"""
在流程结束后写入**内容策略表格** JSON。

输出路径：{OUTPUT_DIR}/{trace_id}/process_trace.json
"""

from __future__ import annotations

import json
import logging
import os
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

from agent.tools import tool, ToolResult
from utils.tool_logging import format_tool_result_for_log, log_tool_call

_LOG_LABEL = "工具调用：exec_summary -> 写入过程 trace JSON"

logger = logging.getLogger(__name__)


def _output_dir_path() -> Path:
    # 与 store_results_mysql / output.json 目录约定一致
    return Path(os.getenv("OUTPUT_DIR", ".cache/output"))


def _parse_payload(summary_json: str) -> Dict[str, Any]:
    """
    解析并规范化 LLM 传入的表格数据。

    - 如果是数组：视为“表格行列表”，包成 {"rows": [...]}
    - 如果是对象：直接返回（用于后续扩展字段）
    """
    data = json.loads(summary_json)
    if isinstance(data, list):
        return {"rows": data}
    if not isinstance(data, dict):
        raise ValueError("summary_json 解析后必须是 JSON 对象或数组")
    return data


def _split_input_features(raw: str) -> List[str]:
    s = (raw or "").strip()
    if not s:
        return []
    parts = s.replace("，", ",").split(",")
    out: List[str] = []
    for p in parts:
        t = p.strip()
        if t:
            out.append(t)
    return out


def _load_output_json(*, trace_id: str) -> Optional[Dict[str, Any]]:
    path = _output_dir_path() / trace_id / "output.json"
    try:
        with path.open("r", encoding="utf-8") as f:
            data = json.load(f)
    except FileNotFoundError:
        return None
    except Exception:
        logger.warning("读取 output.json 失败: %s", str(path), exc_info=True)
        return None
    return data if isinstance(data, dict) else None


def _extract_contents(*, trace_id: str) -> List[Dict[str, Any]]:
    """
    从 output.json 读取最终入选 contents。

    约定：
    - 只允许对 output.json.contents 内的 aweme_id 生成/写入 process_trace rows
    """
    output_json = _load_output_json(trace_id=trace_id) or {}
    contents = output_json.get("contents")
    if not isinstance(contents, list):
        return []
    out: List[Dict[str, Any]] = []
    for item in contents:
        if isinstance(item, dict):
            out.append(item)
    return out


def _map_strategy_type(value: Any) -> str:
    v = str(value or "").strip()
    if v in ("case_based", "case", "case出发"):
        return "case出发"
    if v in ("feature_based", "feature", "特征出发"):
        return "特征出发"
    return v


def _map_channel(value: Any) -> str:
    v = str(value or "").strip()
    mapping = {
        "search": "抖音搜索",
        "author": "订阅账号",
        "ranking": "榜单",
        "other": "其他",
        "抖音搜索": "抖音搜索",
        "订阅账号": "订阅账号",
        "榜单": "榜单",
        "其他": "其他",
    }
    return mapping.get(v, v)


def _map_decision_basis(value: Any) -> str:
    v = str(value or "").strip()
    mapping = {
        "content_portrait": "内容画像匹配",
        "author_portrait": "作者画像匹配",
        "demand_filtering": "需求筛选",
        "other": "其他",
        "画像缺失": "画像缺失",
        "内容画像匹配": "内容画像匹配",
        "作者画像匹配": "作者画像匹配",
        "需求筛选": "需求筛选",
        "其他": "其他",
    }
    return mapping.get(v, v)


def _infer_decision_basis_from_output_content(content: Dict[str, Any]) -> str:
    portrait = content.get("portrait_data") or {}
    source = str(portrait.get("source") or "").strip()
    if source == "content_like":
        return "内容画像匹配"
    if source == "account_fans":
        return "作者画像匹配"
    if source == "none":
        return "画像缺失"
    return ""


def _build_base_row(*, trace_id: str, content: Dict[str, Any], input_features: List[str], query: str) -> Dict[str, Any]:
    return {
        "trace_id": trace_id,
        "aweme_id": str(content.get("aweme_id") or "").strip(),
        "title": str(content.get("title") or "").strip(),
        "author_nickname": str(content.get("author_nickname") or "").strip(),
        "strategy_type": "",
        "from_case_aweme_id": "",
        "from_case_point": "",
        "from_feature": "",
        "search_keyword": str(query or "").strip(),
        "channel": "抖音搜索",
        "decision_basis": _infer_decision_basis_from_output_content(content),
        "decision_notes": str(content.get("reason") or "").strip(),
        "input_features": input_features,
    }


_ROW_KEYS: Tuple[str, ...] = (
    "trace_id",
    "aweme_id",
    "title",
    "author_nickname",
    "strategy_type",
    "from_case_aweme_id",
    "from_case_point",
    "from_feature",
    "search_keyword",
    "channel",
    "decision_basis",
    "decision_notes",
    "input_features",
)


def _sanitize_row(row: Dict[str, Any]) -> Dict[str, Any]:
    """只保留固定字段，并把枚举值规范成中文。"""
    out: Dict[str, Any] = {k: row.get(k, "") for k in _ROW_KEYS}
    out["strategy_type"] = _map_strategy_type(out.get("strategy_type"))
    out["channel"] = _map_channel(out.get("channel"))
    out["decision_basis"] = _map_decision_basis(out.get("decision_basis"))
    # input_features 规范为 list[str]
    feats = out.get("input_features")
    if isinstance(feats, list):
        out["input_features"] = [str(x).strip() for x in feats if str(x).strip()]
    elif isinstance(feats, str):
        out["input_features"] = _split_input_features(feats)
    else:
        out["input_features"] = []
    return out


def _normalize_payload(*, trace_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    # tool 只做最小职责：过滤/补全/规范化；复杂推理由 skill 生成 summary_json 来完成
    raw_rows = payload.get("rows")
    rows_in_payload: List[Dict[str, Any]] = []
    if isinstance(raw_rows, list):
        for item in raw_rows:
            if isinstance(item, dict):
                rows_in_payload.append(item)

    output_json = _load_output_json(trace_id=trace_id) or {}
    query = str(output_json.get("query") or "").strip()
    input_features = _split_input_features(query)
    contents = _extract_contents(trace_id=trace_id)
    contents_by_aweme_id: Dict[str, Dict[str, Any]] = {
        str(c.get("aweme_id") or "").strip(): c for c in contents if str(c.get("aweme_id") or "").strip()
    }

    # 先把 payload rows 归并到 aweme_id
    payload_by_aweme_id: Dict[str, Dict[str, Any]] = {}
    for r in rows_in_payload:
        aweme_id = str(r.get("aweme_id") or r.get("awemeId") or "").strip()
        if not aweme_id:
            continue
        payload_by_aweme_id[aweme_id] = dict(r)

    # 只允许 payload 覆盖“策略/来源/解释”字段，避免覆盖 output.json.contents 的身份字段（title/author 等）
    allowed_payload_keys: set[str] = {
        "strategy_type",
        "from_case_aweme_id",
        "from_case_point",
        "from_feature",
        "search_keyword",
        "channel",
        "decision_basis",
        "decision_notes",
        "input_features",
    }

    # 兼容 payload 的常见别名/驼峰 key（模型输出不稳定时，尽量不丢信息）
    alias_map: Dict[str, Tuple[str, ...]] = {
        "strategy_type": ("strategy_type", "strategyType"),
        "from_case_aweme_id": ("from_case_aweme_id", "fromCaseAwemeId", "case_aweme_id", "caseAwemeId"),
        "from_case_point": ("from_case_point", "fromCasePoint", "case_point", "casePoint"),
        "from_feature": ("from_feature", "fromFeature", "feature", "from_feature_name"),
        "search_keyword": ("search_keyword", "searchKeyword", "keyword"),
        "channel": ("channel", "source_channel", "sourceChannel", "source"),
        "decision_basis": ("decision_basis", "decisionBasis"),
        "decision_notes": ("decision_notes", "decisionNotes", "notes"),
        "input_features": ("input_features", "inputFeatures"),
    }

    def _pick(provided: Dict[str, Any], key: str) -> Any:
        for k in alias_map.get(key, (key,)):
            if k in provided:
                return provided.get(k)
        return None

    normalized: List[Dict[str, Any]] = []
    for aweme_id, content in contents_by_aweme_id.items():
        base = _build_base_row(trace_id=trace_id, content=content, input_features=input_features, query=query)
        provided = payload_by_aweme_id.get(aweme_id) or {}

        merged = dict(base)
        # 只合并允许覆盖的字段
        for k in allowed_payload_keys:
            v = _pick(provided, k)
            if v is not None:
                merged[k] = v

        # 身份字段强制以 output.json.contents 为准（即使 payload 传了也不采纳）
        merged["aweme_id"] = str(content.get("aweme_id") or "").strip()
        merged["title"] = str(content.get("title") or "").strip()
        merged["author_nickname"] = str(content.get("author_nickname") or "").strip()

        # 如果缺失 input_features，用 query 拆分补齐
        if "input_features" not in merged or not merged.get("input_features"):
            merged["input_features"] = input_features

        normalized.append(_sanitize_row(merged))

    # 保持稳定顺序：按 rank（若有）或 aweme_id
    def _sort_key(r: Dict[str, Any]) -> Tuple[int, str]:
        c = contents_by_aweme_id.get(str(r.get("aweme_id") or "").strip()) or {}
        try:
            rank = int(c.get("rank") or 0)
        except Exception:
            rank = 0
        return (rank if rank > 0 else 10**9, str(r.get("aweme_id") or ""))

    normalized.sort(key=_sort_key)
    return {"rows": normalized}


def _write_process_trace(*, trace_id: str, payload: Dict[str, Any]) -> Path:
    out_dir = _output_dir_path() / trace_id
    out_dir.mkdir(parents=True, exist_ok=True)
    path = out_dir / "process_trace.json"
    # 输出格式收敛：只允许 {"rows": [...]}
    doc = {"rows": payload.get("rows") or []}
    with path.open("w", encoding="utf-8") as f:
        json.dump(doc, f, ensure_ascii=False, indent=2)
    return path


@tool(
    description=(
        "在**全部流程执行完毕之后**调用：把每条最终入选内容的「选择策略」整理成表格 JSON，"
        "写入当前任务的 output 目录下的 process_trace.json，便于后续复盘。"
        "参数 summary_json 为 JSON 字符串，可以是数组或对象（对象需包含 rows）。"
        "可选参数 log_path/log_text 用于传入本次运行日志（便于复盘留档/未来扩展）。"
    ),
)
async def exec_summary(
    trace_id: str,
    summary_json: str,
    log_path: str = "",
    log_text: str = "",
) -> ToolResult:
    call_params = {
        "trace_id": trace_id,
        "summary_json": "<json>",
        "log_path": (log_path or "").strip(),
        "log_text": "<text>",
    }
    tid = (trace_id or "").strip()
    if not tid:
        err = ToolResult(
            title="过程摘要",
            output="trace_id 不能为空",
            metadata={"ok": False, "error": "empty trace_id"},
        )
        log_tool_call(_LOG_LABEL, call_params, format_tool_result_for_log(err))
        return err

    try:
        payload = _parse_payload(summary_json)
    except json.JSONDecodeError as e:
        err = ToolResult(
            title="过程摘要",
            output=f"summary_json 不是合法 JSON: {e}",
            metadata={"ok": False, "error": str(e)},
        )
        log_tool_call(_LOG_LABEL, call_params, format_tool_result_for_log(err))
        return err
    except ValueError as e:
        err = ToolResult(
            title="过程摘要",
            output=str(e),
            metadata={"ok": False, "error": str(e)},
        )
        log_tool_call(_LOG_LABEL, call_params, format_tool_result_for_log(err))
        return err

    payload = _normalize_payload(trace_id=tid, payload=payload)

    try:
        path = _write_process_trace(trace_id=tid, payload=payload)
    except OSError as e:
        msg = f"写入 process_trace.json 失败: {e}"
        logger.error(msg, exc_info=True)
        err = ToolResult(title="过程摘要", output=msg, metadata={"ok": False, "error": str(e)})
        log_tool_call(_LOG_LABEL, call_params, format_tool_result_for_log(err))
        return err

    out = ToolResult(
        title="过程摘要",
        output=f"已写入 {path}",
        metadata={
            "ok": True,
            "trace_id": tid,
            "path": str(path),
            "log_path": (log_path or "").strip(),
            "log_text_len": len((log_text or "").strip()),
        },
    )
    log_tool_call(_LOG_LABEL, {"trace_id": tid}, format_tool_result_for_log(out))
    return out