Update app.py

app.py

@@ -3,16 +3,11 @@ import time
 from video_processing import process_video
 from PIL import Image
 import matplotlib
-import threading
 
 matplotlib.rcParams['figure.dpi'] = 300
 matplotlib.rcParams['savefig.dpi'] = 300
 
 def process_and_show_completion(video_input_path, anomaly_threshold_input, fps, progress=gr.Progress()):
-    global processing
-    processing = True
-    start_time = time.time()
-    
     try:
         print("Starting video processing...")
         results = process_video(video_input_path, anomaly_threshold_input, fps, progress=progress)
@@ -20,7 +15,6 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
 
         if isinstance(results[0], str) and results[0].startswith("Error"):
             print(f"Error occurred: {results[0]}")
-            processing = False
             return [results[0]] + [None] * 27
 
         exec_time, results_summary, df, mse_embeddings, mse_posture, mse_voice, \
@@ -37,11 +31,8 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
 
         face_samples_frequent = [Image.open(path) for path in face_samples_frequent] if face_samples_frequent is not None else []
 
-        end_time = time.time()
-        total_exec_time = end_time - start_time
-
         output = [
-            f"Total execution time: {total_exec_time:.2f} seconds", results_summary,
+            exec_time, results_summary,
             df, mse_embeddings, mse_posture, mse_voice,
             mse_plot_embeddings, mse_plot_posture, mse_plot_voice,
             mse_histogram_embeddings, mse_histogram_posture, mse_histogram_voice,
@@ -53,7 +44,6 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
             heatmap_video_path, combined_mse_plot, correlation_heatmap
         ]
 
-        processing = False
         return output
 
     except Exception as e:
@@ -61,69 +51,53 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         print(error_message)
         import traceback
         traceback.print_exc()
-        processing = False
         return [error_message] + [None] * 27
 
-def show_results():
-    return [gr.update(visible=True) for _ in range(4)]
+def show_results(outputs):
+    return gr.Group(visible=True)
 
-def start_execution_timer():
-    return gr.update(visible=True), gr.update(visible=False)
-
-def update_execution_time():
-    current_time = time.time() - start_time
-    return f"Execution time: {current_time:.2f} seconds"
+with gr.Blocks() as iface:
+    gr.Markdown("""
+    # Multimodal Behavioral Anomalies Detection
 
-processing = False
-start_time = 0
+    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.Blocks() as iface:
     with gr.Row():
-        video_input = gr.Video(label="Input Video")
+        video_input = gr.Video()
 
     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)")
     process_btn = gr.Button("Detect Anomalies")
     progress_bar = gr.Progress()
-
-    with gr.Tabs() as all_tabs:
-        description_tab = gr.Tab("Description")
-        with description_tab:
-            description_md = 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.
-            """)
-            execution_time_md = gr.Markdown(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")
-            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")
-
-        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")
-
-        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")
-
-        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")
+    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")
+                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()
@@ -135,24 +109,11 @@ with gr.Blocks() as iface:
     mse_heatmap_posture_store = gr.State()
     mse_heatmap_voice_store = gr.State()
     
-    def start_processing():
-        global start_time, processing
-        start_time = time.time()
-        processing = True
-
     process_btn.click(
-        start_processing,
-        inputs=None,
-        outputs=None
-    ).then(
-        start_execution_timer,
-        inputs=None,
-        outputs=[execution_time_md, description_md]
-    ).then(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],
         outputs=[
-            execution_time_md, results_text, df_store,
+            execution_time, results_text, df_store,
             mse_features_store, mse_posture_store, mse_voice_store,
             mse_features_plot, mse_posture_plot, mse_voice_plot,
             mse_features_hist, mse_posture_hist, mse_voice_hist,
@@ -166,14 +127,7 @@ with gr.Blocks() as iface:
     ).then(
         show_results,
         inputs=None,
-        outputs=all_tabs.children[1:]
-    )
-
-    execution_time_md.change(
-        update_execution_time,
-        inputs=None,
-        outputs=execution_time_md,
-        every=0.1
+        outputs=results_group
     )
 
 if __name__ == "__main__":