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Set up backend and frontend structure for a health and fitness tracker using Python (FastAPI, SQLModel, DSPy) and React. Includes Docker and Compose configs, authentication, nutrition AI module, health/nutrition/user endpoints, database models, and basic frontend with routing and context. Enables tracking nutrition, health metrics, and user management, with architecture ready for future mobile and cloud deployment.
28 lines
1006 B
Python
28 lines
1006 B
Python
from datetime import datetime
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from typing import Optional, List, Dict
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from sqlmodel import Field, SQLModel, JSON
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from pgvector.sqlalchemy import Vector
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from sqlalchemy import Column
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class FoodItem(SQLModel, table=True):
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id: Optional[int] = Field(default=None, primary_key=True)
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name: str = Field(index=True)
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calories: float
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protein: float
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carbs: float
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fats: float
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micros: Dict = Field(default={}, sa_column=Column(JSON))
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embedding: List[float] = Field(sa_column=Column(Vector(1536))) # OpenAI embedding size
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class FoodLog(SQLModel, table=True):
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id: Optional[int] = Field(default=None, primary_key=True)
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user_id: int = Field(foreign_key="user.id")
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food_item_id: Optional[int] = Field(default=None, foreign_key="fooditem.id")
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name: str # In case no food item is linked or custom entry
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calories: float
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protein: float
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carbs: float
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fats: float
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image_url: Optional[str] = None
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timestamp: datetime = Field(default_factory=datetime.utcnow)
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