Update app.py

app.py

@@ -53,13 +53,10 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
 
 with gr.Blocks() as iface:
     gr.Markdown("""
-    # Facial Expression and Body Language Anomaly Detection
+    # Multimodal Behavioral Anomalies Detection
 
-    This application analyzes videos to detect anomalies in facial features and body language. 
-    It processes the video frames to extract facial embeddings and body posture, 
-    then uses machine learning techniques to identify unusual patterns or deviations from the norm.
-
-    For more information, visit: [https://github.com/reab5555/Facial-Expression-Anomaly-Detection](https://github.com/reab5555/Facial-Expression-Anomaly-Detection)
+    The purpose of this tool is to detect anomalies in facial expressions and body language over the timeline of a video.
+    It extracts faces and postures from video frames, detects unique facial features and body postures, and analyzes them to identify anomalies using time series analysis, specifically utilizing a variational autoencoder (VAE) approach.
     """)
 
     with gr.Row():
@@ -101,15 +98,6 @@ with gr.Blocks() as iface:
     mse_heatmap_embeddings_store = gr.State()
     mse_heatmap_posture_store = gr.State()
 
-    def format_anomaly_sentences(anomaly_sentences, anomaly_type):
-        output = f"Sentences before {anomaly_type} Anomalies:\n\n"
-        for anomaly_timecode, sentences in anomaly_sentences:
-            output += f"Anomaly at {anomaly_timecode}:\n"
-            for sentence_timecode, sentence in sentences:
-                output += f"  [{sentence_timecode}] {sentence}\n"
-            output += "\n"
-        return output
-
     process_btn.click(
         process_and_show_completion,
         inputs=[video_input, anomaly_threshold, fps_slider],