Update app.py

app.py

@@ -28,7 +28,7 @@ DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
 print(DEVICE)
 
 yoloModel = YOLO('yolov8x-seg.pt')
-pipe = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to("cuda")
+sdxl = AutoPipelineForInpainting.from_pretrained("diffusers/stable-diffusion-xl-1.0-inpainting-0.1", torch_dtype=torch.float16, variant="fp16").to("cuda")
 sdxl.to("cuda")
 
 image_captioner = pipeline("image-to-text", model="Abdou/vit-swin-base-224-gpt2-image-captioning", device=DEVICE)
@@ -54,8 +54,8 @@ def get_most_similar_string(target_string, string_array):
 
 
 # Yolo
-def getClasses(model, img1):
-    results = model([np.array(img1)], device='cpu')  # Изменение для передачи изображения как массива NumPy
+def getClasses(img1):
+    results = yoloModel([np.array(img1)], device='cpu')  # Изменение для передачи изображения как массива NumPy
     out = []
     for r in results:
         im_array = r.plot()
@@ -101,8 +101,8 @@ def joinClasses(classes):
     return allMask
 
 
-def getSegments(yoloModel, img1):
-    classes, image, results1 = getClasses(yoloModel, img1)
+def getSegments(img1):
+    classes, image, results1 = getClasses(img1)
     allMask = joinClasses(classes)
     return allMask
 
@@ -140,7 +140,7 @@ def ChangeOBJ(img1, response, mask1):
 def full_pipeline(image, target):
     img1 = Image.fromarray(image.astype('uint8'), 'RGB')
     img1 = img1.resize((512, 512))
-    allMask = getSegments(yoloModel, img1)
+    allMask = getSegments(img1)
     tartget_to_remove = get_most_similar_string(target, list(allMask.keys()))
     caption = getDescript(image_captioner, img1)