"""销量预测/分级/新品判断函数"""
from datetime import datetime
from dateutil.relativedelta import relativedelta
from services.db import query
from services.cache import RefreshableCache, register_cache


def is_new_product(created_at_str):
    """判断单品是否为新品（创建时间在5个月以内）"""
    if not created_at_str:
        return '返单品'
    try:
        if isinstance(created_at_str, str):
            created = datetime.strptime(created_at_str[:10], '%Y-%m-%d')
        else:
            created = created_at_str
        if hasattr(created, 'strftime'):
            five_months_ago = datetime.now() - relativedelta(months=5)
            return '新品' if created >= five_months_ago else '返单品'
        return '返单品'
    except:
        return '返单品'


def _load_global_config():
    """加载全局配置"""
    try:
        gc_rows = query("SELECT config_key, config_value FROM global_config")
        return {r['config_key']: r['config_value'] for r in gc_rows}
    except:
        return {
            'safety_stock_days': '7',
            'suggested_return_days': '15',
        }


# 全局配置缓存（文件共享，所有 worker 共用）
_global_config_cache_obj = RefreshableCache(maxsize=1)
register_cache(_global_config_cache_obj)


@_global_config_cache_obj.wrap
def get_global_config():
    """缓存版全局配置（支持主动刷新）"""
    return _load_global_config()


def refresh_global_config():
    """刷新全局配置缓存"""
    _global_config_cache_obj.refresh()


def _sales_predict_config():
    """获取日销量预测基础配置参数"""
    rows = query("SELECT * FROM sales_predict_config ORDER BY id")
    return rows


# 预测配置缓存
predict_config_cache = RefreshableCache(maxsize=1)
register_cache(predict_config_cache)


@predict_config_cache.wrap
def sales_predict_config_cached():
    """缓存版预测配置（支持主动刷新）"""
    return _sales_predict_config()


def refresh_sales_predict_config():
    """刷新销量预测配置缓存，使保存后的权重立即对所有 worker 生效。"""
    predict_config_cache.refresh()


def get_product_grade(monthly_sales, product_name='', product_type=''):
    """根据月销量进行产品分级"""
    # 赠品/晒图卡不参与评级
    if product_type == '赠品' or '晒图卡' in (product_name or ''):
        return '不参与评级'

    # 小样最高C级
    if product_type == '小样':
        return 'C级'

    try:
        ms = float(monthly_sales)
    except:
        return 'C级'
    if ms >= 10000:
        return 'S级'
    elif ms >= 5000:
        return 'A级'
    elif ms >= 3000:
        return 'B级'
    else:
        return 'C级'


def compute_daily_forecast(row, sales_predict_map):
    """计算日销量预测值"""
    avg3 = float(row.get('3天平均销量', 0) or 0)
    avg7 = float(row.get('7天平均销量', 0) or 0)
    avg14 = float(row.get('14天平均销量', 0) or 0)
    max30 = float(row.get('30天最高销量', 0) or 0)
    trend = row.get('销售趋势', '平稳趋势')

    if avg3 == 0 and avg7 == 0 and avg14 == 0 and max30 == 0:
        return 0

    # 根据趋势类型获取预测系数
    cfg = sales_predict_map.get(trend, {})
    w3 = float(cfg.get('avg_3d', 0.5))
    w7 = float(cfg.get('avg_7d', 0.3))
    w14 = float(cfg.get('avg_14d', 0.2))
    adj = float(cfg.get('adjust_factor', 1.0))

    # 计算加权预测值
    forecast = (avg3 * w3 + avg7 * w7 + avg14 * w14) * adj
    return round(forecast)