Update app.py

app.py

@@ -318,7 +318,7 @@ def plot_anomaly_scores(df, anomaly_scores, top_indices, title):
 def plot_emotion(df, emotion, num_anomalies, color):
     fig, ax = plt.subplots(figsize=(16, 8))
     values = df[emotion].values
-    bars = ax.bar(range(len(df)), values, width=0.8, color=color)
+    bars = ax.bar(range(len(df)), values, width=0.9, color=color)
     top_indices = np.argsort(values)[-num_anomalies:][::-1]
     for i in top_indices:
         bars[i].set_color('red')
@@ -343,7 +343,7 @@ def get_random_face_sample(organized_faces_folder, largest_cluster, output_folde
         
         # Read the image and resize it to be smaller
         face_img = cv2.imread(face_path)
-        small_face = cv2.resize(face_img, (80, 80))  # Resize to 80x80 pixels
+        small_face = cv2.resize(face_img, (10, 10))  # Resize to NxN pixels
         cv2.imwrite(output_path, small_face)
         
         return output_path
@@ -402,10 +402,14 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
             emotion_plots = [
                 plot_emotion(df, 'fear', num_anomalies, 'purple'),
                 plot_emotion(df, 'sad', num_anomalies, 'green'),
-                plot_emotion(df, 'angry', num_anomalies, 'orange')
+                plot_emotion(df, 'angry', num_anomalies, 'orange'),
+                plot_emotion(df, 'happy', num_anomalies, 'darkblue'),
+                plot_emotion(df, 'surprise', num_anomalies, 'gold'),
+                plot_emotion(df, 'neutral', num_anomalies, 'grey')
             ]
         except Exception as e:
-            return f"Error generating plots: {str(e)}", None, None, None, None, None, None
+            return f"Error generating plots: {str(e)}", None, None, None, None, None, None, None, None, None
+
 
         # Get a random face sample
         face_sample = get_random_face_sample(organized_faces_folder, largest_cluster, output_folder)
@@ -418,11 +422,11 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
         results += "\n".join([f"{score:.4f} at {timecode}" for score, timecode in 
                               zip(anomaly_scores_comp[top_indices_comp], df['Timecode'].iloc[top_indices_comp].values)])
 
-        for emotion in ['fear', 'sad', 'angry']:
+        for emotion in ['fear', 'sad', 'angry', 'happy', 'surprise', 'neutral']:
             top_indices = np.argsort(df[emotion].values)[-num_anomalies:][::-1]
             results += f"\n\nTop {num_anomalies} {emotion.capitalize()} Scores:\n"
             results += "\n".join([f"{df[emotion].iloc[i]:.4f} at {df['Timecode'].iloc[i]}" for i in top_indices])
-
+    
         return results, face_sample, anomaly_plot_all, anomaly_plot_comp, *emotion_plots
         
 # Gradio interface
@@ -430,7 +434,7 @@ iface = gr.Interface(
     fn=process_video,
     inputs=[
         gr.Video(),
-        gr.Slider(minimum=1, maximum=20, step=1, value=10, label="Number of Anomalies"),
+        gr.Slider(minimum=1, maximum=20, step=1, value=5, label="Number of Anomalies"),
         gr.Slider(minimum=2, maximum=5, step=1, value=3, label="Number of Components"),
         gr.Slider(minimum=1, maximum=30, step=1, value=20, label="Desired FPS"),
         gr.Slider(minimum=1, maximum=64, step=1, value=16, label="Batch Size")
@@ -442,7 +446,10 @@ iface = gr.Interface(
         gr.Plot(label="Anomaly Scores (Components Only)"),
         gr.Plot(label="Fear Anomalies"),
         gr.Plot(label="Sad Anomalies"),
-        gr.Plot(label="Angry Anomalies")
+        gr.Plot(label="Angry Anomalies"),
+        gr.Plot(label="Happy Anomalies"),
+        gr.Plot(label="Surprise Anomalies"),
+        gr.Plot(label="Neutral Anomalies")
     ],
     title="Facial Expressions Anomaly Detection",
     description="""