84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
import uuid
|
|
|
|
from sqlalchemy import Boolean, Column, Date, ForeignKey, Integer, String, Text
|
|
from sqlalchemy.dialects.postgresql import UUID
|
|
|
|
from shared.database import BaseModel
|
|
|
|
|
|
class CalendarEntry(BaseModel):
|
|
__tablename__ = "calendar_entries"
|
|
|
|
uuid = Column(UUID(as_uuid=True), default=uuid.uuid4, unique=True, index=True)
|
|
user_id = Column(Integer, ForeignKey("users.id"), nullable=False, index=True)
|
|
|
|
entry_date = Column(Date, nullable=False, index=True)
|
|
entry_type = Column(
|
|
String(50), nullable=False
|
|
) # period, ovulation, symptoms, medication, etc.
|
|
|
|
# Period tracking
|
|
flow_intensity = Column(String(20)) # light, medium, heavy
|
|
period_symptoms = Column(Text) # cramps, headache, mood, etc.
|
|
|
|
# General health
|
|
mood = Column(String(20)) # happy, sad, anxious, irritated, etc.
|
|
energy_level = Column(Integer) # 1-5 scale
|
|
sleep_hours = Column(Integer)
|
|
|
|
# Symptoms and notes
|
|
symptoms = Column(Text) # Any symptoms experienced
|
|
medications = Column(Text) # Medications taken
|
|
notes = Column(Text) # Personal notes
|
|
|
|
# Predictions and calculations
|
|
is_predicted = Column(Boolean, default=False) # If this is a predicted entry
|
|
confidence_score = Column(Integer) # Prediction confidence 1-100
|
|
|
|
def __repr__(self):
|
|
return f"<CalendarEntry user_id={self.user_id} date={self.entry_date} type={self.entry_type}>"
|
|
|
|
|
|
class CycleData(BaseModel):
|
|
__tablename__ = "cycle_data"
|
|
|
|
user_id = Column(Integer, ForeignKey("users.id"), nullable=False, index=True)
|
|
cycle_start_date = Column(Date, nullable=False)
|
|
cycle_length = Column(Integer) # Length of this cycle
|
|
period_length = Column(Integer) # Length of period in this cycle
|
|
|
|
# Calculated fields
|
|
ovulation_date = Column(Date)
|
|
fertile_window_start = Column(Date)
|
|
fertile_window_end = Column(Date)
|
|
next_period_predicted = Column(Date)
|
|
|
|
# Cycle characteristics
|
|
cycle_regularity_score = Column(Integer) # 1-100, how regular is this cycle
|
|
avg_cycle_length = Column(Integer) # Rolling average
|
|
avg_period_length = Column(Integer) # Rolling average
|
|
|
|
def __repr__(self):
|
|
return f"<CycleData user_id={self.user_id} start={self.cycle_start_date}>"
|
|
|
|
|
|
class HealthInsights(BaseModel):
|
|
__tablename__ = "health_insights"
|
|
|
|
user_id = Column(Integer, ForeignKey("users.id"), nullable=False, index=True)
|
|
insight_type = Column(
|
|
String(50), nullable=False
|
|
) # cycle_pattern, symptom_pattern, etc.
|
|
|
|
title = Column(String(200), nullable=False)
|
|
description = Column(Text, nullable=False)
|
|
recommendation = Column(Text)
|
|
|
|
# Metadata
|
|
confidence_level = Column(String(20)) # high, medium, low
|
|
data_points_used = Column(Integer) # How many data points were used
|
|
is_dismissed = Column(Boolean, default=False)
|
|
|
|
def __repr__(self):
|
|
return f"<HealthInsights user_id={self.user_id} type={self.insight_type}>"
|