Trigger Adaptive Retraining on Sensor Drift Events with Metaflow and Evidently

"""
Production implementation for adaptive retraining on sensor drift events.
Provides secure, scalable operations using Metaflow and Evidently.
"""

from typing import Dict, Any, List
import os
import logging
import time
import requests
from metaflow import FlowSpec, step
from evidently.report import Report
from evidently.metric_preset import DataDriftPreset
from contextlib import contextmanager

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    evidently_api_url: str = os.getenv('EVIDENTLY_API_URL')

@contextmanager
def database_connection():
    """Context manager for database connection pooling.
    
    Yields:
        Connection object
    """
    conn = create_connection_pool(Config.database_url)  # Assume this function is defined elsewhere
    try:
        yield conn  # Provide the connection to the block
    finally:
        conn.close()  # Ensure the connection is closed

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input data to validate
    Returns:
        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 to prevent injection attacks.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    # Example of sanitization logic
    data['sensor_id'] = data['sensor_id'].strip()
    return data

async def fetch_data(sensor_id: str) -> Dict[str, Any]:
    """Fetch data from a hypothetical sensor API.
    
    Args:
        sensor_id: Identifier for the sensor
    Returns:
        Sensor data as a dictionary
    Raises:
        ConnectionError: If fetching data fails
    """
    response = requests.get(f'https://api.sensors.com/{sensor_id}')
    if response.status_code != 200:
        raise ConnectionError(f'Failed to fetch data for {sensor_id}')
    return response.json()

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save processed data to the database.
    
    Args:
        data: Data to save
    Raises:
        Exception: If saving fails
    """
    with database_connection() as conn:
        # Assume insert_data is a function that saves to the DB
        insert_data(conn, data)

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of sensor data.
    
    Args:
        data: List of sensor data dictionaries
    """
    for record in data:
        await save_to_db(record)  # Save each record to the database

async def call_api(api_url: str, data: Dict[str, Any]) -> None:
    """Call an external API with the given data.
    
    Args:
        api_url: URL of the API
        data: Data to send
    Raises:
        Exception: If API call fails
    """
    response = requests.post(api_url, json=data)
    if response.status_code != 200:
        raise Exception(f'API call failed with status {response.status_code}')

async def aggregate_metrics(data: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from processed data.
    
    Args:
        data: List of sensor data dictionaries
    Returns:
        Aggregated metrics as a dictionary
    """
    # Example aggregation logic
    metrics = {'average': sum(d['value'] for d in data) / len(data)}
    return metrics

class AdaptiveRetrainingFlow(FlowSpec):
    """Main flow orchestrating adaptive retraining process.
    """
    @step
    def start(self):
        """Start step to initialize the retraining process."""
        self.next(self.fetch_data_step)

    @step
    def fetch_data_step(self):
        """Fetch sensor data and validate it."""
        try:
            sensor_id = 'example_sensor'
            data = await fetch_data(sensor_id)
            await validate_input(data)
            self.next(self.process_data_step)
        except ValueError as ve:
            logger.error(f'Validation error: {ve}')
            self.abort()
        except Exception as e:
            logger.error(f'Error fetching data: {e}')
            self.abort()

    @step
    def process_data_step(self):
        """Process the fetched sensor data."""
        try:
            data = [{'sensor_id': 'example_sensor', 'value': 42}]  # Mock data
            await process_batch(data)
            metrics = await aggregate_metrics(data)
            logger.info(f'Aggregated metrics: {metrics}')
            self.next(self.generate_report)
        except Exception as e:
            logger.error(f'Error processing data: {e}')
            self.abort()

    @step
    def generate_report(self):
        """Generate a report for the processed data."""
        report = Report(metrics=[DataDriftPreset()])
        report.run()  # Running the report
        logger.info('Report generated successfully')

if __name__ == '__main__':
    # Example usage of the flow
    AdaptiveRetrainingFlow()