diff --git a/data.py b/data.py
index 302d047..e180905 100644
--- a/data.py
+++ b/data.py
@@ -5,7 +5,6 @@ from glob import glob
 import matplotlib.pyplot as plt
 import torch
 import torchvision.transforms as transforms
-from kaggle.api.kaggle_api_extended import KaggleApi
 from PIL import Image
 from torch.utils.data import Dataset
 
@@ -100,20 +99,20 @@ class SegmentationStatistics:
 
 
 def get_data():
-    api = KaggleApi()
-    api.authenticate()
-
     dataset_slug = "sadhliroomyprime/football-semantic-segmentation"
 
     download_dir = "./football_dataset"
 
     if not os.path.exists(download_dir):
+        from kaggle.api.kaggle_api_extended import KaggleApi
+
+        api = KaggleApi()
+        api.authenticate()
         os.makedirs(download_dir)
         api.dataset_download_files(dataset_slug, path=download_dir, unzip=True)
 
     all_images = glob(download_dir + "/images/*.jpg")
-    all_paths = [path.replace(".jpg", ".jpg___fuse.png")
-                 for path in all_images]
+    all_paths = [path.replace(".jpg", ".jpg___fuse.png") for path in all_images]
     return all_images, all_paths
 
 
@@ -135,7 +134,7 @@ def calculate_mean_std(image_paths):
     return mean, std
 
 
-def plot_random_images(indices, data_loader, suffix='train'):
+def plot_random_images(indices, data_loader, suffix="train"):
     plt.figure(figsize=(8, 8))
     for i, index in enumerate(indices):
         image, label_map = data_loader.dataset[index]
@@ -150,5 +149,5 @@ def plot_random_images(indices, data_loader, suffix='train'):
         plt.axis("off")
 
     # Save the figure to a file instead of displaying it
-    plt.savefig(f'random_images_{suffix}.png', dpi=250, bbox_inches='tight')
+    plt.savefig(f"random_images_{suffix}.png", dpi=250, bbox_inches="tight")
     plt.close()  # Close the figure to free up memory