Export Physics-ML Digital Twin Models for Real-Time Inference with PhysicsNeMo and ONNX Runtime

"""
Production implementation for exporting Physics-ML digital twin models for real-time inference.
Ensures secure, scalable operations while utilizing PhysicsNeMo and ONNX Runtime.
"""

from typing import Dict, Any, List
import os
import logging
import time
import json
import requests
from contextlib import contextmanager
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///models.db')
    onnx_runtime_url: str = os.getenv('ONNX_RUNTIME_URL', 'http://onnx-runtime:8080')

# Database connection pooling
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

@contextmanager
def get_db():
    """
    Database session context manager.
    
    Yields:
        Session: SQLAlchemy session object
    """ 
    db = SessionLocal()
    try:
        yield db
    finally:
        db.close()  # Ensure closure of the session

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'model_id' not in data:
        raise ValueError('Missing model_id')  # Validate presence of model_id
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input dictionary
    Returns:
        Sanitized dictionary
    """  
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data  # Strip whitespace from string fields

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize model data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """  
    data['normalized'] = True  # Example normalization step
    return data

async def fetch_data(model_id: str) -> Dict[str, Any]:
    """Fetch model data from a remote service.
    
    Args:
        model_id: Identifier for the model
    Returns:
        Model data
    Raises:
        ConnectionError: If the fetch fails
    """  
    response = requests.get(f'{Config.onnx_runtime_url}/models/{model_id}')
    if response.status_code != 200:
        raise ConnectionError('Failed to fetch model data')  # Handle fetch failure
    return response.json()  # Return JSON response

async def save_to_db(model_data: Dict[str, Any], db) -> None:
    """Save model data to the database.
    
    Args:
        model_data: Model data to save
        db: Database session
    """  
    query = text("INSERT INTO models (id, data) VALUES (:id, :data)")
    db.execute(query, {'id': model_data['id'], 'data': json.dumps(model_data)})  # Insert model data
    db.commit()  # Commit transaction

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call the ONNX runtime inference API.
    
    Args:
        data: Data to send to the API
    Returns:
        API response
    """  
    response = requests.post(f'{Config.onnx_runtime_url}/inference', json=data)
    response.raise_for_status()  # Raise error for bad responses
    return response.json()  # Return API response

async def process_batch(model_ids: List[str]) -> None:
    """Process a batch of model IDs.
    
    Args:
        model_ids: List of model IDs
    """  
    with get_db() as db:
        for model_id in model_ids:
            try:
                await validate_input({'model_id': model_id})  # Validate input
                model_data = await fetch_data(model_id)  # Fetch model data
                sanitized_data = await sanitize_fields(model_data)  # Sanitize fetched data
                normalized_data = await normalize_data(sanitized_data)  # Normalize data
                await save_to_db(normalized_data, db)  # Save to the database
                logger.info(f'Model {model_id} processed successfully.')  # Log success
            except Exception as e:
                logger.error(f'Error processing model {model_id}: {str(e)}')  # Log errors

if __name__ == '__main__':
    # Example usage
    import asyncio
    model_ids_to_process = ['model_1', 'model_2', 'model_3']
    asyncio.run(process_batch(model_ids_to_process))  # Run the batch processing