Update app.py

app.py

@@ -3,7 +3,6 @@ import time
 from video_processing import process_video
 from PIL import Image
 import matplotlib
-
 matplotlib.rcParams['figure.dpi'] = 500
 matplotlib.rcParams['savefig.dpi'] = 500
 
@@ -15,7 +14,7 @@ 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] * 20
+            return [results[0]] + [None] * 18
 
         exec_time, results_summary, df, mse_embeddings, mse_posture, \
             mse_plot_embeddings, mse_histogram_embeddings, \
@@ -30,7 +29,7 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
 
         face_samples_frequent = [Image.open(path) for path in face_samples_frequent]
         face_samples_other = [Image.open(path) for path in face_samples_other]
-        
+
         output = [
             exec_time, results_summary,
             df, mse_embeddings, mse_posture,
@@ -40,7 +39,7 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
             anomaly_faces_embeddings_pil, anomaly_frames_posture_pil,
             face_samples_frequent, face_samples_other,
             aligned_faces_folder, frames_folder,
-            mse_embeddings, mse_posture
+            mse_embeddings, mse_posture,
         ]
 
         return output
@@ -50,14 +49,17 @@ def process_and_show_completion(video_input_path, anomaly_threshold_input, fps,
         print(error_message)
         import traceback
         traceback.print_exc()
-        return [error_message] + [None] * 20
+        return [error_message] + [None] * 18
 
 with gr.Blocks() as iface:
     gr.Markdown("""
-    # Multimodal Behavioral Anomalies 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.
+    # Facial Expression and Body Language Anomaly 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)
     """)
 
     with gr.Row():
@@ -84,11 +86,6 @@ with gr.Blocks() as iface:
             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("Combined"):
-            with gr.Row():
-                kde_plot = gr.Plot(label="KDE Plot: Facial Features vs Body Posture MSE")
-                joint_plot = gr.Plot(label="Joint Plot: Facial Features vs Body Posture MSE")
-
         with gr.Tab("Face Samples"):
             face_samples_most_frequent = gr.Gallery(label="Most Frequent Person Samples (Target)", columns=6, rows=2, height="auto")
             face_samples_others = gr.Gallery(label="Other Persons Samples", columns=6, rows=1, height="auto")