Initial project scaffolding for health tracker app

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.

backend/app/models/food.py

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+from datetime import datetime
+from typing import Optional, List, Dict
+from sqlmodel import Field, SQLModel, JSON
+from pgvector.sqlalchemy import Vector
+from sqlalchemy import Column
+
+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)