Deploy Quantized VLMs for In-Line Assembly Inspection with TensorRT Edge-LLM and ONNX Runtime

"""
Production implementation for deploying quantized VLMs for in-line assembly inspection.
Provides secure, scalable operations with TensorRT and ONNX Runtime.
"""

from typing import Dict, Any, List
import os
import logging
import time
import onnxruntime
from contextlib import contextmanager
import numpy as np

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to handle environment variables.
    """
    model_path: str = os.getenv('MODEL_PATH', 'model.onnx')
    database_url: str = os.getenv('DATABASE_URL')

@contextmanager
def resource_manager() -> None:
    """Context manager for resource cleanup.
    """
    try:
        yield
    finally:
        logger.info('Cleaning up resources...')
        # Placeholder for cleanup logic

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for the model.
    
    Args:
        data: Input data for validation.
    Returns:
        bool: True if valid, raises ValueError if invalid.
    Raises:
        ValueError: If validation fails.
    """  
    if 'image' not in data:
        raise ValueError('Missing image in input data.')
    return True

def load_model(model_path: str) -> onnxruntime.InferenceSession:
    """Load the ONNX model for inference.
    
    Args:
        model_path: Path to the ONNX model.
    Returns:
        onnxruntime.InferenceSession: Loaded model session.
    Raises:
        RuntimeError: If model loading fails.
    """  
    try:
        model = onnxruntime.InferenceSession(model_path)
        logger.info('Model loaded successfully.')
        return model
    except Exception as e:
        logger.error(f'Failed to load model: {e}')
        raise RuntimeError('Model loading failed')

def preprocess_image(image: Any) -> np.ndarray:
    """Preprocess the input image for the model.
    
    Args:
        image: Raw image data.
    Returns:
        np.ndarray: Preprocessed image array.
    """  
    # Placeholder for actual preprocessing logic
    return np.array(image).astype(np.float32)

def run_inference(model: onnxruntime.InferenceSession, input_data: np.ndarray) -> np.ndarray:
    """Run the model inference.
    
    Args:
        model: Loaded ONNX model.
        input_data: Preprocessed input data.
    Returns:
        np.ndarray: Inference results.
    """  
    try:
        result = model.run(None, {'input': input_data})
        logger.info('Inference run successfully.')
        return result
    except Exception as e:
        logger.error(f'Inference failed: {e}')
        raise RuntimeError('Inference failed')

async def save_results(results: Any) -> None:
    """Save inference results to the database.
    
    Args:
        results: Inference results to save.
    """  
    # Placeholder for database save logic
    logger.info('Results saved to database.')

async def main(data: Dict[str, Any]) -> None:
    """Main orchestration function for model inference.
    
    Args:
        data: Input data for inference.
    """  
    try:
        # Validate input data
        await validate_input(data)
        with resource_manager():
            # Load model
            model = load_model(Config.model_path)
            # Preprocess image
            image = preprocess_image(data['image'])
            # Run inference
            results = run_inference(model, image)
            # Save results
            await save_results(results)
    except ValueError as ve:
        logger.error(f'Input validation error: {ve}')
    except RuntimeError as re:
        logger.error(f'Runtime error: {re}')
    except Exception as e:
        logger.error(f'Unexpected error: {e}')

if __name__ == '__main__':
    # Example usage
    input_data = {'image': 'path_to_image'}  # Replace with actual data
    import asyncio
    asyncio.run(main(input_data))