Build Zero-Shot Parts Recognition Pipelines with Grounding DINO and Roboflow Inference

"""
Production implementation for building zero-shot parts recognition pipelines using Grounding DINO and Roboflow.
Provides secure, scalable operations.
"""
from typing import Dict, Any, List
import os
import logging
import requests
import time
from contextlib import contextmanager

# Configure logging to track the flow and any issues
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration settings loaded from environment variables.
    """
    roboflow_api_key: str = os.getenv('ROBOFLOW_API_KEY')
    grounding_dino_url: str = os.getenv('GROUNDING_DINO_URL')

@contextmanager
def resource_manager():
    """Context manager for resource cleanup.
    Handles opening and closing resources securely.
    """  
    try:
        yield
    finally:
        logger.info('Cleaning up resources...')
        # Add cleanup logic if necessary

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for correctness.
    
    Args:
        data: Input data to validate
    Returns:
        True if data is valid
    Raises:
        ValueError: If validation fails
    """  
    if 'image_url' not in data:
        raise ValueError('Missing image_url')  # Must provide image URL
    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()}
    return sanitized_data  # Ensure no leading/trailing spaces

async def fetch_data(image_url: str) -> Dict[str, Any]:
    """Fetch data from Roboflow API.
    
    Args:
        image_url: URL of the image to process
    Returns:
        Response data from API
    Raises:
        RuntimeError: If fetching data fails
    """  
    headers = {'Authorization': f'Bearer {Config.roboflow_api_key}'}
    response = requests.get(f'{Config.grounding_dino_url}/process', headers=headers, params={'image_url': image_url})
    if response.status_code != 200:
        raise RuntimeError(f'Failed to fetch data: {response.text}')  # Handle API failure
    return response.json()

async def transform_records(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Transform raw data into desired format.
    
    Args:
        data: Raw data from the API
    Returns:
        List of transformed records
    """  
    transformed = []
    for item in data.get('predictions', []):  # Process each prediction
        transformed.append({'label': item['label'], 'confidence': item['confidence']})
    return transformed  # Return list of transformed items

async def process_batch(image_urls: List[str]) -> None:
    """Process a batch of images.
    
    Args:
        image_urls: List of image URLs to process
    """  
    for url in image_urls:
        try:
            await validate_input({'image_url': url})  # Validate each URL
            sanitized_url = await sanitize_fields({'image_url': url})
            raw_data = await fetch_data(sanitized_url['image_url'])  # Fetch data from API
            transformed_data = await transform_records(raw_data)  # Transform the response
            logger.info(f'Processed data for {url}: {transformed_data}')  # Logging processed data
        except Exception as e:
            logger.error(f'Error processing {url}: {str(e)}')  # Log any issues

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from processed results.
    
    Args:
        results: List of processed results
    Returns:
        Dictionary of aggregated metrics
    """  
    metrics = {'total': len(results), 'success': sum(1 for r in results if r['confidence'] > 0.5)}
    return metrics  # Return aggregate metrics

async def main(image_urls: List[str]) -> None:
    """Main orchestration function to run the pipeline.
    
    Args:
        image_urls: List of image URLs to process
    """  
    with resource_manager():  # Ensure resources are cleaned up
        results = []
        for url in image_urls:
            result = await process_batch([url])  # Process each batch
            results.extend(result)  # Collect results
        metrics = await aggregate_metrics(results)  # Aggregate metrics
        logger.info(f'Final metrics: {metrics}')  # Log final metrics

if __name__ == '__main__':
    image_urls = ['http://example.com/image1.jpg', 'http://example.com/image2.jpg']  # Example image URLs
    import asyncio
    asyncio.run(main(image_urls))  # Run the main function