Update app.py

app.py

@@ -1,3 +1,61 @@
+import gradio as gr
+import time
+from video_processing import process_video
+from PIL import Image
+import matplotlib
+
+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()):
+    try:
+        print("Starting video processing...")
+        results = process_video(video_input_path, anomaly_threshold_input, fps, progress=progress)
+        print("Video processing completed.")
+
+        if isinstance(results[0], str) and results[0].startswith("Error"):
+            print(f"Error occurred: {results[0]}")
+            return [results[0]] + [None] * 23
+
+        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, \
+            mse_heatmap_embeddings, mse_heatmap_posture, mse_heatmap_voice, \
+            face_samples_frequent, \
+            anomaly_faces_embeddings, anomaly_frames_posture_images, \
+            aligned_faces_folder, frames_folder, \
+            heatmap_video_path = 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 []
+
+        output = [
+            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,
+            mse_heatmap_embeddings, mse_heatmap_posture, mse_heatmap_voice,
+            anomaly_faces_embeddings_pil, anomaly_frames_posture_pil,
+            face_samples_frequent,
+            aligned_faces_folder, frames_folder,
+            mse_embeddings, mse_posture, mse_voice,
+            heatmap_video_path
+        ]
+
+        return output
+
+    except Exception as e:
+        error_message = f"An error occurred: {str(e)}"
+        print(error_message)
+        import traceback
+        traceback.print_exc()
+        return [error_message] + [None] * 23
+
+def show_results(outputs):
+    return gr.Group(visible=True)
+
 with gr.Blocks() as iface:
     gr.Markdown("""
     # Multimodal Behavioral Anomalies Detection
@@ -50,9 +108,6 @@ with gr.Blocks() as iface:
     mse_heatmap_posture_store = gr.State()
     mse_heatmap_voice_store = gr.State()
 
-    def show_results(outputs):
-        return gr.Group(visible=True)
-
     process_btn.click(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],