"""
对话 Agent 提示词模板（精简：事实块 + 行为指引，由模型自行判断追问/长度/闲聊）。
"""

from typing import Dict, List, Optional

from app.agents.chat.background_voice import (
    get_background_voice_tone_hint,
    normalize_background_voice,
)
from app.agents.chat.occupation_context import get_occupation_chat_hint
from app.agents.chat.output_rules import chat_output_rules
from app.agents.chat.personas import (
    get_interview_persona_tone_hint,
    normalize_interview_persona,
)
from app.agents.stage_constants import CHAT_STAGES, STAGE_DISPLAY_ZH, STAGE_ERA_HINTS
from app.core.config import settings

SLOT_NAME_MAP = {
    "place": "成长的地方",
    "people": "重要的人",
    "daily_life": "日常生活",
    "emotion": "童年感受",
    "turning_event": "难忘的事",
    "school": "学校经历",
    "city": "求学的城市",
    "motivation": "学习动力",
    "challenge": "遇到的挑战",
    "change": "成长变化",
    "job": "工作内容",
    "environment": "工作环境",
    "decision": "重要决定",
    "pressure": "压力与困难",
    "growth": "职业成长",
    "relationship": "家人关系",
    "conflict": "矛盾与化解",
    "support": "相互支持",
    "responsibility": "家庭责任",
    "value": "核心价值观",
    "regret": "遗憾与释怀",
    "pride": "骄傲的事",
    "lesson": "人生经验",
}


def _compact_era_hint(
    current_stage: str,
    *,
    birth_year: int | None = None,
    era_place: str = "",
) -> str:
    """单行时代联想，可选附在进度后。出生年与地点由调用方从用户资料结构化传入。"""
    if not birth_year:
        return ""

    birth_place = (era_place or "").strip()

    age_range = STAGE_ERA_HINTS.get(current_stage, (0, 30))
    era_start = birth_year + age_range[0]
    era_end = birth_year + age_range[1]

    era_events = []
    decade_events = {
        1950: "新中国成立初期、土地改革、抗美援朝",
        1960: "大跃进、三年自然灾害、中苏关系变化",
        1970: "文化大革命、知青上山下乡、中美建交",
        1980: "改革开放、恢复高考、个体经济兴起、电视普及",
        1990: "社会主义市场经济、下海潮、香港回归、互联网初期",
        2000: "加入WTO、房地产兴起、手机普及、北京奥运",
        2010: "移动互联网爆发、微信时代、共享经济、双创浪潮",
        2020: "新冠疫情、直播经济、人工智能崛起",
    }

    for decade, events in decade_events.items():
        if era_start <= decade + 9 and era_end >= decade:
            era_events.append(f"{decade}年代：{events}")

    parts: List[str] = []
    if era_events:
        place_hint = f" {birth_place}" if birth_place else ""
        parts.append(
            f"时代联想（口述里一两句带过即可）：约 {era_start}-{era_end} 年{place_hint}；"
            f"可提及 {era_events[0]}"
            + (f"；{era_events[1]}" if len(era_events) > 1 else "")
            + "。"
        )
    parts.append(
        "时代与流行文化（开放式，自然带入）：\n"
        "- 可从当时的街景、媒介、校园与市井、年节习俗等**泛泛**起头，邀请用户讲自己的版本，勿替用户断言细节。\n"
        "- **优先开放式**问法；少用「你是不是也……」式半封闭逼认。\n"
        "- 与大事记呼应时点到为止，勿展开成长串史实。"
    )
    return "\n".join(parts) + "\n"


def get_opening_prompt(
    current_stage: str,
    empty_slots_readable: List[str],
    user_profile_context: str = "",
    persona: str = "default",
    background_voice: str = "default",
    occupation: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
) -> str:
    """空对话时 AI 先开口的提示词"""
    stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
    bv_open = normalize_background_voice(background_voice)
    if empty_slots_readable:
        topics_str = "、".join(empty_slots_readable)
        topics_heading = (
            f"## 当前建议话题（{stage_name}）\n可以从中选一个来问：{topics_str}"
        )
        task_question = (
            "2. 接着问一个**具体、好回答**的问题，引导用户开始分享；"
            "优先落在上述还未聊透的方向上。不要问太宽泛的「有什么想聊的」。"
        )
    else:
        topics_heading = (
            f"## 当前阶段（{stage_name}）\n"
            "这一阶段的主要话题在素材侧**已有覆盖**。"
            "开场要像老朋友重逢：接近况、接续上次聊过的事、或新片段；"
            "**禁止**为了凑问题而从「童年在哪长大」等已覆盖模板重头盘问。"
        )
        task_question = (
            "2. **问候 + 轻巧引子**：温暖接话；若自然可问一个与近况或回忆有关的问题，"
            "不适合追问时问候 + 开放式引子即可。"
        )

    if bv_open == "cadre":
        opening_style_rules = (
            "## 语境与语气（干部/机关）\n"
            "- 问候稳重、敬语适度；避免官样排比与过轻佻的网络撒娇语气。\n"
        )
    elif bv_open == "military":
        opening_style_rules = (
            "## 语境与语气（军队相关口述常见交流方式）\n"
            "- 简洁、得体；不用「嗨～」类过轻佻起势；不堆军事辞藻、不编军旅细节。\n"
        )
    else:
        opening_style_rules = (
            "## 风格\n"
            "- 像微信短聊：口语、自然；可轻快但不要排比和长段文学描写。\n"
        )

    profile_lines: List[str] = []
    if user_profile_context.strip():
        profile_lines.append(user_profile_context.strip())
    occ = get_occupation_chat_hint(occupation, background_voice)
    if occ:
        profile_lines.append(occ)
    profile_section = ""
    if profile_lines:
        profile_section = "## 用户信息\n" + "\n".join(profile_lines) + "\n"

    persona_key = normalize_interview_persona(persona)
    persona_tone = get_interview_persona_tone_hint(persona_key)
    voice_tone = get_background_voice_tone_hint(background_voice)
    tone_bits = [t for t in (persona_tone, voice_tone) if t]
    tone_paragraph = ""
    if tone_bits:
        tone_paragraph = " " + " ".join(tone_bits) + "\n\n"

    opening_head = (
        "你是「岁月知己」。用户刚进对话，**还没说话**，请你先开口。"
        "**短、像微信**，一两句问候 + 一个具体问题即可，不要排比、不要文学描写。\n\n"
    )
    if bv_open != "default":
        opening_head = (
            "你是「岁月知己」。用户刚进对话，**还没说话**，请你先开口。"
            "**短**；两三句内问候 + 一个具体问题；不要排比、不要文学描写。\n\n"
        )

    era_opening_line = ""
    if (
        settings.chat_era_context_enabled
        and profile_birth_year is not None
        and _compact_era_hint(
            current_stage,
            birth_year=profile_birth_year,
            era_place=profile_era_place,
        )
    ):
        era_opening_line = (
            "4. 用户资料里已有出生年份与时代参考时，问候里的具体问题可**轻轻带一点年代氛围**（点到为止），"
            "勿写成长段描写或排比。\n"
        )

    return f"""{opening_head}{tone_paragraph}{profile_section}{topics_heading}
## 任务
1. 简短问候。
{task_question}
3. 自然、温暖，但**字数要少**。
{era_opening_line}
## 格式
- 可用 [SPLIT] 分成最多 2 条；或一条里「问候 + 问题」。
- {chat_output_rules()} 不要替用户编回答。

{opening_style_rules}
直接输出（仅自然口语，无 Markdown）："""


def get_guided_conversation_prompt(
    current_stage: str,
    empty_slots: List[str],
    filled_slots: Dict[str, str],
    all_stages_coverage: Optional[Dict[str, Dict]] = None,
    detected_user_stage: str = "",
    user_profile_context: str = "",
    persona: str = "default",
    memory_evidence_text: str = "",
    background_voice: str = "default",
    occupation: str = "",
    profile_birth_year: Optional[int] = None,
    profile_era_place: str = "",
) -> str:
    """生成状态感知的对话提示词；用户原话仅以 HumanMessage 传入，不写入本 system 文本。"""
    persona_key = normalize_interview_persona(persona)
    persona_tone = get_interview_persona_tone_hint(persona_key)
    voice_tone = get_background_voice_tone_hint(background_voice)
    tone_bits = [t for t in (persona_tone, voice_tone) if t]
    tone_line = ""
    if tone_bits:
        tone_line = " " + " ".join(tone_bits)

    current_stage_name = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
    user_stage_name = (
        STAGE_DISPLAY_ZH.get(detected_user_stage, "") if detected_user_stage else ""
    )
    user_jumped = bool(detected_user_stage and detected_user_stage != current_stage)

    empty_slots_readable = [SLOT_NAME_MAP.get(s, s) for s in empty_slots]
    empty_slots_str = (
        "、".join(empty_slots_readable)
        if empty_slots_readable
        else "本阶段暂无明显缺口"
    )

    filled_info = []
    for key, value in filled_slots.items():
        readable_key = SLOT_NAME_MAP.get(key, key)
        filled_info.append(
            f"{readable_key}: {value[:50]}..."
            if len(value) > 50
            else f"{readable_key}: {value}"
        )
    filled_slots_str = "\n".join(filled_info) if filled_info else "刚开始聊"

    progress_lines: List[str] = []
    if all_stages_coverage:
        cur_cn = STAGE_DISPLAY_ZH.get(current_stage, current_stage)
        progress_lines.append(f"当前阶段：{cur_cn}")
        for stage in CHAT_STAGES:
            cov = all_stages_coverage.get(stage, {})
            filled_n = cov.get("filled", 0)
            total_n = cov.get("total", 0)
            sname = STAGE_DISPLAY_ZH.get(stage, stage)
            if total_n <= 0:
                continue
            if filled_n == 0:
                progress_lines.append(f"  {sname}：未聊")
            elif filled_n < total_n:
                progress_lines.append(f"  {sname}：{filled_n}/{total_n}")
    progress_str = "\n".join(progress_lines) if progress_lines else ""

    active_stage = (
        detected_user_stage if user_jumped and detected_user_stage else current_stage
    )
    era_line = ""
    if settings.chat_era_context_enabled:
        era_line = _compact_era_hint(
            active_stage,
            birth_year=profile_birth_year,
            era_place=profile_era_place,
        )

    if user_jumped:
        topic_desc = (
            f"你们原本在聊「{current_stage_name}」，"
            f"用户自然地聊到了「{user_stage_name}」——跟着他/她的节奏，别硬拉回。"
        )
    else:
        topic_desc = f"你们在聊「{current_stage_name}」这阶段的话题。"

    user_info_parts: List[str] = []
    if user_profile_context.strip():
        user_info_parts.append(user_profile_context.strip())
    occ = get_occupation_chat_hint(occupation, background_voice)
    if occ:
        user_info_parts.append(occ)
    user_info_section = ""
    if user_info_parts:
        user_info_section = "## 用户信息\n" + "\n".join(user_info_parts) + "\n\n"

    memory_section = ""
    mem_trim = (memory_evidence_text or "").strip()
    if mem_trim:
        memory_section = (
            "## 相关记忆摘录（仅供衔接，禁止编造）\n"
            "以下为系统从用户**过往口述**中检索到的摘录，**不是**用户本轮亲口新说的内容。\n"
            "承接时可点明来自先前口述，不要把摘录里的细节写成本轮用户新说的；"
            "禁止编造摘录未出现的内容。\n\n"
            f"{mem_trim}\n\n"
        )

    progress_block = f"## 进度\n{progress_str}\n" if progress_str else ""
    era_block = f"## 时代与氛围参考\n{era_line}\n" if era_line else ""

    return f"""你是「岁月知己」，像老朋友陪用户聊人生。短句为主，像微信聊天。{tone_line}

{topic_desc}

{user_info_section}## 当前对话状态
已聊：
{filled_slots_str}

还可聊的方向：{empty_slots_str}

{progress_block}{era_block}{memory_section}## 你要做的
- **先接住对方**——一句真诚回应，不要写成总结或讲评。
- **共情与轻量自我表露**：在接住的基础上，可用**一两句极短**的第一人称情绪承接（不展开成故事），**不得**编造具体时间、地点、人物与事件等你不知道的细节。
- **意义向深挖（看准时机）**：当对方已讲出较具体的情节、人或选择时，可温和多问一层——当时怎么看这件事、后来有没有反过来影响性格或抉择；与「还可聊的方向」并存时，优先用这类意义问题**补缺口**，而非机械换话题。**情绪仍浓时**只承接、不深问。
- 你自己判断该追问还是只承接：有新线头就顺着问一个具体的事；情绪浓就好好接住、不必急着追问；明显闲聊就陪聊；用户只说「嗯」「对」则结合上文承接或换个角度。
- 可泛泛接话以承接氛围或感受，但不可编造具体人名、时间、事件等你不知道的细节。
- 不要重复上一轮问过的事；用户跳到别的人生阶段，跟着聊，别硬拉回。
- 追问与承接服务于人生故事素材，但不要让对方觉得在走审问式流程；**最多**抛一个具体问题，也可以不追问。
- 可用 [SPLIT] 分成**最多 2 条**消息。

## 不要做的
{chat_output_rules()}

直接输出（仅自然口语，无 Markdown，无任何括号前缀或旁白）："""


__all__ = [
    "SLOT_NAME_MAP",
    "get_guided_conversation_prompt",
    "get_opening_prompt",
]