upd

app.py

@@ -14,49 +14,7 @@ import io
 import os
 # import streamlit as st
 
-print(os.environ.get(["ENDPOINT"]))
-
-providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
-model_path = huggingface_hub.hf_hub_download(
-    "skytnt/anime-seg", "isnetis.onnx")
-rmbg_model = rt.InferenceSession(model_path, providers=providers)
-
-
-def custom_background(background, foreground):
-    foreground = ImageOps.contain(foreground, background.size)
-    x = (background.size[0] - foreground.size[0]) // 2
-    y = (background.size[1] - foreground.size[1]) // 2
-    background.paste(foreground, (x, y), foreground)
-    return background
-
-
-def get_mask(img, s=1024):
-    img = (img / 255).astype(np.float32)
-    h, w = h0, w0 = img.shape[:-1]
-    h, w = (s, int(s * w / h)) if h > w else (int(s * h / w), s)
-    ph, pw = s - h, s - w
-    img_input = np.zeros([s, s, 3], dtype=np.float32)
-    img_input[ph // 2:ph // 2 + h, pw //
-              2:pw // 2 + w] = cv2.resize(img, (w, h))
-    img_input = np.transpose(img_input, (2, 0, 1))
-    img_input = img_input[np.newaxis, :]
-    mask = rmbg_model.run(None, {'img': img_input})[0][0]
-    mask = np.transpose(mask, (1, 2, 0))
-    mask = mask[ph // 2:ph // 2 + h, pw // 2:pw // 2 + w]
-    mask = cv2.resize(mask, (w0, h0))[:, :, np.newaxis]
-    return mask
-
-
-def predict(image, new_background):
-    mask = get_mask(image)
-    image = (mask * image + 255 * (1 - mask)).astype(np.uint8)
-    mask = (mask * 255).astype(np.uint8)
-    image = np.concatenate([image, mask], axis=2, dtype=np.uint8)
-    mask = mask.repeat(3, axis=2)
-    if new_background is not None:
-        foreground = PIL.Image.fromarray(image)
-        return mask, custom_background(new_background, foreground)
-    return mask, image
+print(os.environ.get("ENDPOINT"))
 
 
 def get_mask(img_in):