Update app.py

app.py

@@ -1,58 +1,3 @@
-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
-
 with gr.Blocks() as iface:
     gr.Markdown("""
     # Multimodal Behavioral Anomalies Detection
@@ -73,26 +18,27 @@ with gr.Blocks() as iface:
     with gr.Group(visible=False) as results_group:
         results_text = gr.TextArea(label="Anomaly Detection Results", lines=4)
 
-        with gr.Tab("Facial Features"):
-            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.Tabs():
+            with gr.TabItem("Facial Features"):
+                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.Tab("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("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.Tab("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("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.Tab("Video with Heatmap"):
-            heatmap_video = gr.Video(label="Video with Anomaly Heatmap")
+            with gr.TabItem("Video with Heatmap"):
+                heatmap_video = gr.Video(label="Video with Anomaly Heatmap")
 
     df_store = gr.State()
     mse_features_store = gr.State()
@@ -104,6 +50,9 @@ 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],
@@ -119,6 +68,10 @@ with gr.Blocks() as iface:
             mse_heatmap_embeddings_store, mse_heatmap_posture_store, mse_heatmap_voice_store,
             heatmap_video
         ]
+    ).then(
+        show_results,
+        inputs=None,
+        outputs=results_group
     )
 
 if __name__ == "__main__":