RoyirinRosmontis
0b198e9976
- 驾驶舱改'人才能力变化指挥台'：变化事件流主图+可信度卡+行动卡+播报卡 - 图谱子图同心分层布局、技能变化角标、系统发现折叠入右栏、主操作收敛 - 演化页双 Tab（岗位报告/全岗位扫描）+ 能力生命线 Swimlane + 更替流 - 学习路径成长路线图：概要条/示范路线/预计投入/标记完成联动 - 新增候选审核台抽屉 + GET /api/stats/credibility - 简历裸姓名/专硕提取修复（桌面真实简历验收通过）+ BGE-M3 加载提示
173 lines
7.6 KiB
Python
173 lines
7.6 KiB
Python
"""简历解析：.txt 直接读，.docx 用 python-docx，.pdf 用 pypdf。
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解析后做四件事：脱敏（姓名/手机/邮箱/住址打码）、技能抽取并归一化到词表、
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工作年限与场景识别、结构化信息抽取（性别/学历/住址/分节/证件照），
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供规范简历模板展示与导出。
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"""
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from __future__ import annotations
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import base64
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import logging
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import re
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from pathlib import Path
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from app.extract.base import SkillMatcher, load_lexicon
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from app.models import ResumeProfile
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logger = logging.getLogger(__name__)
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_PHONE_RE = re.compile(r"1[3-9]\d{9}")
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_EMAIL_RE = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
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_NAME_RE = re.compile(r"姓名[:：]\s*([一-龥]{2,4})")
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# 简历常见写法：首行裸姓名（无"姓名："前缀），如" 王伟航 "
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_BARE_NAME_RE = re.compile(r"^\s*([一-龥]{2,4})\s*$", re.MULTILINE)
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_YEARS_RE = re.compile(r"(\d+(?:\.\d+)?)\s*年(?:以上)?[^\n，。；]{0,6}?经验")
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_GENDER_RE = re.compile(r"性别[:：]?\s*(男|女)")
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_EDU_RE = re.compile(r"(博士后|博士|专硕|学硕|硕士|研究生|MBA|本科|大专|专科)")
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_EDU_MAP = {"专硕": "硕士", "学硕": "硕士", "研究生": "硕士"}
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_ADDR_RE = re.compile(r"(?:家庭住址|现居(?:住)?(?:地|址)?|联系地址|通讯地址|籍贯)[:：]\s*([^\n，。；]{2,30})")
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# 常见简历分节标题（整行匹配，可带冒号）
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_SECTION_TITLES = {
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"个人信息", "基本信息", "求职意向", "教育经历", "教育背景",
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"工作经历", "工作经验", "实习经历", "项目经历", "项目经验",
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"技能清单", "专业技能", "核心技能", "技能特长", "技能",
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"自我评价", "个人评价", "荣誉证书", "荣誉奖项", "证书",
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}
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def _extract_name(text: str) -> str:
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"""姓名识别：优先"姓名：X"格式，否则取首个独立的 2-4 字中文裸行（简历常见排版）。"""
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m = _NAME_RE.search(text)
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if m:
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return m.group(1)
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for line in text.splitlines()[:5]: # 只在开头几行找，避免误伤正文
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b = _BARE_NAME_RE.match(line)
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if b:
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return b.group(1)
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return ""
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def mask_text(text: str) -> str:
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"""脱敏：手机、邮箱、姓名（含首行裸姓名）、住址打码。"""
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text = _PHONE_RE.sub(lambda m: m.group(0)[:3] + "****" + m.group(0)[-2:], text)
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text = _EMAIL_RE.sub(lambda m: m.group(0)[0] + "***@" + m.group(0).split("@")[1], text)
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text = _NAME_RE.sub(lambda m: "姓名：" + m.group(1)[0] + "*" * (len(m.group(1)) - 1), text)
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name = _extract_name(text)
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if name: # 裸姓名行整体打码
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text = _BARE_NAME_RE.sub(
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lambda m: m.group(0).replace(name, name[0] + "*" * (len(name) - 1))
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if m.group(1) == name else m.group(0), text, count=1)
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text = _ADDR_RE.sub(lambda m: m.group(0)[:2] + m.group(1)[:6] + "***"
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if len(m.group(1)) > 6 else m.group(0), text)
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return text
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def split_sections(text: str) -> list[dict]:
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"""按常见标题把简历正文分节，返回 [{title, text}]；无标题时整体归入"简历全文"。"""
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sections: list[dict] = []
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cur_title, cur_lines = "基本信息", []
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for ln in text.splitlines():
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key = ln.strip().rstrip(":：").strip()
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if key in _SECTION_TITLES:
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if "\n".join(cur_lines).strip():
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sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
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cur_title, cur_lines = key, []
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else:
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cur_lines.append(ln)
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if "\n".join(cur_lines).strip():
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sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
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if not sections and text.strip():
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sections = [{"title": "简历全文", "text": text.strip()}]
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return sections
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def extract_photo_b64(path: str | Path) -> str:
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"""从 docx/pdf 中提取第一张图片作为证件照，返回 data URI；无图片返回空串。"""
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path = Path(path)
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suffix = path.suffix.lower()
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try:
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if suffix == ".docx":
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import zipfile
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with zipfile.ZipFile(str(path)) as z:
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names = sorted(n for n in z.namelist()
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if n.startswith("word/media/")
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and n.lower().endswith((".png", ".jpg", ".jpeg")))
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if not names:
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return ""
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data = z.read(names[0])
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mime = "image/png" if names[0].lower().endswith(".png") else "image/jpeg"
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return f"data:{mime};base64,{base64.b64encode(data).decode()}"
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if suffix == ".pdf":
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from pypdf import PdfReader
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reader = PdfReader(str(path))
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for page in reader.pages[:2]:
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for img in getattr(page, "images", []):
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mime = "image/png" if img.name.lower().endswith(".png") else "image/jpeg"
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return f"data:{mime};base64,{base64.b64encode(img.data).decode()}"
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except Exception as exc: # 照片是增强信息，提取失败不阻断解析
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logger.warning("简历照片提取失败（已跳过）: %s", exc)
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return ""
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def read_resume_file(path: str | Path) -> str:
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"""按扩展名读取简历文件文本。"""
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path = Path(path)
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suffix = path.suffix.lower()
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if suffix == ".txt":
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return path.read_text(encoding="utf-8")
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if suffix == ".docx":
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from docx import Document # python-docx，requirements 已声明
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doc = Document(str(path))
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return "\n".join(p.text for p in doc.paragraphs)
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if suffix == ".pdf":
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try:
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from pypdf import PdfReader
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except ImportError as exc:
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raise RuntimeError(
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"解析 .pdf 需要可选依赖 pypdf，请先执行：pip install pypdf"
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) from exc
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reader = PdfReader(str(path))
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return "\n".join(page.extract_text() or "" for page in reader.pages)
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raise ValueError(f"不支持的简历格式：{suffix}（支持 .txt/.docx/.pdf）")
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def _mask_addr(addr: str) -> str:
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"""住址脱敏：保留前 6 字（大致到区/县），其余打码。"""
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return addr[:6] + "***" if len(addr) > 6 else addr
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def parse_resume(text: str, matcher: SkillMatcher | None = None,
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photo_b64: str = "") -> ResumeProfile:
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"""从简历文本解析脱敏画像（含结构化基本信息与分节）。"""
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matcher = matcher or SkillMatcher(load_lexicon())
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name_m = _NAME_RE.search(text)
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name = name_m.group(1) if name_m else _extract_name(text)
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phone_m = _PHONE_RE.search(text)
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email_m = _EMAIL_RE.search(text)
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years_m = _YEARS_RE.search(text)
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gender_m = _GENDER_RE.search(text)
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edu_m = _EDU_RE.search(text)
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addr_m = _ADDR_RE.search(text)
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masked = mask_text(text)
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return ResumeProfile(
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name=(name[0] + "*" * (len(name) - 1)) if name else "",
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phone=(phone_m.group(0)[:3] + "****" + phone_m.group(0)[-2:]) if phone_m else "",
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email=(email_m.group(0)[0] + "***@" + email_m.group(0).split("@")[1]) if email_m else "",
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years=float(years_m.group(1)) if years_m else 0.0,
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skills=matcher.find_names(text),
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scenarios=matcher.find_scenarios(text),
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raw_masked_text=masked,
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gender=gender_m.group(1) if gender_m else "",
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education=_EDU_MAP.get(edu_m.group(1), edu_m.group(1)) if edu_m else "",
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address=_mask_addr(addr_m.group(1).strip()) if addr_m else "",
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photo_b64=photo_b64,
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sections=split_sections(masked),
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)
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def parse_resume_file(path: str | Path, matcher: SkillMatcher | None = None) -> ResumeProfile:
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"""读取文件并解析（含证件照提取）。"""
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return parse_resume(read_resume_file(path), matcher,
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photo_b64=extract_photo_b64(path))
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