Update app.py

app.py

@@ -34,7 +34,7 @@ from loguru import logger
 
 from lama_cleaner.model_manager import ModelManager
 from lama_cleaner.schema import Config
-'''
+
 try:
     torch._C._jit_override_can_fuse_on_cpu(False)
     torch._C._jit_override_can_fuse_on_gpu(False)
@@ -104,6 +104,7 @@ def preprocess_mask(mask):
     mask = torch.from_numpy(mask)
     return mask
     
+model = None
 def model_process(init_image, mask):
     global model
     
@@ -117,8 +118,7 @@ def model_process(init_image, mask):
     # image, alpha_channel = load_img(origin_image_bytes)
     # Origin image shape: (512, 512, 3)
     original_shape = init_image.shape
-    interpolation = cv2.INTER_CUBIC
-    
+    interpolation = cv2.INTER_CUBIC    
     
     # form = request.form
     
@@ -160,7 +160,10 @@ def model_process(init_image, mask):
     image = resize_max_size(image, size_limit=size_limit, interpolation=interpolation)
     # logger.info(f"Resized image shape: {image.shape}")
     print(f"Resized image shape: {image.shape}")
-
+    
+    if model is None:
+        return None
+        
     mask, _ = load_img(input["mask"].read(), gray=True)
     mask = resize_max_size(mask, size_limit=size_limit, interpolation=interpolation)
 
@@ -182,6 +185,7 @@ def model_process(init_image, mask):
     ext = get_image_ext(origin_image_bytes)
     return ext
     
+'''
 model = ModelManager(
         name='lama',
         device=device,
@@ -223,7 +227,7 @@ def predict(dict):
     '''
     image = Image.fromarray(dict["image"])
     mask = Image.fromarray(dict["mask"])
-    #output = model_process(image, mask)
+    output1 = model_process(dict["image"], dict["mask"])
     output = mask #output.images[0]
     # output = pipe(prompt = prompt, image=init_image, mask_image=mask,guidance_scale=7.5)