Update app.py

app.py

@@ -1,149 +1,150 @@
-import gradio as gr
-import numpy as np
-import os
-import glob
-import pickle
-import json
-from utils.render import render_smpl
-from periodic_detection_function import run_periodic_detection
-
-DATA_DIR = "data"
-OUTPUT_DIR = "outputs"
-
-os.makedirs(OUTPUT_DIR, exist_ok=True)
-
-def get_candidates():
-    """List all pickle files in data directory."""
-    files = glob.glob(os.path.join(DATA_DIR, "*.pkl"))
-    return [os.path.basename(f) for f in files]
-
-def load_and_render(candidate_file):
-    """
-    Load the selected pickle file, render it to a video, and return the video path.
-    """
-    if not candidate_file:
-        return None
-    
-    pkl_path = os.path.join(DATA_DIR, candidate_file)
-    output_video_path = os.path.join(OUTPUT_DIR, f"{candidate_file.replace('.pkl', '')}_rendered.mp4")
-    
-    # Check for pre-rendered video in data/
-    pre_rendered_path = os.path.join(DATA_DIR, candidate_file.replace('.pkl', '.mp4'))
-    if os.path.exists(pre_rendered_path):
-        print(f"Using pre-rendered video: {pre_rendered_path}")
-        return pre_rendered_path
-
-    # If not found, fall back to rendering (or re-render if desired, but user wants direct use)
-    # Keeping fallback just in case
-    try:
-        with open(pkl_path, 'rb') as f:
-            data = pickle.load(f)
-        
-        # Data shape check
-        if len(data.shape) != 3 or data.shape[1] != 24 or data.shape[2] != 3:
-            raise ValueError(f"Unexpected data shape: {data.shape}. Expected (Frames, 24, 3)")
-            
-        print(f"Rendering {candidate_file}...")
-        render_smpl(data, output_video_path, fps=30)
-        return output_video_path
-    
-    except Exception as e:
-        print(f"Error rendering {candidate_file}: {e}")
-        return None
-
-def run_analysis(candidate_file, rendered_video_path):
-    """
-    Run periodic detection on the rendered video and trajectory data.
-    """
-    if not candidate_file or not rendered_video_path:
-        return None, "Please select a candidate and wait for rendering first."
-    
-    pkl_path = os.path.join(DATA_DIR, candidate_file)
-    output_video_path = os.path.join(OUTPUT_DIR, f"{candidate_file.replace('.pkl', '')}_result.mp4")
-    
-    try:
-        print(f"Running detection on {candidate_file}...")
-        # Note: run_periodic_detection expects [Frames, N_feats] usually or generic trajectory.
-        # The pickle contains (Frames, 24, 3). 
-        # The spatiotemporal_clustering in helper seems to handle reshaping or expects specific shape.
-        # Looking at periodic_detection_function.py line 46: 
-        # trajectories = trajectories.reshape(trajectories.shape[0],-1)
-        # So it flattens (Frames, 24, 3) to (Frames, 72), which is fine.
-        
-        results = run_periodic_detection(
-            video_path=rendered_video_path,
-            trajectory_path=pkl_path,
-            output_video_path=output_video_path,
-            n_clusters=9,
-            sampling_rate=1,
-            make_video=True
-        )
-        
-        if "error" in results:
-            return None, json.dumps(results, indent=2)
-            
-        # Format results for display
-        display_results = {
-            "workflow branches": results.get("workflow"),
-            "period_boundaries": results.get("period_boundaries"),
-            "num_periods": results.get("num_periods"),
-            "window_size": results.get("window_size")
-        }
-        
-        return results.get("output_video"), json.dumps(display_results, indent=2)
-        
-    except Exception as e:
-        import traceback
-        traceback.print_exc()
-        return None, f"Error during analysis: {str(e)}"
-
-def reset_all():
-    return None, None, None, None
-
-# Gradio Interface
-with gr.Blocks(title="Periodic Workflow Detection Demo") as demo:
-    gr.Markdown("# Periodic Workflow Detection Demo")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 1. Select Input")
-            candidate_dropdown = gr.Dropdown(
-                choices=get_candidates(),
-                label="Select Candidates",
-                value=None
-            )
-            
-            gr.Markdown("### Input Visualization")
-            input_video = gr.Video(label="Spatiotemporal Sequence", interactive=False)
-            
-        with gr.Column(scale=1):
-            gr.Markdown("### 2. Run Detection")
-            run_btn = gr.Button("Run Analysis", variant="primary")
-            
-            gr.Markdown("### Results")
-            text_output = gr.JSON(label="Numerical Results")
-            result_video = gr.Video(label="Detection Visualization", interactive=False)
-            
-            reset_btn = gr.Button("Reset", variant="secondary")
-
-    # Interactions
-    candidate_dropdown.change(
-        fn=load_and_render,
-        inputs=[candidate_dropdown],
-        outputs=[input_video]
-    )
-    
-    run_btn.click(
-        fn=run_analysis,
-        inputs=[candidate_dropdown, input_video],
-        outputs=[result_video, text_output]
-    )
-    
-    reset_btn.click(
-        fn=reset_all,
-        inputs=[],
-        outputs=[candidate_dropdown, input_video, result_video, text_output]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+import gradio as gr
+import numpy as np
+import os
+import glob
+import pickle
+import json
+from utils.render import render_smpl
+from periodic_detection_function import run_periodic_detection
+
+DATA_DIR = "data"
+OUTPUT_DIR = "outputs"
+
+os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+def get_candidates():
+    """List all pickle files in data directory."""
+    files = glob.glob(os.path.join(DATA_DIR, "*.pkl"))
+    return [os.path.basename(f) for f in files]
+
+def load_and_render(candidate_file):
+    """
+    Load the selected pickle file, render it to a video, and return the video path.
+    """
+    if not candidate_file:
+        return None
+    
+    pkl_path = os.path.join(DATA_DIR, candidate_file)
+    output_video_path = os.path.join(OUTPUT_DIR, f"{candidate_file.replace('.pkl', '')}_rendered.mp4")
+    
+    # Check for pre-rendered video in data/
+    pre_rendered_path = os.path.join(DATA_DIR, candidate_file.replace('.pkl', '.mp4'))
+    if os.path.exists(pre_rendered_path):
+        print(f"Using pre-rendered video: {pre_rendered_path}")
+        return pre_rendered_path
+
+    # If not found, fall back to rendering (or re-render if desired, but user wants direct use)
+    # Keeping fallback just in case
+    try:
+        with open(pkl_path, 'rb') as f:
+            data = pickle.load(f)
+        
+        # Data shape check
+        if len(data.shape) != 3 or data.shape[1] != 24 or data.shape[2] != 3:
+            raise ValueError(f"Unexpected data shape: {data.shape}. Expected (Frames, 24, 3)")
+            
+        print(f"Rendering {candidate_file}...")
+        render_smpl(data, output_video_path, fps=30)
+        return output_video_path
+    
+    except Exception as e:
+        print(f"Error rendering {candidate_file}: {e}")
+        return None
+
+def run_analysis(candidate_file, rendered_video_path):
+    """
+    Run periodic detection on the rendered video and trajectory data.
+    """
+    if not candidate_file or not rendered_video_path:
+        return None, "Please select a candidate and wait for rendering first."
+    
+    pkl_path = os.path.join(DATA_DIR, candidate_file)
+    output_video_path = os.path.join(OUTPUT_DIR, f"{candidate_file.replace('.pkl', '')}_result.mp4")
+    
+    try:
+        print(f"Running detection on {candidate_file}...")
+        # Note: run_periodic_detection expects [Frames, N_feats] usually or generic trajectory.
+        # The pickle contains (Frames, 24, 3). 
+        # The spatiotemporal_clustering in helper seems to handle reshaping or expects specific shape.
+        # Looking at periodic_detection_function.py line 46: 
+        # trajectories = trajectories.reshape(trajectories.shape[0],-1)
+        # So it flattens (Frames, 24, 3) to (Frames, 72), which is fine.
+        
+        results = run_periodic_detection(
+            video_path=rendered_video_path,
+            trajectory_path=pkl_path,
+            output_video_path=output_video_path,
+            n_clusters=9,
+            sampling_rate=1,
+            make_video=True
+        )
+        
+        if "error" in results:
+            return None, json.dumps(results, indent=2)
+            
+        # Format results for display
+        display_results = {
+            "workflow branches": results.get("workflow"),
+            "period_boundaries": results.get("period_boundaries"),
+            "num_periods": results.get("num_periods"),
+            "window_size": results.get("window_size")
+        }
+        
+        return results.get("output_video"), json.dumps(display_results, indent=2)
+        
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+        return None, f"Error during analysis: {str(e)}"
+
+def reset_all():
+    return None, None, None, None
+
+# Gradio Interface
+with gr.Blocks(title="Periodic Workflow Detection Demo") as demo:
+    gr.Markdown("# Periodic Workflow Detection Demo")
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            gr.Markdown("### 1. Select Input")
+            candidate_dropdown = gr.Dropdown(
+                choices=get_candidates(),
+                label="Select Candidates",
+                value=None
+            )
+            
+            gr.Markdown("### Input Visualization")
+            input_video = gr.Video(label="Spatiotemporal Sequence", interactive=False)
+            
+        with gr.Column(scale=1):
+            gr.Markdown("### 2. Run Detection")
+            run_btn = gr.Button("Run Analysis", variant="primary")
+            
+            gr.Markdown("### Results")
+            text_output = gr.JSON(label="Numerical Results")
+            result_video = gr.Video(label="Detection Visualization", interactive=False)
+            
+            reset_btn = gr.Button("Reset", variant="secondary")
+
+    # Interactions
+    candidate_dropdown.change(
+        fn=load_and_render,
+        inputs=[candidate_dropdown],
+        outputs=[input_video]
+    )
+    
+    run_btn.click(
+        fn=run_analysis,
+        inputs=[candidate_dropdown, input_video],
+        outputs=[result_video, text_output]
+    )
+    
+    reset_btn.click(
+        fn=reset_all,
+        inputs=[],
+        outputs=[candidate_dropdown, input_video, result_video, text_output]
+    )
+
+if __name__ == "__main__":
+    #demo.launch()
+    demo.launch(ssr_mode=False)