Enable 3D Manufacturing Perception with InternVL3 and Roboflow Inference

                      
                     
"""
Production implementation for enabling 3D manufacturing perception using InternVL3 and Roboflow inference.
Provides secure, scalable operations for real-time data processing.
"""
from typing import Dict, Any, List
import os
import logging
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker
from time import sleep

# Logger setup for tracking the flow and errors
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///local.db')
    roboflow_api_key: str = os.getenv('ROBOFLOW_API_KEY')

# SQLAlchemy engine and session setup
engine = create_engine(Config.database_url)
Session = sessionmaker(bind=engine)

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for inference.
    
    Args:
        data: Input data containing necessary fields.
    Returns:
        True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails
    """
    if 'image_url' not in data:
        raise ValueError('Missing image_url')
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Raw input data.
    Returns:
        Safe sanitized data.
    """
    return {key: value.strip() for key, value in data.items()}

def fetch_data(image_url: str) -> Dict[str, Any]:
    """Fetch image data from the given URL.
    
    Args:
        image_url: URL of the image to fetch.
    Returns:
        Dictionary with image data.
    Raises:
        ConnectionError: If fetching fails.
    """
    try:
        response = requests.get(image_url)
        response.raise_for_status()  # Raise an error for bad responses
        return {'image_data': response.content}
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise ConnectionError('Failed to fetch image data.')

def call_api(image_data: Any) -> Dict[str, Any]:
    """Call the Roboflow API for inference.
    
    Args:
        image_data: Image data to send for inference.
    Returns:
        Response data from the API.
    Raises:
        RuntimeError: If API call fails.
    """
    try:
        headers = {'Authorization': f'Bearer {Config.roboflow_api_key}'}
        response = requests.post('https://api.roboflow.com/inference', headers=headers, json=image_data)
        response.raise_for_status()
        return response.json()
    except requests.RequestException as e:
        logger.error(f'API call error: {e}')
        raise RuntimeError('API call failed.')

def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save.
    Raises:
        Exception: If database operation fails.
    """
    with Session() as session:
        try:
            session.execute(text('INSERT INTO results (data) VALUES (:data)'), {'data': data})
            session.commit()
            logger.info('Data saved to database successfully.')
        except Exception as e:
            logger.error(f'Database error: {e}')
            session.rollback()
            raise

def format_output(data: Dict[str, Any]) -> str:
    """Format output data for presentation.
    
    Args:
        data: Data to format.
    Returns:
        Formatted string output.
    """
    return f'Inference results: {data}'

def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of images for inference.
    
    Args:
        batch: List of image data dictionaries.
    """
    for item in batch:
        try:
            validate_input(item)  # Validate input
            sanitized_data = sanitize_fields(item)  # Sanitize fields
            image_data = fetch_data(sanitized_data['image_url'])  # Fetch image
            inference_result = call_api(image_data)  # Call API
            save_to_db(inference_result)  # Save results
            logger.info(f'Successfully processed: {sanitized_data["image_url"]}')  # Log success
        except Exception as e:
            logger.error(f'Error processing item {item}: {e}')  # Log failure
            continue

if __name__ == '__main__':
    # Example usage
    sample_batch = [
        {'image_url': 'http://example.com/image1.jpg'},
        {'image_url': 'http://example.com/image2.jpg'},
    ]
    process_batch(sample_batch)  # Process the sample batch of images