from __future__ import annotations

from copy import deepcopy
from datetime import UTC, datetime
from typing import Any, Protocol

from pydantic import BaseModel, ConfigDict, Field

from .llm_services import OpenAICompatibleStructuredClient
from .settings import Settings


class ProfileSummaryOutput(BaseModel):
    model_config = ConfigDict(extra="forbid")

    content: str = Field(min_length=20, max_length=600)


class ProfileSummaryGenerator(Protocol):
    def generate(self, content: dict[str, Any]) -> str: ...


class RuleBasedProfileSummaryGenerator:
    """Deterministic Chinese summary for tests and configured rule fallback."""

    def generate(self, content: dict[str, Any]) -> str:
        basics = content.get("basics") if isinstance(content.get("basics"), dict) else {}
        target = content.get("target") if isinstance(content.get("target"), dict) else {}
        position = str(target.get("position") or target.get("target_position") or "目标岗位").strip()
        groups = content.get("skill_groups") if isinstance(content.get("skill_groups"), list) else []
        skills = [
            str(skill).strip()
            for group in groups
            if isinstance(group, dict)
            for skill in group.get("skills") or []
            if str(skill).strip()
        ][:5]
        first_entry: dict[str, Any] = {}
        for section in content.get("sections") or []:
            if isinstance(section, dict) and section.get("items"):
                candidate = section["items"][0]
                if isinstance(candidate, dict):
                    first_entry = candidate
                    break
        major = str(first_entry.get("major") or basics.get("major") or "").strip()
        focus = str(
            first_entry.get("company")
            or first_entry.get("project_name")
            or first_entry.get("school")
            or "相关实践"
        ).strip()
        skill_text = "、".join(dict.fromkeys(skills)) or "相关技术与实践能力"
        major_text = f"，具备{major}相关学习背景" if major else ""
        return f"面向{position}{major_text}，具备{skill_text}等能力，拥有{focus}相关经历，能够结合已完成的项目与实践持续提升岗位匹配度。"


class OpenAIProfileSummaryGenerator:
    def __init__(self, completion: OpenAICompatibleStructuredClient) -> None:
        self.completion = completion

    def generate(self, content: dict[str, Any]) -> str:
        output: ProfileSummaryOutput = self.completion.complete(
            schema=ProfileSummaryOutput,
            schema_name="profile_summary",
            system_prompt=(
                "你是中文简历个人总结撰写助手。仅返回 JSON。根据用户已确认的简历内容，"
                "写一段 80 到 180 字、适合置于中文简历开头的个人总结。"
                "只概括目标岗位、教育/经历、项目和技能中的已有事实；不得包含手机、邮箱等隐私信息，"
                "不得编造公司、学校、项目、学历、奖项、证书或量化数字。"
                "内容应自然连贯，不使用标题、列表、Markdown 或解释。"
            ),
            payload={"resume": _summary_source(content)},
        )
        return _validate_summary(output.content)


class FallbackProfileSummaryGenerator:
    def __init__(self, primary: ProfileSummaryGenerator, fallback: ProfileSummaryGenerator) -> None:
        self.primary = primary
        self.fallback = fallback

    def generate(self, content: dict[str, Any]) -> str:
        try:
            return self.primary.generate(content)
        except Exception:
            return self.fallback.generate(content)


def build_profile_summary_generator(
    settings: Settings, client: Any | None = None
) -> ProfileSummaryGenerator:
    rules = RuleBasedProfileSummaryGenerator()
    if not settings.use_openai:
        return rules
    primary = OpenAIProfileSummaryGenerator(OpenAICompatibleStructuredClient(settings, client))
    return FallbackProfileSummaryGenerator(primary, rules) if settings.fallback_to_rules else primary


def generated_summary(content: str) -> dict[str, Any]:
    return {
        "content": _validate_summary(content),
        "source": "ai_generated",
        "generated_at": datetime.now(UTC).isoformat(),
        "stale": False,
    }


def _validate_summary(value: str) -> str:
    clean = " ".join(str(value or "").split())
    if not 20 <= len(clean) <= 600:
        raise ValueError("profile_summary_invalid")
    return clean


def _summary_source(content: dict[str, Any]) -> dict[str, Any]:
    result = deepcopy(content)
    basics = result.get("basics")
    if isinstance(basics, dict):
        for field in ("phone", "email", "masked_phone"):
            basics.pop(field, None)
    result.pop("profile_summary", None)
    return result