Update app.py

app.py

@@ -315,10 +315,10 @@ def plot_anomaly_scores(df, anomaly_scores, top_indices, title):
     plt.tight_layout()
     return fig
 
-def plot_emotion(df, emotion, num_anomalies):
+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='lightgreen')
+    bars = ax.bar(range(len(df)), values, width=0.8, color=color)
     top_indices = np.argsort(values)[-num_anomalies:][::-1]
     for i in top_indices:
         bars[i].set_color('red')
@@ -331,17 +331,14 @@ def plot_emotion(df, emotion, num_anomalies):
     plt.tight_layout()
     return fig
 
-def plot_components(df):
-    fig, ax = plt.subplots(figsize=(16, 8))
-    component_columns = [col for col in df.columns if col.startswith('Comp')]
-    for col in component_columns:
-        ax.plot(df['Time (Minutes)'], df[col], label=col)
-    ax.set_xlabel('Time (Minutes)')
-    ax.set_ylabel('Component Value')
-    ax.set_title('UMAP Components Over Time')
-    ax.legend()
-    plt.tight_layout()
-    return fig
+def get_random_face_sample(organized_faces_folder, largest_cluster):
+    person_folder = os.path.join(organized_faces_folder, f"person_{largest_cluster}")
+    face_files = [f for f in os.listdir(person_folder) if f.endswith('.jpg')]
+    if face_files:
+        random_face = np.random.choice(face_files)
+        face_path = os.path.join(person_folder, random_face)
+        return face_path
+    return None
 
 def process_video(video_path, num_anomalies, num_components, desired_fps, batch_size, progress=gr.Progress()):
     with tempfile.TemporaryDirectory() as temp_dir:
@@ -390,11 +387,17 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
         try:
             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")
-            components_plot = plot_components(df)
-            emotion_plots = [plot_emotion(df, emotion, num_anomalies) for emotion in ['fear', 'sad', 'angry']]
+            emotion_plots = [
+                plot_emotion(df, 'fear', num_anomalies, 'lightblue'),
+                plot_emotion(df, 'sad', num_anomalies, 'lightgreen'),
+                plot_emotion(df, 'angry', num_anomalies, 'salmon')
+            ]
         except Exception as e:
             return f"Error generating plots: {str(e)}", None, None, None, None, None, None
 
+        # Get a random face sample
+        face_sample = get_random_face_sample(organized_faces_folder, largest_cluster)
+
         progress(1.0, "Preparing results")
         results = f"Top {num_anomalies} anomalies (All Features):\n"
         results += "\n".join([f"{score:.4f} at {timecode}" for score, timecode in 
@@ -408,8 +411,8 @@ def process_video(video_path, num_anomalies, num_components, desired_fps, batch_
             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, anomaly_plot_all, anomaly_plot_comp, components_plot, *emotion_plots
-
+        return results, face_sample, anomaly_plot_all, anomaly_plot_comp, *emotion_plots
+        
 # Gradio interface
 iface = gr.Interface(
     fn=process_video,
@@ -422,9 +425,9 @@ iface = gr.Interface(
     ],
     outputs=[
         gr.Textbox(label="Anomaly Detection Results"),
+        gr.Image(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="UMAP Components"),
         gr.Plot(label="Fear Scores"),
         gr.Plot(label="Sad Scores"),
         gr.Plot(label="Angry Scores")