279 lines
10 KiB
Python
279 lines
10 KiB
Python
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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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"""
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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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# Tenant model - define minimal version for container environment
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try:
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from services.tenant.app.models.tenants import Tenant
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except ImportError:
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# If running in training-service container, define minimal Tenant model
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from sqlalchemy import Column, String, Boolean
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from sqlalchemy.dialects.postgresql import UUID as PGUUID
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from sqlalchemy.ext.declarative import declarative_base
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Base = declarative_base()
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class Tenant(Base):
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__tablename__ = "tenants"
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id = Column(PGUUID(as_uuid=True), primary_key=True)
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name = Column(String)
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is_demo = Column(Boolean)
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is_demo_template = Column(Boolean)
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logger = structlog.get_logger()
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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")
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self.tenant_db_url = os.getenv("TENANT_DATABASE_URL")
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if not self.training_db_url or not self.tenant_db_url:
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raise ValueError("Missing required database URLs")
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self.training_db = create_database_manager(self.training_db_url, "demo-ai-seed")
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self.tenant_db = create_database_manager(self.tenant_db_url, "demo-tenant-seed")
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async def get_demo_tenants(self):
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"""Get all demo tenants"""
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async with self.tenant_db.get_session() as session:
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result = await session.execute(
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select(Tenant).where(Tenant.is_demo == True, Tenant.is_demo_template == True)
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)
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return result.scalars().all()
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async def get_tenant_products(self, tenant_id: UUID):
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"""
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Get finished products for a tenant from inventory database.
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We need to query the actual inventory to get real product UUIDs.
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"""
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try:
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inventory_db_url = os.getenv("INVENTORY_DATABASE_URL")
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if not inventory_db_url:
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logger.warning("INVENTORY_DATABASE_URL not set, cannot get products")
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return []
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inventory_db = create_database_manager(inventory_db_url, "demo-inventory-check")
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# Define minimal Ingredient model for querying
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from sqlalchemy import Column, String, Enum as SQLEnum
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from sqlalchemy.dialects.postgresql import UUID as PGUUID
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from sqlalchemy.ext.declarative import declarative_base
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import enum
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Base = declarative_base()
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class IngredientType(str, enum.Enum):
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INGREDIENT = "INGREDIENT"
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FINISHED_PRODUCT = "FINISHED_PRODUCT"
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class Ingredient(Base):
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__tablename__ = "ingredients"
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id = Column(PGUUID(as_uuid=True), primary_key=True)
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tenant_id = Column(PGUUID(as_uuid=True))
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name = Column(String)
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ingredient_type = Column(SQLEnum(IngredientType, name="ingredienttype"))
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async with inventory_db.get_session() as session:
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result = await session.execute(
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select(Ingredient).where(
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Ingredient.tenant_id == tenant_id,
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Ingredient.ingredient_type == IngredientType.FINISHED_PRODUCT
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).limit(10) # Get up to 10 finished products
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)
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products = result.scalars().all()
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product_list = [
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{"id": product.id, "name": product.name}
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for product in products
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]
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logger.info(f"Found {len(product_list)} finished products for tenant",
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tenant_id=str(tenant_id))
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return product_list
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except Exception as e:
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logger.error("Error fetching tenant products", error=str(e), tenant_id=str(tenant_id))
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return []
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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="Demo model - 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: Tenant):
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"""Create fake AI models for a demo tenant"""
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logger.info("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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try:
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# Get products for this tenant
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products = await self.get_tenant_products(tenant.id)
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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("Model already exists, skipping",
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tenant_id=str(tenant.id),
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product_id=product["id"])
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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("Created fake AI model",
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tenant_id=str(tenant.id),
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product_id=product["id"],
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model_id=str(model.id))
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await session.commit()
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logger.info("Successfully created fake AI models for tenant",
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tenant_id=str(tenant.id),
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models_created=models_created)
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except Exception as e:
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logger.error("Error creating fake AI models for tenant",
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tenant_id=str(tenant.id),
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error=str(e))
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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("Starting demo AI models seeding")
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try:
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# Get all demo tenants
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demo_tenants = await self.get_demo_tenants()
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if not demo_tenants:
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logger.warning("No demo tenants found")
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return
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logger.info(f"Found {len(demo_tenants)} demo tenants")
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# Seed models for each tenant
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for tenant in demo_tenants:
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await self.seed_models_for_tenant(tenant)
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logger.info("✅ Demo AI models seeding completed successfully",
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tenants_processed=len(demo_tenants))
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except Exception as e:
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logger.error("❌ Demo AI models seeding failed", error=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 started")
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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("Demo AI models seed completed successfully")
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except Exception as e:
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logger.error("Demo AI models seed failed", error=str(e))
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sys.exit(1)
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if __name__ == "__main__":
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asyncio.run(main())
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