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from pydantic import BaseModel, EmailStr, Field
from typing import Optional, List
from enum import Enum
class UserRole(str, Enum):
ADMIN = "admin"
USER = "user"
class UserCreate(BaseModel):
username: str
email: EmailStr
password: str
class UserLogin(BaseModel):
username: str
password: str
class UserResponse(BaseModel):
id: int
username: str
email: str
role: UserRole
class Config:
from_attributes = True
class Token(BaseModel):
access_token: str
token_type: str
class TokenData(BaseModel):
username: Optional[str] = None
role: Optional[str] = None
class ProductCreate(BaseModel):
name: str
brand: Optional[str] = None
calories: float
protein: float
fat: float
carbohydrates: float
sodium: float
sugar: float
fiber: Optional[float] = None
cholesterol: Optional[float] = None
serving_size: Optional[str] = None
class ProductResponse(BaseModel):
id: int
name: str
brand: Optional[str]
calories: float
protein: float
fat: float
carbohydrates: float
sodium: float
sugar: float
fiber: Optional[float]
cholesterol: Optional[float]
serving_size: Optional[str]
image_path: Optional[str] = None
class Config:
from_attributes = True
class HealthIssueCreate(BaseModel):
issue_type: str = Field(..., description="Type of health issue (e.g., diabetes, hypertension, high cholesterol)")
severity: Optional[str] = None
notes: Optional[str] = None
class HealthIssueResponse(BaseModel):
id: int
user_id: int
issue_type: str
severity: Optional[str]
notes: Optional[str]
class Config:
from_attributes = True
class NutritionAnalysisResponse(BaseModel):
extracted_nutrition: dict
health_rating: float = Field(..., description="Product rating from 1-10")
health_recommendations: List[str]
suggested_alternatives: List[ProductResponse]
analysis_summary: str
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