Update app.py

app.py

@@ -3,11 +3,14 @@ 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:
@@ -17,6 +20,7 @@ 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, \
@@ -37,7 +41,7 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         total_exec_time = end_time - start_time
 
         output = [
-            f"Execution time: {total_exec_time:.2f} seconds", results_summary,
+            f"Total execution time: {total_exec_time:.2f} seconds", 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,
@@ -49,6 +53,7 @@ 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:
@@ -56,21 +61,23 @@ 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 start_execution_timer():
-    return gr.update(value="Execution time: 0 seconds", visible=True)
+    return gr.update(visible=True), gr.update(visible=False)
 
-def update_execution_time(start_time):
+def update_execution_time():
     current_time = time.time() - start_time
     return f"Execution time: {current_time:.2f} seconds"
 
-with gr.Blocks() as iface:
-    start_time = gr.State(0)
+processing = False
+start_time = 0
 
+with gr.Blocks() as iface:
     with gr.Row():
         video_input = gr.Video(label="Input Video")
 
@@ -88,6 +95,7 @@ with gr.Blocks() as iface:
             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:
@@ -127,19 +135,24 @@ 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(
-        lambda: time.time(),
+        start_processing,
         inputs=None,
-        outputs=start_time
+        outputs=None
     ).then(
         start_execution_timer,
         inputs=None,
-        outputs=description_md
+        outputs=[execution_time_md, description_md]
     ).then(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],
         outputs=[
-            description_md, results_text, df_store,
+            execution_time_md, 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,
@@ -156,11 +169,11 @@ with gr.Blocks() as iface:
         outputs=all_tabs.children[1:]
     )
 
-    description_md.change(
+    execution_time_md.change(
         update_execution_time,
-        inputs=[start_time],
-        outputs=description_md,
-        every=1
+        inputs=None,
+        outputs=execution_time_md,
+        every=0.1
     )
 
 if __name__ == "__main__":