Add user role
This commit is contained in:
Urtzi Alfaro
@@ -9,12 +9,14 @@ import structlog
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from fastapi import APIRouter, Depends, HTTPException, status, Query, Path
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from sqlalchemy.ext.asyncio import AsyncSession
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from typing import List, Optional
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from datetime import date
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from datetime import date, datetime
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from sqlalchemy import select, delete, func
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import uuid
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from app.core.database import get_db
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from shared.auth.decorators import (
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get_current_user_dep,
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get_current_tenant_id_dep
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require_admin_role
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)
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from app.services.forecasting_service import ForecastingService
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from app.schemas.forecasts import (
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@@ -22,6 +24,7 @@ from app.schemas.forecasts import (
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BatchForecastResponse, AlertResponse
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)
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from app.models.forecasts import Forecast, PredictionBatch, ForecastAlert
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from app.services.messaging import publish_forecasts_deleted_event
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logger = structlog.get_logger()
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router = APIRouter()
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@@ -318,4 +321,197 @@ async def acknowledge_alert(
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail="Internal server error"
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)
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)
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@router.delete("/forecasts/tenant/{tenant_id}")
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async def delete_tenant_forecasts_complete(
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tenant_id: str,
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current_user = Depends(get_current_user_dep),
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_admin_check = Depends(require_admin_role),
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db: AsyncSession = Depends(get_db)
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):
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"""
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Delete all forecasts and predictions for a tenant.
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**WARNING: This operation is irreversible!**
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This endpoint:
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1. Cancels any active prediction batches
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2. Clears prediction cache
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3. Deletes all forecast records
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4. Deletes prediction batch records
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5. Deletes model performance metrics
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6. Publishes deletion event
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Used by admin user deletion process to clean up all forecasting data.
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"""
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try:
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tenant_uuid = uuid.UUID(tenant_id)
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except ValueError:
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raise HTTPException(
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status_code=status.HTTP_400_BAD_REQUEST,
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detail="Invalid tenant ID format"
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)
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try:
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from app.models.forecasts import Forecast, PredictionBatch
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from app.models.predictions import ModelPerformanceMetric, PredictionCache
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deletion_stats = {
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"tenant_id": tenant_id,
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"deleted_at": datetime.utcnow().isoformat(),
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"batches_cancelled": 0,
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"forecasts_deleted": 0,
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"prediction_batches_deleted": 0,
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"performance_metrics_deleted": 0,
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"cache_entries_deleted": 0,
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"errors": []
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}
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# Step 1: Cancel active prediction batches
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try:
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active_batches_query = select(PredictionBatch).where(
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PredictionBatch.tenant_id == tenant_uuid,
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PredictionBatch.status.in_(["pending", "processing"])
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)
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active_batches_result = await db.execute(active_batches_query)
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active_batches = active_batches_result.scalars().all()
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for batch in active_batches:
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batch.status = "cancelled"
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batch.completed_at = datetime.utcnow()
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deletion_stats["batches_cancelled"] += 1
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if active_batches:
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await db.commit()
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logger.info("Cancelled active prediction batches",
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tenant_id=tenant_id,
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count=len(active_batches))
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except Exception as e:
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error_msg = f"Error cancelling prediction batches: {str(e)}"
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deletion_stats["errors"].append(error_msg)
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logger.error(error_msg)
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# Step 2: Delete prediction cache
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try:
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cache_count_query = select(func.count(PredictionCache.id)).where(
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PredictionCache.tenant_id == tenant_uuid
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)
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cache_count_result = await db.execute(cache_count_query)
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cache_count = cache_count_result.scalar()
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cache_delete_query = delete(PredictionCache).where(
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PredictionCache.tenant_id == tenant_uuid
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)
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await db.execute(cache_delete_query)
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deletion_stats["cache_entries_deleted"] = cache_count
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logger.info("Deleted prediction cache entries",
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tenant_id=tenant_id,
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count=cache_count)
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except Exception as e:
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error_msg = f"Error deleting prediction cache: {str(e)}"
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deletion_stats["errors"].append(error_msg)
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logger.error(error_msg)
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# Step 3: Delete model performance metrics
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try:
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metrics_count_query = select(func.count(ModelPerformanceMetric.id)).where(
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ModelPerformanceMetric.tenant_id == tenant_uuid
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)
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metrics_count_result = await db.execute(metrics_count_query)
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metrics_count = metrics_count_result.scalar()
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metrics_delete_query = delete(ModelPerformanceMetric).where(
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ModelPerformanceMetric.tenant_id == tenant_uuid
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)
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await db.execute(metrics_delete_query)
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deletion_stats["performance_metrics_deleted"] = metrics_count
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logger.info("Deleted performance metrics",
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tenant_id=tenant_id,
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count=metrics_count)
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except Exception as e:
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error_msg = f"Error deleting performance metrics: {str(e)}"
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deletion_stats["errors"].append(error_msg)
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logger.error(error_msg)
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# Step 4: Delete prediction batches
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try:
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batches_count_query = select(func.count(PredictionBatch.id)).where(
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PredictionBatch.tenant_id == tenant_uuid
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)
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batches_count_result = await db.execute(batches_count_query)
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batches_count = batches_count_result.scalar()
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batches_delete_query = delete(PredictionBatch).where(
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PredictionBatch.tenant_id == tenant_uuid
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)
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await db.execute(batches_delete_query)
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deletion_stats["prediction_batches_deleted"] = batches_count
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logger.info("Deleted prediction batches",
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tenant_id=tenant_id,
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count=batches_count)
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except Exception as e:
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error_msg = f"Error deleting prediction batches: {str(e)}"
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deletion_stats["errors"].append(error_msg)
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logger.error(error_msg)
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# Step 5: Delete forecasts (main data)
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try:
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forecasts_count_query = select(func.count(Forecast.id)).where(
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Forecast.tenant_id == tenant_uuid
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)
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forecasts_count_result = await db.execute(forecasts_count_query)
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forecasts_count = forecasts_count_result.scalar()
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forecasts_delete_query = delete(Forecast).where(
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Forecast.tenant_id == tenant_uuid
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)
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await db.execute(forecasts_delete_query)
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deletion_stats["forecasts_deleted"] = forecasts_count
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await db.commit()
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logger.info("Deleted forecasts",
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tenant_id=tenant_id,
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count=forecasts_count)
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except Exception as e:
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await db.rollback()
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error_msg = f"Error deleting forecasts: {str(e)}"
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deletion_stats["errors"].append(error_msg)
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logger.error(error_msg)
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raise HTTPException(
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status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
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detail=error_msg
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)
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# Step 6: Publish deletion event
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try:
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await publish_forecasts_deleted_event(tenant_id, deletion_stats)
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except Exception as e:
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logger.warning("Failed to publish forecasts deletion event", error=str(e))
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return {
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"success": True,
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"message": f"All forecasting data for tenant {tenant_id} deleted successfully",
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"deletion_details": deletion_stats
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}
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except HTTPException:
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raise
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except Exception as e:
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logger.error("Unexpected error deleting tenant forecasts",
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tenant_id=tenant_id,
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error=str(e))
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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 delete tenant forecasts: {str(e)}"
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)
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@@ -9,6 +9,7 @@ import structlog
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import json
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from typing import Dict, Any
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import asyncio
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import datetime
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from shared.messaging.rabbitmq import RabbitMQClient
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from shared.messaging.events import (
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@@ -132,4 +133,20 @@ async def handle_weather_updated(data: Dict[str, Any]):
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# Could trigger re-forecasting if needed
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except Exception as e:
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logger.error("Error handling weather updated event", error=str(e))
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logger.error("Error handling weather updated event", error=str(e))
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async def publish_forecasts_deleted_event(tenant_id: str, deletion_stats: Dict[str, Any]):
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"""Publish forecasts deletion event to message queue"""
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try:
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await rabbitmq_client.publish_event(
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exchange="forecasting_events",
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routing_key="forecasting.tenant.deleted",
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message={
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"event_type": "tenant_forecasts_deleted",
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"tenant_id": tenant_id,
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"timestamp": datetime.utcnow().isoformat(),
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"deletion_stats": deletion_stats
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}
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)
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except Exception as e:
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logger.error("Failed to publish forecasts deletion event", error=str(e))
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