Deploy LLM-Guided Robot Task Planning with ROSA and Open-RMF

"""
Production implementation for deploying LLM-guided robot task planning with ROSA and Open-RMF.
Provides secure, scalable operations for robotic task management.
"""

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

# Setting up logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

def validate_input(data: Dict[str, Any]) -> bool:
    """
    Validate the input data for task planning.
    
    Args:
        data: Input dictionary containing task information.
    Returns:
        True if valid, raises ValueError otherwise.
    Raises:
        ValueError: If validation fails.
    """
    if 'task_id' not in data:
        raise ValueError('Missing task_id')
    if 'robot_id' not in data:
        raise ValueError('Missing robot_id')
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """
    Sanitize input fields to prevent security issues.
    
    Args:
        data: Input dictionary containing task information.
    Returns:
        Sanitized data dictionary.
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    return sanitized_data


def fetch_data(url: str) -> Dict[str, Any]:
    """
    Fetch data from the given URL with retries.
    
    Args:
        url: API endpoint to fetch data from.
    Returns:
        JSON response as dictionary.
    Raises:
        Exception: If request fails after retries.
    """
    for attempt in range(5):  # Retry up to 5 times
        try:
            response = requests.get(url, headers={'Authorization': f'Bearer {Config.api_key}'})
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            logger.warning(f'Fetch attempt {attempt + 1} failed: {e}')
            time.sleep(2 ** attempt)  # Exponential backoff
    raise Exception('Failed to fetch data after multiple attempts')


def transform_records(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """
    Transform raw data records for processing.
    
    Args:
        data: List of raw data records.
    Returns:
        List of transformed data records.
    """
    transformed = []
    for record in data:
        transformed.append({
            'id': record['task_id'],
            'description': record.get('description', ''),
            'robot_id': record['robot_id']
        })
    return transformed


def process_batch(records: List[Dict[str, Any]]) -> None:
    """
    Process a batch of task records for execution.
    
    Args:
        records: List of transformed task records.
    """
    for record in records:
        logger.info(f'Processing task {record["id"]}')
        # Simulate task processing logic here
        # Assume a function send_task_to_robot exists
        try:
            send_task_to_robot(record)
        except Exception as e:
            logger.error(f'Error processing task {record["id"]}: {e}')


def send_task_to_robot(task: Dict[str, Any]) -> None:
    """
    Send task to the specified robot for execution.
    
    Args:
        task: Task information to be sent.
    Raises:
        Exception: If sending task fails.
    """
    logger.info(f'Sending task {task["id"]} to robot {task["robot_id"]}')
    # Simulate sending data to robot API
    # Here you would use requests.post() to send the task to the robot
    # Raise an error if something goes wrong


def aggregate_metrics(task_results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """
    Aggregate metrics from task results for reporting.
    
    Args:
        task_results: List of task execution results.
    Returns:
        Aggregated metrics as a dictionary.
    """
    metrics = {'success': 0, 'failure': 0}
    for result in task_results:
        if result.get('status') == 'success':
            metrics['success'] += 1
        else:
            metrics['failure'] += 1
    return metrics


def handle_errors(e: Exception) -> None:
    """
    Handle errors gracefully and log them.
    
    Args:
        e: Exception instance to handle.
    """
    logger.error(f'An error occurred: {str(e)}')


if __name__ == '__main__':
    # Example usage of the deployment script
    task_data = {
        'task_id': '123',
        'robot_id': 'robot_1',
        'description': 'Pick and place operation'
    }
    try:
        validate_input(task_data)
        sanitized_data = sanitize_fields(task_data)
        data_url = 'http://api.example.com/tasks'
        fetched_data = fetch_data(data_url)
        transformed_data = transform_records(fetched_data)
        process_batch(transformed_data)
        logger.info('All tasks processed successfully.')
    except Exception as e:
        handle_errors(e)