Update app.py

app.py

@@ -31,7 +31,7 @@ def remove_background_rembg(input_path):
     img = Image.open(io.BytesIO(output_image)).convert("RGBA")
     return img
 
-def remove_background_bria(input_image):
+def remove_background_bria(input_path):
     """Remove background using the Bria model."""
     print("Removing background using Bria for the image.")
     
@@ -39,11 +39,11 @@ def remove_background_bria(input_image):
     pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True, device=0)
     
     # Ensure input_image is a PIL image
-    if isinstance(input_image, str):
-        input_image = Image.open(input_image).convert("RGBA")
+    if isinstance(input_path, str):
+        input_image = Image.open(input_path).convert("RGBA")
     
     # Get the segmentation output
-    segmentation_result = pipe(input_image)
+    segmentation_result = pipe(input_path)
 
     # Check if the output contains a mask
     if isinstance(segmentation_result, list) and len(segmentation_result) > 0:
@@ -52,24 +52,24 @@ def remove_background_bria(input_image):
         
         if mask is not None:
             # Create an output image based on the mask
-            output_image = Image.new("RGBA", input_image.size)
-            width, height = input_image.size
+            output_image = Image.new("RGBA", input_path.size)
+            width, height = input_path.size
             
             for y in range(height):
                 for x in range(width):
                     # Check if the mask value is valid (assuming it's binary or a single channel)
                     if mask[y][x] > 0:  # Adjust based on actual mask values
-                        output_image.putpixel((x, y), input_image.getpixel((x, y)))
+                        output_image.putpixel((x, y), input_path.getpixel((x, y)))
                     else:
                         output_image.putpixel((x, y), (0, 0, 0, 0))  # Set to transparent
             
             return output_image
         else:
             print("Mask is None in segmentation result.")
-            return input_image  # Return original if mask is None
+            return input_path  # Return original if mask is None
     else:
         print("No valid segmentation result received.")
-        return input_image  # Return original if no valid result
+        return input_path  # Return original if no valid result
 
 # Fungsi untuk memproses gambar menggunakan prompt
 def text_to_image(prompt):