"""阈值智能推荐 API"""
from fastapi import APIRouter, Depends, Query
from sqlalchemy.orm import Session
from app.database import get_db
from app.auth_middleware import require_auth, require_role
from app.models import KPIDefinition, KPIValue, OperationLog
import json

router = APIRouter(prefix="/api/cma/thresholds", tags=["阈值分析"],
    dependencies=[Depends(require_role("ceo", "finance", "business", "it"))],
)

KPI_TYPES = {
    "higher_better": ["SALES_TOTAL", "CUSTOMER_COUNT", "SALES_PROFIT_RATE", 
                      "CUSTOMER_SATISFACTION", "ORDER_DELIVERY_RATE", "TRAINING_COMPLETION",
                      "RECEIVABLE_TURNOVER", "TURNOVER_RATE"],
    "lower_better": ["COST_CONTROL_RATE"],
    "middle_best": ["TOP5_CUSTOMER_RATIO"],
}

@router.get("/suggest/{kpi_id}")
def suggest_threshold(kpi_id: int, db: Session = Depends(get_db)):
    """根据历史数据自动推荐阈值"""
    kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
    if not kpi:
        return {"error": "KPI不存在"}
    
    values = db.query(KPIValue).filter(KPIValue.kpi_id == kpi_id).order_by(KPIValue.period.asc()).all()
    
    if not values:
        # 无历史数据，按行业标准推荐
        return suggest_by_type(kpi)
    
    nums = [v.actual_value for v in values if v.actual_value is not None]
    
    if len(nums) < 2:
        return suggest_by_type(kpi)
    
    avg = sum(nums) / len(nums)
    # 计算标准差
    variance = sum((x - avg) ** 2 for x in nums) / len(nums)
    std = variance ** 0.5
    
    target = kpi.target_value or avg
    
    # 根据KPI类型生成推荐区间
    if kpi.kpi_code in KPI_TYPES["higher_better"]:
        green_min = round(target * 0.8, 2)
        yellow_min = round(target * 0.5, 2)
        red_max = round(target * 0.5, 2)
        suggestion = {
            "type": "higher_better",
            "description": "越高越好型",
            "green": {"min": green_min, "max": None, "label": f">={green_min}"},
            "yellow": {"min": yellow_min, "max": green_min, "label": f"{yellow_min}~{green_min}"},
            "red": {"min": None, "max": red_max, "label": f"<{red_max}"},
            "current_avg": round(avg, 2),
            "target": target,
        }
    elif kpi.kpi_code in KPI_TYPES["lower_better"]:
        green_max = round(target * 1.2, 2)
        yellow_max = round(target * 2.0, 2)
        suggestion = {
            "type": "lower_better",
            "description": "越低越好型",
            "green": {"min": None, "max": green_max, "label": f"<={green_max}"},
            "yellow": {"min": green_max, "max": yellow_max, "label": f"{green_max}~{yellow_max}"},
            "red": {"min": yellow_max, "max": None, "label": f">{yellow_max}"},
            "current_avg": round(avg, 2),
            "target": target,
        }
    else:
        tolerance = max(std * 1.5, target * 0.2)
        suggestion = {
            "type": "middle_best",
            "description": "适中最好型",
            "green": {"min": round(target - tolerance, 2), "max": round(target + tolerance, 2), "label": f"{round(target-tolerance,2)}~{round(target+tolerance,2)}"},
            "yellow": {"min": round(target - tolerance*2, 2), "max": round(target + tolerance*2, 2), "label": f"偏离{(tolerance*2):.0f}%"},
            "red": {"min": None, "max": round(target - tolerance*2, 2), "label": f"偏离>{tolerance*2:.0f}%"},
            "current_avg": round(avg, 2),
            "target": target,
        }
    
    return {"kpi_id": kpi_id, "kpi_name": kpi.kpi_name, "suggestion": suggestion}

def suggest_by_type(kpi):
    """无历史数据时按类型推荐"""
    target = kpi.target_value or 100
    if kpi.kpi_code in KPI_TYPES["higher_better"]:
        return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
                "suggestion": {"type": "higher_better", "green": {"min": round(target*0.8,2)}, "yellow": {"min": round(target*0.5,2)}, "red": {"max": round(target*0.5,2)}}}
    elif kpi.kpi_code in KPI_TYPES["lower_better"]:
        return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
                "suggestion": {"type": "lower_better", "green": {"max": round(target*1.2,2)}, "yellow": {"max": round(target*2,2)}, "red": {"min": round(target*2,2)}}}
    else:
        return {"kpi_id": kpi.id, "kpi_name": kpi.kpi_name, "message": "无历史数据",
                "suggestion": {"type": "middle_best", "green": {"min": round(target*0.8,2), "max": round(target*1.2,2)}}}