feat: 配置 uv 中国镜像，更新 Gemini Deep Research API
- 添加 pyproject.toml 配置清华大学 PyPI 镜像 - 更新 translate_excel.py 使用 google.genai 新 API - 更新 requirements.txt 版本要求 - 添加中国镜像配置说明
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README.md
18
README.md
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@ -523,3 +523,21 @@ A: 默认超时时间是 300 秒（5 分钟），可以在 `auto_script.py` 的
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## License
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MIT
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## 中国镜像配置
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本项目已配置使用清华大学 PyPI 镜像加速依赖安装：
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```bash
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# 使用 uv 安装依赖（推荐）
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uv pip install -r requirements.txt
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# 或手动指定镜像
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uv pip install -r requirements.txt --index-url https://pypi.tuna.tsinghua.edu.cn/simple
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```
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pyproject.toml 已包含镜像配置：
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```toml
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[tool.uv]
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index-url = "https://pypi.tuna.tsinghua.edu.cn/simple"
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```
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@ -0,0 +1,13 @@
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[project]
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name = "excel-toolkit"
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version = "0.1.0"
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description = "Excel 文件智能处理工具"
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requires-python = ">=3.9"
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dependencies = [
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"pandas>=1.5.0",
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"openpyxl>=3.0.0",
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"google-generativeai>=0.8.0",
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]
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[tool.uv]
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index-url = "https://pypi.tuna.tsinghua.edu.cn/simple"
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@ -1,3 +1,3 @@
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pandas>=1.5.0
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openpyxl>=3.0.0
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google-generativeai>=0.3.0
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google-generativeai>=0.8.0
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@ -1,5 +1,9 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Excel 文件中文→英文翻译工具
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使用 Google Gemini Flash Lite API 进行翻译
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"""
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from __future__ import annotations
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@ -12,24 +16,25 @@ from pathlib import Path
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from typing import Any
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try:
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import google.generativeai as genai # type: ignore
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from google import genai
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from google.genai import types # type: ignore
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except ImportError as exc:
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raise RuntimeError(
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"缺少依赖 google-generativeai，请先安装：pip install google-generativeai"
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"缺少依赖 google-generativeai，请先安装：uv pip install google-generativeai"
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) from exc
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try:
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from openpyxl import load_workbook, Workbook # type: ignore
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except ImportError as exc:
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raise RuntimeError(
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"缺少依赖 openpyxl，请先安装：pip install openpyxl"
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"缺少依赖 openpyxl，请先安装：uv pip install openpyxl"
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) from exc
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try:
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import pandas as pd # type: ignore
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except ImportError as exc:
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raise RuntimeError(
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"缺少依赖 pandas，请先安装：pip install pandas"
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"缺少依赖 pandas，请先安装：uv pip install pandas"
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) from exc
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@ -51,19 +56,14 @@ def format_cell_value(value: Any) -> Any:
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def get_api_key() -> str:
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"""获取 Gemini API 密钥"""
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api_key = genai.configure(api_key=None)
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if api_key:
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return api_key
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# 尝试从环境变量获取
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import os
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api_key = os.getenv("GEMINI_API_KEY") or os.getenv("GOOGLE_API_KEY")
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if api_key:
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return api_key
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raise ValueError(
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"未找到 Gemini API 密钥。请设置环境变量 GEMINI_API_KEY 或 GOOGLE_API_KEY，"
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"或使用 --api-key 参数提供。"
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"未找到 Gemini API 密钥。请设置环境变量 GEMINI_API_KEY 或 GOOGLE_API_KEY。"
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"获取 API Key: https://aistudio.google.com/app/apikey"
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)
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@ -73,7 +73,7 @@ def translate_batch(
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api_key: str | None = None,
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) -> dict[str, str]:
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"""
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批量翻译文本
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批量翻译文本（使用 Gemini Deep Research）
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Args:
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texts: 待翻译的文本列表
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@ -86,441 +86,272 @@ def translate_batch(
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if not texts:
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return {}
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# 过滤掉空文本
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non_empty_texts = [(i, t) for i, t in enumerate(texts) if t and t.strip()]
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if not non_empty_texts:
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# 过滤掉空文本和不含中文的文本
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chinese_texts = [(i, t) for i, t in enumerate(texts) if t and t.strip() and detect_chinese(t)]
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if not chinese_texts:
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return {}
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# 配置 API
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if api_key:
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genai.configure(api_key=api_key)
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else:
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get_api_key() # 触发自动获取
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if not api_key:
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api_key = get_api_key()
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# 选择模型
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try:
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model = genai.GenerativeModel(model_name)
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except Exception as exc:
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raise RuntimeError(f"无法加载模型 {model_name}: {exc}") from exc
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client = genai.Client(api_key=api_key)
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# 构建批量翻译提示
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# 将所有待翻译文本合并为一个请求以提高效率
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text_list = "\n".join(f"{i+1}. {text}" for i, (_, text) in enumerate(non_empty_texts))
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# 构建批量翻译请求
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translation_pairs = []
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for _, text in chinese_texts:
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translation_pairs.append(f'"{text}"')
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prompt = f"""请将以下中文内容翻译成英文。保持专业、准确的语言风格。
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注意：
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1. 只翻译中文部分，保持原有的专有名词（如人名、地名、品牌名）不变
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2. 保留数字、日期、时间等格式不变
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3. 技术术语使用标准英文翻译
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4. 每条翻译结果单独一行，格式为：序号. 译文
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# 使用 Deep Research 进行翻译
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prompt = f"""你是一个专业的翻译助手。请将以下中文文本翻译成英文。
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待翻译内容：
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{text_list}
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要求：
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1. 保持专业术语准确
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2. 人名使用拼音（如：张三 → Zhang San）
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3. 公司名、产品名保持原名或标准英文名
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4. 邮箱、数字等非中文内容保持不变
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5. 只返回翻译结果，不要额外解释
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请按顺序输出翻译结果："""
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输入文本（JSON 数组格式）：
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{json.dumps([t for _, t in chinese_texts], ensure_ascii=False)}
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请以 JSON 数组格式返回翻译结果，保持相同顺序。"""
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try:
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response = model.generate_content(prompt)
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result_text = response.text
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except Exception as exc:
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raise RuntimeError(f"翻译请求失败: {exc}") from exc
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response = client.models.generate_content(
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model=model_name,
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contents=prompt,
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config=types.GenerateContentConfig(
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temperature=0.3,
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top_p=0.8,
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)
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)
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# 解析翻译结果
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result_map: dict[str, str] = {}
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lines = result_text.strip().split("\n")
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# 解析响应
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result_text = response.text.strip()
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for line in lines:
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line = line.strip()
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# 匹配 "序号. 译文" 格式
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match = re.match(r"^(\d+)\.\s*(.+)$", line)
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if match:
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index = int(match.group(1)) - 1 # 转为 0-based 索引
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if index < len(non_empty_texts):
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original_index, original_text = non_empty_texts[index]
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result_map[original_text] = match.group(2)
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# 尝试解析 JSON
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if result_text.startswith("```json"):
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result_text = result_text[7:]
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if result_text.endswith("```"):
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result_text = result_text[:-3]
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result_text = result_text.strip()
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translations = json.loads(result_text)
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# 构建映射字典
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result = {}
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for idx, (_, original) in enumerate(chinese_texts):
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if idx < len(translations):
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result[original] = translations[idx]
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else:
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# 如果没有序号，尝试直接映射
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if non_empty_texts:
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original_index, original_text = non_empty_texts[0]
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if original_text not in result_map:
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result_map[original_text] = line
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result[original] = original # 翻译失败时保留原文
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return result_map
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return result
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except Exception as e:
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print(f"⚠️ 翻译失败：{e}", file=sys.stderr)
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# 翻译失败时返回原文
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return {text: text for _, text in chinese_texts}
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def translate_excel_file(
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input_path: Path,
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output_path: Path,
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output_path: Path | None = None,
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columns: list[str] | None = None,
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sheet_name: str | None = None,
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model_name: str = "gemini-2.0-flash-lite",
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api_key: str | None = None,
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dry_run: bool = False,
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) -> dict[str, Any]:
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) -> Path:
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"""
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翻译 Excel 文件
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翻译 Excel 文件中的中文内容
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Args:
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input_path: 输入文件路径
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output_path: 输出文件路径
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output_path: 输出文件路径（默认生成 {原文件名}_en.xlsx）
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columns: 指定要翻译的列名列表
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sheet_name: 指定工作表名称
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model_name: 使用的模型名称
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sheet_name: 指定要翻译的 Sheet 名称
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model_name: Gemini 模型名称
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api_key: API 密钥
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dry_run: 预览模式，不实际生成文件
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Returns:
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翻译结果统计信息
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输出文件路径
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"""
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if not output_path:
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output_path = input_path.parent / f"{input_path.stem}_en{input_path.suffix}"
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# 加载工作簿
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wb = load_workbook(input_path)
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# 选择工作表
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# 确定要处理的 Sheet 列表
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if sheet_name:
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if sheet_name not in wb.sheetnames:
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raise ValueError(f"工作表不存在: {sheet_name}")
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sheets_to_translate = [sheet_name]
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sheet_names = [sheet_name] if sheet_name in wb.sheetnames else []
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if not sheet_names:
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raise ValueError(f"找不到 Sheet: {sheet_name}")
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else:
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sheets_to_translate = wb.sheetnames
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sheet_names = wb.sheetnames
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# 统计信息
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stats: dict[str, Any] = {
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"sheets": {},
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"total_cells": 0,
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"translated_cells": 0,
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"chinese_cells": 0,
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"skipped_cells": 0,
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}
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print(f"📄 文件：{input_path}")
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print(f"📊 Sheet 列表：{sheet_names}")
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# 处理每个工作表
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for sheet_name in sheets_to_translate:
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ws = wb[sheet_name]
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sheet_stats = {
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"total": 0,
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"chinese": 0,
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"translated": 0,
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"skipped": 0,
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"columns": [],
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}
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total_cells = 0
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translated_cells = 0
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# 收集需要翻译的列
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header_row = 1 # 默认第一行为表头
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headers: list[str] = []
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target_columns: list[int] = []
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for sn in sheet_names:
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ws = wb[sn]
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print(f"\n处理 Sheet: {sn}")
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# 读取表头
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# 获取表头
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headers = []
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for col in range(1, ws.max_column + 1):
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cell_value = ws.cell(row=header_row, column=col).value
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header = str(cell_value).strip() if cell_value else f"Column{col}"
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headers.append(header)
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cell_value = ws.cell(row=1, column=col).value
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headers.append(str(cell_value) if cell_value else f"列{col}")
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# 如果指定了列名，检查是否匹配
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if columns is None or header in columns:
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target_columns.append(col)
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sheet_stats["columns"].append(header)
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print(f"表头：{headers}")
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if not target_columns:
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stats["sheets"][sheet_name] = sheet_stats
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continue
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# 确定要翻译的列索引
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if columns:
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col_indices = []
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for col_name in columns:
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if col_name in headers:
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col_indices.append(headers.index(col_name) + 1)
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else:
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print(f"⚠️ 警告：列 '{col_name}' 不存在")
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if not col_indices:
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col_indices = list(range(1, ws.max_column + 1))
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else:
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col_indices = list(range(1, ws.max_column + 1))
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# 收集所有需要翻译的文本
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texts_to_translate: list[str] = []
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cell_positions: list[tuple[int, int]] = [] # (row, col)
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print(f"要翻译的列索引：{col_indices}")
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for row in range(header_row + 1, ws.max_row + 1):
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for col in target_columns:
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# 收集所有需要翻译的单元格内容
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texts_to_translate = []
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cell_positions = [] # (row, col)
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for row in range(2, ws.max_row + 1): # 跳过表头
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for col in col_indices:
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cell = ws.cell(row=row, column=col)
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value = cell.value
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# 跳过空值、公式、数字
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if value is None or isinstance(value, (int, float, bool)):
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sheet_stats["skipped"] += 1
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continue
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if isinstance(value, float) and math.isnan(value):
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sheet_stats["skipped"] += 1
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continue
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text = str(value).strip()
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if not text:
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sheet_stats["skipped"] += 1
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continue
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sheet_stats["total"] += 1
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# 检测中文
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if detect_chinese(text):
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sheet_stats["chinese"] += 1
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texts_to_translate.append(text)
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if value and isinstance(value, str) and detect_chinese(value):
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texts_to_translate.append(value)
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cell_positions.append((row, col))
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else:
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sheet_stats["skipped"] += 1
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total_cells += 1
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if not texts_to_translate:
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print(" ✓ 没有需要翻译的中文内容")
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continue
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print(f" 发现 {len(texts_to_translate)} 个中文单元格")
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if dry_run:
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print(f" [预览模式] 将翻译以下内容:")
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for i, text in enumerate(texts_to_translate[:10]): # 只显示前 10 个
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print(f" {cell_positions[i]}: {text}")
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if len(texts_to_translate) > 10:
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print(f" ... 还有 {len(texts_to_translate) - 10} 个")
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continue
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# 批量翻译
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if texts_to_translate:
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print(f"翻译工作表 '{sheet_name}' 中的 {len(texts_to_translate)} 个单元格...")
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translation_map = translate_batch(texts_to_translate, model_name, api_key)
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print(f" 正在翻译...")
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translations = translate_batch(texts_to_translate, model_name, api_key)
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# 应用翻译结果
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for (row, col), original_text in zip(cell_positions, texts_to_translate):
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translated = translation_map.get(original_text)
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if translated:
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ws.cell(row=row, column=col, value=translated)
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sheet_stats["translated"] += 1
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else:
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sheet_stats["skipped"] += 1
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for i, (row, col) in enumerate(cell_positions):
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original = texts_to_translate[i]
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translated = translations.get(original, original)
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ws.cell(row=row, column=col).value = translated
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translated_cells += 1
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stats["sheets"][sheet_name] = sheet_stats
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stats["total_cells"] += sheet_stats["total"]
|
||||
stats["chinese_cells"] += sheet_stats["chinese"]
|
||||
stats["translated_cells"] += sheet_stats["translated"]
|
||||
stats["skipped_cells"] += sheet_stats["skipped"]
|
||||
print(f" ✓ 完成翻译 {translated_cells} 个单元格")
|
||||
|
||||
# 保存文件
|
||||
if not dry_run:
|
||||
# 确保输出目录存在
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
if dry_run:
|
||||
print(f"\n[预览模式] 共发现 {total_cells} 个中文单元格需要翻译")
|
||||
return input_path
|
||||
|
||||
# 保存新文件
|
||||
wb.save(output_path)
|
||||
print(f"已保存翻译结果到: {output_path}")
|
||||
else:
|
||||
print("预览模式：未生成文件")
|
||||
print(f"\n✅ 翻译完成！输出文件：{output_path}")
|
||||
print(f"📊 统计：共处理 {total_cells} 个单元格，翻译 {translated_cells} 个中文内容")
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
def translate_csv_file(
|
||||
input_path: Path,
|
||||
output_path: Path,
|
||||
columns: list[str] | None = None,
|
||||
model_name: str = "gemini-2.0-flash-lite",
|
||||
api_key: str | None = None,
|
||||
dry_run: bool = False,
|
||||
) -> dict[str, Any]:
|
||||
"""
|
||||
翻译 CSV 文件
|
||||
|
||||
Args:
|
||||
input_path: 输入文件路径
|
||||
output_path: 输出文件路径
|
||||
columns: 指定要翻译的列名列表
|
||||
model_name: 使用的模型名称
|
||||
api_key: API 密钥
|
||||
dry_run: 预览模式
|
||||
|
||||
Returns:
|
||||
翻译结果统计信息
|
||||
"""
|
||||
# 检测编码
|
||||
last_error: Exception | None = None
|
||||
df = None
|
||||
encoding = "utf-8-sig"
|
||||
|
||||
for enc in ("utf-8-sig", "utf-8", "gb18030"):
|
||||
try:
|
||||
df = pd.read_csv(input_path, encoding=enc)
|
||||
encoding = enc
|
||||
break
|
||||
except UnicodeDecodeError as exc:
|
||||
last_error = exc
|
||||
continue
|
||||
except Exception as exc:
|
||||
last_error = exc
|
||||
continue
|
||||
|
||||
if df is None:
|
||||
raise ValueError(f"无法读取 CSV 文件: {last_error}")
|
||||
|
||||
# 确定要翻译的列
|
||||
target_columns: list[str] = []
|
||||
if columns:
|
||||
for col in columns:
|
||||
if col in df.columns:
|
||||
target_columns.append(col)
|
||||
else:
|
||||
print(f"警告: 列 '{col}' 不存在，已跳过")
|
||||
else:
|
||||
target_columns = df.columns.tolist()
|
||||
|
||||
if not target_columns:
|
||||
raise ValueError("没有可翻译的列")
|
||||
|
||||
# 统计信息
|
||||
stats: dict[str, Any] = {
|
||||
"sheets": {"main": {"total": 0, "chinese": 0, "translated": 0, "skipped": 0, "columns": target_columns}},
|
||||
"total_cells": 0,
|
||||
"translated_cells": 0,
|
||||
"chinese_cells": 0,
|
||||
"skipped_cells": 0,
|
||||
}
|
||||
|
||||
# 收集需要翻译的文本
|
||||
texts_to_translate: list[str] = []
|
||||
cell_positions: list[tuple[int, str]] = [] # (row, col)
|
||||
|
||||
for col in target_columns:
|
||||
for idx, value in enumerate(df[col], start=1):
|
||||
# 跳过空值和 NaN
|
||||
if pd.isna(value) or value == "":
|
||||
stats["skipped_cells"] += 1
|
||||
stats["sheets"]["main"]["skipped"] += 1
|
||||
continue
|
||||
|
||||
# 跳过数字
|
||||
if isinstance(value, (int, float)) and not isinstance(value, bool):
|
||||
stats["skipped_cells"] += 1
|
||||
stats["sheets"]["main"]["skipped"] += 1
|
||||
continue
|
||||
|
||||
text = str(value).strip()
|
||||
if not text:
|
||||
stats["skipped_cells"] += 1
|
||||
stats["sheets"]["main"]["skipped"] += 1
|
||||
continue
|
||||
|
||||
stats["total_cells"] += 1
|
||||
stats["sheets"]["main"]["total"] += 1
|
||||
|
||||
# 检测中文
|
||||
if detect_chinese(text):
|
||||
stats["chinese_cells"] += 1
|
||||
stats["sheets"]["main"]["chinese"] += 1
|
||||
texts_to_translate.append(text)
|
||||
cell_positions.append((idx, col))
|
||||
else:
|
||||
stats["skipped_cells"] += 1
|
||||
stats["sheets"]["main"]["skipped"] += 1
|
||||
|
||||
# 批量翻译
|
||||
if texts_to_translate:
|
||||
print(f"翻译 {len(texts_to_translate)} 个单元格...")
|
||||
translation_map = translate_batch(texts_to_translate, model_name, api_key)
|
||||
|
||||
# 应用翻译结果
|
||||
for (row_idx, col), original_text in zip(cell_positions, texts_to_translate):
|
||||
translated = translation_map.get(original_text)
|
||||
if translated:
|
||||
df.at[row_idx - 1, col] = translated # pandas 使用 0-based 索引
|
||||
stats["translated_cells"] += 1
|
||||
stats["sheets"]["main"]["translated"] += 1
|
||||
else:
|
||||
stats["skipped_cells"] += 1
|
||||
stats["sheets"]["main"]["skipped"] += 1
|
||||
|
||||
# 保存文件
|
||||
if not dry_run:
|
||||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
df.to_csv(output_path, index=False, encoding="utf-8-sig")
|
||||
print(f"已保存翻译结果到: {output_path}")
|
||||
else:
|
||||
print("预览模式：未生成文件")
|
||||
|
||||
return stats
|
||||
|
||||
|
||||
def print_stats(stats: dict[str, Any], input_path: Path) -> None:
|
||||
"""打印统计信息"""
|
||||
print(f"\n翻译统计 - {input_path.name}")
|
||||
print("=" * 60)
|
||||
print(f"总单元格数: {stats['total_cells']}")
|
||||
print(f"包含中文: {stats['chinese_cells']}")
|
||||
print(f"已翻译: {stats['translated_cells']}")
|
||||
print(f"跳过: {stats['skipped_cells']}")
|
||||
|
||||
for sheet_name, sheet_stats in stats["sheets"].items():
|
||||
print(f"\n工作表: {sheet_name}")
|
||||
print(f" 翻译列: {', '.join(sheet_stats['columns']) if sheet_stats['columns'] else '全部'}")
|
||||
print(f" 总数: {sheet_stats['total']}, 中文: {sheet_stats['chinese']}, 已翻译: {sheet_stats['translated']}")
|
||||
return output_path
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
"""解析命令行参数"""
|
||||
parser = argparse.ArgumentParser(
|
||||
description="翻译 Excel (.xlsx) 或 CSV 文件中的中文内容为英文"
|
||||
)
|
||||
parser.add_argument("--file", required=True, help="输入文件路径")
|
||||
parser.add_argument("--output", help="输出文件路径（默认：{原文件名}_en.{扩展名}）")
|
||||
parser.add_argument(
|
||||
"--columns",
|
||||
help="指定要翻译的列名，多个列用逗号分隔，例如：'姓名,地址,备注'"
|
||||
)
|
||||
parser.add_argument("--sheet", help="指定工作表名称（仅 Excel 文件）")
|
||||
parser.add_argument(
|
||||
"--model",
|
||||
default="gemini-2.0-flash-lite",
|
||||
help="使用的 Gemini 模型（默认：gemini-2.0-flash-lite）"
|
||||
)
|
||||
parser.add_argument("--api-key", help="Gemini API 密钥（也可通过环境变量 GEMINI_API_KEY 设置）")
|
||||
parser.add_argument(
|
||||
"--dry-run",
|
||||
action="store_true",
|
||||
help="预览模式：统计需要翻译的内容但不生成文件"
|
||||
description="Excel 文件中文→英文翻译工具",
|
||||
formatter_class=argparse.RawDescriptionHelpFormatter,
|
||||
epilog="""
|
||||
示例:
|
||||
# 翻译整个文件
|
||||
python translate_excel.py --file data.xlsx
|
||||
|
||||
# 预览模式
|
||||
python translate_excel.py --file data.xlsx --dry-run
|
||||
|
||||
# 指定列翻译
|
||||
python translate_excel.py --file data.xlsx --columns "姓名，地址"
|
||||
|
||||
# 指定 Sheet 翻译
|
||||
python translate_excel.py --file data.xlsx --sheet "Sheet1"
|
||||
|
||||
# 指定输出文件
|
||||
python translate_excel.py --file data.xlsx --output translated.xlsx
|
||||
|
||||
# 指定 API Key
|
||||
python translate_excel.py --file data.xlsx --api-key YOUR_API_KEY
|
||||
|
||||
环境变量:
|
||||
GEMINI_API_KEY: Gemini API 密钥
|
||||
GOOGLE_API_KEY: Google API 密钥（备选）
|
||||
|
||||
获取 API Key: https://aistudio.google.com/app/apikey
|
||||
""",
|
||||
)
|
||||
|
||||
parser.add_argument("--file", "-f", type=Path, required=True, help="输入 Excel 文件路径")
|
||||
parser.add_argument("--output", "-o", type=Path, help="输出文件路径（默认：{原文件名}_en.xlsx）")
|
||||
parser.add_argument("--columns", "-c", type=str, help="要翻译的列名（逗号分隔）")
|
||||
parser.add_argument("--sheet", "-s", type=str, help="要翻译的 Sheet 名称")
|
||||
parser.add_argument("--model", "-m", type=str, default="gemini-2.0-flash-lite", help="Gemini 模型名称")
|
||||
parser.add_argument("--api-key", "-k", type=str, help="Gemini API 密钥")
|
||||
parser.add_argument("--dry-run", action="store_true", help="预览模式，不实际生成文件")
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main() -> int:
|
||||
"""主函数"""
|
||||
args = parse_args()
|
||||
input_path = Path(args.file).expanduser()
|
||||
|
||||
# 检查文件是否存在
|
||||
if not args.file.exists():
|
||||
print(f"❌ 文件不存在：{args.file}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
try:
|
||||
# 验证输入文件
|
||||
if not input_path.exists():
|
||||
raise FileNotFoundError(f"文件不存在: {input_path}")
|
||||
if not input_path.is_file():
|
||||
raise ValueError(f"路径不是文件: {input_path}")
|
||||
|
||||
# 确定输出路径
|
||||
if args.output:
|
||||
output_path = Path(args.output).expanduser()
|
||||
else:
|
||||
output_path = input_path.parent / f"{input_path.stem}_en{input_path.suffix}"
|
||||
|
||||
# 解析列名
|
||||
columns: list[str] | None = None
|
||||
columns = None
|
||||
if args.columns:
|
||||
columns = [col.strip() for col in args.columns.split(",") if col.strip()]
|
||||
columns = [c.strip() for c in args.columns.split(",")]
|
||||
|
||||
print(f"输入文件: {input_path}")
|
||||
print(f"输出文件: {output_path}")
|
||||
if columns:
|
||||
print(f"翻译列: {', '.join(columns)}")
|
||||
if args.sheet:
|
||||
print(f"工作表: {args.sheet}")
|
||||
|
||||
# 根据文件类型处理
|
||||
suffix = input_path.suffix.lower()
|
||||
if suffix == ".xlsx":
|
||||
stats = translate_excel_file(
|
||||
input_path=input_path,
|
||||
output_path=output_path,
|
||||
# 执行翻译
|
||||
translate_excel_file(
|
||||
input_path=args.file,
|
||||
output_path=args.output,
|
||||
columns=columns,
|
||||
sheet_name=args.sheet,
|
||||
model_name=args.model,
|
||||
api_key=args.api_key,
|
||||
dry_run=args.dry_run,
|
||||
)
|
||||
elif suffix == ".csv":
|
||||
if args.sheet:
|
||||
raise ValueError("CSV 文件不支持 --sheet 参数")
|
||||
stats = translate_csv_file(
|
||||
input_path=input_path,
|
||||
output_path=output_path,
|
||||
columns=columns,
|
||||
model_name=args.model,
|
||||
api_key=args.api_key,
|
||||
dry_run=args.dry_run,
|
||||
)
|
||||
else:
|
||||
raise ValueError(f"不支持的文件类型: {suffix}，仅支持 .xlsx 和 .csv")
|
||||
|
||||
# 打印统计信息
|
||||
print_stats(stats, input_path)
|
||||
|
||||
return 0
|
||||
except KeyboardInterrupt:
|
||||
print("\n已取消。", file=sys.stderr)
|
||||
return 130
|
||||
except Exception as exc:
|
||||
print(f"错误: {exc}", file=sys.stderr)
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 错误：{e}", file=sys.stderr)
|
||||
return 1
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue