Update app.py

app.py

@@ -4,17 +4,17 @@ import json
 import pandas as pd
 import io
 
-# ----------------- 配置你的服务器API地址 -----------------
-# 将IP地址和端口替换为你自己的
+# ----------------- Configure Your Server API Address -----------------
+# Replace the IP address and port with your own
 SERVER_URL = "http://103.235.229.133:7000/predict"
-# -------------------------------------------------------
+# ---------------------------------------------------------------------
 
-# ----------------- 固定的模型名称 -----------------
-# 根据要求，模型名称是固定的，不再作为UI输入
+# ----------------- Fixed Model Name -----------------
+# As per requirements, the model name is fixed and not a UI input.
 FIXED_MODEL_NAME = "Qwen2.5-1.5B-Instruct"
-# -------------------------------------------------------
+# ----------------------------------------------------
 
-# 默认的输入数据，方便用户测试
+# Default input data for easy testing
 DEFAULT_DATAFRAME_VALUE = [
     ["France", "capital city of", "Paris", "she traveled to France for the first time"],
     ["Germany", "capital city of", "Berlin", "Germany is a country in Central Europe"],
@@ -23,29 +23,34 @@ DEFAULT_DATAFRAME_VALUE = [
 
 def call_api(only_final_result, data_df):
     """
-    这个函数会被Gradio调用，它负责收集UI上的输入，
-    将其转换成API需要的格式，然后发送请求。
+    This function is called by Gradio. It collects inputs from the UI,
+    formats them for the API, and sends the request.
     """
     if data_df is None or data_df.empty:
-        raise gr.Error("输入数据不能为空！请在表格中至少添加一行数据。")
+        raise gr.Error("Input data cannot be empty! Please add at least one row to the table.")
 
+    # Create a copy and add an 'id' column
     data_df_with_id = data_df.copy()
     data_df_with_id.insert(0, 'id', [f'{i:03d}' for i in range(len(data_df_with_id))])
     
+    # Convert DataFrame to CSV string
     output = io.StringIO()
     data_df_with_id.to_csv(output, index=False)
     csv_data = output.getvalue()
     
+    # Prepare the payload for the API
     payload = {
         "data_csv": csv_data,
         "only_final_result": only_final_result
     }
 
     try:
+        # Send the request to the server
         response = requests.post(SERVER_URL, json=payload, timeout=300, stream=True)
-        response.raise_for_status()
+        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
         result_json = response.json()
         
+        # Extract the result data for the DataFrame
         dataframe_result_data = result_json.get("result", [])
         
         if isinstance(dataframe_result_data, list) and len(dataframe_result_data) > 0:
@@ -56,65 +61,61 @@ def call_api(only_final_result, data_df):
         return result_json, output_df
 
     except requests.exceptions.HTTPError as e:
-        error_details = f"API返回错误 (状态码: {e.response.status_code}):\n{e.response.text}"
-        raise gr.Error(f"请求失败: {error_details}")
+        error_details = f"API returned an error (Status Code: {e.response.status_code}):\n{e.response.text}"
+        raise gr.Error(f"Request failed: {error_details}")
     except requests.exceptions.RequestException as e:
-        raise gr.Error(f"连接服务器失败: {e}")
+        raise gr.Error(f"Failed to connect to the server: {e}")
     except Exception as e:
-        raise gr.Error(f"发生未知错误: {e}")
+        raise gr.Error(f"An unknown error occurred: {e}")
 
 
-# --- 使用Gradio Blocks创建更灵活、更美观的界面 ---
+# --- Use Gradio Blocks for a more flexible and beautiful interface ---
 with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container {max-width: 1280px !important; margin: auto;}") as demo:
     gr.Markdown(
         """
-        # 🚀 Ml_patch 函数推理接口
-        一个为 `Ml_patch` 函数设计的交互式Web界面。请在下方配置模型参数并输入您要测试的数据。
+        # 🚀 Ml_patch Function Inference Interface
+        An interactive web interface designed for the `Ml_patch` function. 
+        Please configure the model parameters and enter your test data below.
         """
     )
     
     with gr.Column():
-        gr.Markdown("### 1. 配置参数")
+        gr.Markdown("### 1. Configure Parameters")
         with gr.Group():
-            gr.Markdown(f"**当前模型 (固定):** `{FIXED_MODEL_NAME}`")
+            gr.Markdown(f"**Current Model (Fixed):** `{FIXED_MODEL_NAME}`")
             only_final_result_input = gr.Checkbox(
-                label="仅返回最终结果 (only_final_result)",
+                label="Only Return Final Result (only_final_result)",
                 value=False,
-                info="勾选此项，API将只返回最终聚合结果，而不是所有层的详细信息。"
+                info="If checked, the API will only return the final aggregated result, not the detailed information for all layers."
             )
         
-        gr.Markdown("### 2. 输入数据")
-        gr.Markdown("ℹ️ **操作提示**: 点击表格右下方的 **✏️ Edit** 按钮，然后点击 **+ Add row** 来添加新行，或点击垃圾桶图标删除行。")
+        gr.Markdown("### 2. Input Data")
+        gr.Markdown("ℹ️ **Instructions**: Click the **✏️ Edit** button at the bottom right of the table, then click **+ Add row** to add a new row, or the trash can icon to delete a row.")
         
-        # -------------------- 主要修改部分在这里 --------------------
-        # 移除了 'height' 参数，因为它在旧版Gradio中不受支持。
-        # 我们通过增加 row_count 的第一个参数（初始行数）来为表格提供更多初始垂直空间。
         data_input = gr.DataFrame(
-            label="数据表格",
+            label="Data Table",
             headers=["subject", "relation", "object", "prompt_source"],
             value=DEFAULT_DATAFRAME_VALUE,
-            # 将初始行数从3增加到5，使表格在加载时就更高。
-            # (要显示的行数, "dynamic"表示用户可以增删行)
+            # Set the initial number of rows to 5 to give the table more vertical space on load.
+            # (rows_to_display, "dynamic" allows user to add/remove rows)
             row_count=(5, "dynamic"),
             col_count=(4, "fixed"),
             interactive=True
-            # 已移除不受支持的 'height' 参数
         )
-        # -------------------- 修改结束 --------------------
         
-        submit_btn = gr.Button("提交推理", variant="primary")
+        submit_btn = gr.Button("Submit for Inference", variant="primary")
 
     with gr.Column():
-        gr.Markdown("### 3. 查看推理结果")
+        gr.Markdown("### 3. View Inference Results")
         with gr.Tabs():
-            with gr.TabItem("结果表格"):
+            with gr.TabItem("Results Table"):
                 output_dataframe = gr.DataFrame(
-                    label="推理结果详情", 
+                    label="Inference Result Details", 
                     interactive=False,
                     headers=["id", "subject", "relation", "object", "prompt_source", "prompt_target", "layer_source", "is_correct_patched", "generations"]
                 )
-            with gr.TabItem("原始JSON响应"):
-                output_json = gr.JSON(label="API 返回的完整JSON")
+            with gr.TabItem("Raw JSON Response"):
+                output_json = gr.JSON(label="Full JSON response from API")
             
     submit_btn.click(
         fn=call_api,
@@ -122,6 +123,6 @@ with gr.Blocks(theme=gr.themes.Glass(), css=".gradio-container {max-width: 1280p
         outputs=[output_json, output_dataframe]
     )
 
-# 启动Gradio应用
+# Launch the Gradio app
 if __name__ == "__main__":
     demo.launch()