Run VLM-Powered Quality Inspection on Industrial Edge Devices with TensorRT Edge-LLM and OpenCV

"""
Production implementation for VLM-Powered Quality Inspection.
This module interfaces with TensorRT and OpenCV.
Handles image processing and quality checks.
"""

from typing import Dict, Any, List
import os
import logging
import cv2
import numpy as np
import time
from functools import wraps

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    model_path: str = os.getenv('MODEL_PATH', '/models/my_model.trt')
    image_dir: str = os.getenv('IMAGE_DIR', '/images')
    db_url: str = os.getenv('DATABASE_URL', 'sqlite:///mydb.sqlite')

def retry(exceptions: Exception, tries: int = 4, delay: float = 1.0):
    """
    Retry decorator to handle transient errors.
    
    Args:
        exceptions: Exception types to catch
        tries: Number of retry attempts
        delay: Delay between retries
    """  
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):
            mtries, mdelay = tries, delay  
            while mtries > 0:
                try:
                    return func(*args, **kwargs)
                except exceptions as e:
                    logger.warning(f'Error: {e}, retrying in {mdelay} seconds...')
                    time.sleep(mdelay)
                    mtries -= 1
                    mdelay *= 2  # Exponential backoff
            raise
        return wrapper
    return decorator

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for quality inspection.
    
    Args:
        data: Input data dictionary
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_id' not in data:
        raise ValueError('Missing image_id')
    if 'image_file' not in data:
        raise ValueError('Missing image_file')
    return True

def load_image(image_path: str) -> np.ndarray:
    """Load and preprocess the image.
    
    Args:
        image_path: Path to the image file
    Returns:
        np.ndarray: Preprocessed image
    Raises:
        FileNotFoundError: If image file does not exist
    """  
    if not os.path.exists(image_path):
        raise FileNotFoundError(f'Image file not found: {image_path}')
    image = cv2.imread(image_path)
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)  # Convert color space
    return image

def preprocess_image(image: np.ndarray) -> np.ndarray:
    """Resize and normalize the image for model input.
    
    Args:
        image: Input image as a numpy array
    Returns:
        np.ndarray: Normalized image
    """  
    image = cv2.resize(image, (224, 224))  # Resize to model input size
    image = image.astype(np.float32) / 255.0  # Normalize
    return image

@retry(Exception)
def run_inference(image: np.ndarray) -> List[float]:
    """Run inference using TensorRT model.
    
    Args:
        image: Preprocessed image
    Returns:
        List[float]: Inference results
    """  
    # Placeholder for TensorRT inference code
    results = [0.5, 0.2, 0.3]  # Mockup results
    return results

def process_batch(image_files: List[str]) -> List[Dict[str, Any]]:
    """Process a batch of images for quality inspection.
    
    Args:
        image_files: List of image file paths
    Returns:
        List[Dict[str, Any]]: Results of quality inspection
    """  
    results = []
    for image_file in image_files:
        try:
            image = load_image(image_file)
            image = preprocess_image(image)
            inference_results = run_inference(image)
            results.append({'image_file': image_file, 'results': inference_results})
        except Exception as e:
            logger.error(f'Error processing {image_file}: {e}')
            results.append({'image_file': image_file, 'error': str(e)})
    return results

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save results to the database.
    
    Args:
        results: List of results to save
    """  
    # Placeholder for database saving logic
    logger.info('Results saved to the database.')

async def main(image_files: List[str]) -> None:
    """Main orchestration function for quality inspection.
    
    Args:
        image_files: List of image file paths to inspect
    """  
    try:
        await validate_input({'image_files': image_files})
        results = process_batch(image_files)
        await save_to_db(results)
    except Exception as e:
        logger.error(f'Error in main execution: {e}')

if __name__ == '__main__':
    # Example usage
    image_files = [os.path.join(Config.image_dir, f) for f in os.listdir(Config.image_dir)]
    try:
        import asyncio
        asyncio.run(main(image_files))
    except Exception as e:
        logger.error(f'Failed to run: {e}')