# -*- coding: utf-8 -*-
"""Create only a blank manual-decision review template.

This script is intentionally not allowed to generate final human_decision_*,
decision_time, or decision_source fields. The V1 external manual decision source
must be filled by case_analysis.analyst / laoan outside this script, then applied
with tools/apply_manual_decision_repair.py.
"""

from __future__ import annotations

import csv
import json
from pathlib import Path

import pandas as pd


ROOT = Path(__file__).resolve().parents[1]
WORKBOOK = "manual_decision_review_workbook.csv"
TEMPLATE = "manual_decision_external_source_template.csv"


def read_csv(name: str) -> pd.DataFrame:
    return pd.read_csv(ROOT / name, dtype=str, keep_default_na=False).fillna("")


def code_reason(row: pd.Series) -> str:
    return row.get("code_evidence_reason_cn", "") or row.get("code_suggested_reason_cn", "") or row.get("human_decision_reason_cn", "")


def build_workbook() -> pd.DataFrame:
    strict = read_csv("strict_sell_candidate_ledger.csv")
    rolling = read_csv("rolling_low_buy_candidate_ledger.csv")
    rows: list[dict[str, str]] = []

    for _, row in strict.iterrows():
        rows.append(
            {
                "artifact_type": "STRICT_SELL_SIGNAL",
                "signal_id": row.get("signal_id", ""),
                "rolling_signal_id": "",
                "case_id": row.get("case_id", ""),
                "candidate_id": row.get("candidate_id", ""),
                "symbol": row.get("symbol", ""),
                "signal_type": row.get("signal_type", ""),
                "trade_date": row.get("observation_trade_date", ""),
                "trade_time": row.get("candidate_time", ""),
                "code_suggested_action": row.get("code_suggested_action", ""),
                "code_suggested_reason_cn": code_reason(row),
                "review_input_chart_path": row.get("review_input_chart_path", "") or row.get("chart_path", ""),
                "numeric_context": json.dumps(
                    {
                        "gain_pct": row.get("gain_pct", ""),
                        "action_price": row.get("action_price", ""),
                        "entry_support_price": row.get("entry_support_price", ""),
                        "trend_first3_time": row.get("trend_first3_time", ""),
                        "fast_breakout_minutes": row.get("fast_breakout_minutes", ""),
                    },
                    ensure_ascii=False,
                ),
            }
        )

    for _, row in rolling.iterrows():
        rows.append(
            {
                "artifact_type": "ROLLING_LOW_BUY_SIGNAL",
                "signal_id": "",
                "rolling_signal_id": row.get("rolling_signal_id", ""),
                "case_id": row.get("case_id", ""),
                "candidate_id": row.get("candidate_id", ""),
                "symbol": row.get("symbol", ""),
                "signal_type": row.get("signal_type", ""),
                "trade_date": row.get("rolling_trade_date", ""),
                "trade_time": row.get("rolling_time", ""),
                "code_suggested_action": row.get("code_suggested_action", ""),
                "code_suggested_reason_cn": code_reason(row),
                "review_input_chart_path": row.get("review_input_chart_path", "") or row.get("chart_path", ""),
                "numeric_context": json.dumps(
                    {
                        "rolling_price": row.get("rolling_price", ""),
                        "ma5_close": row.get("ma5_close", ""),
                        "near_ma5_pct": row.get("near_ma5_pct", ""),
                        "volume_ratio_vs_prev20m": row.get("volume_ratio_vs_prev20m", ""),
                    },
                    ensure_ascii=False,
                ),
            }
        )

    workbook = pd.DataFrame(rows)
    workbook.to_csv(ROOT / WORKBOOK, index=False, encoding="utf-8-sig")
    return workbook


def build_blank_template(workbook: pd.DataFrame) -> pd.DataFrame:
    fieldnames = [
        "external_decision_id",
        "artifact_type",
        "signal_id",
        "rolling_signal_id",
        "case_id",
        "candidate_id",
        "symbol",
        "code_suggested_action",
        "accept_code_suggestion_flag",
        "human_decision_action",
        "human_decision_reason_cn",
        "decision_operator",
        "decision_time",
        "decision_source",
        "review_input_chart_path",
        "review_input_chart_sha256",
        "manual_draft_path",
        "manual_draft_sha256",
        "reviewer_notes",
    ]
    rows = []
    for seq, (_, row) in enumerate(workbook.iterrows(), start=1):
        rows.append(
            {
                "external_decision_id": f"EXT-MANUAL-RUN-ANA-WUJI-STRICT-SELL-ROLLING-REPAIR-20260609-001-{seq:06d}",
                "artifact_type": row.get("artifact_type", ""),
                "signal_id": row.get("signal_id", ""),
                "rolling_signal_id": row.get("rolling_signal_id", ""),
                "case_id": row.get("case_id", ""),
                "candidate_id": row.get("candidate_id", ""),
                "symbol": row.get("symbol", ""),
                "code_suggested_action": row.get("code_suggested_action", ""),
                "accept_code_suggestion_flag": "",
                "human_decision_action": "",
                "human_decision_reason_cn": "",
                "decision_operator": "",
                "decision_time": "",
                "decision_source": "",
                "review_input_chart_path": row.get("review_input_chart_path", ""),
                "review_input_chart_sha256": "",
                "manual_draft_path": "",
                "manual_draft_sha256": "",
                "reviewer_notes": "",
            }
        )
    template = pd.DataFrame(rows, columns=fieldnames)
    template.to_csv(ROOT / TEMPLATE, index=False, encoding="utf-8-sig", quoting=csv.QUOTE_MINIMAL)
    return template


def main() -> None:
    workbook = build_workbook()
    template = build_blank_template(workbook)
    print(
        json.dumps(
            {
                "workbook": WORKBOOK,
                "template": TEMPLATE,
                "rows": len(template),
                "note": "Template only. Fill manual_decision_external_source_ledger.csv manually before running apply_manual_decision_repair.py.",
            },
            ensure_ascii=False,
        )
    )


if __name__ == "__main__":
    main()