"""用于把模型生成的 Markdown 摘要做轻量解析并用更产品化的方式展示。

说明：不引入复杂依赖，不要求模型输出 JSON。
通过解析固定标题：
- # 一句话结论
- # 关键决策要点（Top 5）
- # 主要风险（Top 3）
- # 下一步行动清单
- # 需要进一步澄清的问题

另外提供：
- render_opinion_preview：观点一句话预览
- summarize_round_opinions：把一轮的多角色观点压缩为可扫读摘要（用于时间线）
"""
from __future__ import annotations

import re
import streamlit as st


SECTION_TITLES = [
    "一句话结论",
    "关键决策要点（Top 5）",
    "主要风险（Top 3）",
    "下一步行动清单",
    "需要进一步澄清的问题",
]


def _split_sections(md: str) -> dict[str, str]:
    if not md:
        return {}

    pattern = r"^#\s+(.*)$"
    lines = md.splitlines()
    sections: dict[str, list[str]] = {}

    current = None
    for line in lines:
        m = re.match(pattern, line.strip())
        if m:
            title = m.group(1).strip()
            current = title
            sections.setdefault(current, [])
            continue
        if current is not None:
            sections[current].append(line)

    return {k: "\n".join(v).strip() for k, v in sections.items()}


def _extract_decision_badge(one_liner: str) -> tuple[str, str]:
    t = (one_liner or "").strip()
    if any(k in t for k in ["不建议", "否决", "不推荐"]):
        return ("不建议", "error")
    if any(k in t for k in ["谨慎", "有条件", "需要修改"]):
        return ("谨慎推进", "warning")
    if any(k in t for k in ["推荐", "可行", "建议推进"]):
        return ("推荐", "success")
    return ("结论", "info")


def render_decision_summary(md: str) -> None:
    """以更产品化的方式渲染结论摘要。"""
    sections = _split_sections(md)

    one_liner = sections.get("一句话结论", "").strip()
    badge_text, badge_kind = _extract_decision_badge(one_liner)

    col1, col2 = st.columns([1, 5])
    with col1:
        if badge_kind == "success":
            st.success(badge_text)
        elif badge_kind == "warning":
            st.warning(badge_text)
        elif badge_kind == "error":
            st.error(badge_text)
        else:
            st.info(badge_text)

    with col2:
        if one_liner:
            st.markdown(f"**{one_liner}**")
        else:
            st.markdown("**（未识别到一句话结论，请检查模型输出格式）**")

    st.markdown("---")

    c1, c2 = st.columns(2)

    with c1:
        st.subheader("🎯 关键决策要点")
        content = sections.get("关键决策要点（Top 5）", "").strip()
        st.markdown(content if content else "（暂无）")

        st.subheader("✅ 下一步行动")
        actions = sections.get("下一步行动清单", "").strip()
        items = [
            re.sub(r"^-\s*\[.\]\s*", "", l).strip()
            for l in actions.splitlines()
            if l.strip().startswith("-")
        ]
        if items:
            for i, it in enumerate(items):
                st.checkbox(it, value=False, key=f"action_{i}")
        else:
            st.markdown(actions if actions else "（暂无）")

    with c2:
        st.subheader("⚠️ 主要风险")
        risks = sections.get("主要风险（Top 3）", "").strip()
        st.markdown(risks if risks else "（暂无）")

        st.subheader("❓ 需要澄清")
        qs = sections.get("需要进一步澄清的问题", "").strip()
        st.markdown(qs if qs else "（暂无）")


def render_opinion_preview(opinion: str, limit: int = 160) -> str:
    """返回用于预览的短文本。"""
    if not opinion:
        return ""
    t = opinion.strip().replace("\n", " ")
    if len(t) <= limit:
        return t
    return t[:limit].rstrip() + "..."


def summarize_round_opinions(opinions: dict, role_descriptions: dict, limit_per_role: int = 60) -> str:
    """把一轮 opinions 生成适合扫读的摘要（不调用模型，避免额外成本与不稳定）。

    输出格式示例：
    - 📊 产品经理：xxx...
    - 💻 技术专家：xxx...
    """
    if not opinions:
        return ""

    lines: list[str] = []
    for role, text in opinions.items():
        info = role_descriptions.get(role, {"icon": "👤", "name": role})
        preview = render_opinion_preview(str(text or ""), limit=limit_per_role)
        if not preview:
            preview = "（本轮未生成/生成失败）"
        lines.append(f"- {info['icon']} {info['name']}：{preview}")

    return "\n".join(lines)