Inspect Assembly Welds with Vision-Language Models Using LMDeploy and Supervision

"""
Production implementation for inspecting assembly welds using vision-language models.
Provides secure, scalable operations with LMDeploy and Supervision.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from time import sleep

# Setting up logging configurations
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

# Validating input data
async def validate_input_data(data: Dict[str, Any]) -> bool:
    """Validate input data for weld inspection.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image' not in data:
        raise ValueError('Missing image data')  # Image field is required
    return True

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

# Transforming the data for processing
def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for model input.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    # Perform normalization here (e.g., resizing images)
    return data

# Processing a batch of weld images
async def process_batch(images: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of images to inspect welds.
    
    Args:
        images: List of image data
    Returns:
        List of results from inspection
    """
    results = []  # To hold results
    for image in images:
        try:
            normalized = normalize_data(image)
            result = await call_api(normalized)  # Call to the model API
            results.append(result)
            logger.info(f'Processed image: {image.get("id")}')  # Log each processed image
        except Exception as e:
            logger.error(f'Error processing image: {e}')  # Log error
    return results

# Fetching data from an external source
async def fetch_data(url: str) -> Any:
    """Fetch data from the specified URL.
    
    Args:
        url: URL to fetch data from
    Returns:
        JSON response from the API
    Raises:
        Exception: If fetching fails
    """
    response = requests.get(url)
    if response.status_code != 200:
        raise Exception('Failed to fetch data')
    return response.json()

# Saving results to the database
async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save inspection results to the database.
    
    Args:
        results: List of results to save
    """
    # Implement database save logic here
    logger.info('Results saved to database')  # Log successful save

# API call to the model for inspection
async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call the vision-language model API for inspection.
    
    Args:
        data: Input data to send
    Returns:
        API response
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(Config.api_url, json=data)
        response.raise_for_status()
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'API request failed: {e}')  # Log error
        raise

# Formatting output for better readability
def format_output(results: List[Dict[str, Any]]) -> str:
    """Format results for output.
    
    Args:
        results: List of results to format
    Returns:
        Formatted string output
    """
    return '\n'.join(str(result) for result in results)

# Main orchestrator class
class WeldInspection:
    """Main class to orchestrate weld inspection processes.
    """
    def __init__(self, data: List[Dict[str, Any]]) -> None:
        self.data = data

    async def run_inspection(self) -> None:
        """Run the entire inspection process.
        """
        try:
            # Validate and sanitize input data
            for image in self.data:
                await validate_input_data(image)  # Validate each input
                image = sanitize_fields(image)  # Sanitize fields
            results = await process_batch(self.data)  # Process images
            await save_to_db(results)  # Save results
            logger.info('Inspection completed successfully')  # Log completion
        except Exception as e:
            logger.error(f'Inspection process failed: {e}')  # Log failure

if __name__ == '__main__':
    # Example usage
    images_to_inspect = [{'image': 'path/to/image1.jpg'}, {'image': 'path/to/image2.jpg'}]
    inspection = WeldInspection(images_to_inspect)
    import asyncio
    asyncio.run(inspection.run_inspection())  # Run the inspection asynchronously