Optimize Factory Vision Models with OpenVINO and ExecuTorch

                      
                     
"""
Production implementation for optimizing factory vision models using OpenVINO and ExecuTorch.
Includes secure, scalable operations for model inference and processing.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
import numpy as np
from contextlib import contextmanager

# Set up logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """Configuration class to manage environment variables."""
    model_url: str = os.getenv('MODEL_URL')
    database_url: str = os.getenv('DATABASE_URL')

@contextmanager
def database_connection():
    """Context manager for managing database connections."""
    conn = None
    try:
        conn = create_connection(Config.database_url)  # Assuming a function that creates a database connection
        yield conn
    except Exception as e:
        logger.error(f"Database connection error: {e}")
    finally:
        if conn:
            conn.close()  # Ensure the connection is closed

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent security issues.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    return sanitized_data

async def fetch_data(url: str) -> Dict[str, Any]:
    """Fetch data from a given URL.
    
    Args:
        url: The URL to fetch data from
    Returns:
        Dict[str, Any]: The response JSON
    Raises:
        requests.RequestException: If the request fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f"Failed to fetch data: {e}")
        raise

async def normalize_data(data: List[float]) -> List[float]:
    """Normalize data for model input.
    
    Args:
        data: Raw data to normalize
    Returns:
        List[float]: Normalized data
    """
    data_min = np.min(data)
    data_max = np.max(data)
    normalized = [(x - data_min) / (data_max - data_min) for x in data]
    return normalized

async def process_batch(batch: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Process a batch of data for model inference.
    
    Args:
        batch: List of data entries
    Returns:
        List[Dict[str, Any]]: Inference results
    """
    results = []
    for item in batch:
        try:
            normalized_data = await normalize_data(item['data'])
            # Simulate model inference call
            result = model_inference(normalized_data)  # Placeholder for model inference function
            results.append(result)
        except Exception as e:
            logger.error(f"Error processing item {item}: {e}")
    return results

async def save_to_db(data: List[Dict[str, Any]], conn):
    """Save processed results to the database.
    
    Args:
        data: List of results to save
        conn: Database connection
    """
    try:
        for item in data:
            insert_query = "INSERT INTO results (data) VALUES (?)"  # Adjust for your DB
            conn.execute(insert_query, (item,))
        conn.commit()  # Commit transaction
    except Exception as e:
        logger.error(f"Failed to save data: {e}")
        raise

def model_inference(data: List[float]) -> Dict[str, Any]:
    """Simulate model inference.
    
    Args:
        data: Normalized input data
    Returns:
        Dict[str, Any]: Inference results
    """
    # Placeholder for actual model inference logic
    return {'result': sum(data)}  # Simple sum as a dummy inference result

if __name__ == '__main__':
    # Example usage
    input_data = {'image': 'path/to/image.jpg', 'data': [1.0, 2.0, 3.0]}
    try:
        await validate_input(input_data)
        sanitized = await sanitize_fields(input_data)
        async with database_connection() as conn:
            results = await process_batch([sanitized])
            await save_to_db(results, conn)
    except Exception as e:
        logger.error(f"Main block error: {e}")