.

app.py

@@ -63,6 +63,7 @@ def create_ui(examples_path: str):
                         gr.Markdown(f"#Summary: {example['analysis']['video_description']}")
                         gr.Markdown(f"#Highlights to search for: {example['analysis']['highlight_types']}")
 
+
         gr.Markdown("## Try It Yourself!")
         with gr.Row():
             with gr.Column(scale=1):
@@ -81,113 +82,116 @@ def create_ui(examples_path: str):
 
                 status = gr.Markdown()
         
-                # Replace Accordion with Column for visibility control
-                with gr.Column(visible=False) as analysis_column:
-                    gr.Markdown("## Model chain of thought details")
+                analysis_accordion = gr.Accordion(
+                    "Model chain of thought details", 
+                    open=True, 
+                    visible=False
+                )
+                
+                with analysis_accordion:
                     video_description = gr.Markdown("", elem_id="video_desc")
                     highlight_types = gr.Markdown("", elem_id="highlight_types")
 
         @spaces.GPU
-        def process_video(video):
+        def on_process(video):
             if not video:
-                return [
-                    "Please upload a video",
-                    "",
-                    "",
-                    gr.update(visible=False),
-                    gr.update(visible=False)
-                ]
+                yield {
+                    "status": "Please upload a video",  # Changed to string key
+                    "video_description": gr.update(value=""),  # Added gr.update
+                    "highlight_types": gr.update(value=""),  # Added gr.update
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=False)
+                }
+                return
             
             try:
                 duration = get_video_duration_seconds(video)
                 if duration > 1200:  # 20 minutes
-                    return [
-                        "Video must be shorter than 20 minutes",
-                        "",
-                        "",
-                        gr.update(visible=False),
-                        gr.update(visible=False)
-                    ]
-
-                # Load model
+                    yield {
+                        "status": "Video must be shorter than 20 minutes",
+                        "video_description": gr.update(value=""),
+                        "highlight_types": gr.update(value=""),
+                        "output_video": gr.update(visible=False),
+                        "analysis_accordion": gr.update(visible=False)
+                    }
+                    return
+
+                # Make accordion visible as soon as processing starts
+                yield {
+                    "status": "Loading model...",
+                    "video_description": gr.update(value=""),
+                    "highlight_types": gr.update(value=""),
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=True)
+                }
+
                 model, processor = load_model()
                 detector = BatchedVideoHighlightDetector(model, processor, batch_size=8)
 
-                # Analyze content
+                yield {
+                    "status": "Analyzing video content...",
+                    "video_description": gr.update(value=""),
+                    "highlight_types": gr.update(value=""),
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=True)
+                }
+                
                 video_desc = detector.analyze_video_content(video)
                 formatted_desc = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}"
                 
-                # Determine highlights
+                # Update description as soon as it's available
+                yield {
+                    "status": "Determining highlight types...",
+                    "video_description": gr.update(value=formatted_desc),
+                    "highlight_types": gr.update(value=""),
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=True)
+                }
+                
                 highlights = detector.determine_highlights(video_desc)
                 formatted_highlights = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}"
+                
+                # Update highlights as soon as they're available
+                yield {
+                    "status": "Detecting and extracting highlights...",
+                    "video_description": gr.update(value=formatted_desc),
+                    "highlight_types": gr.update(value=formatted_highlights),
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=True)
+                }
 
-                # Create highlight video
                 with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
                     temp_output = tmp_file.name
                 detector.create_highlight_video(video, temp_output)
                 
-                return [
-                    "Processing complete!",
-                    formatted_desc,
-                    formatted_highlights,
-                    gr.update(value=temp_output, visible=True),
-                    gr.update(visible=True)
-                ]
+                yield {
+                    "status": "Processing complete!",
+                    "video_description": gr.update(value=formatted_desc),
+                    "highlight_types": gr.update(value=formatted_highlights),
+                    "output_video": gr.update(value=temp_output, visible=True),
+                    "analysis_accordion": gr.update(visible=True)
+                }
 
             except Exception as e:
-                return [
-                    f"Error processing video: {str(e)}",
-                    "",
-                    "",
-                    gr.update(visible=False),
-                    gr.update(visible=False)
-                ]
-
-        def process_with_updates(video):
-            # Initial state
-            yield [
-                "Loading model...",
-                "",
-                "",
-                gr.update(visible=False),
-                gr.update(visible=True)
-            ]
-
-            # Analyzing video
-            yield [
-                "Analyzing video content...",
-                "",
-                "",
-                gr.update(visible=False),
-                gr.update(visible=True)
-            ]
-            
-            # Get final results
-            results = process_video(video)
-            
-            # If we're still processing, show an intermediate state
-            if results[0] != "Processing complete!":
-                yield [
-                    "Detecting and extracting highlights...",
-                    results[1],  # description
-                    results[2],  # highlights
-                    gr.update(visible=False),
-                    gr.update(visible=True)
-                ]
-            
-            # Return final state
-            yield results
+                yield {
+                    "status": f"Error processing video: {str(e)}",
+                    "video_description": gr.update(value=""),
+                    "highlight_types": gr.update(value=""),
+                    "output_video": gr.update(visible=False),
+                    "analysis_accordion": gr.update(visible=False)
+                }
 
         process_btn.click(
-            process_with_updates,
+            on_process,
             inputs=[input_video],
             outputs=[
                 status,
                 video_description,
                 highlight_types,
                 output_video,
-                analysis_column
-            ]
+                analysis_accordion
+            ],
+            queue=True,  # Added queue=True
         )
 
     return app