#!/usr/bin/env python3
"""
Prompt 验收脚本（修改 prompts/ 后运行）。

  ./310py/bin/python prompts/smoke.py           # 仅校验文件加载与渲染（无需 API Key）
  ./310py/bin/python prompts/smoke.py --live    # 调用模型跑 smoke/reviews.yaml（需 DEEPSEEK_API_KEY）
"""
from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path

PROJECT_ROOT = Path(__file__).resolve().parent.parent
if str(PROJECT_ROOT) not in sys.path:
    sys.path.insert(0, str(PROJECT_ROOT))

import yaml

from prompts.loader import (
    build_batch_extraction_system,
    build_batch_extraction_user,
    get_schema,
    product_feedback_categories,
    validate_prompt_files,
)


def _load_smoke_reviews() -> dict:
    path = Path(__file__).resolve().parent / "smoke/reviews.yaml"
    with path.open(encoding="utf-8") as f:
        return yaml.safe_load(f) or {}


def run_dry() -> int:
    errors = validate_prompt_files()
    if errors:
        print("❌ Prompt 文件校验失败：")
        for e in errors:
            print(f"  - {e}")
        return 1
    smoke = _load_smoke_reviews()
    industry = smoke.get("industry", "Test")
    product = smoke.get("product_name", "Test")
    reviews = smoke.get("reviews") or []
    keys = [str(r["id"]) for r in reviews]
    tagged = "\n".join(f"[{r['id']}] {r['text']}" for r in reviews)
    keys_literal = ", ".join(json.dumps(k) for k in keys)
    system = build_batch_extraction_system(
        industry,
        product,
        n_keys=len(keys),
        keys_literal=keys_literal,
    )
    user = build_batch_extraction_user(
        n_keys=len(keys),
        keys_literal=keys_literal,
        tagged_input=tagged,
    )
    print("✅ 外置 Prompt 加载与渲染通过")
    print(f"   示例评论数: {len(reviews)}")
    print(f"   system 长度: {len(system)} 字符")
    print(f"   user 长度: {len(user)} 字符")
    return 0


def run_live() -> int:
    rc = run_dry()
    if rc != 0:
        return rc
    try:
        from 结构化_server import _call_dashscope_chat, _validate_extraction_strict
    except ImportError as e:
        print(f"❌ 无法导入结构化_server: {e}")
        return 1

    smoke = _load_smoke_reviews()
    industry = smoke["industry"]
    product = smoke["product_name"]
    reviews = smoke["reviews"]
    keys = [str(r["id"]) for r in reviews]
    tagged = "\n".join(f"[{r['id']}] {r['text']}" for r in reviews)
    keys_literal = ", ".join(json.dumps(k) for k in keys)
    system = build_batch_extraction_system(
        industry,
        product,
        n_keys=len(keys),
        keys_literal=keys_literal,
    )
    user = build_batch_extraction_user(
        n_keys=len(keys),
        keys_literal=keys_literal,
        tagged_input=tagged,
    )
    print("⏳ 调用模型进行 smoke 结构化…")
    try:
        raw = _call_dashscope_chat(
            [{"role": "system", "content": system}, {"role": "user", "content": user}],
            max_tokens=8192,
        )
    except Exception as e:
        print(f"❌ 模型调用失败: {e}")
        return 1

    if not isinstance(raw, dict):
        print(f"❌ 期望 JSON 对象，得到: {type(raw).__name__}")
        return 1

    ok = 0
    for k in keys:
        if k not in raw:
            print(f"❌ 缺少键 {k}")
            continue
        try:
            _validate_extraction_strict(raw[k], k)
            ok += 1
            print(f"✅ {k} 校验通过")
        except ValueError as e:
            print(f"❌ {k} 校验失败: {e}")

    schema_cats = product_feedback_categories(get_schema())
    print(f"   锁定 category 枚举: {', '.join(sorted(schema_cats))}")
    return 0 if ok == len(keys) else 1


def main() -> int:
    parser = argparse.ArgumentParser(description="Prompt smoke 验收")
    parser.add_argument(
        "--live",
        action="store_true",
        help="调用 DashScope 跑 smoke 评论（需 API Key）",
    )
    args = parser.parse_args()
    return run_live() if args.live else run_dry()


if __name__ == "__main__":
    raise SystemExit(main())