Scale Industrial Demand Forecasting to the Cloud with NeuralForecast and Amazon Forecast SDK

                      
                     
"""
Production implementation for scaling industrial demand forecasting using NeuralForecast and Amazon Forecast SDK.
This service provides secure, scalable operations for demand predictions.
"""
from typing import Dict, Any, List
import os
import logging
import json
import boto3
import time
from pydantic import BaseModel, conlist

# Setup logging for tracking operations
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Configuration class to manage environment variables
class Config:
    forecast_model: str = os.getenv('FORECAST_MODEL')
    aws_region: str = os.getenv('AWS_REGION', 'us-east-1')
    dynamodb_table: str = os.getenv('DYNAMODB_TABLE')

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

# Sanitize fields to prevent injection
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """  
    return {k: str(v).strip() for k, v in data.items()}

# Fetch data from DynamoDB
async def fetch_data(product_id: str) -> List[Dict[str, Any]]:
    """Fetch historical data for the product from DynamoDB.
    
    Args:
        product_id: ID of the product
    Returns:
        List of historical records
    """  
    dynamo_client = boto3.resource('dynamodb', region_name=Config.aws_region)
    table = dynamo_client.Table(Config.dynamodb_table)
    response = table.get_item(Key={'product_id': product_id})
    return response.get('Item', {}).get('historical_data', [])

# Normalize data for model input
def normalize_data(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize historical data for model consumption.
    
    Args:
        records: List of historical records
    Returns:
        Normalized records
    """  
    # Implement normalization logic here
    return records

# Call the Amazon Forecast API
async def call_forecast_api(normalized_data: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Call the Amazon Forecast SDK to get predictions.
    
    Args:
        normalized_data: Normalized input data
    Returns:
        Prediction results
    Raises:
        Exception: If API call fails
    """  
    # Placeholder for actual API call
    logger.info('Calling Forecast API...')
    # Simulate network delay
    time.sleep(2)  
    return {'predictions': [{'timestamp': '2023-10-01', 'demand': 100}]}  

# Save predictions to DynamoDB
async def save_to_db(product_id: str, predictions: Dict[str, Any]) -> None:
    """Save forecast predictions back to DynamoDB.
    
    Args:
        product_id: ID of the product
        predictions: Prediction results
    Raises:
        Exception: If saving fails
    """  
    dynamo_client = boto3.resource('dynamodb', region_name=Config.aws_region)
    table = dynamo_client.Table(Config.dynamodb_table)
    try:
        table.put_item(Item={'product_id': product_id, 'predictions': predictions})
    except Exception as e:
        logger.error(f'Failed to save predictions: {e}')
        raise

class DemandForecastService:
    """Main orchestrator for demand forecasting.
    """  
    @staticmethod
    async def forecast(data: Dict[str, Any]) -> Dict[str, Any]:
        """Main function to handle forecasting workflow.
        
        Args:
            data: Input data for forecasting
        Returns:
            Forecast predictions
        Raises:
            ValueError: If input validation fails
        """  
        await validate_input(data)  # Validate input
        sanitized_data = sanitize_fields(data)  # Sanitize input
        historical_data = await fetch_data(sanitized_data['product_id'])  # Fetch historical data
        normalized_data = normalize_data(historical_data)  # Normalize data
        predictions = await call_forecast_api(normalized_data)  # Call the API
        await save_to_db(sanitized_data['product_id'], predictions)  # Save predictions
        return predictions  # Return predictions

if __name__ == '__main__':
    # Example usage
    sample_data = {'product_id': 'prod-123', 'timestamp': '2023-10-01'}
    result = DemandForecastService.forecast(sample_data)
    print(json.dumps(result, indent=2))