my-gradio-app / health_data /pydantic_models.py
Nguyen Trong Lap
Recreate history without binary blobs
eeb0f9c
"""
Pydantic Models for Health Data
Provides automatic validation and parsing
"""
from datetime import datetime
from typing import Optional, List, Dict, Any, Union
from pydantic import BaseModel, Field, field_validator, model_validator
from enum import Enum
from .validators import HealthDataParser, HealthDataValidator
class Gender(str, Enum):
"""Gender enum"""
MALE = "male"
FEMALE = "female"
OTHER = "other"
class ActivityLevel(str, Enum):
"""Activity level enum"""
SEDENTARY = "sedentary" # Ít vận động
LIGHT = "light" # Vận động nhẹ
MODERATE = "moderate" # Vận động vừa
ACTIVE = "active" # Vận động nhiều
VERY_ACTIVE = "very_active" # Vận động rất nhiều
class FitnessLevel(str, Enum):
"""Fitness level enum"""
BEGINNER = "beginner"
INTERMEDIATE = "intermediate"
ADVANCED = "advanced"
class RecordType(str, Enum):
"""Health record type"""
NUTRITION = "nutrition"
EXERCISE = "exercise"
SYMPTOM = "symptom"
MENTAL_HEALTH = "mental_health"
GENERAL_HEALTH = "general_health"
class HealthRecord(BaseModel):
"""
Health Record with Pydantic validation
Automatically validates and normalizes health data
"""
record_id: str = Field(default_factory=lambda: str(__import__('uuid').uuid4()))
user_id: str
record_type: RecordType
data: Dict[str, Any] = Field(default_factory=dict)
timestamp: datetime = Field(default_factory=datetime.now)
agent_name: Optional[str] = None
confidence: float = Field(default=0.5, ge=0.0, le=1.0)
# Health metrics (optional, extracted from data)
height: Optional[float] = Field(None, description="Height in cm")
weight: Optional[float] = Field(None, description="Weight in kg")
age: Optional[int] = Field(None, description="Age in years")
gender: Optional[Gender] = None
bmi: Optional[float] = Field(None, description="BMI")
class Config:
use_enum_values = True
json_encoders = {
datetime: lambda v: v.isoformat()
}
@field_validator('height', mode='before')
@classmethod
def parse_height(cls, v):
"""Parse height from various formats"""
if v is None:
return None
parsed = HealthDataParser.parse_height(v)
if parsed is not None:
is_valid, error = HealthDataValidator.validate_height(parsed)
if not is_valid:
raise ValueError(error)
return parsed
@field_validator('weight', mode='before')
@classmethod
def parse_weight(cls, v):
"""Parse weight from various formats"""
if v is None:
return None
parsed = HealthDataParser.parse_weight(v)
if parsed is not None:
is_valid, error = HealthDataValidator.validate_weight(parsed)
if not is_valid:
raise ValueError(error)
return parsed
@field_validator('age', mode='before')
@classmethod
def parse_age(cls, v):
"""Parse age from various formats"""
if v is None:
return None
parsed = HealthDataParser.parse_age(v)
if parsed is not None:
is_valid, error = HealthDataValidator.validate_age(parsed)
if not is_valid:
raise ValueError(error)
return parsed
@field_validator('bmi', mode='before')
@classmethod
def parse_bmi(cls, v):
"""Parse BMI"""
if v is None:
return None
parsed = HealthDataParser.parse_bmi(v)
if parsed is not None:
is_valid, error = HealthDataValidator.validate_bmi(parsed)
if not is_valid:
raise ValueError(error)
return parsed
@model_validator(mode='after')
def calculate_bmi_if_missing(self):
"""Auto-calculate BMI if weight and height are provided"""
if self.bmi is None and self.weight and self.height:
self.bmi = HealthDataValidator.calculate_bmi(self.weight, self.height)
return self
class UserHealthProfile(BaseModel):
"""
User Health Profile with Pydantic validation
"""
user_id: str
age: Optional[int] = Field(None, ge=13, le=150)
gender: Optional[Gender] = None
weight: Optional[float] = Field(None, ge=20, le=300, description="Weight in kg")
height: Optional[float] = Field(None, ge=50, le=300, description="Height in cm")
bmi: Optional[float] = Field(None, ge=10, le=60)
activity_level: Optional[ActivityLevel] = None
fitness_level: Optional[FitnessLevel] = None
health_conditions: List[str] = Field(default_factory=list)
medications: List[str] = Field(default_factory=list)
allergies: List[str] = Field(default_factory=list)
dietary_restrictions: List[str] = Field(default_factory=list)
created_at: datetime = Field(default_factory=datetime.now)
updated_at: datetime = Field(default_factory=datetime.now)
class Config:
use_enum_values = True
json_encoders = {
datetime: lambda v: v.isoformat()
}
@field_validator('height', mode='before')
@classmethod
def parse_height(cls, v):
"""Parse height from various formats"""
if v is None:
return None
return HealthDataParser.parse_height(v)
@field_validator('weight', mode='before')
@classmethod
def parse_weight(cls, v):
"""Parse weight from various formats"""
if v is None:
return None
return HealthDataParser.parse_weight(v)
@field_validator('age', mode='before')
@classmethod
def parse_age(cls, v):
"""Parse age from various formats"""
if v is None:
return None
return HealthDataParser.parse_age(v)
@model_validator(mode='after')
def calculate_bmi_if_missing(self):
"""Auto-calculate BMI if weight and height are provided"""
if self.bmi is None and self.weight and self.height:
self.bmi = HealthDataValidator.calculate_bmi(self.weight, self.height)
return self
def get_bmi_category(self) -> str:
"""Get BMI category"""
return HealthDataValidator.get_bmi_category(self.bmi)
def is_complete(self) -> bool:
"""Check if profile has all essential data"""
return all([
self.age is not None,
self.gender is not None,
self.weight is not None,
self.height is not None
])
def get_missing_fields(self) -> List[str]:
"""Get list of missing essential fields"""
missing = []
if self.age is None:
missing.append('age')
if self.gender is None:
missing.append('gender')
if self.weight is None:
missing.append('weight')
if self.height is None:
missing.append('height')
return missing
class NutritionRecord(HealthRecord):
"""Nutrition-specific health record"""
record_type: RecordType = Field(default=RecordType.NUTRITION, frozen=True)
calories: Optional[float] = Field(None, ge=0, le=10000)
protein: Optional[float] = Field(None, ge=0, le=500)
carbs: Optional[float] = Field(None, ge=0, le=1000)
fat: Optional[float] = Field(None, ge=0, le=500)
meal_type: Optional[str] = None # breakfast/lunch/dinner/snack
class ExerciseRecord(HealthRecord):
"""Exercise-specific health record"""
record_type: RecordType = Field(default=RecordType.EXERCISE, frozen=True)
exercise_type: Optional[str] = None # cardio/strength/flexibility/sports
duration_minutes: Optional[int] = Field(None, ge=0, le=600)
intensity: Optional[str] = None # low/medium/high
calories_burned: Optional[float] = Field(None, ge=0, le=5000)
class SymptomRecord(HealthRecord):
"""Symptom-specific health record"""
record_type: RecordType = Field(default=RecordType.SYMPTOM, frozen=True)
symptoms: List[str] = Field(default_factory=list)
severity: Optional[int] = Field(None, ge=1, le=10)
duration_days: Optional[int] = Field(None, ge=0, le=365)
body_part: Optional[str] = None
class MentalHealthRecord(HealthRecord):
"""Mental health-specific health record"""
record_type: RecordType = Field(default=RecordType.MENTAL_HEALTH, frozen=True)
mood: Optional[str] = None
stress_level: Optional[int] = Field(None, ge=1, le=10)
sleep_hours: Optional[float] = Field(None, ge=0, le=24)
sleep_quality: Optional[int] = Field(None, ge=1, le=10)