Update app.py

app.py

@@ -5,10 +5,8 @@ from PIL import Image
 
 # Dummy segmentation function: replace with your actual segmentation model inference if available.
 def segment_foreground(img):
-    # Convert input image to a NumPy array
     np_img = np.array(img.convert("RGB"))
     h, w, _ = np_img.shape
-    # Create a circular mask as a dummy example
     mask = np.zeros((h, w), dtype=np.uint8)
     center = (w // 2, h // 2)
     radius = min(center) - 10
@@ -19,11 +17,8 @@ def segment_foreground(img):
 def gaussian_blur_background(img, sigma=15):
     mask = segment_foreground(img)
     np_img = np.array(img.convert("RGB"))
-    # Apply Gaussian blur to the entire image
     blurred = cv2.GaussianBlur(np_img, (0, 0), sigma)
-    # Prepare the mask in 3 channels
     mask_3d = np.stack([mask] * 3, axis=-1) / 255.0
-    # Combine the original (foreground) with the blurred (background)
     combined = np_img * mask_3d + blurred * (1 - mask_3d)
     return Image.fromarray(combined.astype(np.uint8))
 
@@ -31,7 +26,6 @@ def gaussian_blur_background(img, sigma=15):
 def estimate_depth(img):
     np_img = np.array(img.convert("RGB"))
     h, w, _ = np_img.shape
-    # Create a gradient depth map: top of the image is close (0), bottom is far (1)
     depth = np.tile(np.linspace(0, 1, h)[:, None], (1, w))
     return depth
 
@@ -41,17 +35,14 @@ def depth_based_blur(img, max_sigma=20):
     np_img = np.array(img.convert("RGB"))
     output = np.zeros_like(np_img)
     
-    # Normalize the depth map to [0, 1]
     depth_norm = (depth - depth.min()) / (depth.max() - depth.min() + 1e-8)
     
-    # Apply a variable Gaussian blur to each row based on the depth value (using the first column as representative)
     for i in range(np_img.shape[0]):
         sigma = max_sigma * depth_norm[i, 0]
         row = cv2.GaussianBlur(np_img[i:i+1, :, :], (0, 0), sigma)
         output[i, :, :] = row
     return Image.fromarray(output.astype(np.uint8))
 
-# Function that dispatches the processing based on user selection.
 def process_image(img, effect):
     if effect == "Gaussian Blur Background":
         return gaussian_blur_background(img)
@@ -60,14 +51,14 @@ def process_image(img, effect):
     else:
         return img
 
-# Create the Gradio interface with an image input and a radio button to select the effect.
+# Updated Gradio interface using the new API components.
 iface = gr.Interface(
     fn=process_image,
     inputs=[
-        gr.inputs.Image(type="pil", label="Input Image"),
-        gr.inputs.Radio(["Gaussian Blur Background", "Depth-based Lens Blur"], label="Select Effect")
+        gr.Image(type="pil", label="Input Image"),
+        gr.Radio(choices=["Gaussian Blur Background", "Depth-based Lens Blur"], label="Select Effect")
     ],
-    outputs=gr.outputs.Image(type="pil", label="Output Image"),
+    outputs=gr.Image(type="pil", label="Output Image"),
     title="Blur Effects Demo",
     description="Upload an image and choose an effect to apply either a Gaussian Blur to the background or a Depth-based Lens Blur."
 )