demo seed change
This commit is contained in:
Urtzi Alfaro
@@ -13,6 +13,7 @@ from typing import Optional
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import os
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import sys
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from pathlib import Path
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import json
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sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent))
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from shared.utils.demo_dates import adjust_date_for_demo, BASE_REFERENCE_DATE
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@@ -21,7 +22,7 @@ from app.core.database import get_db
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from app.models.forecasts import Forecast, PredictionBatch
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logger = structlog.get_logger()
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router = APIRouter(prefix="/internal/demo", tags=["internal"])
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router = APIRouter()
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# Base demo tenant IDs
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DEMO_TENANT_PROFESSIONAL = "a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6"
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@@ -36,7 +37,7 @@ def verify_internal_api_key(x_internal_api_key: Optional[str] = Header(None)):
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return True
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@router.post("/clone")
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@router.post("/internal/demo/clone")
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async def clone_demo_data(
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base_tenant_id: str,
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virtual_tenant_id: str,
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@@ -49,50 +50,95 @@ async def clone_demo_data(
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"""
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Clone forecasting service data for a virtual demo tenant
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Clones:
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- Forecasts (historical predictions)
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- Prediction batches (batch prediction records)
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This endpoint creates fresh demo data by:
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1. Loading seed data from JSON files
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2. Applying XOR-based ID transformation
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3. Adjusting dates relative to session creation time
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4. Creating records in the virtual tenant
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Args:
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base_tenant_id: Template tenant UUID to clone from
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base_tenant_id: Template tenant UUID (for reference)
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virtual_tenant_id: Target virtual tenant UUID
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demo_account_type: Type of demo account
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session_id: Originating session ID for tracing
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session_created_at: ISO timestamp when demo session was created (for date adjustment)
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session_created_at: Session creation timestamp for date adjustment
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db: Database session
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Returns:
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Cloning status and record counts
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Dictionary with cloning results
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Raises:
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HTTPException: On validation or cloning errors
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"""
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start_time = datetime.now(timezone.utc)
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# Parse session_created_at or fallback to now
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if session_created_at:
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try:
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session_time = datetime.fromisoformat(session_created_at.replace('Z', '+00:00'))
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except (ValueError, AttributeError) as e:
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logger.warning(
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"Invalid session_created_at format, using current time",
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session_created_at=session_created_at,
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error=str(e)
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)
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session_time = datetime.now(timezone.utc)
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else:
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logger.warning("session_created_at not provided, using current time")
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session_time = datetime.now(timezone.utc)
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logger.info(
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"Starting forecasting data cloning",
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base_tenant_id=base_tenant_id,
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virtual_tenant_id=virtual_tenant_id,
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demo_account_type=demo_account_type,
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session_id=session_id,
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session_time=session_time.isoformat()
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)
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try:
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# Validate UUIDs
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base_uuid = uuid.UUID(base_tenant_id)
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virtual_uuid = uuid.UUID(virtual_tenant_id)
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# Parse session creation time for date adjustment
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if session_created_at:
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try:
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session_time = datetime.fromisoformat(session_created_at.replace('Z', '+00:00'))
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except (ValueError, AttributeError):
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session_time = start_time
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else:
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session_time = start_time
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logger.info(
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"Starting forecasting data cloning with date adjustment",
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base_tenant_id=base_tenant_id,
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virtual_tenant_id=str(virtual_uuid),
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demo_account_type=demo_account_type,
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session_id=session_id,
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session_time=session_time.isoformat()
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)
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# Load seed data using shared utility
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try:
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from shared.utils.seed_data_paths import get_seed_data_path
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if demo_account_type == "enterprise":
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profile = "enterprise"
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else:
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profile = "professional"
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json_file = get_seed_data_path(profile, "10-forecasting.json")
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except ImportError:
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# Fallback to original path
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seed_data_dir = Path(__file__).parent.parent.parent.parent / "shared" / "demo" / "fixtures"
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if demo_account_type == "enterprise":
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json_file = seed_data_dir / "enterprise" / "parent" / "10-forecasting.json"
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else:
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json_file = seed_data_dir / "professional" / "10-forecasting.json"
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if not json_file.exists():
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raise HTTPException(
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status_code=404,
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detail=f"Seed data file not found: {json_file}"
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)
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# Load JSON data
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with open(json_file, 'r', encoding='utf-8') as f:
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seed_data = json.load(f)
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# Check if data already exists for this virtual tenant (idempotency)
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existing_check = await db.execute(
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select(Forecast).where(Forecast.tenant_id == virtual_uuid).limit(1)
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)
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existing_forecast = existing_check.scalar_one_or_none()
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if existing_forecast:
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logger.warning(
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"Demo data already exists, skipping clone",
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virtual_tenant_id=str(virtual_uuid)
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)
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return {
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"status": "skipped",
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"reason": "Data already exists",
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"records_cloned": 0
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}
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# Track cloning statistics
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stats = {
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@@ -100,93 +146,150 @@ async def clone_demo_data(
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"prediction_batches": 0
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}
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# Clone Forecasts
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result = await db.execute(
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select(Forecast).where(Forecast.tenant_id == base_uuid)
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)
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base_forecasts = result.scalars().all()
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# Transform and insert forecasts
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for forecast_data in seed_data.get('forecasts', []):
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# Transform ID using XOR
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from shared.utils.demo_id_transformer import transform_id
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try:
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forecast_uuid = uuid.UUID(forecast_data['id'])
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tenant_uuid = uuid.UUID(virtual_tenant_id)
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transformed_id = transform_id(forecast_data['id'], tenant_uuid)
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except ValueError as e:
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logger.error("Failed to parse UUIDs for ID transformation",
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forecast_id=forecast_data['id'],
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virtual_tenant_id=virtual_tenant_id,
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error=str(e))
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raise HTTPException(
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status_code=400,
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detail=f"Invalid UUID format in forecast data: {str(e)}"
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)
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# Transform dates
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for date_field in ['forecast_date', 'created_at']:
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if date_field in forecast_data:
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try:
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date_value = forecast_data[date_field]
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if isinstance(date_value, str):
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original_date = datetime.fromisoformat(date_value)
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elif hasattr(date_value, 'isoformat'):
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original_date = date_value
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else:
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logger.warning("Skipping invalid date format",
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date_field=date_field,
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date_value=date_value)
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continue
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adjusted_forecast_date = adjust_date_for_demo(
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original_date,
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session_time,
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BASE_REFERENCE_DATE
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)
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forecast_data[date_field] = adjusted_forecast_date
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except (ValueError, AttributeError) as e:
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logger.warning("Failed to parse date, skipping",
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date_field=date_field,
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date_value=forecast_data[date_field],
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error=str(e))
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forecast_data.pop(date_field, None)
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# Create forecast
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# Map product_id to inventory_product_id if needed
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inventory_product_id = forecast_data.get('inventory_product_id') or forecast_data.get('product_id')
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logger.info(
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"Found forecasts to clone",
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count=len(base_forecasts),
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base_tenant=str(base_uuid)
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)
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for forecast in base_forecasts:
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adjusted_forecast_date = adjust_date_for_demo(
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forecast.forecast_date,
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session_time,
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BASE_REFERENCE_DATE
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) if forecast.forecast_date else None
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# Map predicted_quantity to predicted_demand if needed
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predicted_demand = forecast_data.get('predicted_demand') or forecast_data.get('predicted_quantity')
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new_forecast = Forecast(
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id=uuid.uuid4(),
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id=transformed_id,
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tenant_id=virtual_uuid,
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inventory_product_id=forecast.inventory_product_id, # Keep product reference
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product_name=forecast.product_name,
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location=forecast.location,
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forecast_date=adjusted_forecast_date,
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created_at=session_time,
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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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day_of_week=forecast.day_of_week,
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is_holiday=forecast.is_holiday,
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is_weekend=forecast.is_weekend,
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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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processing_time_ms=forecast.processing_time_ms,
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features_used=forecast.features_used
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inventory_product_id=inventory_product_id,
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product_name=forecast_data.get('product_name'),
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location=forecast_data.get('location'),
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forecast_date=forecast_data.get('forecast_date'),
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created_at=forecast_data.get('created_at', session_time),
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predicted_demand=predicted_demand,
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confidence_lower=forecast_data.get('confidence_lower'),
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confidence_upper=forecast_data.get('confidence_upper'),
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confidence_level=forecast_data.get('confidence_level', 0.8),
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model_id=forecast_data.get('model_id'),
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model_version=forecast_data.get('model_version'),
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algorithm=forecast_data.get('algorithm', 'prophet'),
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business_type=forecast_data.get('business_type', 'individual'),
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day_of_week=forecast_data.get('day_of_week'),
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is_holiday=forecast_data.get('is_holiday', False),
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is_weekend=forecast_data.get('is_weekend', False),
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weather_temperature=forecast_data.get('weather_temperature'),
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weather_precipitation=forecast_data.get('weather_precipitation'),
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weather_description=forecast_data.get('weather_description'),
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traffic_volume=forecast_data.get('traffic_volume'),
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processing_time_ms=forecast_data.get('processing_time_ms'),
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features_used=forecast_data.get('features_used')
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)
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db.add(new_forecast)
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stats["forecasts"] += 1
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# Clone Prediction Batches
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result = await db.execute(
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select(PredictionBatch).where(PredictionBatch.tenant_id == base_uuid)
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)
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base_batches = result.scalars().all()
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logger.info(
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"Found prediction batches to clone",
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count=len(base_batches),
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base_tenant=str(base_uuid)
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)
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for batch in base_batches:
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adjusted_requested_at = adjust_date_for_demo(
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batch.requested_at,
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session_time,
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BASE_REFERENCE_DATE
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) if batch.requested_at else None
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adjusted_completed_at = adjust_date_for_demo(
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batch.completed_at,
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session_time,
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BASE_REFERENCE_DATE
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) if batch.completed_at else None
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# Transform and insert prediction batches
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for batch_data in seed_data.get('prediction_batches', []):
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# Transform ID using XOR
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from shared.utils.demo_id_transformer import transform_id
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try:
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batch_uuid = uuid.UUID(batch_data['id'])
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tenant_uuid = uuid.UUID(virtual_tenant_id)
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transformed_id = transform_id(batch_data['id'], tenant_uuid)
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except ValueError as e:
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logger.error("Failed to parse UUIDs for ID transformation",
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batch_id=batch_data['id'],
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virtual_tenant_id=virtual_tenant_id,
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error=str(e))
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raise HTTPException(
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status_code=400,
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detail=f"Invalid UUID format in batch data: {str(e)}"
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)
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# Transform dates
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for date_field in ['requested_at', 'completed_at']:
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if date_field in batch_data:
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try:
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date_value = batch_data[date_field]
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if isinstance(date_value, str):
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original_date = datetime.fromisoformat(date_value)
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elif hasattr(date_value, 'isoformat'):
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original_date = date_value
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else:
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logger.warning("Skipping invalid date format",
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date_field=date_field,
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date_value=date_value)
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continue
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adjusted_batch_date = adjust_date_for_demo(
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original_date,
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session_time,
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BASE_REFERENCE_DATE
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)
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batch_data[date_field] = adjusted_batch_date
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except (ValueError, AttributeError) as e:
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logger.warning("Failed to parse date, skipping",
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date_field=date_field,
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date_value=batch_data[date_field],
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error=str(e))
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batch_data.pop(date_field, None)
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# Create prediction batch
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new_batch = PredictionBatch(
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id=uuid.uuid4(),
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id=transformed_id,
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tenant_id=virtual_uuid,
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batch_name=batch.batch_name,
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requested_at=adjusted_requested_at,
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completed_at=adjusted_completed_at,
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status=batch.status,
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total_products=batch.total_products,
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completed_products=batch.completed_products,
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failed_products=batch.failed_products,
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forecast_days=batch.forecast_days,
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business_type=batch.business_type,
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error_message=batch.error_message,
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processing_time_ms=batch.processing_time_ms,
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cancelled_by=batch.cancelled_by
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batch_name=batch_data.get('batch_name'),
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requested_at=batch_data.get('requested_at'),
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completed_at=batch_data.get('completed_at'),
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status=batch_data.get('status'),
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total_products=batch_data.get('total_products'),
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completed_products=batch_data.get('completed_products'),
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failed_products=batch_data.get('failed_products'),
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forecast_days=batch_data.get('forecast_days'),
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business_type=batch_data.get('business_type'),
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error_message=batch_data.get('error_message'),
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processing_time_ms=batch_data.get('processing_time_ms'),
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cancelled_by=batch_data.get('cancelled_by')
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)
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db.add(new_batch)
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stats["prediction_batches"] += 1
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@@ -198,11 +301,12 @@ async def clone_demo_data(
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duration_ms = int((datetime.now(timezone.utc) - start_time).total_seconds() * 1000)
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logger.info(
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"Forecasting data cloning completed",
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virtual_tenant_id=virtual_tenant_id,
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total_records=total_records,
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stats=stats,
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duration_ms=duration_ms
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"Forecasting data cloned successfully",
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virtual_tenant_id=str(virtual_uuid),
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records_cloned=total_records,
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duration_ms=duration_ms,
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forecasts_cloned=stats["forecasts"],
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batches_cloned=stats["prediction_batches"]
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)
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return {
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@@ -210,11 +314,15 @@ async def clone_demo_data(
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"status": "completed",
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"records_cloned": total_records,
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"duration_ms": duration_ms,
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"details": stats
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"details": {
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"forecasts": stats["forecasts"],
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"prediction_batches": stats["prediction_batches"],
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"virtual_tenant_id": str(virtual_uuid)
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}
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}
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except ValueError as e:
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logger.error("Invalid UUID format", error=str(e))
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logger.error("Invalid UUID format", error=str(e), virtual_tenant_id=virtual_tenant_id)
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raise HTTPException(status_code=400, detail=f"Invalid UUID: {str(e)}")
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except Exception as e:
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@@ -248,3 +356,73 @@ async def clone_health_check(_: bool = Depends(verify_internal_api_key)):
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"clone_endpoint": "available",
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"version": "2.0.0"
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}
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@router.delete("/tenant/{virtual_tenant_id}")
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async def delete_demo_tenant_data(
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virtual_tenant_id: uuid.UUID,
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db: AsyncSession = Depends(get_db),
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_: bool = Depends(verify_internal_api_key)
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):
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"""
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Delete all demo data for a virtual tenant.
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This endpoint is idempotent - safe to call multiple times.
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"""
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from sqlalchemy import delete
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start_time = datetime.now(timezone.utc)
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records_deleted = {
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"forecasts": 0,
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"prediction_batches": 0,
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"total": 0
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}
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try:
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# Delete in reverse dependency order
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# 1. Delete prediction batches
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result = await db.execute(
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delete(PredictionBatch)
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.where(PredictionBatch.tenant_id == virtual_tenant_id)
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)
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records_deleted["prediction_batches"] = result.rowcount
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# 2. Delete forecasts
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result = await db.execute(
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delete(Forecast)
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.where(Forecast.tenant_id == virtual_tenant_id)
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)
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||||
records_deleted["forecasts"] = result.rowcount
|
||||
|
||||
records_deleted["total"] = sum(records_deleted.values())
|
||||
|
||||
await db.commit()
|
||||
|
||||
logger.info(
|
||||
"demo_data_deleted",
|
||||
service="forecasting",
|
||||
virtual_tenant_id=str(virtual_tenant_id),
|
||||
records_deleted=records_deleted
|
||||
)
|
||||
|
||||
return {
|
||||
"service": "forecasting",
|
||||
"status": "deleted",
|
||||
"virtual_tenant_id": str(virtual_tenant_id),
|
||||
"records_deleted": records_deleted,
|
||||
"duration_ms": int((datetime.now(timezone.utc) - start_time).total_seconds() * 1000)
|
||||
}
|
||||
|
||||
except Exception as e:
|
||||
await db.rollback()
|
||||
logger.error(
|
||||
"demo_data_deletion_failed",
|
||||
service="forecasting",
|
||||
virtual_tenant_id=str(virtual_tenant_id),
|
||||
error=str(e)
|
||||
)
|
||||
raise HTTPException(
|
||||
status_code=500,
|
||||
detail=f"Failed to delete demo data: {str(e)}"
|
||||
)
|
||||
Reference in New Issue
Block a user