.

app.py

@@ -1,24 +1,24 @@
-import gradio
-from huggingface_hub import Repository
 import os
 
-from utils.utils import norm_crop, estimate_norm, inverse_estimate_norm, transform_landmark_points, get_lm
-from networks.layers import AdaIN, AdaptiveAttention
-from tensorflow_addons.layers import InstanceNormalization
-import numpy as np
 import cv2
+import gradio
+import numpy as np
+from huggingface_hub import Repository
 from scipy.ndimage import gaussian_filter
-
 from tensorflow.keras.models import load_model
+from tensorflow_addons.layers import InstanceNormalization
+
+from networks.layers import AdaIN, AdaptiveAttention
 from options.swap_options import SwapOptions
+from utils.utils import (estimate_norm, get_lm, inverse_estimate_norm,
+                         norm_crop, transform_landmark_points)
 
 # .
 token = os.environ['model_fetch']
 
 opt = SwapOptions().parse()
 
-retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/retinaface_resnet50",
-                         private=True, use_auth_token=token, git_user="felixrosberg")
+retina_repo = Repository(local_dir="retina_model", clone_from="felixrosberg/RetinaFace")
 
 from retina_model.models import *
 
@@ -29,8 +29,7 @@ RetinaFace = load_model("retina_model/retinaface_res50.h5",
                                         "LandmarkHead": LandmarkHead,
                                         "ClassHead": ClassHead})
 
-arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/arcface_tf",
-                      private=True, use_auth_token=token)
+arc_repo = Repository(local_dir="arcface_model", clone_from="felixrosberg/ArcFace")
 ArcFace = load_model("arcface_model/arc_res50.h5")
 ArcFaceE = load_model("arcface_model/arc_res50e.h5")