Update app.py

app.py

@@ -324,7 +324,7 @@ def plot_emotion(df, emotion, num_anomalies, color):
         bars[i].set_color('red')
     ax.set_xlabel('Timecode')
     ax.set_ylabel(f'{emotion.capitalize()} Score')
-    ax.set_title(f'{emotion.capitalize()} Scores Over Time (Top {num_anomalies} in Red)')
+    ax.set_title(f'{emotion.capitalize()} Anomalies Over Time (Top {num_anomalies} in Red)')
     ax.xaxis.set_major_locator(MaxNLocator(nbins=100))
     ticks = ax.get_xticks()
     ax.set_xticklabels([df['Timecode'].iloc[int(tick)] if tick >= 0 and tick < len(df) else '' for tick in ticks], rotation=90, ha='right')
@@ -340,7 +340,12 @@ def get_random_face_sample(organized_faces_folder, largest_cluster, output_folde
         random_face = np.random.choice(face_files)
         face_path = os.path.join(person_folder, random_face)
         output_path = os.path.join(output_folder, "random_face_sample.jpg")
-        shutil.copy(face_path, output_path)
+        
+        # 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
+        cv2.imwrite(output_path, small_face)
+        
         return output_path
     return None
 
@@ -377,18 +382,17 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
         progress(0.8, "Saving person data")
         df, largest_cluster = save_person_data_to_csv(embeddings_by_frame, emotions_by_frame, clusters, desired_fps, original_fps, temp_dir, num_components)
 
-        progress(0.9, "Performing anomaly detection")
-        feature_columns = [col for col in df.columns if col not in ['Frame', 'Timecode', 'Time (Minutes)', 'Embedding_Index']]
-        X = df[feature_columns].values
-        print(f"Shape of input data: {X.shape}")
-        print(f"Feature columns: {feature_columns}")
+        progress(0.95, "Generating plots")
         try:
-            anomalies_all, anomaly_scores_all, top_indices_all, anomalies_comp, anomaly_scores_comp, top_indices_comp, _ = lstm_anomaly_detection(X, feature_columns, num_anomalies=num_anomalies, batch_size=batch_size)
+            anomaly_plot_all = plot_anomaly_scores(df, anomaly_scores_all, top_indices_all, "All Features")
+            anomaly_plot_comp = plot_anomaly_scores(df, anomaly_scores_comp, top_indices_comp, "Components Only")
+            emotion_plots = [
+                plot_emotion(df, 'fear', num_anomalies, 'purple'),
+                plot_emotion(df, 'sad', num_anomalies, 'green'),
+                plot_emotion(df, 'angry', num_anomalies, 'orange')
+            ]
         except Exception as e:
-            print(f"Error details: {str(e)}")
-            print(f"X shape: {X.shape}")
-            print(f"X dtype: {X.dtype}")
-            return f"Error in anomaly detection: {str(e)}", None, None, None, None, None, None
+            return f"Error generating plots: {str(e)}", None, None, None, None, None, None
         
         progress(0.95, "Generating plots")
         try:
@@ -435,9 +439,9 @@ iface = gr.Interface(
         gr.Image(type="filepath", label="Random Face Sample of Most Frequent Person"),
         gr.Plot(label="Anomaly Scores (All Features)"),
         gr.Plot(label="Anomaly Scores (Components Only)"),
-        gr.Plot(label="Fear Scores"),
-        gr.Plot(label="Sad Scores"),
-        gr.Plot(label="Angry Scores")
+        gr.Plot(label="Fear Anomalies"),
+        gr.Plot(label="Sad Anomalies"),
+        gr.Plot(label="Angry Anomalies")
     ],
     title="Facial Expressions Anomaly Detection",
     description="""