from datetime import datetime
from typing import Dict, List, Optional

from pgvector.sqlalchemy import Vector
from sqlalchemy import Column
from sqlmodel import JSON, Field, SQLModel


class FoodItem(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    name: str = Field(index=True)
    calories: float
    protein: float
    carbs: float
    fats: float
    micros: Dict = Field(default={}, sa_column=Column(JSON))
    embedding: List[float] = Field(sa_column=Column(Vector(1536)))  # OpenAI embedding size


class FoodLog(SQLModel, table=True):
    id: Optional[int] = Field(default=None, primary_key=True)
    user_id: int = Field(foreign_key="user.id")
    food_item_id: Optional[int] = Field(default=None, foreign_key="fooditem.id")
    name: str  # In case no food item is linked or custom entry
    calories: float
    protein: float
    carbs: float
    fats: float
    image_url: Optional[str] = None
    timestamp: datetime = Field(default_factory=datetime.utcnow)