"""Book deconstruction (拆书) service: novel architect that produces
concept, world, characters, outline, and foreshadowing sections.

Pure functions — no Flask imports.  Each section is streamed via
:func:`llm.make_token_stream` with ``section_start`` / ``section_end``
SSE bookends.
"""

import logging

from services import llm

logger = logging.getLogger(__name__)

SECTIONS = ["concept", "world", "characters", "outline", "foreshadowing"]

SYSTEM_PROMPT = (
    "你是一位资深小说架构师, 精通网文结构设计与节奏把控。"
    "请根据用户的需求, 逐步构建完整的小说框架。"
    "输出纯文本, 不要使用 Markdown 标题标记 (# / ## 等), 用换行分段即可。"
)

# ---- per-section Chinese user prompts -----------------------------------

_SECTION_PROMPTS = {
    "concept": (
        "请为小说《{title}》设计核心概念, 包括:\n"
        "1. 主题 (这部小说想表达什么)\n"
        "2. 核心冲突 (主角面临的主要矛盾)\n"
        "3. 一句话卖点 (最能吸引读者的概括)\n"
        "类型: {genre} | 风格: {style} | 目标字数: {target_words}"
    ),
    "world": (
        "请为小说《{title}》设计世界观设定, 包括:\n"
        "1. 时代背景\n"
        "2. 主要地点\n"
        "3. 力量体系 (如有)\n"
        "4. 社会结构\n"
        "类型: {genre} | 风格: {style}"
    ),
    "characters": (
        "请为小说《{title}》设计主要角色表。\n"
        "每个角色单独一段, 以【角色】名字 开头, 包含:\n"
        "名字 / 定位 / 性格 / 动机 / 成长弧线。\n"
        "类型: {genre} | 风格: {style}"
    ),
    "outline": (
        "请为小说《{title}》规划三幕结构与卷章大纲。\n"
        "格式: 第N卷 / 第N章 标题 — 一句话概要\n"
        "预估总章数按目标字数 {target_words} 除以每章 5000 字计算。\n"
        "类型: {genre} | 风格: {style}"
    ),
    "foreshadowing": (
        "请为小说《{title}》设计伏笔计划。\n"
        "每条伏笔单独一段, 以【伏笔】开头, 包含:\n"
        "描述 / 埋设位置 (第几卷第几章) / 回收位置。\n"
        "类型: {genre} | 风格: {style}"
    ),
}


def _format_inputs(inputs):
    """Return (title, genre, style, target_words, reference_novel, confirmed)."""
    return (
        inputs.get("title", "未命名"),
        inputs.get("genre", "未指定"),
        inputs.get("style", "未指定"),
        inputs.get("target_words", "未指定"),
        inputs.get("reference_novel"),
        inputs.get("confirmed", {}),
    )


def _confirmed_context(confirmed):
    """Render previously confirmed sections as context for the next prompt."""
    if not confirmed:
        return ""
    parts = []
    for sec in SECTIONS:
        text = confirmed.get(sec)
        if text:
            parts.append(f"【已确认 - {sec}】\n{text}")
    return "\n\n".join(parts)


def build_section_prompt(section, inputs):
    """Build the messages list for a given section.

    ``inputs`` is a dict with keys: title, genre, style, target_words,
    reference_novel (optional), confirmed (dict of previous section texts).
    Returns a list of ``{"role": ..., "content": ...}`` dicts ready for
    the LLM chat API.
    """
    title, genre, style, target_words, reference_novel, confirmed = _format_inputs(inputs)

    template = _SECTION_PROMPTS.get(section)
    if template is None:
        raise ValueError(f"unknown section: {section}")

    user_prompt = template.format(
        title=title,
        genre=genre,
        style=style,
        target_words=target_words,
    )

    if reference_novel:
        user_prompt += f"\n\n参考小说: 《{reference_novel}》, 参考其节奏与结构但不抄袭。"

    ctx = _confirmed_context(confirmed)
    if ctx:
        user_prompt = f"以下是已确认的前序设定:\n\n{ctx}\n\n---\n\n{user_prompt}"

    return [
        {"role": "system", "content": SYSTEM_PROMPT},
        {"role": "user", "content": user_prompt},
    ]


def stream_section(section, inputs):
    """Generator yielding SSE frames for a section.

    Emits ``section_start`` → token frames → ``section_end`` (with full
    accumulated content).  Uses :func:`llm.make_token_stream` under the hood.
    """
    messages = build_section_prompt(section, inputs)
    yield llm._sse("section_start", {"section": section})

    parts = []

    def _on_token(t):
        parts.append(t)

    yield from llm.make_token_stream(messages, _on_token, max_tokens=4096)

    full_text = "".join(parts)
    yield llm._sse("section_end", {"section": section, "content": full_text})