Update app.py

app.py

@@ -19,7 +19,7 @@ from transformers import pipeline, AutoImageProcessor, AutoModelForDepthEstimati
 from scipy.ndimage import gaussian_filter
 from scipy import interpolate
 import cv2
-from backgroundremover.bg import remove
+from rembg import remove
 
 app = Flask(__name__)
 CORS(app)
@@ -86,20 +86,25 @@ def allowed_file(filename):
     return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
 
 def remove_background(image_path):
-    with open(image_path, "rb") as img_file:
-        img_data = img_file.read()
-    result = remove(img_data, alpha_matting=True, alpha_matting_foreground_threshold=240, alpha_matting_background_threshold=10)
-    img = Image.open(io.BytesIO(result)).convert("RGBA")
-    
-    # Check if image is fully transparent (no object)
-    img_array = np.array(img)
-    if np.all(img_array[:, :, 3] == 0):
-        return None
-    
-    # Create black background
-    black_bg = Image.new("RGB", img.size, (0, 0, 0))
-    black_bg.paste(img, (0, 0), img)
-    return black_bg
+    try:
+        with open(image_path, "rb") as img_file:
+            img_data = img_file.read()
+        result = remove(img_data)
+        img = Image.open(io.BytesIO(result)).convert("RGBA")
+        
+        # Check if image is fully transparent (no object)
+        img_array = np.array(img)
+        if np.all(img_array[:, :, 3] == 0):
+            print(f"Warning: Image {image_path} is fully transparent or no object detected")
+            return None
+        
+        # Create black background
+        black_bg = Image.new("RGB", img.size, (0, 0, 0))
+        black_bg.paste(img, (0, 0), img)
+        return black_bg
+    except Exception as e:
+        print(f"Error in remove_background for {image_path}: {str(e)}")
+        raise
 
 def preprocess_image(image_path):
     # Remove background and add black background