Inspect Automotive Assembly Components with TensorRT Edge-LLM and Supervision

"""
Production implementation for inspecting automotive assembly components.
Provides secure, scalable operations using TensorRT and edge processing.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import time
import tensorflow as tf
from tensorflow import keras
import numpy as np

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    def __init__(self):
        self.model_path: str = os.getenv('MODEL_PATH', 'model.tflite')
        self.db_url: str = os.getenv('DATABASE_URL')

config = Config()

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for component inspection.
    
    Args:
        data: Input dictionary containing inspection data
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'component_id' not in data or 'image' not in data:
        raise ValueError('Missing required fields: component_id, image')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input dictionary to sanitize
    Returns:
        Sanitized dictionary
    """
    return {key: str(value).strip() for key, value in data.items()}

async def load_model(model_path: str) -> keras.Model:
    """Load the TensorRT model for inference.
    
    Args:
        model_path: Path to the TensorRT model file
    Returns:
        Loaded keras model
    """
    try:
        model = keras.models.load_model(model_path)
        logger.info('Model loaded successfully.')
        return model
    except Exception as e:
        logger.error('Failed to load model: %s', e)
        raise

async def preprocess_image(image: Any) -> np.ndarray:
    """Preprocess the input image for model prediction.
    
    Args:
        image: Raw image input
    Returns:
        Preprocessed image as numpy array
    """
    image = tf.image.decode_image(image)
    image = tf.image.resize(image, [224, 224])  # Resize to model input size
    image = np.expand_dims(image, axis=0)  # Add batch dimension
    logger.info('Image preprocessed.')
    return image

async def predict(model: keras.Model, image: np.ndarray) -> str:
    """Make a prediction using the loaded model.
    
    Args:
        model: Loaded keras model
        image: Preprocessed image
    Returns:
        Prediction result as string
    """
    predictions = model.predict(image)
    result = 'Defective' if predictions[0][0] > 0.5 else 'Non-defective'
    logger.info('Prediction made: %s', result)
    return result

async def save_to_db(component_id: str, result: str) -> None:
    """Save inspection result to the database.
    
    Args:
        component_id: ID of the inspected component
        result: Result of the inspection
    Raises:
        Exception: If database operation fails
    """
    try:
        # Simulate database saving operation
        logger.info('Saving result for component %s: %s', component_id, result)
        # Here, you would implement actual database saving logic
    except Exception as e:
        logger.error('Error saving to database: %s', e)
        raise

async def handle_inspection(data: Dict[str, Any]) -> None:
    """Process the inspection of an automotive component.
    
    Args:
        data: Inspection data
    Raises:
        ValueError: If input validation fails
    """
    await validate_input(data)  # Validate input
    sanitized_data = await sanitize_fields(data)  # Sanitize fields
    model = await load_model(config.model_path)  # Load model
    image = await preprocess_image(sanitized_data['image'])  # Preprocess image
    result = await predict(model, image)  # Make prediction
    await save_to_db(sanitized_data['component_id'], result)  # Save result

if __name__ == '__main__':
    # Example usage
    inspection_data = {
        'component_id': '12345',
        'image': 'path/to/image.jpg'  # Replace with actual image data
    }
    try:
        await handle_inspection(inspection_data)
    except Exception as e:
        logger.error('Inspection failed: %s', e)