2 en

app.py

@@ -87,14 +87,14 @@ if __name__ == "__main__":
     gr.Interface(
         fn=infer,
         inputs=[
-            gr.Image(label="上传图片 Upload an image", type="filepath"),
+            gr.Image(label="Upload an image", type="filepath"),
             gr.Dropdown(
-                label="选择权重 Select a model",
+                label="Select a model",
                 choices=models,
                 value=models[0],
             ),
         ],
-        outputs=gr.Textbox(label="识别结果 Recognition result", show_copy_button=True),
+        outputs=gr.Textbox(label="Recognition result", show_copy_button=True),
         examples=samples,
         flagging_mode="never",
         cache_examples=False,

model.py

@@ -7,7 +7,6 @@ import torch.nn as nn
 
 class Model(torch.jit.ScriptModule):
     CHECKPOINT_FILENAME_PATTERN = "model-{}.pth"
-
     __constants__ = [
         "_hidden1",
         "_hidden2",
@@ -31,7 +30,6 @@ class Model(torch.jit.ScriptModule):
 
     def __init__(self):
         super(Model, self).__init__()
-
         self._hidden1 = nn.Sequential(
             nn.Conv2d(in_channels=3, out_channels=48, kernel_size=5, padding=2),
             nn.BatchNorm2d(num_features=48),
@@ -90,7 +88,6 @@ class Model(torch.jit.ScriptModule):
         )
         self._hidden9 = nn.Sequential(nn.Linear(192 * 7 * 7, 3072), nn.ReLU())
         self._hidden10 = nn.Sequential(nn.Linear(3072, 3072), nn.ReLU())
-
         self._digit_length = nn.Sequential(nn.Linear(3072, 7))
         self._digit1 = nn.Sequential(nn.Linear(3072, 11))
         self._digit2 = nn.Sequential(nn.Linear(3072, 11))
@@ -111,14 +108,12 @@ class Model(torch.jit.ScriptModule):
         x = x.view(x.size(0), 192 * 7 * 7)
         x = self._hidden9(x)
         x = self._hidden10(x)
-
         length_logits = self._digit_length(x)
         digit1_logits = self._digit1(x)
         digit2_logits = self._digit2(x)
         digit3_logits = self._digit3(x)
         digit4_logits = self._digit4(x)
         digit5_logits = self._digit5(x)
-
         return (
             length_logits,
             digit1_logits,
@@ -154,5 +149,4 @@ class Model(torch.jit.ScriptModule):
         self.load_state_dict(
             torch.load(path_to_checkpoint_file, map_location=torch.device("cpu"))
         )
-        step = int(path_to_checkpoint_file.split("model-")[-1][:-4])
-        return step
+        return int(path_to_checkpoint_file.split("model-")[-1][:-4])