Upload 2 files

eda.py

@@ -27,24 +27,15 @@ def run():
     
 
     st.markdown('---')
-        
-    # Define the path to the dataset
+    main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
     
-    # Load the dataset from Hugging Face
-    from datasets import load_dataset
-    dataset = load_dataset("andrewsunanda/fast_food_image_classification")
-    
-    # Define the batch size and image size
+    # Define batch size and image size
     batch_size = 256
     img_size = (64, 64)
-    
-    # Define the paths to the train, validation, and test folders
-    dataset_path = "andrewsunanda/fast_food_image_classification"
-    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 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')
     # Create data generators for training, validation, and testing
     train_datagen = ImageDataGenerator(
         rescale=1./255, 

prediction.py

@@ -20,25 +20,15 @@ def preprocess_input_image(img_path):
     x /= 255.
     return x, img1
 
-import os
-import torch
-import torchvision.transforms as transforms
-from torch.utils.data import DataLoader
-from datasets import load_dataset
-
-# Load the dataset from Hugging Face
-from datasets import load_dataset
-dataset = load_dataset("andrewsunanda/fast_food_image_classification")
+main_path= 'D:\\tugas_andrew_DS\\phase_2\\m2\\food'
     
-# Define the batch size and image size
+# Define batch size and image size
 batch_size = 256
 img_size = (64, 64)
-    
-# Define the paths to the train, validation, and test folders
-dataset_path = "andrewsunanda/fast_food_image_classification"
-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 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')
 
 # Create data generators for training, validation, and testing
 train_datagen = ImageDataGenerator(