Autoscale Industrial AI Services Based on Inference Queue Depth with KServe and Prometheus Client

"""
Production implementation for Autoscale Industrial AI Services based on Inference Queue Depth with KServe and Prometheus Client.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import asyncio
import httpx
import prometheus_client

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class for environment variables."""
    kserve_url: str = os.getenv('KSERVE_URL', 'http://localhost:8080')
    prometheus_url: str = os.getenv('PROMETHEUS_URL', 'http://localhost:9090')

def get_prometheus_client() -> prometheus_client:
    """Returns a Prometheus client instance."""
    return prometheus_client

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_name' not in data:
        raise ValueError('Missing model_name')  # Ensure model_name is present
    return True

async def fetch_data(model_name: str) -> Dict[str, Any]:
    """Fetch inference data from KServe.
    
    Args:
        model_name: Name of the model to fetch data for
    Returns:
        Inference results
    Raises:
        Exception: If fetching fails
    """
    try:
        logger.info(f'Fetching data for model: {model_name}')
        async with httpx.AsyncClient() as client:
            response = await client.get(f'{Config.kserve_url}/v1/models/{model_name}/predict')
            response.raise_for_status()
            return response.json()  # Return JSON response
    except Exception as e:
        logger.error(f'Failed to fetch data: {e}')
        raise

async def process_inference_response(response: Dict[str, Any]) -> None:
    """Process the inference response data.
    
    Args:
        response: Inference response data
    """
    logger.info('Processing inference response...')
    # Logic to process response and trigger further actions

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save inference results to the database.
    
    Args:
        data: Inference results to save
    """
    logger.info('Saving data to database...')
    # Simulate database save with asyncio.sleep
    await asyncio.sleep(1)  # Replace with actual DB save logic

async def aggregate_metrics(metrics: List[float]) -> float:
    """Aggregate metrics from inference results.
    
    Args:
        metrics: List of metrics to aggregate
    Returns:
        Average metric value
    """
    return sum(metrics) / len(metrics) if metrics else 0.0

async def main_process(model_name: str) -> None:
    """Main process for handling inference requests.
    
    Args:
        model_name: The name of the model to process
    """
    try:
        await validate_input({'model_name': model_name})  # Validate input
        response = await fetch_data(model_name)  # Get inference data
        await process_inference_response(response)  # Process response
        await save_to_db(response)  # Save to DB
    except Exception as e:
        logger.error(f'Error in main process: {e}')  # Handle errors gracefully

if __name__ == '__main__':
    model_name = 'example_model'  # Example model name
    asyncio.run(main_process(model_name))  # Run main process