.mp4'], ['01_140.mp4']], inputs=[video])
 
     with gr.Row(variant='panel'):
         with gr.Column():
             video = gr.Video(label="Video", format="mp4", include_audio=False, sources=["upload"], interactive=False)
             abstract_symbols_btn = gr.Button("Abstract Symbols", variant='secondary')
         symbol_output = gr.HTML(label="Output")
-
+    examples = gr.Examples(examples = [['01_10.mp4'], ['01_11.mp4'], ['01_16.mp4'], ['01_33.mp4'], ['01_76.mp4'], ['01_140.mp4']], inputs=[video])
     gr.Markdown(
         """
         ## Step 2: Calculate Logic-Based Errors and Generate Detailed Score Report