Update eda.py

eda.py

@@ -27,15 +27,47 @@ def run():
     
 
     st.markdown('---')
-    main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
+    import os
+    import torch
+    import torchvision.transforms as transforms
+    from torch.utils.data import DataLoader
+    from datasets import load_dataset
     
-    # Define batch size and image size
+    # Define the path to the dataset
+    dataset_path = 'andrewsunanda/fast_food_image_classification'
+    
+    # Load the dataset from Hugging Face
+    dataset = load_dataset(dataset_path)
+    
+    # Define the batch size and image size
     batch_size = 256
     img_size = (64, 64)
-    # Define paths to the data folders
-    train_path = os.path.join(main_path, 'Train')
-    valid_path = os.path.join(main_path, 'Valid')
-    test_path = os.path.join(main_path, 'Test')
+    
+    # Define the paths to the train, validation, and test folders
+    train_path = os.path.join(dataset_path, 'Train')
+    valid_path = os.path.join(dataset_path, 'Valid')
+    test_path = os.path.join(dataset_path, 'Test')
+    
+    # Define the transforms for the dataset
+    transform = transforms.Compose([
+        transforms.Resize(img_size),
+        transforms.ToTensor(),
+    ])
+    
+    # Load the training dataset
+    train_dataset = dataset['train']
+    train_dataset = train_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    
+    # Load the validation dataset
+    valid_dataset = dataset['validation']
+    valid_dataset = valid_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
+    valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)
+    
+    # Load the testing dataset
+    test_dataset = dataset['test']
+    test_dataset = test_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
     # Create data generators for training, validation, and testing
     train_datagen = ImageDataGenerator(
         rescale=1./255,