Detect Casting Defects with YOLO26 and MetaLog

                      
                     
"""
Production implementation for detecting casting defects using YOLO26 and MetaLog.
Provides secure, scalable operations for defect detection in manufacturing.
"""

from typing import Dict, Any, List
import os
import logging
import time
from pydantic import BaseModel, ValidationError
import requests

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

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

class InputData(BaseModel):
    """Model for input data validation."""
    image_path: str
    batch_id: str

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
    """
    try:
        InputData(**data)  # Validate using Pydantic model
    except ValidationError as e:
        logger.error(f'Input validation error: {e}')
        raise ValueError('Invalid input data')
    return True

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

async def fetch_data(batch_id: str) -> Dict[str, Any]:
    """Fetch batch data from the database.
    
    Args:
        batch_id: ID of the batch
    Returns:
        Data associated with the batch
    Raises:
        Exception: If data fetching fails
    """
    try:
        response = requests.get(f'{Config.api_url}/batches/{batch_id}')
        response.raise_for_status()
        logger.info(f'Fetched data for batch {batch_id}')
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Data fetch failed')

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    # Simulated DB saving logic
    try:
        logger.info('Saving data to the database...')  # Simulate save operation
        time.sleep(1)  # Simulate delay
        logger.info('Data saved successfully')
    except Exception as e:
        logger.error(f'Error saving data: {e}')
        raise Exception('Database save failed')

async def call_api(image_path: str) -> str:
    """Call the YOLO26 API for defect detection.
    
    Args:
        image_path: Path to the image file
    Returns:
        Result of the detection
    Raises:
        Exception: If API call fails
    """
    try:
        logger.info(f'Calling YOLO26 API for image {image_path}')
        # Simulated API call
        time.sleep(2)  # Simulate processing time
        result = 'defect'  # Simulated result
        logger.info('API call completed')
        return result
    except Exception as e:
        logger.error(f'API call error: {e}')
        raise Exception('API call failed')

async def process_batch(batch_id: str) -> None:
    """Process a batch of images for defects.
    
    Args:
        batch_id: ID of the batch to process
    Raises:
        Exception: If processing fails
    """
    try:
        data = await fetch_data(batch_id)
        # Assume data contains image_paths
        for image_path in data.get('image_paths', []):
            result = await call_api(image_path)
            await save_to_db({'image_path': image_path, 'result': result})
        logger.info(f'Processed batch {batch_id}')
    except Exception as e:
        logger.error(f'Error processing batch: {e}')

async def aggregate_metrics(results: List[str]) -> Dict[str, int]:
    """Aggregate metrics from results.
    
    Args:
        results: List of detection results
    Returns:
        Aggregated metrics
    """
    metrics = {'defects': results.count('defect'), 'total': len(results)}
    logger.info('Aggregated metrics')
    return metrics

if __name__ == '__main__':
    import asyncio
    # Example usage
    try:
        batch_id = 'batch_001'
        asyncio.run(process_batch(batch_id))
    except Exception as e:
        logger.error(f'Error in main execution: {e}')  # Handle main errors