Update app.py

app.py

@@ -26,7 +26,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme='Dark'):
         raw_cm_path = os.path.join(raw_folder, "confusion_matrix_raw.png")
         plot_confusion_matrix_beamPred(raw_cm, 
                                        classes=np.arange(raw_cm.shape[0]), 
-                                       title=f"Raw Confusion Matrix\n{data_percentage}% data, {task_complexity} beams", 
+                                       title=f"Confusion Matrix (Raw Channels)\n{data_percentage}% data, {task_complexity} beams", 
                                        save_path=raw_cm_path, 
                                        theme=theme)
         raw_img = Image.open(raw_cm_path)
@@ -39,7 +39,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme='Dark'):
         embeddings_cm_path = os.path.join(embeddings_folder, "confusion_matrix_embeddings.png")
         plot_confusion_matrix_beamPred(embeddings_cm, 
                                        classes=np.arange(embeddings_cm.shape[0]), 
-                                       title=f"Embeddings Confusion Matrix\n{data_percentage}% data, {task_complexity} beams", 
+                                       title=f"Confusion Matrix (LWM Embeddings)\n{data_percentage}% data, {task_complexity} beams", 
                                        save_path=embeddings_cm_path, 
                                        theme=theme)
         embeddings_img = Image.open(embeddings_cm_path)
@@ -191,14 +191,14 @@ def plot_confusion_matrix_from_csv(csv_file_path, title, save_path, light_mode=F
     sns.heatmap(cm, annot=True, fmt="d", cmap=cmap, cbar=False, annot_kws={"size": 12}, linewidths=0.5, linecolor='white')
 
     # Add F1-score to the title
-    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color=text_color, fontsize=14)
+    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color=text_color, fontsize=24)
 
     # Customize tick labels for light/dark mode
-    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
-    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
+    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
+    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
 
-    plt.ylabel('True label', color=text_color, fontsize=12)
-    plt.xlabel('Predicted label', color=text_color, fontsize=12)
+    plt.ylabel('True label', color=text_color, fontsize=18)
+    plt.xlabel('Predicted label', color=text_color, fontsize=18)
     plt.tight_layout()
 
     # Save the plot as an image
@@ -220,14 +220,14 @@ def display_confusion_matrices_los(percentage):
     raw_csv_file = os.path.join(raw_folder, f"test_predictions_raw_{percentage/100:.3f}_los.csv")
     raw_cm_img_path = os.path.join(raw_folder, "confusion_matrix_raw.png")
     raw_img = plot_confusion_matrix_from_csv(raw_csv_file, 
-                                             f"Raw Confusion Matrix ({percentage:.1f}% data)", 
+                                             f"Confusion Matrix (Raw Channels)\n{percentage:.1f}% data", 
                                              raw_cm_img_path)
 
     # Process embeddings confusion matrix
     embeddings_csv_file = os.path.join(embeddings_folder, f"test_predictions_embedding_{percentage/100:.3f}_los.csv")
     embeddings_cm_img_path = os.path.join(embeddings_folder, "confusion_matrix_embeddings.png")
     embeddings_img = plot_confusion_matrix_from_csv(embeddings_csv_file, 
-                                                    f"Embeddings Confusion Matrix ({percentage:.1f}% data)", 
+                                                    f"Confusion Matrix (LWM Embeddings)\n{percentage:.1f}% data", 
                                                     embeddings_cm_img_path)
 
     return raw_img, embeddings_img
@@ -362,14 +362,14 @@ def plot_confusion_matrix(y_true, y_pred, title, light_mode=False):
     sns.heatmap(cm, annot=True, fmt="d", cmap=cmap, cbar=False, annot_kws={"size": 12}, linewidths=0.5, linecolor='white')
     
     # Add F1-score to the title
-    plt.title(f"{title}\n(F1 Score: {f1:.3f})", color=text_color, fontsize=14)
+    plt.title(f"{title}\nF1 Score: {f1:.3f}", color=text_color, fontsize=23)
     
     # Customize tick labels for dark mode
-    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
-    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=10)
+    plt.xticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
+    plt.yticks([0.5, 1.5], labels=['Class 0', 'Class 1'], color=text_color, fontsize=14)
     
-    plt.ylabel('True label', color=text_color, fontsize=12)
-    plt.xlabel('Predicted label', color=text_color, fontsize=12)
+    plt.ylabel('True label', color=text_color, fontsize=18)
+    plt.xlabel('Predicted label', color=text_color, fontsize=18)
     plt.tight_layout()
     
     # Save the plot as an image