261 lines
9.4 KiB
Python
261 lines
9.4 KiB
Python
#!/usr/bin/env python3
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"""根据 glossary 的核查结果，在 final_zh.md 上做精确的文本替换。
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原理：
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- build_glossary.py 会给每条术语标 `zh`（正确中文）和 `issue`（有发现问题）
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- 本脚本扫描所有 `issue` 非空且 `confidence in {high, medium}` 的条目
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- 对这些条目，在正文中把「当前错误译名」替换为「正确译名」
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- 同时处理英文拼写错误（例如 Maywavee → Mabwell）
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规则：
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- 安全第一：只做字面替换，不做上下文改写
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- 明确可见：每一处替换都打印出来，方便 diff
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- 幂等：多次跑结果一致
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- 支持 --dry-run 预览
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用法：
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uv run python scripts/apply_glossary.py <project_slug>
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uv run python scripts/apply_glossary.py <project_slug> --dry-run
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uv run python scripts/apply_glossary.py <project_slug> --min-confidence high
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"""
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from __future__ import annotations
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import argparse
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import json
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import re
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import sys
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from dataclasses import dataclass
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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@dataclass
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class Correction:
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"""一条要应用的修正。"""
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kind: str # "en" (英文拼写) / "zh" (中文译名)
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wrong: str # 当前文中的错误写法
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correct: str # 正确写法
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term_key: str # glossary 里这条的 key（人工 debug 用）
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confidence: str # high / medium / low
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reason: str # 为什么要改（从 issue 字段提取）
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_ISSUE_WRONG_EN_RE = re.compile(
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r"(?:'|\"|term\s*)([A-Z][A-Za-z0-9_\- ]{2,40})['\"]*\s*(?:为|是)[^。]*?"
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r"(?:拼写错误|拼写有误|应为|正确英文名为|正确为|正确拼写为)",
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)
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_CORRECT_EN_RE = re.compile(
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r"(?:正确英文名为|正确为|正确拼写为|应为|正确写法为)\s*['\"]?([A-Z][A-Za-z0-9_\- ]{2,40})['\"]?",
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)
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_WRONG_ZH_RE = re.compile(
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r"current_zh\s*['\"]?([\u4e00-\u9fff][^'\"。，；]{1,40})['\"]?\s*(?:为|是|存在)[^。]*?(?:错误|错译|误译|不准确|应为|应该)",
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)
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def resolve_project(arg: str) -> Path:
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p = Path(arg)
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if p.is_dir():
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return p
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cand = Path.cwd() / "projects" / arg
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if cand.is_dir():
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return cand
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raise SystemExit(f"project not found: {arg}")
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def load_glossary(path: Path) -> dict:
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return json.loads(path.read_text(encoding="utf-8"))
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# 中文替换的保守性：只对"公司名、机构名、产品名"类这种具体化的词做替换
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# 规避常见缩写词在不同上下文有不同含义的情况（如 PDE = 磷酸二酯酶 OR 允许日暴露量）
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_AMBIGUOUS_ABBREVS = {
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"PDE", "ASGPR", "BLA", "IND", "NDA", "CMC", "API", "QC", "QA",
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"ADC", "CRO", "CDMO", "CMO", "GMP", "PK", "PD", "TRL",
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"FDA", "EMA", "NMPA", "ICH", "WHO", "CFDA", "CDE",
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"LNP", "RNP", "AAV", "mRNA", "RNAi", "siRNA", "ASO",
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"DNA", "RNA", "cDNA", "dsRNA", "ssRNA",
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"OTP", "HCV", "HBV", "HPV", "HIV",
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}
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def _looks_like_proper_noun(term_key: str) -> bool:
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"""判断这条术语是否是"专有名词"（公司/机构/产品/药物名）。"""
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# 全大写 2-4 字符缩写 → 视作通用缩写，跳过（歧义风险高）
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if term_key.upper() == term_key and 2 <= len(term_key) <= 5:
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return False
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if term_key in _AMBIGUOUS_ABBREVS:
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return False
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# 其他情况：视作专有名词
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return True
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def extract_corrections(glossary: dict, min_conf: str) -> list[Correction]:
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"""从 glossary 抽出可应用的修正。
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保守策略：
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- 英文拼写错误（term_key 本身错）：高信度直接修
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- 中文译名错误（issue 里说 current_zh 错）：仅对"专有名词"类（公司/产品/机构）修
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- 缩写/通用术语（PDE/ASGPR 等）：不自动修，避免上下文歧义
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"""
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rank = {"high": 3, "medium": 2, "low": 1}
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threshold = rank.get(min_conf, 2)
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corrections: list[Correction] = []
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for term_key, entry in glossary.items():
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if not isinstance(entry, dict):
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continue
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conf = entry.get("confidence", "low")
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if rank.get(conf, 0) < threshold:
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continue
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issue = entry.get("issue", "")
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zh = entry.get("zh", "")
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en_full = entry.get("en_full", "")
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if not issue:
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continue
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# 1. 英文拼写错误：key 本身错了，en_full 是对的
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if (
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en_full
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and term_key != en_full
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and re.search(r"拼写错误|拼写有误|spelled|should be", issue, re.IGNORECASE)
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and term_key[0].isupper() # 通常是公司/产品名
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):
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# 排除太短的（容易误伤）或包含空格的原始 key
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if len(term_key) >= 4 and len(en_full) >= 4:
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corrections.append(
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Correction(
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kind="en",
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wrong=term_key,
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correct=en_full.split("(")[0].strip(), # 去掉括号内的法人全称
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term_key=term_key,
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confidence=conf,
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reason=issue[:200],
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)
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)
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# 2. 中文译名错误：只对"专有名词"修（排除缩写/通用术语的歧义风险）
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if not _looks_like_proper_noun(term_key):
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continue
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m = _WRONG_ZH_RE.search(issue)
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if m and zh:
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wrong_zh = m.group(1).strip()
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if wrong_zh != zh and len(wrong_zh) >= 2:
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corrections.append(
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Correction(
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kind="zh",
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wrong=wrong_zh,
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correct=zh,
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term_key=term_key,
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confidence=conf,
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reason=issue[:200],
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)
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)
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return corrections
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def apply_corrections(text: str, corrections: list[Correction]) -> tuple[str, list[tuple[Correction, int]]]:
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"""返回 (新文本, [(correction, 替换次数)])。"""
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results: list[tuple[Correction, int]] = []
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new_text = text
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for c in corrections:
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# 英文术语用 \b 边界；中文直接替换
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if c.kind == "en":
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pattern = r"\b" + re.escape(c.wrong) + r"\b"
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new_text, count = re.subn(pattern, c.correct, new_text)
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else:
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count = new_text.count(c.wrong)
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if count:
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new_text = new_text.replace(c.wrong, c.correct)
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results.append((c, count))
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return new_text, results
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def main() -> int:
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parser = argparse.ArgumentParser(description="用 glossary 修正正文术语")
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parser.add_argument("project", help="项目 slug 或路径")
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parser.add_argument(
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"--input", default="phase4/final_zh.md",
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help="待修正的 Markdown",
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)
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parser.add_argument(
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"--output", default=None,
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help="输出路径（默认原地覆盖）",
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)
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parser.add_argument(
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"--glossary", default="phase4/glossary.json",
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help="glossary.json 路径",
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)
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parser.add_argument(
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"--min-confidence",
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choices=["high", "medium", "low"],
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default="medium",
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help="只应用 >= 此置信度的修正（默认 medium）",
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)
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parser.add_argument(
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"--dry-run", action="store_true",
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help="只打印会发生的修改，不实际写入",
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)
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args = parser.parse_args()
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project_root = resolve_project(args.project)
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input_path = project_root / args.input
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output_path = project_root / (args.output or args.input)
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glossary_path = project_root / args.glossary
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if not input_path.exists():
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raise SystemExit(f"输入文件不存在：{input_path}")
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if not glossary_path.exists():
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raise SystemExit(f"glossary 不存在：{glossary_path}")
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glossary = load_glossary(glossary_path)
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corrections = extract_corrections(glossary, args.min_confidence)
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print(f"Project: {project_root.name}")
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print(f"Input: {input_path.relative_to(project_root)}")
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print(f"Output: {output_path.relative_to(project_root)}")
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print(f"Glossary: {glossary_path.relative_to(project_root)} ({len(glossary)} terms)")
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print(f"Min confidence: {args.min_confidence}")
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print(f"候选修正: {len(corrections)} 条")
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print()
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if not corrections:
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print("没有需要应用的修正。")
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return 0
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original = input_path.read_text(encoding="utf-8")
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new_text, results = apply_corrections(original, corrections)
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applied = [(c, n) for c, n in results if n > 0]
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skipped = [(c, n) for c, n in results if n == 0]
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print(f"=== 已应用 {len(applied)} 条修正（共替换 {sum(n for _, n in applied)} 处）===")
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for c, n in sorted(applied, key=lambda x: -x[1]):
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marker = "EN" if c.kind == "en" else "ZH"
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print(f" [{marker}][{c.confidence}] {c.wrong!r} → {c.correct!r} (×{n})")
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print(f" 原因: {c.reason[:120]}")
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if skipped:
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print(f"\n=== 跳过 {len(skipped)} 条（正文未出现该错误写法）===")
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for c, _ in skipped[:10]:
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marker = "EN" if c.kind == "en" else "ZH"
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print(f" [{marker}] {c.wrong!r} → {c.correct!r} (0 hit)")
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if len(skipped) > 10:
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print(f" …还有 {len(skipped) - 10} 条")
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if args.dry_run:
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print("\n(--dry-run：未写入)")
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return 0
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output_path.write_text(new_text, encoding="utf-8")
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delta = len(new_text) - len(original)
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print(f"\n✓ 已写入 {output_path.relative_to(project_root)} (字符变化 {delta:+d})")
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return 0
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if __name__ == "__main__":
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sys.exit(main())
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