Detect and Segment Production Line Defects in Real Time with RF-DETR and Supervision

"""
Production implementation for Detect and Segment Production Line Defects in Real Time with RF-DETR and Supervision.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
from contextlib import asynccontextmanager
import httpx
import asyncio

# Set up logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

@asynccontextmanager
async def get_db_connection():
    """Context manager for database connection pooling.
    
    Yields:
        Connection object for the database.
    """
    try:
        conn = await connect_to_db(Config.database_url)
        yield conn  # Provide the connection to the caller
    finally:
        await conn.close()  # Ensure the connection is closed

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data dictionary
    """
    return {k: v.strip() for k, v in data.items() if isinstance(v, str)}

async def fetch_data(image_path: str) -> Any:
    """Fetch image data from the provided path.
    
    Args:
        image_path: Path to the image file
    Returns:
        Image data
    Raises:
        IOError: If file cannot be accessed
    """
    try:
        with open(image_path, 'rb') as f:
            return f.read()
    except IOError as e:
        logger.error('Error reading image file: %s', e)
        raise

async def transform_records(raw_data: Any) -> List[Dict[str, Any]]:
    """Transform raw image data into a suitable format for processing.
    
    Args:
        raw_data: Raw image data
    Returns:
        List of transformed records
    """
    # Placeholder for transformation logic
    return [{'image': raw_data, 'metadata': {}}]

async def process_batch(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of images for defect detection.
    
    Args:
        data: List of data records to process
    Returns:
        List of detection results
    """
    results = []
    for record in data:
        # Simulate processing with RF-DETR
        results.append({'id': record['image'], 'defects': []})  # Placeholder
    return results

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results to the database.
    
    Args:
        results: Results from processing
    Raises:
        ValueError: If saving fails
    """
    if not results:
        raise ValueError('No results to save')
    # Simulate database save logic
    logger.info('Results saved to database.')

async def call_api(data: Dict[str, Any]) -> Dict[str, Any]:
    """Call an external API to notify the results.
    
    Args:
        data: Data to send to the API
    Returns:
        Response from the API
    """
    async with httpx.AsyncClient() as client:
        response = await client.post(Config.api_url, json=data)
        response.raise_for_status()
        return response.json()

class DefectDetectionOrchestrator:
    """Main orchestrator for defect detection workflow.
    
    Handles input validation, processing, and output storage.
    """

    async def execute(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Execute the defect detection workflow.
        
        Args:
            input_data: Input image data
        Returns:
            Final results of the detection
        """
        try:
            await validate_input(input_data)  # Validate the input
            sanitized_data = await sanitize_fields(input_data)  # Sanitize inputs
            raw_data = await fetch_data(sanitized_data['image'])  # Fetch raw data
            transformed_data = await transform_records(raw_data)  # Transform data
            results = await process_batch(transformed_data)  # Process data
            await save_to_db(results)  # Save results to DB
            await call_api({'results': results})  # Notify API
            return {'status': 'success', 'data': results}
        except Exception as e:
            logger.error('Error during execution: %s', e)
            return {'status': 'error', 'message': str(e)}

if __name__ == '__main__':
    # Example usage of the orchestrator
    orchestrator = DefectDetectionOrchestrator()
    input_data = {'image': 'path/to/image.jpg'}
    result = asyncio.run(orchestrator.execute(input_data))
    logger.info('Execution result: %s', result)