Quantize Factory Vision Models for Low-Resource Deployment with Hugging Face Optimum and ONNX Runtime

"""
Production implementation for quantizing factory vision models for low-resource deployment.
Utilizes Hugging Face Optimum and ONNX Runtime for efficient model inference.
"""

from typing import Dict, Any, List
import os
import logging
import onnx
from onnxruntime import InferenceSession
from transformers import AutoModelForImageClassification, AutoTokenizer

# Setup logger for tracking information and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    model_name: str = os.getenv('MODEL_NAME', 'facebook/deit-base-distilled-patch16-224')
    onnx_model_path: str = os.getenv('ONNX_MODEL_PATH', './model.onnx')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the request data for model inference.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image' not in data:
        raise ValueError('Missing image field in input data.')
    logger.info('Input data validated successfully.')
    return True

async def fetch_data(image_path: str) -> Any:
    """Fetch and preprocess the input image for model inference.
    
    Args:
        image_path: Path to the image file
    Returns:
        Preprocessed image
    Raises:
        FileNotFoundError: If the image file is not found
    """
    if not os.path.isfile(image_path):
        raise FileNotFoundError(f'Image file not found: {image_path}')
    logger.info(f'Fetched image data from {image_path}.')
    # Add image loading and preprocessing logic here...
    return image_data

async def load_model(model_name: str) -> InferenceSession:
    """Load the ONNX model for inference.
    
    Args:
        model_name: Name of the model to load
    Returns:
        InferenceSession for the model
    """
    logger.info(f'Loading model: {model_name}')
    model = onnx.load(Config.onnx_model_path)
    session = InferenceSession(model.SerializeToString())
    logger.info('Model loaded successfully.')
    return session

def transform_records(image_data: Any) -> List[float]:
    """Transform image data into a tensor format suitable for ONNX.
    
    Args:
        image_data: Preprocessed image data
    Returns:
        Tensor representation of the image
    """
    # Logic to transform image_data to tensor
    tensor = [0.0]  # Placeholder for actual tensor conversion
    logger.info('Image data transformed into tensor format.')
    return tensor

async def process_batch(session: InferenceSession, inputs: List[float]) -> Any:
    """Process a batch of inputs through the model.
    
    Args:
        session: Inference session for the model
        inputs: List of input tensors
    Returns:
        Model predictions
    """
    results = session.run(None, {'input': inputs})
    logger.info('Batch processed successfully.')
    return results

async def save_to_db(results: Any) -> None:
    """Save model results to a database or external service.
    
    Args:
        results: Model inference results
    """
    # Logic to save results to DB
    logger.info('Results saved to database.')

async def format_output(results: Any) -> Dict[str, Any]:
    """Format the model results into a JSON-friendly structure.
    
    Args:
        results: Raw model results
    Returns:
        Dictionary with formatted results
    """
    formatted = {'predictions': results}
    logger.info('Output formatted successfully.')
    return formatted

async def handle_errors(func):
    """Decorator for handling errors in async functions.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function with error handling
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error occurred in {func.__name__}: {e}')
            raise
    return wrapper

class VisionModel:
    """Main orchestrator for vision model inference pipeline.
    """
    def __init__(self):
        self.session = None

    async def initialize(self) -> None:
        """Initialize the model session.
        """ 
        self.session = await load_model(Config.model_name)

    @handle_errors
    async def run_inference(self, image_path: str) -> Dict[str, Any]:
        """Run inference on a given image.
        
        Args:
            image_path: Path to the image for inference
        Returns:
            Inference results formatted for output
        """ 
        await validate_input({'image': image_path})
        image_data = await fetch_data(image_path)
        inputs = transform_records(image_data)
        results = await process_batch(self.session, inputs)
        return await format_output(results)

if __name__ == '__main__':
    # Example usage of the VisionModel
    model = VisionModel()
    import asyncio
    asyncio.run(model.initialize())
    try:
        output = asyncio.run(model.run_inference('path/to/image.jpg'))
        print(output)
    except Exception as e:
        logger.error(f'Failed to run inference: {e}')