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Typer_Body
151 lines
5.1 KiB
Python
151 lines
5.1 KiB
Python
#!/usr/bin/env python3
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"""修复 ru-RU.ts 文件中包含中文字符的翻译"""
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import re
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import os
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from pathlib import Path
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from anthropic import Anthropic
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def contains_chinese(text: str) -> bool:
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"""检查文本是否包含中文字符"""
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return bool(re.search(r'[\u4e00-\u9fff]', text))
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def extract_workflows_section(file_path: Path) -> dict:
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"""从 .ts 文件中提取 workflows 部分的键值对"""
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with open(file_path, 'r', encoding='utf-8') as f:
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content = f.read()
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# 找到 workflows 部分
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workflows_match = re.search(r'workflows:\s*\{(.*?)\n\s*\},?\s*\n', content, re.DOTALL)
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if not workflows_match:
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return {}
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workflows_content = workflows_match.group(1)
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# 提取所有键值对
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pattern = r"(\w+):\s*['\"]([^'\"]*(?:\\.[^'\"]*)*)['\"]"
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matches = re.findall(pattern, workflows_content)
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result = {}
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for key, value in matches:
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# 处理转义字符
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value = value.replace("\\'", "'").replace('\\"', '"')
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result[key] = value
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return result
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def translate_text(client: Anthropic, text: str, target_lang: str = "Russian") -> str:
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"""使用 Claude API 翻译文本"""
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try:
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message = client.messages.create(
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model="claude-3-5-sonnet-20241022",
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max_tokens=1000,
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messages=[{
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"role": "user",
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"content": f"""Translate the following English text to {target_lang}.
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Only provide the translation, no explanations or additional text.
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Keep any {{variable}} placeholders unchanged.
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Text to translate: {text}"""
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}]
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)
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translation = message.content[0].text.strip()
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# 移除可能的引号
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translation = translation.strip('"').strip("'")
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return translation
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except Exception as e:
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print(f"翻译错误: {e}")
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return text
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def main():
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# 检查 API key
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api_key = os.getenv('ANTHROPIC_API_KEY')
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if not api_key:
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print("错误: 未找到 ANTHROPIC_API_KEY 环境变量")
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print("请设置: export ANTHROPIC_API_KEY='your-api-key'")
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return
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client = Anthropic(api_key=api_key)
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base_dir = Path(__file__).parent
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en_us_file = base_dir / "src/i18n/locales/en-US.ts"
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ru_ru_file = base_dir / "src/i18n/locales/ru-RU.ts"
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# 提取英文和俄文的 workflows 部分
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print("正在提取英文原文...")
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en_workflows = extract_workflows_section(en_us_file)
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print(f"提取了 {len(en_workflows)} 个英文键")
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print("\n正在提取俄文翻译...")
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ru_workflows = extract_workflows_section(ru_ru_file)
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print(f"提取了 {len(ru_workflows)} 个俄文键")
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# 找出包含中文的俄文翻译
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problematic_keys = []
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for key, ru_text in ru_workflows.items():
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if contains_chinese(ru_text):
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en_text = en_workflows.get(key, '')
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if en_text:
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problematic_keys.append({
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'key': key,
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'en_text': en_text,
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'ru_text': ru_text
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})
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print(f"\n发现 {len(problematic_keys)} 个需要修复的翻译")
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if not problematic_keys:
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print("没有需要修复的翻译！")
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return
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# 翻译前10个作为示例
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print("\n开始翻译前10个键...")
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fixed_translations = {}
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for i, item in enumerate(problematic_keys[:10], 1):
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key = item['key']
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en_text = item['en_text']
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old_ru_text = item['ru_text']
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print(f"\n[{i}/10] 翻译键: {key}")
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print(f" 英文: {en_text}")
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print(f" 旧俄文: {old_ru_text}")
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new_ru_text = translate_text(client, en_text, "Russian")
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print(f" 新俄文: {new_ru_text}")
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fixed_translations[key] = new_ru_text
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# 保存修复结果到文件
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output_file = base_dir / "russian_fixes.txt"
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with open(output_file, 'w', encoding='utf-8') as f:
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f.write(f"需要修复的翻译总数: {len(problematic_keys)}\n\n")
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f.write("=" * 80 + "\n")
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f.write("已翻译的前10个键:\n")
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f.write("=" * 80 + "\n\n")
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for key, new_text in fixed_translations.items():
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old_item = next(item for item in problematic_keys if item['key'] == key)
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f.write(f"键: {key}\n")
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f.write(f"英文: {old_item['en_text']}\n")
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f.write(f"旧俄文: {old_item['ru_text']}\n")
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f.write(f"新俄文: {new_text}\n")
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f.write("-" * 80 + "\n\n")
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f.write("\n" + "=" * 80 + "\n")
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f.write("剩余需要翻译的键:\n")
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f.write("=" * 80 + "\n\n")
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for item in problematic_keys[10:]:
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f.write(f"键: {item['key']}\n")
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f.write(f"英文: {item['en_text']}\n")
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f.write(f"旧俄文: {item['ru_text']}\n")
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f.write("-" * 80 + "\n\n")
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print(f"\n修复结果已保存到: {output_file}")
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print(f"\n注意: 由于翻译数量较多({len(problematic_keys)}个)，建议分批处理")
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print("您可以修改脚本中的 [:10] 来处理更多键")
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if __name__ == '__main__':
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main()
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