Update app.py

app.py

@@ -53,8 +53,8 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         traceback.print_exc()
         return [error_message] + [None] * 27
 
-def show_results(outputs):
-    return [gr.Tab.update(visible=True) for _ in range(4)] + [gr.Tab.update(visible=False)], gr.Group(visible=True)
+def show_results():
+    return [gr.Tab.update(visible=True) for _ in range(4)] + [gr.Tab.update(visible=False)], gr.Group.update(visible=True)
 
 def hide_description_show_results():
     return [gr.Tab.update(visible=False)] + [gr.Tab.update(visible=True) for _ in range(4)]
@@ -73,7 +73,8 @@ with gr.Blocks() as iface:
         execution_time = gr.Number(label="Execution Time (seconds)")
 
     with gr.Tabs() as all_tabs:
-        with gr.Tab("Description", visible=True):
+        description_tab = gr.Tab("Description")
+        with description_tab:
             gr.Markdown("""
             # Multimodal Behavioral Anomalies Detection
 
@@ -81,7 +82,8 @@ with gr.Blocks() as iface:
             It extracts faces, postures, and voice from video frames, and analyzes them to identify anomalies using time series analysis and a variational autoencoder (VAE) approach.
             """)
 
-        with gr.Tab("Facial Features", visible=False):
+        facial_features_tab = gr.Tab("Facial Features", visible=False)
+        with facial_features_tab:
             results_text = gr.TextArea(label="Faces Breakdown", lines=5)
             mse_features_plot = gr.Plot(label="MSE: Facial Features")
             mse_features_hist = gr.Plot(label="MSE Distribution: Facial Features")
@@ -89,18 +91,21 @@ with gr.Blocks() as iface:
             anomaly_frames_features = gr.Gallery(label="Anomaly Frames (Facial Features)", columns=6, rows=2, height="auto")
             face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples", columns=10, rows=2, height="auto")
 
-        with gr.Tab("Body Posture", visible=False):
+        body_posture_tab = gr.Tab("Body Posture", visible=False)
+        with body_posture_tab:
             mse_posture_plot = gr.Plot(label="MSE: Body Posture")
             mse_posture_hist = gr.Plot(label="MSE Distribution: Body Posture")
             mse_posture_heatmap = gr.Plot(label="MSE Heatmap: Body Posture")
             anomaly_frames_posture = gr.Gallery(label="Anomaly Frames (Body Posture)", columns=6, rows=2, height="auto")
 
-        with gr.Tab("Voice", visible=False):
+        voice_tab = gr.Tab("Voice", visible=False)
+        with voice_tab:
             mse_voice_plot = gr.Plot(label="MSE: Voice")
             mse_voice_hist = gr.Plot(label="MSE Distribution: Voice")
             mse_voice_heatmap = gr.Plot(label="MSE Heatmap: Voice")
 
-        with gr.Tab("Combined", visible=False):
+        combined_tab = gr.Tab("Combined", visible=False)
+        with combined_tab:
             heatmap_video = gr.Video(label="Video with Anomaly Heatmap")
             combined_mse_plot = gr.Plot(label="Combined MSE Plot")
             correlation_heatmap_plot = gr.Plot(label="Correlation Heatmap")
@@ -118,7 +123,7 @@ with gr.Blocks() as iface:
     process_btn.click(
         hide_description_show_results,
         inputs=None,
-        outputs=all_tabs.children
+        outputs=[description_tab, facial_features_tab, body_posture_tab, voice_tab, combined_tab]
     ).then(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],
@@ -137,7 +142,7 @@ with gr.Blocks() as iface:
     ).then(
         show_results,
         inputs=None,
-        outputs=[all_tabs.children, execution_time_group]
+        outputs=[all_tabs, execution_time_group]
     )
 
 if __name__ == "__main__":