Improve the frontend and repository layer
This commit is contained in:
Urtzi Alfaro
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# services/forecasting/app/repositories/forecasting_alert_repository.py
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"""
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Forecasting Alert Repository
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Data access layer for forecasting-specific alert detection and analysis
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"""
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from typing import List, Dict, Any
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from uuid import UUID
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from sqlalchemy import text
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from sqlalchemy.ext.asyncio import AsyncSession
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import structlog
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logger = structlog.get_logger()
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class ForecastingAlertRepository:
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"""Repository for forecasting alert data access"""
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def __init__(self, session: AsyncSession):
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self.session = session
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async def get_weekend_demand_surges(self, tenant_id: UUID) -> List[Dict[str, Any]]:
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"""
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Get predicted weekend demand surges
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Returns forecasts showing significant growth over previous weeks
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"""
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try:
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query = text("""
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WITH weekend_forecast AS (
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SELECT
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f.tenant_id,
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f.inventory_product_id,
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f.product_name,
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f.predicted_demand,
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f.forecast_date,
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LAG(f.predicted_demand, 7) OVER (
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PARTITION BY f.tenant_id, f.inventory_product_id
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ORDER BY f.forecast_date
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) as prev_week_demand,
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AVG(f.predicted_demand) OVER (
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PARTITION BY f.tenant_id, f.inventory_product_id
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ORDER BY f.forecast_date
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ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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) as avg_weekly_demand
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FROM forecasts f
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WHERE f.forecast_date >= CURRENT_DATE + INTERVAL '1 day'
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AND f.forecast_date <= CURRENT_DATE + INTERVAL '3 days'
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AND EXTRACT(DOW FROM f.forecast_date) IN (6, 0)
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AND f.tenant_id = :tenant_id
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),
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surge_analysis AS (
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SELECT *,
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CASE
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WHEN prev_week_demand > 0 THEN
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(predicted_demand - prev_week_demand) / prev_week_demand * 100
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ELSE 0
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END as growth_percentage,
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CASE
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WHEN avg_weekly_demand > 0 THEN
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(predicted_demand - avg_weekly_demand) / avg_weekly_demand * 100
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ELSE 0
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END as avg_growth_percentage
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FROM weekend_forecast
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)
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SELECT * FROM surge_analysis
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WHERE growth_percentage > 50 OR avg_growth_percentage > 50
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ORDER BY growth_percentage DESC
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""")
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result = await self.session.execute(query, {"tenant_id": tenant_id})
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return [dict(row._mapping) for row in result.fetchall()]
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except Exception as e:
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logger.error("Failed to get weekend demand surges", error=str(e), tenant_id=str(tenant_id))
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raise
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async def get_weather_impact_forecasts(self, tenant_id: UUID) -> List[Dict[str, Any]]:
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"""
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Get weather impact on demand forecasts
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Returns forecasts with rain or significant demand changes
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"""
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try:
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query = text("""
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WITH weather_impact AS (
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SELECT
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f.tenant_id,
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f.inventory_product_id,
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f.product_name,
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f.predicted_demand,
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f.forecast_date,
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f.weather_precipitation,
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f.weather_temperature,
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f.traffic_volume,
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AVG(f.predicted_demand) OVER (
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PARTITION BY f.tenant_id, f.inventory_product_id
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ORDER BY f.forecast_date
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ROWS BETWEEN 6 PRECEDING AND CURRENT ROW
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) as avg_demand
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FROM forecasts f
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WHERE f.forecast_date >= CURRENT_DATE + INTERVAL '1 day'
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AND f.forecast_date <= CURRENT_DATE + INTERVAL '2 days'
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AND f.tenant_id = :tenant_id
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),
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rain_impact AS (
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SELECT *,
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CASE
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WHEN weather_precipitation > 2.0 THEN true
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ELSE false
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END as rain_forecast,
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CASE
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WHEN traffic_volume < 80 THEN true
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ELSE false
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END as low_traffic_expected,
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(predicted_demand - avg_demand) / avg_demand * 100 as demand_change
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FROM weather_impact
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)
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SELECT * FROM rain_impact
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WHERE rain_forecast = true OR demand_change < -15
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ORDER BY demand_change ASC
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""")
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result = await self.session.execute(query, {"tenant_id": tenant_id})
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return [dict(row._mapping) for row in result.fetchall()]
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except Exception as e:
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logger.error("Failed to get weather impact forecasts", error=str(e), tenant_id=str(tenant_id))
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raise
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async def get_holiday_demand_spikes(self, tenant_id: UUID) -> List[Dict[str, Any]]:
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"""
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Get historical holiday demand spike analysis
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Returns products with significant holiday demand increases
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"""
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try:
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query = text("""
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WITH holiday_demand AS (
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SELECT
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f.tenant_id,
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f.inventory_product_id,
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f.product_name,
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AVG(f.predicted_demand) as avg_holiday_demand,
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AVG(CASE WHEN f.is_holiday = false THEN f.predicted_demand END) as avg_normal_demand,
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COUNT(*) as forecast_count
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FROM forecasts f
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WHERE f.created_at > CURRENT_DATE - INTERVAL '365 days'
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AND f.tenant_id = :tenant_id
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GROUP BY f.tenant_id, f.inventory_product_id, f.product_name
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HAVING COUNT(*) >= 10
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),
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demand_spike_analysis AS (
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SELECT *,
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CASE
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WHEN avg_normal_demand > 0 THEN
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(avg_holiday_demand - avg_normal_demand) / avg_normal_demand * 100
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ELSE 0
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END as spike_percentage
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FROM holiday_demand
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)
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SELECT * FROM demand_spike_analysis
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WHERE spike_percentage > 25
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ORDER BY spike_percentage DESC
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""")
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result = await self.session.execute(query, {"tenant_id": tenant_id})
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return [dict(row._mapping) for row in result.fetchall()]
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except Exception as e:
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logger.error("Failed to get holiday demand spikes", error=str(e), tenant_id=str(tenant_id))
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raise
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async def get_demand_pattern_analysis(self, tenant_id: UUID) -> List[Dict[str, Any]]:
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"""
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Get weekly demand pattern analysis for optimization
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Returns products with significant demand variations
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"""
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try:
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query = text("""
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WITH weekly_patterns AS (
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SELECT
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f.tenant_id,
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f.inventory_product_id,
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f.product_name,
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EXTRACT(DOW FROM f.forecast_date) as day_of_week,
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AVG(f.predicted_demand) as avg_demand,
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STDDEV(f.predicted_demand) as demand_variance,
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COUNT(*) as data_points
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FROM forecasts f
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WHERE f.created_at > CURRENT_DATE - INTERVAL '60 days'
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AND f.tenant_id = :tenant_id
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GROUP BY f.tenant_id, f.inventory_product_id, f.product_name, EXTRACT(DOW FROM f.forecast_date)
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HAVING COUNT(*) >= 5
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),
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pattern_analysis AS (
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SELECT
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tenant_id, inventory_product_id, product_name,
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MAX(avg_demand) as peak_demand,
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MIN(avg_demand) as min_demand,
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AVG(avg_demand) as overall_avg,
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MAX(avg_demand) - MIN(avg_demand) as demand_range
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FROM weekly_patterns
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GROUP BY tenant_id, inventory_product_id, product_name
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)
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SELECT * FROM pattern_analysis
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WHERE demand_range > overall_avg * 0.3
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AND peak_demand > overall_avg * 1.5
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ORDER BY demand_range DESC
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""")
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result = await self.session.execute(query, {"tenant_id": tenant_id})
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return [dict(row._mapping) for row in result.fetchall()]
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except Exception as e:
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logger.error("Failed to get demand pattern analysis", error=str(e), tenant_id=str(tenant_id))
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raise
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