"""
报告整合器 —— 将多次分析结果合并为一份完整报告
"""
import json

from core.config import LLM_CONFIG
from core.utils import get_llm_client, llm_chat
from layers.context import AnalysisSession


CONSOLIDATE_PROMPT = """你是一个高级数据分析总监。下面是你的团队针对同一份数据做的多次分析，请整合为一份完整的综合报告。

## 核心问题
{question}

## 各次分析结果
{sections}

## 可用图表
{charts_text}

## 整合要求
1. **执行摘要**：3-5 句话概括全局结论
2. **核心发现**：从所有分析中提炼最重要的发现，去重，按重要性排列
3. **交叉洞察**：不同维度之间的关联
4. **图表引用**：用 `![标题](路径)` 嵌入相关段落
5. **风险与建议**：按优先级排列
6. **数据附录**：关键统计数字

中文，专业简报风格。先结论后细节。"""


class ReportConsolidator:
    """报告整合器"""

    def __init__(self):
        self.client, self.model = get_llm_client(LLM_CONFIG)

    def consolidate(self, sessions: list[AnalysisSession], question: str = "",
                    charts: list[dict] | None = None) -> str:
        if not sessions:
            return "（无分析数据可整合）"
        if not question:
            question = sessions[0].question

        sections = self._build_sections(sessions)
        charts_text = "\n".join(f"{i}. {c['title']}: {c['path']}" for i, c in enumerate(charts or [], 1)) or "无图表。"

        try:
            return llm_chat(
                self.client, self.model,
                messages=[
                    {"role": "system", "content": "你是高级数据分析总监，整合多维度分析结果。"},
                    {"role": "user", "content": CONSOLIDATE_PROMPT.format(question=question, sections=sections, charts_text=charts_text)},
                ],
                temperature=0.3, max_tokens=4096,
            )
        except Exception as e:
            print(f"  ⚠️ LLM 整合失败: {e}，使用拼接模式")
            return self._fallback_concat(sessions, charts)

    def _build_sections(self, sessions: list[AnalysisSession]) -> str:
        parts = []
        for i, session in enumerate(sessions, 1):
            section = f"### 分析 {i}: {session.question}\n"
            section += f"类型: {session.plan.get('analysis_type', '未知')}\n\n"
            for step in session.steps:
                if not step.success or not step.rows or step.action == "done":
                    continue
                section += f"- {step.purpose} ({step.row_count} 行)\n"
                section += f"  数据: {json.dumps(step.rows[:8], ensure_ascii=False)}\n\n"
            if session.insights:
                section += "#### 洞察\n" + "\n".join(f"- {i}" for i in session.insights) + "\n"
            parts.append(section)
        return "\n---\n".join(parts)

    def _fallback_concat(self, sessions: list[AnalysisSession], charts: list[dict] | None) -> str:
        parts = ["# 综合分析报告\n"]
        for i, s in enumerate(sessions, 1):
            parts.append(f"## 第 {i} 部分: {s.question}\n{s.report}\n")
        if charts:
            parts.append("\n## 可视化\n" + "\n".join(f"![{c['title']}]({c['path']})" for c in charts))
        return "\n".join(parts)