CareerNexus/app/match/resume.py

"""简历解析：.txt 直接读，.docx 用 python-docx，.pdf 用 pypdf。

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、
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），
供规范简历模板展示与导出。
"""
from __future__ import annotations

import base64
import logging
import re
from pathlib import Path

from app.extract.base import SkillMatcher, load_lexicon
from app.models import ResumeProfile

logger = logging.getLogger(__name__)

_PHONE_RE = re.compile(r"1[3-9]\d{9}")
_EMAIL_RE = re.compile(r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}")
_NAME_RE = re.compile(r"姓名[:：]\s*([一-龥]{2,4})")
_YEARS_RE = re.compile(r"(\d+(?:\.\d+)?)\s*年(?:以上)?[^\n，。；]{0,6}?经验")
_GENDER_RE = re.compile(r"性别[:：]?\s*(男|女)")
_EDU_RE = re.compile(r"(博士后|博士|硕士|研究生|MBA|本科|大专|专科)")
_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})")

# 常见简历分节标题（整行匹配，可带冒号）
_SECTION_TITLES = {
    "个人信息", "基本信息", "求职意向", "教育经历", "教育背景",
    "工作经历", "工作经验", "实习经历", "项目经历", "项目经验",
    "技能清单", "专业技能", "核心技能", "技能特长", "技能",
    "自我评价", "个人评价", "荣誉证书", "荣誉奖项", "证书",
}


def mask_text(text: str) -> str:
    """脱敏：手机、邮箱、姓名、住址打码。"""
    text = _PHONE_RE.sub(lambda m: m.group(0)[:3] + "****" + m.group(0)[-2:], text)
    text = _EMAIL_RE.sub(lambda m: m.group(0)[0] + "***@" + m.group(0).split("@")[1], text)
    text = _NAME_RE.sub(lambda m: "姓名：" + m.group(1)[0] + "*" * (len(m.group(1)) - 1), text)
    text = _ADDR_RE.sub(lambda m: m.group(0)[:2] + m.group(1)[:6] + "***"
                        if len(m.group(1)) > 6 else m.group(0), text)
    return text


def split_sections(text: str) -> list[dict]:
    """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"。"""
    sections: list[dict] = []
    cur_title, cur_lines = "基本信息", []
    for ln in text.splitlines():
        key = ln.strip().rstrip(":：").strip()
        if key in _SECTION_TITLES:
            if "\n".join(cur_lines).strip():
                sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
            cur_title, cur_lines = key, []
        else:
            cur_lines.append(ln)
    if "\n".join(cur_lines).strip():
        sections.append({"title": cur_title, "text": "\n".join(cur_lines).strip()})
    if not sections and text.strip():
        sections = [{"title": "简历全文", "text": text.strip()}]
    return sections


def extract_photo_b64(path: str | Path) -> str:
    """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。"""
    path = Path(path)
    suffix = path.suffix.lower()
    try:
        if suffix == ".docx":
            import zipfile
            with zipfile.ZipFile(str(path)) as z:
                names = sorted(n for n in z.namelist()
                               if n.startswith("word/media/")
                               and n.lower().endswith((".png", ".jpg", ".jpeg")))
                if not names:
                    return ""
                data = z.read(names[0])
                mime = "image/png" if names[0].lower().endswith(".png") else "image/jpeg"
                return f"data:{mime};base64,{base64.b64encode(data).decode()}"
        if suffix == ".pdf":
            from pypdf import PdfReader
            reader = PdfReader(str(path))
            for page in reader.pages[:2]:
                for img in getattr(page, "images", []):
                    mime = "image/png" if img.name.lower().endswith(".png") else "image/jpeg"
                    return f"data:{mime};base64,{base64.b64encode(img.data).decode()}"
    except Exception as exc:  # 照片是增强信息，提取失败不阻断解析
        logger.warning("简历照片提取失败（已跳过）: %s", exc)
    return ""


def read_resume_file(path: str | Path) -> str:
    """按扩展名读取简历文件文本。"""
    path = Path(path)
    suffix = path.suffix.lower()
    if suffix == ".txt":
        return path.read_text(encoding="utf-8")
    if suffix == ".docx":
        from docx import Document  # python-docx，requirements 已声明
        doc = Document(str(path))
        return "\n".join(p.text for p in doc.paragraphs)
    if suffix == ".pdf":
        try:
            from pypdf import PdfReader
        except ImportError as exc:
            raise RuntimeError(
                "解析 .pdf 需要可选依赖 pypdf，请先执行：pip install pypdf"
            ) from exc
        reader = PdfReader(str(path))
        return "\n".join(page.extract_text() or "" for page in reader.pages)
    raise ValueError(f"不支持的简历格式：{suffix}（支持 .txt/.docx/.pdf）")


def _mask_addr(addr: str) -> str:
    """住址脱敏：保留前 6 字（大致到区/县），其余打码。"""
    return addr[:6] + "***" if len(addr) > 6 else addr


def parse_resume(text: str, matcher: SkillMatcher | None = None,
                 photo_b64: str = "") -> ResumeProfile:
    """从简历文本解析脱敏画像（含结构化基本信息与分节）。"""
    matcher = matcher or SkillMatcher(load_lexicon())
    name_m = _NAME_RE.search(text)
    phone_m = _PHONE_RE.search(text)
    email_m = _EMAIL_RE.search(text)
    years_m = _YEARS_RE.search(text)
    gender_m = _GENDER_RE.search(text)
    edu_m = _EDU_RE.search(text)
    addr_m = _ADDR_RE.search(text)
    masked = mask_text(text)
    return ResumeProfile(
        name=(name_m.group(1)[0] + "*" * (len(name_m.group(1)) - 1)) if name_m else "",
        phone=(phone_m.group(0)[:3] + "****" + phone_m.group(0)[-2:]) if phone_m else "",
        email=(email_m.group(0)[0] + "***@" + email_m.group(0).split("@")[1]) if email_m else "",
        years=float(years_m.group(1)) if years_m else 0.0,
        skills=matcher.find_names(text),
        scenarios=matcher.find_scenarios(text),
        raw_masked_text=masked,
        gender=gender_m.group(1) if gender_m else "",
        education=edu_m.group(1) if edu_m else "",
        address=_mask_addr(addr_m.group(1).strip()) if addr_m else "",
        photo_b64=photo_b64,
        sections=split_sections(masked),
    )


def parse_resume_file(path: str | Path, matcher: SkillMatcher | None = None) -> ResumeProfile:
    """读取文件并解析（含证件照提取）。"""
    return parse_resume(read_resume_file(path), matcher,
                        photo_b64=extract_photo_b64(path))