Update app.py

app.py

@@ -160,7 +160,7 @@ from sklearn.metrics import f1_score
 import seaborn as sns
 
 # Function to compute confusion matrix, F1-score and plot it with dark mode style
-def plot_confusion_matrix_from_csv(csv_file_path, title, save_path, light_mode=True):
+def plot_confusion_matrix_from_csv(csv_file_path, title, save_path, light_mode=False):
     # Load CSV file
     data = pd.read_csv(csv_file_path)
     
@@ -181,8 +181,9 @@ def plot_confusion_matrix_from_csv(csv_file_path, title, save_path, light_mode=T
         cmap = 'Blues'  # Light-mode-friendly colormap
     else:
         plt.style.use('dark_background')  # Dark mode styling
-        text_color = 'white'
-        cmap = 'magma'  # Dark-mode-friendly colormap
+        text_color = 'gray'
+        #cmap = 'magma'  # Dark-mode-friendly colormap
+        cmap = 'coolwarm'
     
     plt.figure(figsize=(5, 5))
 
@@ -336,7 +337,7 @@ def classify_based_on_distance(train_data, train_labels, test_data):
     
     return torch.tensor(predictions)  # Return predictions as a PyTorch tensor
 
-def plot_confusion_matrix(y_true, y_pred, title, light_mode=True):
+def plot_confusion_matrix(y_true, y_pred, title, light_mode=False):
     cm = confusion_matrix(y_true, y_pred)
     
     # Calculate F1 Score
@@ -351,8 +352,9 @@ def plot_confusion_matrix(y_true, y_pred, title, light_mode=True):
         cmap = 'Blues'  # Light-mode-friendly colormap
     else:
         plt.style.use('dark_background')  # Dark mode styling
-        text_color = 'white'
-        cmap = 'magma'  # Dark-mode-friendly colormap
+        text_color = 'gray'
+        #cmap = 'magma'  # Dark-mode-friendly colormap
+        cmap = 'coolwarm'
         
     plt.figure(figsize=(5, 5))