AI 工具开发实战（1）：开发一个命令行 AI 翻译工具——支持中英互译、多语言、文件翻译

aitrans/
├── aitrans.py          # 主程序（Click CLI）
├── translators.py      # 翻译引擎
├── config.py           # 配置管理
├── requirements.txt
└── .env                # API Key

mkdir aitrans && cd aitrans
python -m venv venv && source venv/bin/activate

# requirements.txt
click==8.1.7
python-dotenv==1.0.0
openai==1.50.0

pip install -r requirements.txt

# config.py
import os
from dotenv import load_dotenv
load_dotenv()

# 支持多个模型后端，按需切换
MODELS = {
    "deepseek": {
        "api_key": os.getenv("DEEPSEEK_API_KEY", ""),
        "base_url": "https://api.deepseek.com/v1",
        "model": "deepseek-chat",
    },
    "doubao": {
        "api_key": os.getenv("DOUBAO_API_KEY", ""),
        "base_url": "https://ark.cn-beijing.volces.com/api/v3",
        "model": "doubao-2.1-pro",
    },
}

DEFAULT_BACKEND = os.getenv("AI_BACKEND", "deepseek")
DEFAULT_TARGET = os.getenv("AI_DEFAULT_LANG", "中文")

# translators.py
from openai import OpenAI
from config import MODELS, DEFAULT_BACKEND

class AITranslator:
    """AI 翻译引擎。"""

    def __init__(self, backend=None):
        backend = backend or DEFAULT_BACKEND
        cfg = MODELS[backend]
        self.client = OpenAI(
            api_key=cfg["api_key"],
            base_url=cfg["base_url"],
        )
        self.model = cfg["model"]

    def translate(self, text, target_lang="中文", source_lang=None):
        """翻译文本。"""
        source_hint = f"，源语言是{source_lang}" if source_lang else ""
        prompt = f"将以下文本翻译成{target_lang}{source_hint}。只输出译文，不要解释：\n\n{text}"

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[
                {"role": "system", "content": f"你是一个专业的翻译助手。输出简洁的译文，不添加任何解释。"},
                {"role": "user", "content": prompt},
            ],
            temperature=0.1,  # 翻译场景用低温度
            max_tokens=min(len(text) * 2, 4096),
        )
        return response.choices[0].message.content.strip()

    def batch_translate(self, texts, target_lang="中文"):
        """批量翻译（一次调用完成）。"""
        numbered = "\n\n---\n\n".join(
            f"[{i+1}] {t}" for i, t in enumerate(texts)
        )
        prompt = f"将以下{len(texts)}段文本分别翻译成{target_lang}，保持编号：\n\n{numbered}"

        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
        )
        # 解析带编号的返回
        result = response.choices[0].message.content
        return self._parse_batch(result, len(texts))

    def _parse_batch(self, response, count):
        """解析批量翻译结果。"""
        import re
        translations = []
        for i in range(1, count + 1):
            pattern = rf"\[{i}\](.*?)(?=\[\d+\]|$)"
            match = re.search(pattern, response, re.DOTALL)
            if match:
                translations.append(match.group(1).strip())
            else:
                translations.append("")
        return translations

#!/usr/bin/env python3
# aitrans.py
import click
from translators import AITranslator

LANG_MAP = {
    "en": "英文", "zh": "中文", "ja": "日文",
    "ko": "韩文", "fr": "法文", "de": "德文",
    "es": "西班牙文", "ru": "俄文", "ar": "阿拉伯文",
}


@click.group()
def cli():
    """aitrans - AI 驱动的命令行翻译工具"""
    pass


@cli.command()
@click.argument("text")
@click.option("--to", "-t", default="中文", help="目标语言")
@click.option("--from", "-f", "from_lang", default=None, help="源语言（可选）")
@click.option("--backend", "-b", default=None, help="模型后端（deepseek/doubao）")
def trans(text, to, from_lang, backend):
    """翻译一段文本。"""
    t = AITranslator(backend)
    result = t.translate(text, target_lang=to, source_lang=from_lang)
    click.echo(result)


@cli.command()
@click.argument("filepath", type=click.Path(exists=True))
@click.option("--to", "-t", default="中文", help="目标语言")
@click.option("--output", "-o", default=None, help="输出文件（默认覆盖原文件加 .trans 后缀）")
@click.option("--backend", "-b", default=None)
def file(filepath, to, output, backend):
    """翻译一个文件。"""
    with open(filepath, "r", encoding="utf-8") as f:
        content = f.read()

    t = AITranslator(backend)

    # 分段翻译长文件
    chunks = content.split("\n\n")
    if len(content) > 3000 and len(chunks) > 1:
        results = t.batch_translate(chunks[:10], target_lang=to)
        translated = "\n\n".join(results)
    else:
        translated = t.translate(content, target_lang=to)

    output = output or f"{filepath}.trans"
    with open(output, "w", encoding="utf-8") as f:
        f.write(translated)
    click.echo(f"✅ 已翻译并保存到 {output}")


@cli.command()
@click.option("--backend", "-b", default=None)
def repl(backend):
    """交互式翻译模式。"""
    t = AITranslator(backend)
    click.echo("交互翻译模式（输入 :q 退出，:en 切换目标为英文，:zh 切换为中文）")
    target = "中文"

    while True:
        text = click.prompt(">", prompt_suffix=" ")
        if text == ":q":
            break
        elif text.startswith(":"):
            lang = LANG_MAP.get(text[1:], text[1:])
            click.echo(f"  目标语言切换为：{lang}")
            target = lang
            continue

        result = t.translate(text, target_lang=target)
        click.echo(f"  {result}")
        click.echo()


if __name__ == "__main__":
    cli()

# .env 配置
DEEPSEEK_API_KEY=sk-your-key
AI_BACKEND=deepseek

# 翻译文本
python aitrans.py trans "Hello, this is a test message"
# → 你好，这是一条测试消息。

# 指定目标语言
python aitrans.py trans "Hello world" --to 日文
# → こんにちは、世界

# 翻译文件
python aitrans.py file README.md --to 中文
# → ✅ 已翻译并保存到 README.md.trans

# 交互模式
python aitrans.py repl
> The transformer architecture is widely used
  转换器架构被广泛应用于自然语言处理。
> :en                                   # 切换目标为英文
  目标语言切换为：英文
> 今天天气真好
  The weather is really nice today.

# 在 ~/.zshrc 或 ~/.bashrc 中加
alias aitrans='python /path/to/aitrans/aitrans.py'

# 之后直接在终端用
aitrans trans "Hello world"