209 lines
7.7 KiB
Python
209 lines
7.7 KiB
Python
# shared/clients/forecast_client.py
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"""
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Forecast Service Client - Updated for refactored backend structure
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Handles all API calls to the forecasting service
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Backend structure:
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- ATOMIC: /forecasting/forecasts (CRUD)
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- BUSINESS: /forecasting/operations/* (single, multi-day, batch, etc.)
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- ANALYTICS: /forecasting/analytics/* (predictions-performance)
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"""
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from typing import Dict, Any, Optional, List
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from datetime import date
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from .base_service_client import BaseServiceClient
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from shared.config.base import BaseServiceSettings
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class ForecastServiceClient(BaseServiceClient):
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"""Client for communicating with the forecasting service"""
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def __init__(self, config: BaseServiceSettings, calling_service_name: str = "unknown"):
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super().__init__(calling_service_name, config)
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def get_service_base_path(self) -> str:
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return "/api/v1"
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# ================================================================
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# ATOMIC: Forecast CRUD Operations
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# ================================================================
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async def get_forecast(self, tenant_id: str, forecast_id: str) -> Optional[Dict[str, Any]]:
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"""Get forecast details by ID"""
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return await self.get(f"forecasting/forecasts/{forecast_id}", tenant_id=tenant_id)
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async def list_forecasts(
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self,
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tenant_id: str,
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inventory_product_id: Optional[str] = None,
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start_date: Optional[date] = None,
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end_date: Optional[date] = None,
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limit: int = 50,
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offset: int = 0
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) -> Optional[List[Dict[str, Any]]]:
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"""List forecasts for a tenant with optional filters"""
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params = {"limit": limit, "offset": offset}
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if inventory_product_id:
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params["inventory_product_id"] = inventory_product_id
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if start_date:
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params["start_date"] = start_date.isoformat()
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if end_date:
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params["end_date"] = end_date.isoformat()
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return await self.get("forecasting/forecasts", tenant_id=tenant_id, params=params)
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async def delete_forecast(self, tenant_id: str, forecast_id: str) -> Optional[Dict[str, Any]]:
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"""Delete a forecast"""
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return await self.delete(f"forecasting/forecasts/{forecast_id}", tenant_id=tenant_id)
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# ================================================================
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# BUSINESS: Forecasting Operations
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# ================================================================
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async def generate_single_forecast(
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self,
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tenant_id: str,
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inventory_product_id: str,
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forecast_date: date,
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include_recommendations: bool = False
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) -> Optional[Dict[str, Any]]:
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"""Generate a single product forecast"""
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data = {
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"inventory_product_id": inventory_product_id,
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"forecast_date": forecast_date.isoformat(),
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"include_recommendations": include_recommendations
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}
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return await self.post("forecasting/operations/single", data=data, tenant_id=tenant_id)
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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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inventory_product_id: str,
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forecast_date: date,
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forecast_days: int = 7,
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include_recommendations: bool = False
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) -> Optional[Dict[str, Any]]:
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"""Generate multiple daily forecasts for the specified period"""
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data = {
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"inventory_product_id": inventory_product_id,
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"forecast_date": forecast_date.isoformat(),
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"forecast_days": forecast_days,
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"include_recommendations": include_recommendations
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}
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return await self.post("forecasting/operations/multi-day", data=data, tenant_id=tenant_id)
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async def generate_batch_forecast(
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self,
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tenant_id: str,
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inventory_product_ids: List[str],
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forecast_date: date,
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forecast_days: int = 1
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) -> Optional[Dict[str, Any]]:
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"""Generate forecasts for multiple products in batch"""
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data = {
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"inventory_product_ids": inventory_product_ids,
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"forecast_date": forecast_date.isoformat(),
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"forecast_days": forecast_days
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}
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return await self.post("forecasting/operations/batch", data=data, tenant_id=tenant_id)
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async def generate_realtime_prediction(
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self,
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tenant_id: str,
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inventory_product_id: str,
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model_id: str,
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features: Dict[str, Any],
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model_path: Optional[str] = None,
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confidence_level: float = 0.8
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) -> Optional[Dict[str, Any]]:
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"""Generate real-time prediction"""
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data = {
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"inventory_product_id": inventory_product_id,
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"model_id": model_id,
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"features": features,
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"confidence_level": confidence_level
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}
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if model_path:
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data["model_path"] = model_path
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return await self.post("forecasting/operations/realtime", data=data, tenant_id=tenant_id)
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async def validate_predictions(
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self,
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tenant_id: str,
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start_date: date,
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end_date: date
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) -> Optional[Dict[str, Any]]:
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"""Validate predictions against actual sales data"""
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params = {
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"start_date": start_date.isoformat(),
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"end_date": end_date.isoformat()
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}
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return await self.post("forecasting/operations/validate-predictions", params=params, tenant_id=tenant_id)
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async def get_forecast_statistics(
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self,
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tenant_id: str,
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start_date: Optional[date] = None,
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end_date: Optional[date] = None
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) -> Optional[Dict[str, Any]]:
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"""Get forecast statistics"""
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params = {}
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if start_date:
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params["start_date"] = start_date.isoformat()
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if end_date:
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params["end_date"] = end_date.isoformat()
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return await self.get("forecasting/operations/statistics", tenant_id=tenant_id, params=params)
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async def clear_prediction_cache(self, tenant_id: str) -> Optional[Dict[str, Any]]:
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"""Clear prediction cache"""
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return await self.delete("forecasting/operations/cache", tenant_id=tenant_id)
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# ================================================================
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# ANALYTICS: Forecasting Analytics
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# ================================================================
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async def get_predictions_performance(
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self,
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tenant_id: str,
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start_date: Optional[date] = None,
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end_date: Optional[date] = None
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) -> Optional[Dict[str, Any]]:
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"""Get predictions performance analytics"""
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params = {}
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if start_date:
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params["start_date"] = start_date.isoformat()
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if end_date:
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params["end_date"] = end_date.isoformat()
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return await self.get("forecasting/analytics/predictions-performance", tenant_id=tenant_id, params=params)
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# ================================================================
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# Legacy/Compatibility Methods (deprecated)
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# ================================================================
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async def create_forecast(
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self,
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tenant_id: str,
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model_id: str,
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start_date: str,
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end_date: str,
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product_ids: Optional[List[str]] = None,
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include_confidence_intervals: bool = True,
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**kwargs
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) -> Optional[Dict[str, Any]]:
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"""
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DEPRECATED: Use generate_single_forecast or generate_batch_forecast instead
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Legacy method for backward compatibility
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"""
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# Map to new batch forecast operation
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if product_ids:
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return await self.generate_batch_forecast(
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tenant_id=tenant_id,
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inventory_product_ids=product_ids,
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forecast_date=date.fromisoformat(start_date),
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forecast_days=1
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)
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return None
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