try upd req

app.py

@@ -2,21 +2,46 @@ import os
 import torch
 import random
 import warnings
+import modelscope
+import huggingface_hub
 import gradio as gr
 from PIL import Image
 from model import Model
 from torchvision import transforms
-from huggingface_hub import snapshot_download
 
+EN_US = os.getenv("LANG") != "zh_CN.UTF-8"
 
-MODEL_DIR = snapshot_download("Genius-Society/svhn", cache_dir="./__pycache__")
+MODEL_DIR = (
+    huggingface_hub.snapshot_download(
+        "Genius-Society/svhn",
+        cache_dir="./__pycache__",
+    )
+    if EN_US
+    else modelscope.snapshot_download(
+        "Genius-Society/svhn",
+        cache_dir="./__pycache__",
+    )
+)
+
+ZH2EN = {
+    "上传图片": "Upload an image",
+    "状态栏": "Status",
+    "选择模型": "Select a model",
+    "识别结果": "Recognition result",
+    "门牌号识别": "Door Number Recognition",
+}
+
+
+def _L(zh_txt: str):
+    return ZH2EN[zh_txt] if EN_US else zh_txt
 
 
 def infer(input_img: str, checkpoint_file: str):
+    status = "Success"
+    outstr = None
     try:
         model = Model()
         model.restore(f"{MODEL_DIR}/{checkpoint_file}")
-        outstr = ""
         with torch.no_grad():
             transform = transforms.Compose(
                 [
@@ -55,10 +80,10 @@ def infer(input_img: str, checkpoint_file: str):
             for i in range(length_prediction.item()):
                 outstr += str(output[i])
 
-        return outstr
-
     except Exception as e:
-        return f"{e}"
+        status = f"{e}"
+
+    return status, outstr
 
 
 def get_files(dir_path=MODEL_DIR, ext=".pth"):
@@ -87,16 +112,19 @@ if __name__ == "__main__":
     gr.Interface(
         fn=infer,
         inputs=[
-            gr.Image(label="Upload an image", type="filepath"),
+            gr.Image(label=_L("上传图片"), type="filepath"),
             gr.Dropdown(
-                label="Select a model",
+                label=_L("选择模型"),
                 choices=models,
                 value=models[0],
             ),
         ],
-        outputs=gr.Textbox(label="Recognition result", show_copy_button=True),
+        outputs=[
+            gr.Textbox(label=_L("状态栏"), show_copy_button=True),
+            gr.Textbox(label=_L("识别结果"), show_copy_button=True),
+        ],
         examples=samples,
-        title="Door Number Recognition",
+        title=_L("门牌号识别"),
         flagging_mode="never",
         cache_examples=False,
-    ).launch()
+    ).launch(ssr_mode=False)

requirements.txt

@@ -1,3 +1,4 @@
 torch==2.6.0+cu118
 -f https://download.pytorch.org/whl/torch
-torchvision
+torchvision==0.21.0+cu118
+-f https://download.pytorch.org/whl/torchvision