Classify Manufacturing Defects with GLM-4.5V and Weights & Biases

                      
                     
"""
Production implementation for Classifying Manufacturing Defects using GLM-4.5V and Weights & Biases.
Provides secure, scalable operations with robust error handling and logging.
"""

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

# Setup logging with appropriate levels
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'features' not in data:
        raise ValueError('Missing features field')
    if not isinstance(data['features'], list):
        raise ValueError('Features must be a list')
    return True

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

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize input features for model compatibility.
    
    Args:
        data: Input features
    Returns:
        Normalized features
    """
    normalized_data = {key: (value - 100) / 50 for key, value in data.items()}  # Example normalization
    logger.info('Normalized data')
    return normalized_data

async def fetch_data() -> List[Dict[str, Any]]:
    """Fetch data from external API.
    
    Returns:
        List of records
    """
    try:
        response = requests.get(Config.api_url)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Data fetched successfully')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of input data for defect classification.
    
    Args:
        data: List of input records
    Returns:
        List of processed records
    """
    processed_records = []
    for record in data:
        try:
            await validate_input(record)  # Validate input
            sanitized = await sanitize_fields(record)  # Sanitize fields
            normalized = await normalize_data(sanitized)  # Normalize features
            processed_records.append(normalized)
        except ValueError as e:
            logger.warning(f'Skipping record due to validation error: {e}')  # Log warnings
    return processed_records

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save processed records to the database.
    
    Args:
        records: List of records to save
    """
    # Here, implement actual database saving logic; using placeholder
    logger.info(f'Saving {len(records)} records to the database')

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call external API for classification.
    
    Args:
        data: Input data for classification
    Returns:
        Classification results
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post(Config.api_url + '/classify', json=data)
        response.raise_for_status()  # Raise an error for bad responses
        logger.info('Classification API call successful')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics for reporting.
    
    Args:
        results: Classification results
    Returns:
        Aggregated metrics
    """
    metrics = {'defects': 0, 'total': len(results)}
    for result in results:
        if result['status'] == 'defective':
            metrics['defects'] += 1
    logger.info('Aggregated metrics calculated')
    return metrics

class DefectClassifier:
    """Main orchestrator for defect classification workflow."""

    async def classify_defects(self) -> None:
        """Main method to classify defects from fetched data."""
        try:
            raw_data = await fetch_data()  # Fetch data
            processed_data = await process_batch(raw_data)  # Process data
            await save_to_db(processed_data)  # Save processed data
            metrics = await aggregate_metrics(processed_data)  # Aggregate metrics
            logger.info(f'Metrics: {metrics}')
        except Exception as e:
            logger.error(f'Error during classification: {e}')  # Handle any errors gracefully

if __name__ == '__main__':
    # Example usage
    classifier = DefectClassifier()
    # Here, you would typically use an event loop to run the async method
    import asyncio
    loop = asyncio.get_event_loop()
    loop.run_until_complete(classifier.classify_defects())
    loop.close()