"""Deterministic display categories for confirmed resume skills."""

from __future__ import annotations

from collections.abc import Iterable, Mapping

_EXACT_CATEGORY_RULES: dict[str, str] = {
    "sql analysis": "产品、设计与分析",
    "data analysis": "产品、设计与分析",
    "user research": "产品、设计与分析",
    "数据分析": "产品、设计与分析",
    "用户研究": "产品、设计与分析",
}

_CATEGORY_RULES: tuple[tuple[str, tuple[str, ...]], ...] = (
    ("编程语言与框架", (
        "python", "java", "javascript", "typescript", "go", "golang", "c++", "c#",
        "fastapi", "django", "flask", "spring", "spring boot", "node.js", "nodejs",
        "react native", "pytorch", "tensorflow", "编程语言", "软件工程",
    )),
    ("前端", (
        "vue", "react", "angular", "html", "css", "sass", "tailwind", "webpack", "vite",
        "前端", "小程序",
    )),
    ("后端与数据存储", (
        "postgresql", "postgres", "mysql", "sqlite", "redis", "mongodb", "elasticsearch",
        "kafka", "rabbitmq", "sql", "clickhouse", "后端", "数据库", "缓存", "消息队列",
    )),
    ("AI 与数据智能", (
        "langgraph", "langchain", "llamaindex", "rag", "pgvector", "bge-m3", "bge", "tei",
        "机器学习", "深度学习", "人工智能", "计算机视觉", "自然语言处理", "pandas", "numpy",
    )),
    ("云、DevOps 与工具", (
        "docker", "kubernetes", "k8s", "git", "github actions", "gitlab ci", "jenkins",
        "linux", "terraform", "aws", "azure", "aliyun", "云原生", "容器",
    )),
    ("产品、设计与分析", (
        "figma", "axure", "tableau", "power bi", "excel", "data analysis", "sql analysis",
        "product", "user research", "产品", "原型", "需求分析", "项目管理",
    )),
)


def classify_skills(
    skills: Iterable[object], preferred: Mapping[str, str] | None = None
) -> list[dict[str, list[str] | str]]:
    """Group confirmed user skills without changing their display order.

    `preferred` maps skill -> category assigned by the recommender (LLM); it wins
    over the keyword rules, which remain the fallback for manual edits.
    """
    preferred_normalized = {
        " ".join(str(skill).casefold().split()): str(category).strip()
        for skill, category in (preferred or {}).items()
        if str(category).strip()
    }
    grouped: dict[str, list[str]] = {category: [] for category, _ in _CATEGORY_RULES}
    grouped["其他技能"] = []
    seen: set[str] = set()
    for value in skills:
        skill = str(value or "").strip()
        normalized = " ".join(skill.casefold().split())
        if not normalized or normalized in seen:
            continue
        seen.add(normalized)
        category = preferred_normalized.get(normalized) or _EXACT_CATEGORY_RULES.get(normalized) or next(
            (name for name, keywords in _CATEGORY_RULES if any(keyword in normalized for keyword in keywords)),
            "其他技能",
        )
        grouped.setdefault(category, []).append(skill)
    return [
        {"category": category, "skills": values}
        for category, values in grouped.items()
        if values
    ]