Update app.py

app.py

@@ -54,16 +54,9 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         return [error_message] + [None] * 27
 
 def show_results(outputs):
-    return gr.Group(visible=True), gr.Group(visible=True), gr.Markdown(visible=False)
+    return gr.Tabs(visible=True), gr.Group(visible=True)
 
 with gr.Blocks() as iface:
-    description = gr.Markdown("""
-    # Multimodal Behavioral Anomalies Detection
-
-    This tool detects anomalies in facial expressions, body language, and voice over the timeline of a video.
-    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.Row():
         video_input = gr.Video(label="Input Video")
 
@@ -76,31 +69,38 @@ with gr.Blocks() as iface:
     with execution_time_group:
         execution_time = gr.Number(label="Execution Time (seconds)")
 
-    with gr.Group(visible=False) as results_group:
-        with gr.Tabs():
-            with gr.TabItem("Facial Features"):
-                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")
-                mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features")
-                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"):
-                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"):
-                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)
-                combined_mse_plot = gr.Plot(label="Combined MSE Plot")
-                correlation_heatmap_plot = gr.Plot(label="Correlation Heatmap")
+    with gr.Tabs(visible=True) as results_tabs:
+        with gr.TabItem("Description"):
+            gr.Markdown("""
+            # Multimodal Behavioral Anomalies Detection
+
+            This tool detects anomalies in facial expressions, body language, and voice over the timeline of a video.
+            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"):
+            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")
+            mse_features_heatmap = gr.Plot(label="MSE Heatmap: Facial Features")
+            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"):
+            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"):
+            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)
+            combined_mse_plot = gr.Plot(label="Combined MSE Plot")
+            correlation_heatmap_plot = gr.Plot(label="Correlation Heatmap")
                 
     df_store = gr.State()
     mse_features_store = gr.State()
@@ -130,7 +130,7 @@ with gr.Blocks() as iface:
     ).then(
         show_results,
         inputs=None,
-        outputs=[results_group, execution_time_group, description]
+        outputs=[results_tabs, execution_time_group]
     )
 
 if __name__ == "__main__":