Imporve the predicciones page
This commit is contained in:
Urtzi Alfaro
@@ -11,8 +11,8 @@ import uuid
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from app.services.forecasting_service import EnhancedForecastingService
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from app.schemas.forecasts import (
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ForecastRequest, ForecastResponse, BatchForecastRequest,
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BatchForecastResponse
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ForecastRequest, ForecastResponse, BatchForecastRequest,
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BatchForecastResponse, MultiDayForecastResponse
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)
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from shared.auth.decorators import (
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get_current_user_dep,
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@@ -66,7 +66,7 @@ async def create_enhanced_single_forecast(
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forecast_id=forecast.id)
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return forecast
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except ValueError as e:
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if metrics:
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metrics.increment_counter("enhanced_forecast_validation_errors_total")
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@@ -89,6 +89,70 @@ async def create_enhanced_single_forecast(
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)
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@router.post("/tenants/{tenant_id}/forecasts/multi-day", response_model=MultiDayForecastResponse)
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@track_execution_time("enhanced_multi_day_forecast_duration_seconds", "forecasting-service")
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async def create_enhanced_multi_day_forecast(
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request: ForecastRequest,
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tenant_id: str = Path(..., description="Tenant ID"),
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request_obj: Request = None,
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enhanced_forecasting_service: EnhancedForecastingService = Depends(get_enhanced_forecasting_service)
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):
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"""Generate multiple daily forecasts for the specified period using enhanced repository pattern"""
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metrics = get_metrics_collector(request_obj)
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try:
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logger.info("Generating enhanced multi-day forecast",
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tenant_id=tenant_id,
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inventory_product_id=request.inventory_product_id,
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forecast_days=request.forecast_days,
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forecast_date=request.forecast_date.isoformat())
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# Record metrics
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if metrics:
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metrics.increment_counter("enhanced_multi_day_forecasts_total")
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# Validate forecast_days parameter
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if request.forecast_days <= 0 or request.forecast_days > 30:
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raise ValueError("forecast_days must be between 1 and 30")
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# Generate multi-day forecast using enhanced service
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forecast_result = await enhanced_forecasting_service.generate_multi_day_forecast(
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tenant_id=tenant_id,
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request=request
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)
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if metrics:
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metrics.increment_counter("enhanced_multi_day_forecasts_success_total")
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logger.info("Enhanced multi-day forecast generated successfully",
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tenant_id=tenant_id,
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inventory_product_id=request.inventory_product_id,
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forecast_days=len(forecast_result.get("forecasts", [])))
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return MultiDayForecastResponse(**forecast_result)
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except ValueError as e:
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if metrics:
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metrics.increment_counter("enhanced_multi_day_forecast_validation_errors_total")
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logger.error("Enhanced multi-day forecast validation error",
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error=str(e),
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tenant_id=tenant_id)
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail=str(e)
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)
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except Exception as e:
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if metrics:
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metrics.increment_counter("enhanced_multi_day_forecasts_errors_total")
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logger.error("Enhanced multi-day forecast generation failed",
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error=str(e),
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tenant_id=tenant_id)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Enhanced multi-day forecast generation failed"
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)
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@router.post("/tenants/{tenant_id}/forecasts/batch", response_model=BatchForecastResponse)
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@track_execution_time("enhanced_batch_forecast_duration_seconds", "forecasting-service")
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async def create_enhanced_batch_forecast(
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@@ -95,4 +95,15 @@ class BatchForecastResponse(BaseModel):
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forecasts: Optional[List[ForecastResponse]]
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error_message: Optional[str]
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class MultiDayForecastResponse(BaseModel):
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"""Response schema for multi-day forecast results"""
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tenant_id: str = Field(..., description="Tenant ID")
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inventory_product_id: str = Field(..., description="Inventory product ID")
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forecast_start_date: date = Field(..., description="Start date of forecast period")
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forecast_days: int = Field(..., description="Number of forecasted days")
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forecasts: List[ForecastResponse] = Field(..., description="Daily forecasts")
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total_predicted_demand: float = Field(..., description="Total demand across all days")
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average_confidence_level: float = Field(..., description="Average confidence across all days")
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processing_time_ms: int = Field(..., description="Total processing time")
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@@ -345,7 +345,101 @@ class EnhancedForecastingService:
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inventory_product_id=request.inventory_product_id,
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processing_time=processing_time)
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raise
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async def generate_multi_day_forecast(
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self,
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tenant_id: str,
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request: ForecastRequest
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) -> Dict[str, Any]:
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"""
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Generate multiple daily forecasts for the specified period.
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"""
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start_time = datetime.utcnow()
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forecasts = []
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try:
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logger.info("Generating multi-day forecast",
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tenant_id=tenant_id,
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inventory_product_id=request.inventory_product_id,
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forecast_days=request.forecast_days,
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start_date=request.forecast_date.isoformat())
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# Generate a forecast for each day
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for day_offset in range(request.forecast_days):
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# Calculate the forecast date for this day
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current_date = request.forecast_date
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if isinstance(current_date, str):
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from dateutil.parser import parse
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current_date = parse(current_date).date()
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if day_offset > 0:
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from datetime import timedelta
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current_date = current_date + timedelta(days=day_offset)
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# Create a new request for this specific day
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daily_request = ForecastRequest(
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inventory_product_id=request.inventory_product_id,
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forecast_date=current_date,
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forecast_days=1, # Single day for each iteration
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location=request.location,
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confidence_level=request.confidence_level
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)
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# Generate forecast for this day
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daily_forecast = await self.generate_forecast(tenant_id, daily_request)
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forecasts.append(daily_forecast)
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# Calculate summary statistics
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total_demand = sum(f.predicted_demand for f in forecasts)
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avg_confidence = sum(f.confidence_level for f in forecasts) / len(forecasts)
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processing_time = int((datetime.utcnow() - start_time).total_seconds() * 1000)
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# Convert forecasts to dictionary format for the response
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forecast_dicts = []
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for forecast in forecasts:
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forecast_dicts.append({
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"id": forecast.id,
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"tenant_id": forecast.tenant_id,
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"inventory_product_id": forecast.inventory_product_id,
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"location": forecast.location,
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"forecast_date": forecast.forecast_date.isoformat() if hasattr(forecast.forecast_date, 'isoformat') else str(forecast.forecast_date),
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"predicted_demand": forecast.predicted_demand,
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"confidence_lower": forecast.confidence_lower,
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"confidence_upper": forecast.confidence_upper,
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"confidence_level": forecast.confidence_level,
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"model_id": forecast.model_id,
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"model_version": forecast.model_version,
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"algorithm": forecast.algorithm,
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"business_type": forecast.business_type,
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"is_holiday": forecast.is_holiday,
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"is_weekend": forecast.is_weekend,
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"day_of_week": forecast.day_of_week,
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"weather_temperature": forecast.weather_temperature,
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"weather_precipitation": forecast.weather_precipitation,
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"weather_description": forecast.weather_description,
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"traffic_volume": forecast.traffic_volume,
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"created_at": forecast.created_at.isoformat() if hasattr(forecast.created_at, 'isoformat') else str(forecast.created_at),
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"processing_time_ms": forecast.processing_time_ms,
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"features_used": forecast.features_used
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})
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return {
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"tenant_id": tenant_id,
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"inventory_product_id": request.inventory_product_id,
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"forecast_start_date": request.forecast_date.isoformat() if hasattr(request.forecast_date, 'isoformat') else str(request.forecast_date),
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"forecast_days": request.forecast_days,
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"forecasts": forecast_dicts,
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"total_predicted_demand": total_demand,
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"average_confidence_level": avg_confidence,
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"processing_time_ms": processing_time
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}
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except Exception as e:
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logger.error("Multi-day forecast generation failed",
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tenant_id=tenant_id,
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error=str(e))
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raise
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async def get_forecast_history(
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self,
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tenant_id: str,
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