Recognize Industrial Components with GLM-4.5V and Hugging Face Transformers

                      
                     
"""
Production implementation for recognizing industrial components using GLM-4.5V and Hugging Face Transformers.
Provides secure, scalable operations, including data validation, transformation, and processing.
"""

from typing import Dict, Any, List
import os
import logging
import time
from transformers import pipeline

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    model_name: str = os.getenv('MODEL_NAME', 'GLM-4.5V')
    database_url: str = os.getenv('DATABASE_URL')

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

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for component recognition.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'components' not in data:
        raise ValueError('Missing components in input data')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to avoid security risks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {k: str(v).strip() for k, v in data.items()}

async def transform_records(data: Dict[str, Any]) -> List[str]:
    """Transform records into a suitable format for processing.
    
    Args:
        data: Input data to transform
    Returns:
        List of transformed component descriptions
    """
    return [f"Component: {desc}" for desc in data.get('components', [])]

async def process_batch(components: List[str]) -> List[str]:
    """Process a batch of components through the model.
    
    Args:
        components: List of component descriptions
    Returns:
        List of recognized components
    """
    results = []
    for component in components:
        output = model(component)
        results.append(output[0]['generated_text'])  # Extracting the generated text
    return results

async def fetch_data() -> Dict[str, Any]:
    """Fetch data from an external source (stub for now).
    
    Returns:
        Mock input data
    """
    return {'components': ['gear', 'motor', 'sensor']}

async def save_to_db(results: List[str]) -> None:
    """Save recognition results to the database (stub for now).
    
    Args:
        results: List of recognized components
    """
    # Simulate saving to DB
    logger.info('Saving results to the database...')

async def call_api(data: Dict[str, Any]) -> None:
    """Call an external API to process data (stub for now).
    
    Args:
        data: Data to send to the API
    """
    logger.info('Calling external API...')

async def format_output(results: List[str]) -> str:
    """Format the output for display or logging.
    
    Args:
        results: List of recognized components
    Returns:
        Formatted string of results
    """
    return '\n'.join(results)

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: The function to be wrapped
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {str(e)}')
            return []  # Return empty list on error
    return wrapper

class ComponentRecognizer:
    """Main orchestrator for recognizing components.
    """

    async def recognize_components(self) -> str:
        """Main workflow for recognizing components.
        
        Returns:
            Formatted recognition results
        """
        try:
            # Fetch data
            data = await fetch_data()  # Simulate fetching data
            await validate_input(data)  # Validate input data
            sanitized_data = await sanitize_fields(data)  # Sanitize data
            components = await transform_records(sanitized_data)  # Transform records
            results = await process_batch(components)  # Process batch
            await save_to_db(results)  # Save results to DB
            return format_output(results)  # Format output
        except ValueError as ve:
            logger.warning(f'Validation error: {str(ve)}')  # Log validation error
            return str(ve)
        except Exception as e:
            logger.error(f'Unexpected error: {str(e)}')  # Log unexpected errors
            return 'An error occurred during processing.'

if __name__ == '__main__':
    # Example usage of the ComponentRecognizer
    recognizer = ComponentRecognizer()
    results = await recognizer.recognize_components()  # Call the recognition method
    print(results)  # Display the results