Update app.py

app.py

@@ -590,7 +590,7 @@ transform = transforms.Compose([
     transforms.ToTensor(),
 ])
 
-dataset = datasets.ImageFolder(root='Datasets/Bananas', transform=transform)
+dataset = datasets.ImageFolder(root='Datasets/Bananas/Dataset', transform=transform)
 
 # Create a DataLoader
 loader = DataLoader(dataset, batch_size=32, shuffle=False)
@@ -626,7 +626,7 @@ transform = transforms.Compose([
     transforms.Normalize(mean=mean, std=std)
 ])
 
-test_dataset = datasets.ImageFolder(root='/Datasets/Bananas/Dataset', transform=transform)
+test_dataset = datasets.ImageFolder(root='Datasets/Bananas/Dataset', transform=transform)
 
 # Extracting features from Efficientnet model
 def extract_features(test_dataset):
@@ -691,7 +691,6 @@ print(f"Covariance Matrix Shape: {covariance_matrix.shape}")
 
 # Defining the function to calculate the Mahalanobis distance
 
-import torch
 
 def mahalanobis(x=None, feature_mean=None, feature_cov=None):
     """Compute the Mahalanobis Distance between each row of x and the data