.

app.py

@@ -104,10 +104,8 @@ 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']}")
 
-        # Main interface section
         gr.Markdown("## Try It Yourself!")
         with gr.Row():
-            # Left column: Upload and Process
             with gr.Column(scale=1):
                 input_video = gr.Video(
                     label="Upload your video (max 20 minutes)",
@@ -115,10 +113,7 @@ def create_ui(examples_path: str):
                 )
                 process_btn = gr.Button("Process Video", variant="primary")
             
-            # Right column: Progress and Analysis
             with gr.Column(scale=1):
-
-                # Output video (initially hidden)
                 output_video = gr.Video(
                     label="Highlight Video",
                     visible=False,
@@ -127,19 +122,52 @@ def create_ui(examples_path: str):
 
                 status = gr.Markdown()
         
-                with gr.Accordion("Model chain of thought details", open=True, visible=True) as analysis_accordion:
+                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")
+        # # Main interface section
+        # gr.Markdown("## Try It Yourself!")
+        # with gr.Row():
+        #     # Left column: Upload and Process
+        #     with gr.Column(scale=1):
+        #         input_video = gr.Video(
+        #             label="Upload your video (max 20 minutes)",
+        #             interactive=True
+        #         )
+        #         process_btn = gr.Button("Process Video", variant="primary")
+            
+        #     # Right column: Progress and Analysis
+        #     with gr.Column(scale=1):
+
+        #         # Output video (initially hidden)
+        #         output_video = gr.Video(
+        #             label="Highlight Video",
+        #             visible=False,
+        #             interactive=False,
+        #         )
+
+        #         status = gr.Markdown()
+        
+        #         with gr.Accordion("Model chain of thought details", open=True, visible=True) as analysis_accordion:
+        #             video_description = gr.Markdown("", elem_id="video_desc")
+        #             highlight_types = gr.Markdown("", elem_id="highlight_types")
                 
 
         @spaces.GPU
-        def on_process(video, progress=gr.Progress()):
+        def on_process(video):
             if not video:
                 return {
                     status: "Please upload a video",
                     video_description: "",
                     highlight_types: "",
-                    output_video: gr.update(visible=False)
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=False)
                 }
             
             try:
@@ -149,45 +177,64 @@ def create_ui(examples_path: str):
                         status: "Video must be shorter than 20 minutes",
                         video_description: "",
                         highlight_types: "",
-                        output_video: gr.update(visible=False)
+                        output_video: gr.update(visible=False),
+                        analysis_accordion: gr.update(visible=False)
                     }
 
-                progress(0.1, desc="Loading model...")
-                status.value = "Loading model..."
+                # Make accordion visible as soon as processing starts
+                yield {
+                    status: "Loading model...",
+                    video_description: "",
+                    highlight_types: "",
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=True)
+                }
+
                 model, processor = load_model()
                 detector = BatchedVideoHighlightDetector(model, processor, batch_size=8)
 
-                progress(0.2, desc="Analyzing video content...")
-                status.value = "Analyzing video content..."
+                yield {
+                    status: "Analyzing video content...",
+                    video_description: "",
+                    highlight_types: "",
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=True)
+                }
+                
                 video_desc = detector.analyze_video_content(video)
-                # Update description in real-time
-                video_description.value = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}"
+                formatted_desc = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}"
+                
+                # Update description as soon as it's available
+                yield {
+                    status: "Determining highlight types...",
+                    video_description: formatted_desc,
+                    highlight_types: "",
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=True)
+                }
                 
-                progress(0.3, desc="Determining highlight types...")
-                status.value = "Determining highlight types..."
                 highlights = detector.determine_highlights(video_desc)
-                # Update highlights in real-time
-                highlight_types.value = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}"
+                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: formatted_desc,
+                    highlight_types: formatted_highlights,
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=True)
+                }
 
-                progress(0.4, desc="Detecting and extracting highlights...")
-                status.value = "Detecting and extracting highlights..."
                 with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
                     temp_output = tmp_file.name
                 detector.create_highlight_video(video, temp_output)
                 
-                # progress(0.9, desc="Adding watermark...")
-                # status.value = "Adding watermark..."
-                # output_path = temp_output.replace('.mp4', '_watermark.mp4')
-                # add_watermark(temp_output, output_path)
-                
-                # os.unlink(temp_output)
-                progress(1.0, desc="Complete!")
-                
                 return {
                     status: "Processing complete!",
-                    video_description: video_description.value,
-                    highlight_types: highlight_types.value,
-                    output_video: gr.update(value=temp_output, visible=True)
+                    video_description: formatted_desc,
+                    highlight_types: formatted_highlights,
+                    output_video: gr.update(value=temp_output, visible=True),
+                    analysis_accordion: gr.update(visible=True)
                 }
 
             except Exception as e:
@@ -195,78 +242,88 @@ def create_ui(examples_path: str):
                     status: f"Error processing video: {str(e)}",
                     video_description: "",
                     highlight_types: "",
-                    output_video: gr.update(visible=False)
+                    output_video: gr.update(visible=False),
+                    analysis_accordion: gr.update(visible=False)
                 }
 
         process_btn.click(
             on_process,
             inputs=[input_video],
-            outputs=[status, video_description, highlight_types, output_video]
+            outputs=[status, video_description, highlight_types, output_video, analysis_accordion]
         )
 
     return app
-    #     gr.Markdown("## Try It Yourself!")
-    #     with gr.Row():
-    #         input_video = gr.Video(
-    #             label="Upload your video (max 20 minutes)",
-    #             interactive=True
-    #         )
-        
-    #     gr.Progress()
-    #     process_btn = gr.Button("Process Video", variant="primary")
-
-    #     status = gr.Markdown(visible=True)
-        
-    #     with gr.Row() as results_row:
-    #         with gr.Column():
-    #             video_description = gr.Markdown(visible=False)
-    #         with gr.Column():
-    #             highlight_types = gr.Markdown(visible=False)
-        
-    #     with gr.Row() as output_row:
-    #         output_video = gr.Video(label="Highlight Video", visible=False)
-    #         download_btn = gr.Button("Download Highlights", visible=False)
 
+    #     @spaces.GPU
     #     def on_process(video, progress=gr.Progress()):
     #         if not video:
     #             return {
     #                 status: "Please upload a video",
-    #                 video_description: gr.update(visible=False),
-    #                 highlight_types: gr.update(visible=False),
-    #                 output_video: gr.update(visible=False),
-    #                 download_btn: gr.update(visible=False)
+    #                 video_description: "",
+    #                 highlight_types: "",
+    #                 output_video: gr.update(visible=False)
     #             }
             
-    #         status.value = "Processing video..."
-    #         output_path, desc, highlights, err = process_video(video, progress=progress)
-            
-    #         if err:
+    #         try:
+    #             duration = get_video_duration_seconds(video)
+    #             if duration > 1200:  # 20 minutes
+    #                 return {
+    #                     status: "Video must be shorter than 20 minutes",
+    #                     video_description: "",
+    #                     highlight_types: "",
+    #                     output_video: gr.update(visible=False)
+    #                 }
+
+    #             progress(0.1, desc="Loading model...")
+    #             status.value = "Loading model..."
+    #             model, processor = load_model()
+    #             detector = BatchedVideoHighlightDetector(model, processor, batch_size=8)
+
+    #             progress(0.2, desc="Analyzing video content...")
+    #             status.value = "Analyzing video content..."
+    #             video_desc = detector.analyze_video_content(video)
+    #             # Update description in real-time
+    #             video_description.value = f"#Summary: {video_desc[:500] + '...' if len(video_desc) > 500 else video_desc}"
+                
+    #             progress(0.3, desc="Determining highlight types...")
+    #             status.value = "Determining highlight types..."
+    #             highlights = detector.determine_highlights(video_desc)
+    #             # Update highlights in real-time
+    #             highlight_types.value = f"#Highlights to search for: {highlights[:500] + '...' if len(highlights) > 500 else highlights}"
+
+    #             progress(0.4, desc="Detecting and extracting highlights...")
+    #             status.value = "Detecting and extracting highlights..."
+    #             with tempfile.NamedTemporaryFile(suffix='.mp4', delete=False) as tmp_file:
+    #                 temp_output = tmp_file.name
+    #             detector.create_highlight_video(video, temp_output)
+                
+    #             # progress(0.9, desc="Adding watermark...")
+    #             # status.value = "Adding watermark..."
+    #             # output_path = temp_output.replace('.mp4', '_watermark.mp4')
+    #             # add_watermark(temp_output, output_path)
+                
+    #             # os.unlink(temp_output)
+    #             progress(1.0, desc="Complete!")
+                
     #             return {
-    #                 status: f"Error: {err}",
-    #                 video_description: gr.update(visible=False),
-    #                 highlight_types: gr.update(visible=False),
-    #                 output_video: gr.update(visible=False),
-    #                 download_btn: gr.update(visible=False)
+    #                 status: "Processing complete!",
+    #                 video_description: video_description.value,
+    #                 highlight_types: highlight_types.value,
+    #                 output_video: gr.update(value=temp_output, visible=True)
+    #             }
+
+    #         except Exception as e:
+    #             return {
+    #                 status: f"Error processing video: {str(e)}",
+    #                 video_description: "",
+    #                 highlight_types: "",
+    #                 output_video: gr.update(visible=False)
     #             }
-            
-    #         return {
-    #             status: "Processing complete!",
-    #             video_description: gr.update(value=desc, visible=True),
-    #             highlight_types: gr.update(value=highlights, visible=True),
-    #             output_video: gr.update(value=output_path, visible=True),
-    #             download_btn: gr.update(visible=True)
-    #         }
 
     #     process_btn.click(
     #         on_process,
     #         inputs=[input_video],
-    #         outputs=[status, video_description, highlight_types, output_video, download_btn]
-    #     )
-
-    #     download_btn.click(
-    #         lambda x: x,
-    #         inputs=[output_video],
-    #         outputs=[output_video]
+    #         outputs=[status, video_description, highlight_types, output_video]
     #     )
 
     # return app