Coordinate Predictive Maintenance Agents with Claude Agent SDK and CrewAI

"""
Production implementation for coordinating predictive maintenance agents using Claude Agent SDK and CrewAI.
Provides secure, scalable operations for predictive maintenance.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import requests
import time

# Logging configuration for tracking application behavior.
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

    def __init__(self):
        if not self.database_url or not self.api_key:
            logger.error("Missing required environment variables.")
            raise ValueError("DATABASE_URL and API_KEY must be set")

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for predictive maintenance.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'device_id' not in data:
        raise ValueError('Missing device_id')
    if 'status' not in data:
        raise ValueError('Missing status')
    logger.info("Input data validated successfully.")
    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 input data
    """
    sanitized_data = {key: str(value).strip() for key, value in data.items()}
    logger.info("Input data sanitized.")
    return sanitized_data

async def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    data['status'] = data['status'].lower()
    logger.info("Data normalized.")
    return data

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform input records into desired format.
    
    Args:
        data: Input data to transform
    Returns:
        Transformed data
    """
    transformed_data = {'id': data['device_id'], 'state': data['status']}
    logger.info("Records transformed.")
    return transformed_data

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from external API with retries.
    
    Args:
        api_url: URL of the API to fetch data from
    Returns:
        List of records fetched
    Raises:
        Exception: If fetch fails after retries
    """
    retries = 5
    for attempt in range(retries):
        try:
            response = requests.get(api_url, timeout=10)
            response.raise_for_status()
            logger.info("Data fetched successfully.")
            return response.json()
        except requests.HTTPError as e:
            logger.warning(f"Fetch attempt {attempt + 1} failed: {e}")
            time.sleep(2 ** attempt)  # Exponential backoff
    logger.error("Failed to fetch data after retries.")
    raise Exception("Fetch data failed")

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save transformed data to the database.
    
    Args:
        data: Data to save to the database
    Raises:
        Exception: If saving fails
    """
    logger.info("Saving data to the database...")
    # Simulated database save operation
    # Implement actual DB logic here
    logger.info("Data saved successfully")

async def handle_errors(func):
    """Decorator to handle errors in async functions.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """
    async def wrapper(*args, **kwargs):
        try:
            return await func(*args, **kwargs)
        except Exception as e:
            logger.error(f"An error occurred: {e}")
            raise
    return wrapper

class PredictiveMaintenanceAgent:
    """Orchestrator class for predictive maintenance agents."""

    def __init__(self, config: Config):
        self.config = config
        self.api_url = f"{self.config.database_url}/devices"

    @handle_errors
    async def process_batch(self, batch_data: List[Dict[str, Any]]) -> None:
        """Process a batch of predictive maintenance data.
        
        Args:
            batch_data: List of data to process
        """
        for data in batch_data:
            await validate_input(data)
            sanitized_data = await sanitize_fields(data)
            normalized_data = await normalize_data(sanitized_data)
            transformed_data = await transform_records(normalized_data)
            await save_to_db(transformed_data)
            logger.info("Batch processed successfully.")

if __name__ == '__main__':
    # Example usage
    config = Config()
    agent = PredictiveMaintenanceAgent(config)
    sample_data = [{'device_id': '123', 'status': 'active'}, {'device_id': '456', 'status': 'inactive'}]
    import asyncio
    asyncio.run(agent.process_batch(sample_data))