Update app.py

app.py

@@ -24,7 +24,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme):
     raw_cm = compute_average_confusion_matrix(raw_folder)
     if raw_cm is not None:
         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)", save_path=raw_cm_path, theme)
+        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)", save_path=raw_cm_path, theme=theme)
         raw_img = Image.open(raw_cm_path)
     else:
         raw_img = None
@@ -33,7 +33,7 @@ def beam_prediction_task(data_percentage, task_complexity, theme):
     embeddings_cm = compute_average_confusion_matrix(embeddings_folder)
     if embeddings_cm is not None:
         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)", save_path=embeddings_cm_path, theme)
+        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)", save_path=embeddings_cm_path, theme=theme)
         embeddings_img = Image.open(embeddings_cm_path)
     else:
         embeddings_img = None
@@ -59,7 +59,7 @@ def compute_f1_score(cm):
     f1 = np.nan_to_num(f1)  # Replace NaN with 0
     return np.mean(f1)  # Return the mean F1-score across all classes
 
-def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme):
+def plot_confusion_matrix_beamPred(cm, classes, title, save_path, theme=None):
     # Compute the average F1-score
     avg_f1 = compute_f1_score(cm)