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] * 27  # Increased to 27 to account for the new audio waveform plot
+            return [results[0]] + [None] * 27
     
         exec_time, results_summary, df, mse_embeddings, mse_posture, mse_voice, \
             mse_plot_embeddings, mse_plot_posture, mse_plot_voice, \
@@ -26,9 +26,22 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
             aligned_faces_folder, frames_folder, \
             stacked_heatmap, audio_waveform_plot = 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 []
+        # Handle potential string values for image data
+        def safe_image_conversion(image_data):
+            if isinstance(image_data, np.ndarray):
+                return Image.fromarray(image_data)
+            elif isinstance(image_data, str):
+                print(f"Warning: Expected image data, got string: {image_data[:100]}...")  # Print first 100 chars
+                return None
+            else:
+                print(f"Warning: Unexpected data type for image: {type(image_data)}")
+                return None
+    
+        anomaly_faces_embeddings_pil = [safe_image_conversion(face) for face in anomaly_faces_embeddings] if isinstance(anomaly_faces_embeddings, list) else []
+        anomaly_frames_posture_pil = [safe_image_conversion(frame) for frame in anomaly_frames_posture_images] if isinstance(anomaly_frames_posture_images, list) else []
+        
+        # For face_samples_frequent, we're expecting paths, so we'll keep it as is
+        face_samples_frequent = [Image.open(path) for path in face_samples_frequent] if isinstance(face_samples_frequent, list) else []
     
         output = [
             exec_time, results_summary,
@@ -50,7 +63,7 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         print(error_message)
         import traceback
         traceback.print_exc()
-        return [error_message] + [None] * 26 
+        return [error_message] + [None] * 26
 
 def show_results(outputs):
     return gr.Group(visible=True)