Run Multimodal Weld Quality VLMs on Jetson with TensorRT Edge-LLM and SGLang

"""
Production implementation for running Multimodal Weld Quality VLMs on Jetson with TensorRT Edge-LLM and SGLang.
Provides secure, scalable operations for real-time welding quality assessment.
"""

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

# Logger setup for tracking application behavior
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/quality_model.trt')
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

@contextmanager
def database_connection():
    """
    Context manager for managing database connections.
    Ensures connections are properly closed after use.
    """
    connection = create_db_connection(Config.database_url)
    try:
        yield connection  # Providing connection to the caller
    finally:
        connection.close()  # Close connection after use

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate incoming data for welding quality assessment.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'weld_id' not in data:
        raise ValueError('Missing weld_id')
    if 'parameters' not in data:
        raise ValueError('Missing parameters')
    return True  # Data is valid

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input data to prevent injection attacks.
    
    Args:
        data: Raw input data
    Returns:
        Sanitized input data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data  # Returning sanitized data

async def fetch_data(weld_id: str) -> Dict[str, Any]:
    """
    Fetch welding data from the API.
    
    Args:
        weld_id: Identifier for the weld
    Returns:
        Welding data
    Raises:
        Exception: If fetching fails
    """
    response = requests.get(f'{Config.api_endpoint}/weld/{weld_id}')
    if response.status_code != 200:
        raise Exception('Failed to fetch data')
    return response.json()  # Returning fetched data

def process_batch(data: List[Dict[str, Any]]) -> Dict[str, float]:
    """
    Process a batch of welding quality data.
    
    Args:
        data: List of welding quality metrics
    Returns:
        Aggregated metrics
    """
    metrics = {'average_quality': 0.0, 'total_count': len(data)}
    metrics['average_quality'] = sum(d['quality'] for d in data) / len(data)  # Calculate average
    return metrics  # Returning processed metrics

async def save_to_db(data: Dict[str, Any]) -> None:
    """
    Save processed data to the database.
    
    Args:
        data: Data to be saved
    Raises:
        Exception: If save fails
    """
    try:
        with database_connection() as conn:
            conn.execute('INSERT INTO weld_quality (data) VALUES (?)', (json.dumps(data),))
    except Exception as e:
        logger.error('Error saving data to database: %s', e)
        raise  # Re-raise exception for further handling

def format_output(data: Dict[str, Any]) -> str:
    """
    Format output data for presentation.
    
    Args:
        data: Data to format
    Returns:
        Formatted string output
    """
    return json.dumps(data, indent=4)  # Return pretty JSON string

class WeldQualityProcessor:
    """
    Main orchestrator for the welding quality assessment process.
    """

    def __init__(self):
        pass  # Initialize if needed

    async def run_quality_assessment(self, weld_id: str) -> None:
        """
        Execute the quality assessment workflow.
        
        Args:
            weld_id: Identifier for the weld
        """
        try:
            raw_data = await fetch_data(weld_id)  # Fetch data from API
            validated_data = sanitize_fields(raw_data)  # Sanitize the data
            validate_input(validated_data)  # Validate the sanitized data
            metrics = process_batch([validated_data])  # Process batch data
            await save_to_db(metrics)  # Save processed metrics
            logger.info('Quality assessment completed successfully.')
        except Exception as e:
            logger.error('Failed to run quality assessment: %s', e)  # Log error

if __name__ == '__main__':
    # Example usage
    processor = WeldQualityProcessor()
    weld_id = 'WLD1234'
    try:
        import asyncio
        asyncio.run(processor.run_quality_assessment(weld_id))  # Run assessment asynchronously
    except Exception as e:
        logger.error('Error in main execution: %s', e)  # Log any errors raised in main