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Urtzi Alfaro
297
services/forecasting/app/api/retraining.py
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297
services/forecasting/app/api/retraining.py
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# ================================================================
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# services/forecasting/app/api/retraining.py
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# ================================================================
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"""
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Retraining API - Trigger and manage model retraining based on performance
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"""
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from fastapi import APIRouter, Depends, HTTPException, Path, Query, status
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from typing import Dict, Any, List
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from uuid import UUID
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import structlog
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from pydantic import BaseModel, Field
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from app.services.retraining_trigger_service import RetrainingTriggerService
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from shared.auth.decorators import get_current_user_dep
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from shared.auth.access_control import require_user_role
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from shared.routing import RouteBuilder
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from app.core.database import get_db
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from sqlalchemy.ext.asyncio import AsyncSession
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route_builder = RouteBuilder('forecasting')
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router = APIRouter(tags=["retraining"])
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logger = structlog.get_logger()
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# ================================================================
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# Request/Response Schemas
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# ================================================================
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class EvaluateRetrainingRequest(BaseModel):
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"""Request model for retraining evaluation"""
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auto_trigger: bool = Field(
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default=False,
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description="Automatically trigger retraining for poor performers"
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)
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class TriggerProductRetrainingRequest(BaseModel):
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"""Request model for single product retraining"""
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inventory_product_id: UUID = Field(..., description="Product to retrain")
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reason: str = Field(..., description="Reason for retraining")
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priority: str = Field(
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default="normal",
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description="Priority level: low, normal, high"
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)
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class TriggerBulkRetrainingRequest(BaseModel):
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"""Request model for bulk retraining"""
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product_ids: List[UUID] = Field(..., description="List of products to retrain")
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reason: str = Field(
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default="Bulk retraining requested",
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description="Reason for bulk retraining"
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)
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class ScheduledRetrainingCheckRequest(BaseModel):
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"""Request model for scheduled retraining check"""
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max_model_age_days: int = Field(
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default=30,
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ge=1,
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le=90,
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description="Maximum acceptable model age"
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)
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# ================================================================
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# Endpoints
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# ================================================================
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@router.post(
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route_builder.build_base_route("retraining/evaluate"),
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status_code=status.HTTP_200_OK
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)
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@require_user_role(['admin', 'owner'])
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async def evaluate_retraining_needs(
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request: EvaluateRetrainingRequest,
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Evaluate performance and optionally trigger retraining
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Analyzes 30-day performance and identifies products needing retraining.
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If auto_trigger=true, automatically triggers retraining for poor performers.
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"""
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try:
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logger.info(
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"Evaluating retraining needs",
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tenant_id=tenant_id,
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auto_trigger=request.auto_trigger,
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user_id=current_user.get("user_id")
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)
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service = RetrainingTriggerService(db)
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result = await service.evaluate_and_trigger_retraining(
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tenant_id=tenant_id,
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auto_trigger=request.auto_trigger
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)
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return result
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except Exception as e:
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logger.error(
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"Failed to evaluate retraining needs",
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tenant_id=tenant_id,
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error=str(e)
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to evaluate retraining: {str(e)}"
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)
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@router.post(
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route_builder.build_base_route("retraining/trigger-product"),
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status_code=status.HTTP_200_OK
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)
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@require_user_role(['admin', 'owner'])
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async def trigger_product_retraining(
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request: TriggerProductRetrainingRequest,
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Trigger retraining for a specific product
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Manually trigger model retraining for a single product.
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"""
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try:
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logger.info(
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"Triggering product retraining",
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tenant_id=tenant_id,
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product_id=request.inventory_product_id,
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reason=request.reason,
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user_id=current_user.get("user_id")
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)
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service = RetrainingTriggerService(db)
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result = await service._trigger_product_retraining(
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tenant_id=tenant_id,
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inventory_product_id=request.inventory_product_id,
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reason=request.reason,
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priority=request.priority
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)
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return result
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except Exception as e:
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logger.error(
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"Failed to trigger product retraining",
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tenant_id=tenant_id,
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product_id=request.inventory_product_id,
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error=str(e)
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to trigger retraining: {str(e)}"
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)
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@router.post(
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route_builder.build_base_route("retraining/trigger-bulk"),
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status_code=status.HTTP_200_OK
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)
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@require_user_role(['admin', 'owner'])
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async def trigger_bulk_retraining(
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request: TriggerBulkRetrainingRequest,
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Trigger retraining for multiple products
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Bulk retraining operation for multiple products at once.
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"""
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try:
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logger.info(
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"Triggering bulk retraining",
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tenant_id=tenant_id,
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product_count=len(request.product_ids),
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reason=request.reason,
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user_id=current_user.get("user_id")
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)
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service = RetrainingTriggerService(db)
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result = await service.trigger_bulk_retraining(
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tenant_id=tenant_id,
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product_ids=request.product_ids,
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reason=request.reason
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)
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return result
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except Exception as e:
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logger.error(
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"Failed to trigger bulk retraining",
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tenant_id=tenant_id,
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error=str(e)
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to trigger bulk retraining: {str(e)}"
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)
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@router.get(
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route_builder.build_base_route("retraining/recommendations"),
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status_code=status.HTTP_200_OK
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)
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@require_user_role(['admin', 'owner', 'member'])
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async def get_retraining_recommendations(
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Get retraining recommendations without triggering
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Returns recommendations for manual review and decision-making.
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"""
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try:
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logger.info(
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"Getting retraining recommendations",
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tenant_id=tenant_id,
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user_id=current_user.get("user_id")
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)
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service = RetrainingTriggerService(db)
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recommendations = await service.get_retraining_recommendations(
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tenant_id=tenant_id
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)
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return recommendations
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except Exception as e:
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logger.error(
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"Failed to get recommendations",
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tenant_id=tenant_id,
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error=str(e)
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to get recommendations: {str(e)}"
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)
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@router.post(
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route_builder.build_base_route("retraining/check-scheduled"),
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status_code=status.HTTP_200_OK
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)
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@require_user_role(['admin', 'owner'])
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async def check_scheduled_retraining(
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request: ScheduledRetrainingCheckRequest,
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tenant_id: UUID = Path(..., description="Tenant ID"),
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current_user: Dict[str, Any] = Depends(get_current_user_dep),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Check for models needing scheduled retraining based on age
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Identifies models that haven't been updated in max_model_age_days.
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"""
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try:
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logger.info(
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"Checking scheduled retraining needs",
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tenant_id=tenant_id,
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max_model_age_days=request.max_model_age_days,
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user_id=current_user.get("user_id")
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)
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service = RetrainingTriggerService(db)
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result = await service.check_and_trigger_scheduled_retraining(
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tenant_id=tenant_id,
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max_model_age_days=request.max_model_age_days
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)
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return result
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except Exception as e:
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logger.error(
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"Failed to check scheduled retraining",
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tenant_id=tenant_id,
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error=str(e)
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)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=f"Failed to check scheduled retraining: {str(e)}"
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)
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