Deploy Edge LLMs for Factory Diagnostics with LiteRT-LM and Hugging Face Transformers

                      
                     
"""
Production implementation for deploying Edge LLMs for factory diagnostics.
Provides secure, scalable operations using LiteRT-LM and Hugging Face Transformers.
"""

from typing import Dict, Any, List
import os
import logging
import asyncio
import httpx
from transformers import pipeline

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

class Config:
    """
    Configuration class to hold environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    model_name: str = os.getenv('MODEL_NAME', 'default-model')

# Initialize the Hugging Face model pipeline
model_pipeline = pipeline('text-generation', model=Config.model_name)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data structure.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'sensor_data' not in data:
        raise ValueError('Missing sensor_data key in input.')  # Validation error
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields for security.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}  # Strip whitespace
    logger.info('Sanitized input fields.')  # Log sanitization
    return sanitized_data

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize incoming data for processing.
    
    Args:
        data: Raw data to normalize
    Returns:
        Normalized data
    """
    # Example normalization logic, you can customize this based on your data
    normalized_data = {key: value / 100 for key, value in data.items() if isinstance(value, (int, float))}
    logger.info('Normalized data.')  # Log normalization
    return normalized_data

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data entries.
    
    Args:
        data: List of data entries to process
    Returns:
        Processed data entries
    """
    results = []
    for entry in data:
        logger.info(f'Processing entry: {entry}')  # Log entry processing
        # Here we would have the core logic to handle each entry
        results.append({'entry': entry, 'result': 'success'})  # Placeholder result
    return results

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from external source.
    
    Returns:
        List of fetched data entries
    """
    async with httpx.AsyncClient() as client:
        response = await client.get(Config.database_url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Fetched data successfully from external source.')  # Log success
        return response.json()  # Assuming the response is JSON

async def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to be saved
    """
    # Implement database saving logic here, for now, we just log it
    logger.info(f'Saving {len(data)} entries to the database.')  # Log saving action

async def format_output(data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Format output for the client.
    
    Args:
        data: Data to format
    Returns:
        Formatted output
    """
    return {'results': data, 'status': 'success', 'message': 'Data processed.'}  # Example output format

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Function to wrap
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in function {func.__name__}: {str(e)}')  # Log error
            return {'status': 'error', 'message': str(e)}  # Return error message
    return wrapper

class DiagnosticsProcessor:
    """Main orchestrator for diagnostics processing.
    
    Handles end-to-end diagnostics workflow from fetching to saving data.
    """

    @handle_errors
    async def run(self) -> None:
        """Run the full diagnostics processing workflow.
        """
        raw_data = await fetch_data()  # Fetch the data from the source
        validated_data = await validate_input(raw_data)  # Validate the data
        sanitized_data = await sanitize_fields(validated_data)  # Sanitize the data
        normalized_data = await normalize_data(sanitized_data)  # Normalize the data
        processed_data = await process_batch(normalized_data)  # Process the normalized data
        await save_to_db(processed_data)  # Save the processed data
        formatted_output = await format_output(processed_data)  # Format the output
        logger.info('Processing completed successfully.')  # Log completion
        return formatted_output  # Return the final output

if __name__ == '__main__':
    # Example usage of the DiagnosticsProcessor
    processor = DiagnosticsProcessor()
    asyncio.run(processor.run())  # Run the main processing workflow