#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Demo Retail Stock Seeding Script for Inventory Service Creates realistic stock levels for finished products at child retail outlets This script runs as a Kubernetes init job inside the inventory-service container. It populates child retail tenants with stock levels for FINISHED PRODUCTS ONLY. Usage: python /app/scripts/demo/seed_demo_stock_retail.py Environment Variables Required: INVENTORY_DATABASE_URL - PostgreSQL connection string for inventory database DEMO_MODE - Set to 'production' for production seeding LOG_LEVEL - Logging level (default: INFO) """ import asyncio import uuid import sys import os import random from datetime import datetime, timezone, timedelta from pathlib import Path from decimal import Decimal # Add app to path sys.path.insert(0, str(Path(__file__).parent.parent.parent)) # Add shared to path for demo utilities sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent.parent)) from sqlalchemy.ext.asyncio import AsyncSession, create_async_engine from sqlalchemy.orm import sessionmaker from sqlalchemy import select import structlog from shared.utils.demo_dates import BASE_REFERENCE_DATE from app.models.inventory import Ingredient, Stock, ProductType # Configure logging structlog.configure( processors=[ structlog.stdlib.add_log_level, structlog.processors.TimeStamper(fmt="iso"), structlog.dev.ConsoleRenderer() ] ) logger = structlog.get_logger() # Fixed Demo Tenant IDs (must match tenant service) DEMO_TENANT_ENTERPRISE_CHAIN = uuid.UUID("c3d4e5f6-a7b8-49c0-d1e2-f3a4b5c6d7e8") # Enterprise parent (Obrador) DEMO_TENANT_CHILD_1 = uuid.UUID("d4e5f6a7-b8c9-40d1-e2f3-a4b5c6d7e8f9") # Madrid Centro DEMO_TENANT_CHILD_2 = uuid.UUID("e5f6a7b8-c9d0-41e2-f3a4-b5c6d7e8f9a0") # Barcelona Gràcia DEMO_TENANT_CHILD_3 = uuid.UUID("f6a7b8c9-d0e1-42f3-a4b5-c6d7e8f9a0b1") # Valencia Ruzafa # Child tenant configurations CHILD_TENANTS = [ (DEMO_TENANT_CHILD_1, "Madrid Centro", 1.2), # Larger store, 20% more stock (DEMO_TENANT_CHILD_2, "Barcelona Gràcia", 1.0), # Medium store, baseline stock (DEMO_TENANT_CHILD_3, "Valencia Ruzafa", 0.8) # Smaller store, 20% less stock ] # Retail stock configuration for finished products # Daily sales estimates (units per day) for each product type DAILY_SALES_BY_SKU = { "PRO-BAG-001": 80, # Baguette Tradicional - high volume "PRO-CRO-001": 50, # Croissant de Mantequilla - popular breakfast item "PRO-PUE-001": 30, # Pan de Pueblo - specialty item "PRO-NAP-001": 40 # Napolitana de Chocolate - pastry item } # Storage locations for retail outlets RETAIL_STORAGE_LOCATIONS = ["Display Case", "Back Room", "Cooling Shelf", "Storage Area"] def generate_retail_batch_number(tenant_id: uuid.UUID, product_sku: str, days_ago: int) -> str: """Generate a realistic batch number for retail stock""" tenant_short = str(tenant_id).split('-')[0].upper()[:4] date_code = (BASE_REFERENCE_DATE - timedelta(days=days_ago)).strftime("%Y%m%d") return f"RET-{tenant_short}-{product_sku}-{date_code}" def calculate_retail_stock_quantity( product_sku: str, size_multiplier: float, create_some_low_stock: bool = False ) -> float: """ Calculate realistic retail stock quantity based on daily sales Args: product_sku: SKU of the finished product size_multiplier: Store size multiplier (0.8 for small, 1.0 for medium, 1.2 for large) create_some_low_stock: If True, 20% chance of low stock scenario Returns: Stock quantity in units """ daily_sales = DAILY_SALES_BY_SKU.get(product_sku, 20) # Retail outlets typically stock 1-3 days worth (fresh bakery products) if create_some_low_stock and random.random() < 0.2: # Low stock: 0.3-0.8 days worth (need restock soon) days_of_supply = random.uniform(0.3, 0.8) else: # Normal: 1-2.5 days worth days_of_supply = random.uniform(1.0, 2.5) quantity = daily_sales * days_of_supply * size_multiplier # Add realistic variability quantity *= random.uniform(0.85, 1.15) return max(5.0, round(quantity)) # Minimum 5 units async def seed_retail_stock_for_tenant( db: AsyncSession, tenant_id: uuid.UUID, tenant_name: str, size_multiplier: float ) -> dict: """ Seed realistic stock levels for a child retail tenant Creates multiple stock batches per product with varied freshness levels, simulating realistic retail bakery inventory with: - Fresh stock from today's/yesterday's delivery - Some expiring soon items - Varied batch sizes and locations Args: db: Database session tenant_id: UUID of the child tenant tenant_name: Name of the tenant (for logging) size_multiplier: Store size multiplier for stock quantities Returns: Dict with seeding statistics """ logger.info("─" * 80) logger.info(f"Seeding retail stock for: {tenant_name}") logger.info(f"Tenant ID: {tenant_id}") logger.info(f"Size Multiplier: {size_multiplier}x") logger.info("─" * 80) # Get all finished products for this tenant result = await db.execute( select(Ingredient).where( Ingredient.tenant_id == tenant_id, Ingredient.product_type == ProductType.FINISHED_PRODUCT, Ingredient.is_active == True ) ) products = result.scalars().all() if not products: logger.warning(f"No finished products found for tenant {tenant_id}") return { "tenant_id": str(tenant_id), "tenant_name": tenant_name, "stock_batches_created": 0, "products_stocked": 0 } created_batches = 0 for product in products: # Create 2-4 batches per product (simulating multiple deliveries/batches) num_batches = random.randint(2, 4) for batch_index in range(num_batches): # Vary delivery dates (0-2 days ago for fresh bakery products) days_ago = random.randint(0, 2) received_date = BASE_REFERENCE_DATE - timedelta(days=days_ago) # Calculate expiration based on shelf life shelf_life_days = product.shelf_life_days or 2 # Default 2 days for bakery expiration_date = received_date + timedelta(days=shelf_life_days) # Calculate quantity for this batch # Split total quantity across batches with variation batch_quantity_factor = random.uniform(0.3, 0.7) # Each batch is 30-70% of average quantity = calculate_retail_stock_quantity( product.sku, size_multiplier, create_some_low_stock=(batch_index == 0) # First batch might be low ) * batch_quantity_factor # Determine if product is still good days_until_expiration = (expiration_date - BASE_REFERENCE_DATE).days is_expired = days_until_expiration < 0 is_available = not is_expired quality_status = "expired" if is_expired else "good" # Random storage location storage_location = random.choice(RETAIL_STORAGE_LOCATIONS) # Create stock batch stock_batch = Stock( id=uuid.uuid4(), tenant_id=tenant_id, ingredient_id=product.id, supplier_id=DEMO_TENANT_ENTERPRISE_CHAIN, # Supplied by parent (Obrador) batch_number=generate_retail_batch_number(tenant_id, product.sku, days_ago), lot_number=f"LOT-{BASE_REFERENCE_DATE.strftime('%Y%m%d')}-{batch_index+1:02d}", supplier_batch_ref=f"OBRADOR-{received_date.strftime('%Y%m%d')}-{random.randint(1000, 9999)}", production_stage="fully_baked", # Retail receives fully baked products transformation_reference=None, current_quantity=quantity, reserved_quantity=0.0, available_quantity=quantity if is_available else 0.0, received_date=received_date, expiration_date=expiration_date, best_before_date=expiration_date - timedelta(hours=12) if shelf_life_days == 1 else None, original_expiration_date=None, transformation_date=None, final_expiration_date=expiration_date, unit_cost=Decimal(str(product.average_cost or 0.5)), total_cost=Decimal(str(product.average_cost or 0.5)) * Decimal(str(quantity)), storage_location=storage_location, warehouse_zone=None, # Retail outlets don't have warehouse zones shelf_position=None, requires_refrigeration=False, # Most bakery products don't require refrigeration requires_freezing=False, storage_temperature_min=None, storage_temperature_max=25.0 if product.is_perishable else None, # Room temp storage_humidity_max=65.0 if product.is_perishable else None, shelf_life_days=shelf_life_days, storage_instructions=product.storage_instructions if hasattr(product, 'storage_instructions') else None, is_available=is_available, is_expired=is_expired, quality_status=quality_status, created_at=received_date, updated_at=BASE_REFERENCE_DATE ) db.add(stock_batch) created_batches += 1 logger.debug( f" ✅ Created stock batch: {product.name} - " f"{quantity:.0f} units, expires in {days_until_expiration} days" ) # Commit all changes for this tenant await db.commit() logger.info(f" 📊 Stock batches created: {created_batches} across {len(products)} products") logger.info("") return { "tenant_id": str(tenant_id), "tenant_name": tenant_name, "stock_batches_created": created_batches, "products_stocked": len(products) } async def seed_retail_stock(db: AsyncSession): """ Seed retail stock for all child tenant templates Args: db: Database session Returns: Dict with overall seeding statistics """ logger.info("=" * 80) logger.info("📦 Starting Demo Retail Stock Seeding") logger.info("=" * 80) logger.info("Creating stock levels for finished products at retail outlets") logger.info("") results = [] # Seed for each child retail outlet for child_tenant_id, child_tenant_name, size_multiplier in CHILD_TENANTS: logger.info("") result = await seed_retail_stock_for_tenant( db, child_tenant_id, f"{child_tenant_name} (Retail Outlet)", size_multiplier ) results.append(result) # Calculate totals total_batches = sum(r["stock_batches_created"] for r in results) total_products = sum(r["products_stocked"] for r in results) logger.info("=" * 80) logger.info("✅ Demo Retail Stock Seeding Completed") logger.info("=" * 80) return { "service": "inventory_stock_retail", "tenants_seeded": len(results), "total_batches_created": total_batches, "total_products_stocked": total_products, "results": results } async def main(): """Main execution function""" logger.info("Demo Retail Stock Seeding Script Starting") logger.info("Mode: %s", os.getenv("DEMO_MODE", "development")) logger.info("Log Level: %s", os.getenv("LOG_LEVEL", "INFO")) # Get database URL from environment database_url = os.getenv("INVENTORY_DATABASE_URL") or os.getenv("DATABASE_URL") if not database_url: logger.error("❌ INVENTORY_DATABASE_URL or DATABASE_URL environment variable must be set") return 1 # Convert to async URL if needed if database_url.startswith("postgresql://"): database_url = database_url.replace("postgresql://", "postgresql+asyncpg://", 1) logger.info("Connecting to inventory database") # Create engine and session engine = create_async_engine( database_url, echo=False, pool_pre_ping=True, pool_size=5, max_overflow=10 ) async_session = sessionmaker( engine, class_=AsyncSession, expire_on_commit=False ) try: async with async_session() as session: result = await seed_retail_stock(session) logger.info("") logger.info("📊 Retail Stock Seeding Summary:") logger.info(f" ✅ Retail outlets seeded: {result['tenants_seeded']}") logger.info(f" ✅ Total stock batches: {result['total_batches_created']}") logger.info(f" ✅ Products stocked: {result['total_products_stocked']}") logger.info("") # Print per-tenant details for tenant_result in result['results']: logger.info( f" {tenant_result['tenant_name']}: " f"{tenant_result['stock_batches_created']} batches, " f"{tenant_result['products_stocked']} products" ) logger.info("") logger.info("🎉 Success! Retail stock levels are ready for cloning.") logger.info("") logger.info("Stock characteristics:") logger.info(" ✓ Multiple batches per product (2-4 batches)") logger.info(" ✓ Varied freshness levels (0-2 days old)") logger.info(" ✓ Realistic quantities based on store size") logger.info(" ✓ Some low-stock scenarios for demo alerts") logger.info(" ✓ Expiration tracking enabled") logger.info("") logger.info("Next steps:") logger.info(" 1. Seed retail sales history") logger.info(" 2. Seed customer data") logger.info(" 3. Test stock alerts and reorder triggers") logger.info("") return 0 except Exception as e: logger.error("=" * 80) logger.error("❌ Demo Retail Stock Seeding Failed") logger.error("=" * 80) logger.error("Error: %s", str(e)) logger.error("", exc_info=True) return 1 finally: await engine.dispose() if __name__ == "__main__": exit_code = asyncio.run(main()) sys.exit(exit_code)