Accelerate In-Vehicle AI with TensorRT Edge-LLM and Jetson T4000

                      
                     
"""
Production implementation for Accelerating In-Vehicle AI with TensorRT Edge-LLM and Jetson T4000.
Provides secure, scalable operations for real-time AI inference.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from contextlib import contextmanager

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

class Config:
    """Configuration class to manage environment settings."""
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH')

@contextmanager
def connection_pool():
    """Context manager for database connection pooling."""
    try:
        # Simulating a database connection pool
        logger.info('Connecting to the database...')
        yield 'db_connection'
    finally:
        # Clean up the connection
        logger.info('Closing the database connection.')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for AI inference.
    
    Args:
        data: Input data for validation
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image' not in data:
        raise ValueError('Missing image field')
    if not isinstance(data['image'], str):
        raise ValueError('Image must be a base64 encoded string')
    return True

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

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from an external API.
    
    Args:
        url: API endpoint to fetch data from
    Returns:
        Response data
    Raises:
        Exception: If the request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error('Failed to fetch data: %s', e)
        raise Exception('API request failed')

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data for inference.
    
    Args:
        data: List of input data records
    Returns:
        Processed results
    Raises:
        Exception: If processing fails
    """
    results = []
    for record in data:
        # Simulated processing
        result = {'processed': record['image']}  # Placeholder for actual processing
        results.append(result)
    return results

def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of processed results
    Returns:
        Aggregated metrics
    """
    # Placeholder for metrics aggregation
    return {'total_processed': len(results)}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    with connection_pool() as conn:
        logger.info('Saving data to the database...')
        # Simulate saving data
        # Actual database logic would go here

class InVehicleAI:
    """Main orchestrator class for managing AI inference workflow."""

    def __init__(self, config: Config) -> None:
        self.config = config

    async def run_inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Main method to run inference.
        
        Args:
            data: Input data for inference
        Returns:
            Result of inference
        Raises:
            ValueError: If input data is invalid
        """
        await validate_input(data)  # Validate input
        sanitized_data = sanitize_fields(data)  # Sanitize input
        results = await process_batch([sanitized_data])  # Process data
        await save_to_db(results)  # Save results to DB
        return {'status': 'success', 'results': results}

if __name__ == '__main__':
    # Example usage
    config = Config()  # Load configurations
    ai_system = InVehicleAI(config)
    example_data = {'image': 'base64encodedimage...'}
    try:
        # Run inference and print results
        result = ai_system.run_inference(example_data)
        print(result)
    except ValueError as ve:
        logger.error('Input error: %s', ve)
    except Exception as e:
        logger.error('An error occurred: %s', e)