Use Python's stdlib to hash files

requirements.txt

@@ -10,4 +10,3 @@ requests>=2.24.0
 scipy>=1.5.4
 pymatting>=1.1.1
 filetype>=1.0.7
-hsh>=1.1.0

src/rembg/u2net/detect.py

@@ -9,7 +9,7 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torchvision
-from hsh.library.hash import Hasher
+from hashlib import md5
 from PIL import Image
 from skimage import transform
 from torchvision import transforms
@@ -52,7 +52,7 @@ def download_file_from_google_drive(id, fname, destination):
 
 
 def load_model(model_name: str = "u2net"):
-    hasher = Hasher()
+    hashfile = lambda f: md5(open(f,"rb").read()).hexdigest()
 
     if model_name == "u2netp":
         net = u2net.U2NETP(3, 1)
@@ -62,7 +62,7 @@ def load_model(model_name: str = "u2net"):
         )
         if (
             not os.path.exists(path)
-            or hasher.md5(path) != "e4f636406ca4e2af789941e7f139ee2e"
+            or hashfile(path) != "e4f636406ca4e2af789941e7f139ee2e"
         ):
             download_file_from_google_drive(
                 "1rbSTGKAE-MTxBYHd-51l2hMOQPT_7EPy",
@@ -78,7 +78,7 @@ def load_model(model_name: str = "u2net"):
         )
         if (
             not os.path.exists(path)
-            or hasher.md5(path) != "347c3d51b01528e5c6c071e3cff1cb55"
+            or hashfile(path) != "347c3d51b01528e5c6c071e3cff1cb55"
         ):
             download_file_from_google_drive(
                 "1ao1ovG1Qtx4b7EoskHXmi2E9rp5CHLcZ",
@@ -94,7 +94,7 @@ def load_model(model_name: str = "u2net"):
         )
         if (
             not os.path.exists(path)
-            or hasher.md5(path) != "09fb4e49b7f785c9f855baf94916840a"
+            or hashfile(path) != "09fb4e49b7f785c9f855baf94916840a"
         ):
             download_file_from_google_drive(
                 "1-Yg0cxgrNhHP-016FPdp902BR-kSsA4P",