Spaces:
Build error
Build error
File size: 2,001 Bytes
4c4571b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 |
from pydantic import BaseModel, Field, computed_field, field_validator
from typing import Literal, Annotated
from config.city_tier import tier_1_cities, tier_2_cities
# pydantic model to validate incoming data
class UserInput(BaseModel):
age: Annotated[int, Field(..., gt=0, lt=120, description='Age of the user')]
weight: Annotated[float, Field(..., gt=0, description='Weight of the user')]
height: Annotated[float, Field(..., gt=0, lt=2.5, description='Height of the user')]
income_lpa: Annotated[float, Field(..., gt=0, description='Annual salary of the user in lpa')]
smoker: Annotated[bool, Field(..., description='Is user a smoker')]
city: Annotated[str, Field(..., description='The city that the user belongs to')]
occupation: Annotated[Literal['retired', 'freelancer', 'student', 'government_job',
'business_owner', 'unemployed', 'private_job'], Field(..., description='Occupation of the user')]
@field_validator('city')
@classmethod
def normalize_city(cls, v: str) -> str:
v = v.strip().title()
return v
@computed_field
@property
def bmi(self) -> float:
return self.weight/(self.height**2)
@computed_field
@property
def lifestyle_risk(self) -> str:
if self.smoker and self.bmi > 30:
return "high"
elif self.smoker or self.bmi > 27:
return "medium"
else:
return "low"
@computed_field
@property
def age_group(self) -> str:
if self.age < 25:
return "young"
elif self.age < 45:
return "adult"
elif self.age < 60:
return "middle_aged"
return "senior"
@computed_field
@property
def city_tier(self) -> int:
if self.city in tier_1_cities:
return 1
elif self.city in tier_2_cities:
return 2
else:
return 3 |