demo seed change
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Urtzi Alfaro
@@ -1,268 +0,0 @@
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"""
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Demo AI Models Seed Script
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Creates fake AI models for demo tenants to populate the models list
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without having actual trained model files.
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This script uses hardcoded tenant and product IDs to avoid cross-database dependencies.
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"""
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import asyncio
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import sys
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import os
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from uuid import UUID
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from datetime import datetime, timezone, timedelta
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from decimal import Decimal
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# Add project root to path
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../..")))
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from sqlalchemy import select
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from shared.database.base import create_database_manager
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import structlog
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# Import models - these paths work both locally and in container
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try:
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# Container environment (training-service image)
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from app.models.training import TrainedModel
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except ImportError:
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# Local environment
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from services.training.app.models.training import TrainedModel
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logger = structlog.get_logger()
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# ============================================================================
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# HARDCODED DEMO DATA (from seed scripts)
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# ============================================================================
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# Demo Tenant IDs (from seed_demo_tenants.py)
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DEMO_TENANT_PROFESSIONAL = UUID("a1b2c3d4-e5f6-47a8-b9c0-d1e2f3a4b5c6") # Panadería Artesana Madrid
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DEMO_TENANT_ENTERPRISE_CHAIN = UUID("c3d4e5f6-a7b8-49c0-d1e2-f3a4b5c6d7e8") # Enterprise parent (Obrador)
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DEMO_PRODUCTS = {
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DEMO_TENANT_PROFESSIONAL: [
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{"id": UUID("20000000-0000-0000-0000-000000000001"), "name": "Baguette Tradicional"},
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{"id": UUID("20000000-0000-0000-0000-000000000002"), "name": "Croissant de Mantequilla"},
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{"id": UUID("20000000-0000-0000-0000-000000000003"), "name": "Pan de Pueblo"},
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{"id": UUID("20000000-0000-0000-0000-000000000004"), "name": "Napolitana de Chocolate"},
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],
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DEMO_TENANT_ENTERPRISE_CHAIN: [
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# Same products as professional but for enterprise parent (Obrador)
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{"id": UUID("20000000-0000-0000-0000-000000000001"), "name": "Baguette Tradicional"},
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{"id": UUID("20000000-0000-0000-0000-000000000002"), "name": "Croissant de Mantequilla"},
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{"id": UUID("20000000-0000-0000-0000-000000000003"), "name": "Pan de Pueblo"},
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{"id": UUID("20000000-0000-0000-0000-000000000004"), "name": "Napolitana de Chocolate"},
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]
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}
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class DemoAIModelSeeder:
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"""Seed fake AI models for demo tenants"""
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def __init__(self):
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self.training_db_url = os.getenv("TRAINING_DATABASE_URL") or os.getenv("DATABASE_URL")
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if not self.training_db_url:
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raise ValueError("Missing TRAINING_DATABASE_URL or DATABASE_URL")
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# Convert to async URL if needed
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if self.training_db_url.startswith("postgresql://"):
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self.training_db_url = self.training_db_url.replace(
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"postgresql://", "postgresql+asyncpg://", 1
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)
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self.training_db = create_database_manager(self.training_db_url, "demo-ai-seed")
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async def create_fake_model(self, session, tenant_id: UUID, product_info: dict):
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"""Create a fake AI model entry for a product"""
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now = datetime.now(timezone.utc)
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training_start = now - timedelta(days=90)
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training_end = now - timedelta(days=7)
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fake_model = TrainedModel(
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tenant_id=tenant_id,
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inventory_product_id=product_info["id"],
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model_type="prophet_optimized",
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model_version="1.0-demo",
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job_id=f"demo-job-{tenant_id}-{product_info['id']}",
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# Fake file paths (files don't actually exist)
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model_path=f"/fake/models/{tenant_id}/{product_info['id']}/model.pkl",
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metadata_path=f"/fake/models/{tenant_id}/{product_info['id']}/metadata.json",
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# Fake but realistic metrics
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mape=Decimal("12.5"), # Mean Absolute Percentage Error
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mae=Decimal("2.3"), # Mean Absolute Error
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rmse=Decimal("3.1"), # Root Mean Squared Error
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r2_score=Decimal("0.85"), # R-squared
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training_samples=60, # 60 days of training data
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# Fake hyperparameters
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hyperparameters={
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"changepoint_prior_scale": 0.05,
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"seasonality_prior_scale": 10.0,
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"holidays_prior_scale": 10.0,
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"seasonality_mode": "multiplicative"
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},
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# Features used
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features_used=["weekday", "month", "is_holiday", "temperature", "precipitation"],
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# Normalization params (fake)
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normalization_params={
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"temperature": {"mean": 15.0, "std": 5.0},
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"precipitation": {"mean": 2.0, "std": 1.5}
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},
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# Model status
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is_active=True,
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is_production=False, # Demo models are not production-ready
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# Training data info
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training_start_date=training_start,
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training_end_date=training_end,
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data_quality_score=Decimal("0.75"), # Good but not excellent
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# Metadata
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notes=f"Demo model for {product_info['name']} - No actual trained file exists. For demonstration purposes only.",
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created_by="demo-seed-script",
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created_at=now,
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updated_at=now,
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last_used_at=None
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)
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session.add(fake_model)
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return fake_model
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async def seed_models_for_tenant(self, tenant_id: UUID, tenant_name: str, products: list):
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"""Create fake AI models for a demo tenant"""
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logger.info(
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"Creating fake AI models for demo tenant",
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tenant_id=str(tenant_id),
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tenant_name=tenant_name,
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product_count=len(products)
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)
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try:
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async with self.training_db.get_session() as session:
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models_created = 0
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for product in products:
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# Check if model already exists
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result = await session.execute(
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select(TrainedModel).where(
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TrainedModel.tenant_id == tenant_id,
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TrainedModel.inventory_product_id == product["id"]
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)
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)
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existing_model = result.scalars().first()
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if existing_model:
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logger.info(
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"Model already exists, skipping",
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tenant_id=str(tenant_id),
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product_name=product["name"],
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product_id=str(product["id"])
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)
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continue
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# Create fake model
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model = await self.create_fake_model(session, tenant_id, product)
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models_created += 1
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logger.info(
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"Created fake AI model",
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tenant_id=str(tenant_id),
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product_name=product["name"],
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product_id=str(product["id"]),
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model_id=str(model.id)
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)
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await session.commit()
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logger.info(
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"✅ Successfully created fake AI models for tenant",
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tenant_id=str(tenant_id),
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tenant_name=tenant_name,
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models_created=models_created
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)
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return models_created
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except Exception as e:
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logger.error(
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"❌ Error creating fake AI models for tenant",
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tenant_id=str(tenant_id),
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tenant_name=tenant_name,
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error=str(e),
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exc_info=True
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)
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raise
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async def seed_all_demo_models(self):
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"""Seed fake AI models for all demo tenants"""
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logger.info("=" * 80)
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logger.info("🤖 Starting Demo AI Models Seeding")
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logger.info("=" * 80)
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total_models_created = 0
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try:
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# Professional Bakery (single location)
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professional_count = await self.seed_models_for_tenant(
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tenant_id=DEMO_TENANT_PROFESSIONAL,
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tenant_name="Panadería Artesana Madrid (Professional)",
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products=DEMO_PRODUCTS[DEMO_TENANT_PROFESSIONAL]
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)
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total_models_created += professional_count
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# Enterprise Parent (central production - Obrador)
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enterprise_count = await self.seed_models_for_tenant(
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tenant_id=DEMO_TENANT_ENTERPRISE_CHAIN,
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tenant_name="Panadería Central - Obrador Madrid (Enterprise Parent)",
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products=DEMO_PRODUCTS[DEMO_TENANT_ENTERPRISE_CHAIN]
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)
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total_models_created += enterprise_count
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logger.info("=" * 80)
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logger.info(
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"✅ Demo AI Models Seeding Completed",
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total_models_created=total_models_created,
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tenants_processed=2
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)
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logger.info("=" * 80)
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except Exception as e:
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logger.error("=" * 80)
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logger.error("❌ Demo AI Models Seeding Failed")
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logger.error("=" * 80)
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logger.error("Error: %s", str(e))
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raise
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async def main():
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"""Main entry point"""
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logger.info("Demo AI Models Seed Script Starting")
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logger.info("Mode: %s", os.getenv("DEMO_MODE", "development"))
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logger.info("Log Level: %s", os.getenv("LOG_LEVEL", "INFO"))
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try:
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seeder = DemoAIModelSeeder()
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await seeder.seed_all_demo_models()
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logger.info("")
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logger.info("🎉 Success! Demo AI models are ready.")
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logger.info("")
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logger.info("Note: These are fake models for demo purposes only.")
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logger.info(" No actual model files exist on disk.")
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logger.info("")
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return 0
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except Exception as e:
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logger.error("Demo AI models seed failed", error=str(e), exc_info=True)
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return 1
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if __name__ == "__main__":
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exit_code = asyncio.run(main())
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sys.exit(exit_code)
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