38 lines
1.9 KiB
Python
38 lines
1.9 KiB
Python
"""食谱模型 - 支持热量识别与营养分析"""
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from .base import Base
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from sqlalchemy.orm import Mapped, mapped_column
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from sqlalchemy import BigInteger, Integer, VARCHAR, Text, Float, JSON
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from typing import Optional
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class Recipe(Base):
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__tablename__ = "recipes"
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id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
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name: Mapped[str] = mapped_column(VARCHAR(200), nullable=False)
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description: Mapped[Optional[str]] = mapped_column(Text, nullable=True)
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image_url: Mapped[Optional[str]] = mapped_column(VARCHAR(500), nullable=True)
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# 营养信息
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calories: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="热量（千卡）")
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protein: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="蛋白质（克）")
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fat: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="脂肪（克）")
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carbs: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="碳水化合物（克）")
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fiber: Mapped[Optional[float]] = mapped_column(Float, nullable=True, comment="膳食纤维（克）")
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# AI 识别相关
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ai_recognition_confidence: Mapped[Optional[float]] = mapped_column(
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Float, nullable=True, comment="AI 识别置信度 0-1"
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)
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ai_analysis_result: Mapped[Optional[dict]] = mapped_column(
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JSON, nullable=True, comment="AI 识别完整结果"
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)
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# 分类
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category: Mapped[Optional[str]] = mapped_column(VARCHAR(50), nullable=True, comment="菜品分类")
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cuisine: Mapped[Optional[str]] = mapped_column(VARCHAR(50), nullable=True, comment="菜系")
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# 状态
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status: Mapped[str] = mapped_column(
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VARCHAR(20), nullable=False, default="draft", comment="draft/published/archived"
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)
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# 用户标注
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user_notes: Mapped[Optional[str]] = mapped_column(Text, nullable=True, comment="用户备注")
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is_favorite: Mapped[bool] = mapped_column(default=False, comment="是否收藏")
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