Update app.py

app.py

@@ -56,9 +56,12 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
 def show_results(outputs):
     return gr.Tabs(visible=True), gr.Group(visible=True)
 
+def hide_description_show_results():
+    return gr.Tabs(visible=True), gr.Tab(visible=False)
+
 with gr.Blocks() as iface:
     with gr.Row():
-        video_input = gr.Video(label="Input Video")
+        video_input = gr.Video(label="Input Video", visible=False)
 
     anomaly_threshold = gr.Slider(minimum=1, maximum=5, step=0.1, value=3, label="Anomaly Detection Threshold (Standard deviation)")
     fps_slider = gr.Slider(minimum=5, maximum=20, step=1, value=10, label="Frames Per Second (FPS)")
@@ -69,8 +72,8 @@ with gr.Blocks() as iface:
     with execution_time_group:
         execution_time = gr.Number(label="Execution Time (seconds)")
 
-    with gr.Tabs(visible=True) as results_tabs:
-        with gr.TabItem("Description"):
+    with gr.Tabs() as all_tabs:
+        with gr.Tab("Description", elem_id="description_tab"):
             gr.Markdown("""
             # Multimodal Behavioral Anomalies Detection
 
@@ -78,7 +81,7 @@ 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.TabItem("Facial Features"):
+        with gr.Tab("Facial Features", visible=False):
             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")
@@ -86,19 +89,19 @@ 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.TabItem("Body Posture"):
+        with gr.Tab("Body Posture", visible=False):
             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.TabItem("Voice"):
+        with gr.Tab("Voice", visible=False):
             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.TabItem("Combined"):
-            heatmap_video = gr.Video(label="Video with Anomaly Heatmap", visible=False)
+        with gr.Tab("Combined", visible=False):
+            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")
                 
@@ -113,6 +116,10 @@ with gr.Blocks() as iface:
     mse_heatmap_voice_store = gr.State()
     
     process_btn.click(
+        hide_description_show_results,
+        inputs=None,
+        outputs=[all_tabs, all_tabs.select("Description")]
+    ).then(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],
         outputs=[
@@ -130,7 +137,7 @@ with gr.Blocks() as iface:
     ).then(
         show_results,
         inputs=None,
-        outputs=[results_tabs, execution_time_group]
+        outputs=[all_tabs, execution_time_group]
     )
 
 if __name__ == "__main__":