Update app.py

app.py

@@ -1,54 +1,55 @@
+import gradio as gr
 import numpy as np
 from PIL import Image
-from rembg import new_session, remove
-from skimage import filters
-import gradio as gr
-import cv2  # Added for dilation and GaussianBlur
+from rembg import remove, new_session
 
-# Initialize models
-isnet_session = new_session("isnet-general-use")
-u2net_session = new_session("u2net")
+# Initialize session with proper settings to prevent cropping
+session = new_session("u2net")
+bg_removal_kwargs = {
+    "alpha_matting": False,  # Disable advanced features that cause cropping
+    "session": session,
+    "only_mask": False,
+    "post_process_mask": True  # Clean edges without cropping
+}
 
-def perfect_remove_bg(img):
+def remove_background(input_image):
     try:
-        # Convert input
-        if isinstance(img, np.ndarray):
-            img = Image.fromarray(img)
-        
-        # ISNet for details
-        result = remove(img, session=isnet_session)
-        mask = np.array(result.split()[-1])
-        
-        # Edge refinement
-        mask = filters.rank.mean(
-            mask.astype(np.uint8),
-            footprint=np.ones((3,3), np.uint8)
-        )
-        
-        # Enhanced edge preservation
-        mask = cv2.dilate(mask, np.ones((1,1), np.uint8), iterations=1)
-        mask = cv2.GaussianBlur(mask, (3,3), 0)
-        
-        # U²Net mask
-        u2net_result = remove(img, session=u2net_session)
-        u2net_mask = np.array(u2net_result.split()[-1])
-        
-        # Combine masks
-        final_mask = np.where(u2net_mask > 200, mask, u2net_mask)
-        result.putalpha(Image.fromarray(final_mask))
+        # Convert any input to PIL Image
+        if isinstance(input_image, np.ndarray):
+            img = Image.fromarray(input_image)
+        elif isinstance(input_image, dict):  # Handle paste/drop
+            img = Image.open(input_image["name"])
+        else:
+            img = input_image
         
+        # Preserve original size (disable auto-resizing)
+        result = remove(img, **bg_removal_kwargs)
         return result
-        
+    
     except Exception as e:
-        print(f"Error: {e}")
-        return remove(img, session=u2net_session)
+        print(f"Error: {str(e)}")
+        return input_image  # Return original if fails
 
-# Gradio interface
+# Gradio interface with proper image handling
 with gr.Blocks() as demo:
-    gr.Markdown("## ✨ Professional BG Remover")
+    gr.Markdown("## 🖼️ Background Remover (No Cropping)")
+    
     with gr.Row():
-        input_img = gr.Image(label="Input", type="pil")
-        output_img = gr.Image(label="Output", type="pil")
-    gr.Button("Process").click(perfect_remove_bg, inputs=input_img, outputs=output_img)
+        input_img = gr.Image(
+            label="Original", 
+            type="pil",  # Ensures we get PIL Images
+            height=400
+        )
+        output_img = gr.Image(
+            label="Result", 
+            type="pil",
+            height=400
+        )
+    
+    gr.Button("Remove Background").click(
+        remove_background,
+        inputs=input_img,
+        outputs=output_img
+    )
 
 demo.launch()