Update app.py

app.py

@@ -36,32 +36,27 @@ tokenizer = AutoTokenizer.from_pretrained(
 )
 
 
-def preprocessing(image, image_size=448):
-    """
-    Apply enhancement filters and pad the image to match the target size while keeping full content.
-    """
-    # Convert input to a PIL Image (if it isn’t already)
-    image = Image.fromarray(np.array(image))
-    
-    # Apply enhancement filters
-    image = ImageEnhance.Sharpness(image).enhance(2.0)   # Increase sharpness
-    image = ImageEnhance.Contrast(image).enhance(1.5)    # Increase contrast
-    image = ImageEnhance.Brightness(image).enhance(0.8)  # Reduce brightness
+from PIL import Image, ImageEnhance
+import numpy as np
+import torch
 
-    # Get original dimensions
-    orig_width, orig_height = image.size
+def preprocessing(image):
+    """Apply three enhancement filters without resizing or cropping."""
+    
+    # Ensure the image is a PIL Image
+    if not isinstance(image, Image.Image):
+        image = Image.fromarray(np.array(image))
 
-    # Determine the padding needed to fit the image within a square of size `image_size`
-    pad_x = max(image_size - orig_width, 0)
-    pad_y = max(image_size - orig_height, 0)
+    # Apply enhancements
+    image = ImageEnhance.Sharpness(image).enhance(2.0)  # Increase sharpness
+    image = ImageEnhance.Contrast(image).enhance(1.5)   # Increase contrast
+    image = ImageEnhance.Brightness(image).enhance(0.8) # Reduce brightness
 
-    # Create a new blank image with a white background
-    padded_image = Image.new("RGB", (orig_width + pad_x, orig_height + pad_y), (255, 255, 255))
+    # Convert to tensor without resizing
+    image_tensor = torch.tensor(np.array(image)).permute(2, 0, 1).float() / 255.0  # Shape: [C, H, W]
 
-    # Paste the original image in the center
-    padded_image.paste(image, (pad_x // 2, pad_y // 2))
+    return image_tensor
 
-    return padded_image