Update app.py

app.py

@@ -18,18 +18,8 @@ if huggingface_token is None:
     raise ValueError("Hugging Face token not found. Please set the HUGGINGFACE_TOKEN environment variable.")
 
 
-# Define a custom transform for Gaussian blur
-def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=9, sigma_min=0.1, sigma_max=2):
-    if x.ndim == 4:
-        for i in range(x.shape[0]):
-            if random.random() < p:
-                kernel_size = random.randrange(kernel_size_min, kernel_size_max + 1, 2)
-                sigma = random.uniform(sigma_min, sigma_max)
-                x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
-    return x
-
 # # Define a custom transform for Gaussian blur
-# def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=20, sigma_min=0.1, sigma_max=3):
+# def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=9, sigma_min=0.1, sigma_max=2):
 #     if x.ndim == 4:
 #         for i in range(x.shape[0]):
 #             if random.random() < p:
@@ -38,6 +28,16 @@ def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=9, sigma_min=0.1,
 #                 x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
 #     return x
 
+# Define a custom transform for Gaussian blur
+def gaussian_blur(x, p=0.5, kernel_size_min=3, kernel_size_max=20, sigma_min=0.1, sigma_max=3):
+    if x.ndim == 4:
+        for i in range(x.shape[0]):
+            if random.random() < p:
+                kernel_size = random.randrange(kernel_size_min, kernel_size_max + 1, 2)
+                sigma = random.uniform(sigma_min, sigma_max)
+                x[i] = GaussianBlur(kernel_size=kernel_size, sigma=sigma)(x[i])
+    return x
+
 # this function only describes how much a singular value in al ist stands out.
 # if all values in the lsit are high or low this is 1
 # the smaller the proportiopn of number of disimilar vlaues are to other more similar values the lower this number