"""食谱模型 - 支持热量识别与营养分析"""
from .base import Base
from sqlalchemy.orm import Mapped, mapped_column
from sqlalchemy import BigInteger, Integer, VARCHAR, Text, Float, JSON
from typing import Optional


class Recipe(Base):
    __tablename__ = "recipes"

    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    name: Mapped[str] = mapped_column(VARCHAR(200), nullable=False)
    description: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
    image_url: Mapped[Optional[str]] = mapped_column(VARCHAR(500), nullable=True)
    # 营养信息
    calories: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="热量（千卡）")
    protein: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="蛋白质（克）")
    fat: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="脂肪（克）")
    carbs: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="碳水化合物（克）")
    fiber: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="膳食纤维（克）")
    # AI 识别相关
    ai_recognition_confidence: Mapped[Optional[float]] = mapped_column(
        Float, nullable=True, comment="AI 识别置信度 0-1"
    )
    ai_analysis_result: Mapped[Optional[dict]] = mapped_column(
        JSON, nullable=True, comment="AI 识别完整结果"
    )
    # 分类
    category: Mapped[Optional[str]] = mapped_column(VARCHAR(50), nullable=True, comment="菜品分类")
    cuisine: Mapped[Optional[str]] = mapped_column(VARCHAR(50), nullable=True, comment="菜系")
    # 状态
    status: Mapped[str] = mapped_column(
        VARCHAR(20), nullable=False, default="draft", comment="draft/published/archived"
    )
    # 用户标注
    user_notes: Mapped[Optional[str]] = mapped_column(Text, nullable=True, comment="用户备注")
    is_favorite: Mapped[bool] = mapped_column(default=False, comment="是否收藏")