Update app.py

app.py

@@ -3,17 +3,55 @@ import cv2
 import numpy as np
 import torch
 import gradio as gr
-import spaces  # Required for @spaces.GPU
-
+import spaces
+from gradio.themes.base import Base
+from gradio.themes.utils import colors, fonts, sizes
 from PIL import Image, ImageOps
 from transformers import AutoModelForImageSegmentation
 from torchvision import transforms
 
+# Custom White Theme with Inter font
+class WhiteTheme(Base):
+    def __init__(
+        self,
+        *,
+        primary_hue: colors.Color | str = colors.orange,
+        font: fonts.Font | str = fonts.GoogleFont("Inter"),
+        font_mono: fonts.Font | str = fonts.GoogleFont("Inter")
+    ):
+        super().__init__(
+            primary_hue=primary_hue,
+            font=font,
+            font_mono=font_mono,
+        )
+        
+        self.set(
+            body_background_fill="white",
+            block_background_fill="white",
+            panel_background_fill="white",
+            body_text_color="black",
+            block_label_text_color="black",
+            block_border_color="white",
+            panel_border_color="white",
+            input_border_color="lightgray",
+            button_primary_background_fill="*primary_500",
+            button_primary_background_fill_hover="*primary_600",
+            button_primary_text_color="white",
+            button_secondary_background_fill="white",
+            button_secondary_border_color="lightgray",
+            block_shadow="none",
+            button_shadow="none",
+            input_shadow="none",
+            slider_color="*primary_500",
+            slider_track_color="lightgray",
+        )
+
+# Your existing setup code
 torch.set_float32_matmul_precision('high')
 torch.jit.script = lambda f: f
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Keep all your existing functions unchanged
 def refine_foreground(image, mask, r=90):
     if mask.size != image.size:
         mask = mask.resize(image.size)
@@ -33,14 +71,11 @@ def FB_blur_fusion_foreground_estimator(image, F, B, alpha, r=90):
     if isinstance(image, Image.Image):
         image = np.array(image) / 255.0
     blurred_alpha = cv2.blur(alpha, (r, r))[:, :, None]
-
     blurred_FA = cv2.blur(F * alpha, (r, r))
     blurred_F = blurred_FA / (blurred_alpha + 1e-5)
-
     blurred_B1A = cv2.blur(B * (1 - alpha), (r, r))
     blurred_B = blurred_B1A / ((1 - blurred_alpha) + 1e-5)
-    F = blurred_F + alpha * \
-        (image - alpha * blurred_F - (1 - alpha) * blurred_B)
+    F = blurred_F + alpha * (image - alpha * blurred_F - (1 - alpha) * blurred_B)
     F = np.clip(F, 0, 1)
     return F, blurred_B
 
@@ -67,42 +102,33 @@ def remove_background_wrapper(image):
     if image is None:
         raise gr.Error("Please upload an image.")
     image_ori = Image.fromarray(image).convert('RGB')
-    # Call the processing function
     foreground, background, pred_pil, reverse_mask = remove_background(image_ori)
     return foreground, background, pred_pil, reverse_mask
 
-@spaces.GPU  # Decorate the processing function
+@spaces.GPU
 def remove_background(image_ori):
     original_size = image_ori.size
-
-    # Preprocess the image
     image_preprocessor = ImagePreprocessor(resolution=(1024, 1024))
     image_proc = image_preprocessor.proc(image_ori)
     image_proc = image_proc.unsqueeze(0)
-
-    # Prediction
+    
     with torch.no_grad():
         preds = birefnet(image_proc.to(device))[-1].sigmoid().cpu()
     pred = preds[0].squeeze()
-
-    # Process Results
+    
     pred_pil = transforms.ToPILImage()(pred)
-    pred_pil = pred_pil.resize(original_size, Image.BICUBIC)  # Resize mask to original size
-
-    # Create reverse mask (background mask)
+    pred_pil = pred_pil.resize(original_size, Image.BICUBIC)
+    
     reverse_mask = ImageOps.invert(pred_pil)
-
-    # Create foreground image (object with transparent background)
+    
     foreground = image_ori.copy()
     foreground.putalpha(pred_pil)
-
-    # Create background image
+    
     background = image_ori.copy()
     background.putalpha(reverse_mask)
-
+    
     torch.cuda.empty_cache()
-
-    # Return images in the specified order
+    
     return foreground, background, pred_pil, reverse_mask
 
 # Custom CSS for button styling
@@ -123,11 +149,12 @@ custom_css = """
     animation: gradient-animation 15s ease infinite;
     border-radius: 12px;
     color: black;
+    font-family: 'Inter', sans-serif;
 }
 """
 
-with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
-    # Interface setup with input and output
+# Create the interface with the custom theme
+with gr.Blocks(css=custom_css, theme=WhiteTheme()) as demo:
     with gr.Row():
         with gr.Column():
             image_input = gr.Image(type="numpy", sources=['upload'], label="Upload Image")
@@ -138,7 +165,6 @@ with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
             output_foreground_mask = gr.Image(type="pil", label="Foreground Mask")
             output_background_mask = gr.Image(type="pil", label="Background Mask")
 
-    # Link the button to the processing function
     btn.click(fn=remove_background_wrapper, inputs=image_input, outputs=[
         output_foreground, output_background, output_foreground_mask, output_background_mask])