Files
bakery-ia/services/inventory/scripts/demo/seed_demo_stock_retail.py
2025-11-30 09:12:40 +01:00

395 lines
14 KiB
Python

#!/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)