fix(webui): change labels

Author: ChenZiHong-Gavin

webui/app.py

@@ -371,7 +371,7 @@ def sum_tokens(client):
                     value="",
                     info="https://cloud.siliconflow.cn/account/ak")
             with gr.Column(scale=1):
-                test_connection_btn = gr.Button("Test Connection")
+                test_connection_btn = gr.Button(_("Test Connection"))
 
         with gr.Blocks():
             with gr.Row(equal_height=True):
@@ -399,7 +399,7 @@ def sum_tokens(client):
             with gr.Row(equal_height=True):
                 with gr.Column(scale=1):
                     upload_file = gr.File(
-                        label="Upload File",
+                        label=_("Upload File"),
                         file_count="single",
                         file_types=[".txt", ".json", ".jsonl"],
                         interactive=True,
@@ -411,7 +411,7 @@ def sum_tokens(client):
                         [os.path.join(examples_dir, "chunked_demo.json")],
                     ],
                                 inputs=upload_file,
-                                label="Example Files",
+                                label=_("Example Files"),
                                 examples_per_page=3)
                 with gr.Column(scale=1):
                     output = gr.File(
@@ -428,7 +428,7 @@ def sum_tokens(client):
                          visible=False,
                          wrap=True)
 
-        submit_btn = gr.Button("Run GraphGen")
+        submit_btn = gr.Button(_("Run GraphGen"))
 
         # Test Connection
         test_connection_btn.click(

webui/translation.json

@@ -1,24 +1,32 @@
 {
   "en": {
     "Title": "✨Easy-to-use LLM Training Data Generation Framework✨",
-    "Intro": "is a framework for synthetic data generation guided by knowledge graphs, designed to tackle challenges for knowledge-intensive QA generation. \n\nBy uploading your text chunks (such as knowledge in agriculture, healthcare, or marine science) and filling in the LLM API key, you can generate the training data required by **LLaMA-Factory** and **xtuner** online. We will automatically delete user information after completion.",
+    "Intro": "is a framework for synthetic data generation guided by knowledge graphs, designed to tackle challenges for knowledge-intensive QA generation. \n\nBy uploading your text chunks (such as knowledge in agriculture, healthcare, or marine science) and filling in the LLM API key, you can generate the training data required by **[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)** and **[xtuner](https://github.com/InternLM/xtuner)** online. We will automatically delete user information after completion.",
     "Use Trainee Model": "Use Trainee Model to identify knowledge blind spots, please keep disable for SiliconCloud",
     "Base URL Info": "Base URL for the API, use SiliconFlow as default",
     "Synthesizer Model Info": "Model for constructing KGs and generating QAs",
     "Trainee Model Info": "Model for training",
     "Model Config": "Model Configuration",
     "Generation Config": "Generation Config",
-    "SiliconCloud Token": "SiliconCloud API Key"
+    "SiliconCloud Token": "SiliconCloud API Key",
+    "Test Connection": "Test Connection",
+    "Run GraphGen": "Run GraphGen",
+    "Upload File": "Upload File",
+    "Example Files": "Example Files"
   },
   "zh": {
     "Title": "✨开箱即用的LLM训练数据生成框架✨",
-    "Intro": "是一个基于知识图谱的合成数据生成框架，旨在解决知识密集型问答生成的挑战。\n\n 上传你的文本块（如农业、医疗、海洋知识），填写 LLM api key，即可在线生成 **LLaMA-Factory**、**xtuner** 所需训练数据。结束后我们将自动删除用户信息。",
+    "Intro": "是一个基于知识图谱的合成数据生成框架，旨在解决知识密集型问答生成的挑战。\n\n 上传你的文本块（如农业、医疗、海洋知识），填写 LLM api key，即可在线生成 **[LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)**、**[xtuner](https://github.com/InternLM/xtuner)** 所需训练数据。结束后我们将自动删除用户信息。",
     "Use Trainee Model": "使用Trainee Model来识别知识盲区，使用硅基流动时请保持禁用",
     "Base URL Info": "调用模型API的URL，默认使用硅基流动",
     "Synthesizer Model Info": "用于构建知识图谱和生成问答的模型",
     "Trainee Model Info": "用于训练的模型",
     "Model Config": "模型配置",
     "Generation Config": "生成配置",
-    "SiliconCloud Token": "硅基流动 API Key"
+    "SiliconCloud Token": "硅基流动 API Key",
+    "Test Connection": "测试接口",
+    "Run GraphGen": "运行GraphGen",
+    "Upload File": "上传文件",
+    "Example Files": "示例文件"
   }
 }