Gate Digital Twin Retraining on Sensor Data Quality with Evidently and Vertex AI SDK

                      
                     
"""
Production implementation for Gate Digital Twin Retraining on Sensor Data Quality.
Provides secure, scalable operations with Evidently and Vertex AI SDK.
"""

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

# Logger setup
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to load environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    vertex_ai_endpoint: str = os.getenv('VERTEX_AI_ENDPOINT')

@contextmanager
def db_connection() -> Any:
    """Context manager for database connection pooling.
    """  
    engine = create_engine(Config.database_url, pool_pre_ping=True)  # Connection pooling
    with engine.connect() as connection:
        yield connection  # Yield the database connection

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """  
    if 'sensor_id' not in data:
        raise ValueError('Missing sensor_id')
    if not isinstance(data['sensor_id'], str):
        raise ValueError('sensor_id must be a string')
    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:
        Dict[str, Any]: Sanitized data
    """  
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.info('Sanitized data: %s', sanitized_data)
    return sanitized_data

async def fetch_data(sensor_id: str) -> List[Dict[str, Any]]:
    """Fetch sensor data from the API.
    
    Args:
        sensor_id: The identifier for the sensor
    Returns:
        List[Dict[str, Any]]: List of sensor records
    """  
    try:
        response = requests.get(f'{Config.vertex_ai_endpoint}/sensors/{sensor_id}')
        response.raise_for_status()  # Raise error for bad responses
        data = response.json()
        logger.info('Fetched data for sensor_id %s: %s', sensor_id, data)
        return data
    except requests.RequestException as e:
        logger.error('Error fetching sensor data: %s', e)
        raise RuntimeError('Failed to fetch data')

async def normalize_data(data: List[Dict[str, Any]]) -> np.ndarray:
    """Normalize sensor data for model training.
    
    Args:
        data: List of sensor records
    Returns:
        np.ndarray: Normalized data array
    """  
    if not data:
        raise ValueError('No data to normalize')
    # Convert to a structured numpy array for processing
    normalized_array = np.array([list(record.values()) for record in data], dtype=float)
    logger.info('Normalized data shape: %s', normalized_array.shape)
    return normalized_array

async def process_batch(data: np.ndarray) -> None:
    """Process a batch of normalized sensor data.
    
    Args:
        data: Normalized numpy array of sensor data
    """  
    # Placeholder for data processing logic (e.g., retraining model)
    logger.info('Processing batch of size: %d', len(data))
    # Implement retraining logic here

async def save_to_db(sensor_id: str, result: Any) -> None:
    """Save results to the database.
    
    Args:
        sensor_id: The identifier for the sensor
        result: Result to save
    """  
    with db_connection() as connection:
        connection.execute(text('INSERT INTO results (sensor_id, result) VALUES (:sensor_id, :result)'),
                           {'sensor_id': sensor_id, 'result': result})
    logger.info('Saved result for sensor_id %s', sensor_id)

async def call_api(endpoint: str, payload: Dict[str, Any]) -> Any:
    """Call external API with payload.
    
    Args:
        endpoint: API endpoint to call
        payload: Data to send to the API
    Returns:
        Any: Response from the API
    """  
    try:
        response = requests.post(endpoint, json=payload)
        response.raise_for_status()
        logger.info('API response: %s', response.json())
        return response.json()
    except requests.RequestException as e:
        logger.error('API call failed: %s', e)
        raise RuntimeError('Failed to call API')

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from the processed data.
    
    Args:
        data: List of processed data records
    Returns:
        Dict[str, float]: Aggregated metrics
    """  
    metrics = {'mean': np.mean(data), 'stddev': np.std(data)}
    logger.info('Aggregated metrics: %s', metrics)
    return metrics

async def main(sensor_id: str) -> None:
    """Main orchestration function.
    
    Args:
        sensor_id: The identifier for the sensor
    """  
    try:
        # Validate input
        await validate_input({'sensor_id': sensor_id})
        # Fetch and sanitize data
        raw_data = await fetch_data(sensor_id)
        sanitized_data = await sanitize_fields(raw_data)
        # Normalize and process
        normalized_data = await normalize_data(sanitized_data)
        await process_batch(normalized_data)
        # Save results
        result = {}  # Placeholder for actual result
        await save_to_db(sensor_id, result)
    except Exception as e:
        logger.error('Error in main processing: %s', e)

if __name__ == '__main__':
    # Example usage
    import asyncio
    sensor_id = 'sensor_123'
    asyncio.run(main(sensor_id))