Update app.py

app.py

@@ -15,7 +15,7 @@ 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]}")
-            return [results[0]] + [None] * 23
+            return [results[0]] + [None] * 25  # Update the tuple size to match the number of outputs
 
         exec_time, results_summary, df, mse_embeddings, mse_posture, mse_voice, \
             mse_plot_embeddings, mse_plot_posture, mse_plot_voice, \
@@ -24,19 +24,13 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
             face_samples_frequent, \
             anomaly_faces_embeddings, anomaly_frames_posture_images, \
             aligned_faces_folder, frames_folder, \
-            heatmap_video_path = results
+            heatmap_video_path, correlation_heatmap, scatter_plot_3d = results
 
         anomaly_faces_embeddings_pil = [Image.fromarray(face) for face in anomaly_faces_embeddings] if anomaly_faces_embeddings is not None else []
         anomaly_frames_posture_pil = [Image.fromarray(frame) for frame in anomaly_frames_posture_images] if anomaly_frames_posture_images is not None else []
 
         face_samples_frequent = [Image.open(path) for path in face_samples_frequent] if face_samples_frequent is not None else []
 
-        # Generate the correlation heatmap
-        correlation_heatmap = plot_correlation_heatmap(mse_embeddings, mse_posture, mse_voice)
-        
-        # Generate the 3D scatter plot
-        scatter_plot_3d = plot_3d_scatter(mse_embeddings, mse_posture, mse_voice)
-
         output = [
             exec_time, results_summary,
             df, mse_embeddings, mse_posture, mse_voice,
@@ -139,4 +133,4 @@ with gr.Blocks() as iface:
     )
 
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()