Forecast Demand Volatility in Industrial Supply Chains with Lag-Llama and Darts

"""
Production implementation for forecasting demand volatility in industrial supply chains using Lag-Llama and Darts.
This module provides secure, scalable operations with robust error handling and logging.
"""
from typing import Dict, Any, List
import os
import logging
import time
import backoff
import pandas as pd
from darts import TimeSeries
from darts.models import ExponentialSmoothing

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

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    lag_lama_api_key: str = os.getenv('LAG_LAMA_API_KEY')

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')  # Ensure product_id is present
    if 'date_range' not in data:
        raise ValueError('Missing date_range')  # Ensure date_range is provided
    return True

def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Raw input data
    Returns:
        Cleaned data
    """
    # Here you can implement sanitization logic, like trimming spaces or removing special characters
    return data

@backoff.on_exception(backoff.expo, Exception, max_time=60)
def fetch_data(product_id: str, date_range: List[str]) -> pd.DataFrame:
    """Fetch historical demand data from the database.
    
    Args:
        product_id: The ID of the product to fetch data for
        date_range: The range of dates to fetch
    Returns:
        DataFrame containing historical demand data
    Raises:
        Exception: If data fetching fails
    """
    logger.info(f'Fetching data for product {product_id} from {date_range}')
    # Simulate database fetch
    # In production, replace this with actual database logic
    data = pd.DataFrame({
        'date': pd.date_range(start=date_range[0], end=date_range[1]),
        'demand': [100 + i for i in range((pd.to_datetime(date_range[1]) - pd.to_datetime(date_range[0])).days + 1)]
    })
    return data

def transform_records(data: pd.DataFrame) -> TimeSeries:
    """Transform DataFrame records to TimeSeries for Darts.
    
    Args:
        data: Historical demand data as DataFrame
    Returns:
        Darts TimeSeries object
    """
    logger.info('Transforming records to TimeSeries')
    ts = TimeSeries.from_dataframe(data, time_col='date', value_cols='demand')
    return ts

def aggregate_metrics(ts: TimeSeries) -> Dict[str, float]:
    """Aggregate demand metrics.
    
    Args:
        ts: TimeSeries object
    Returns:
        Dictionary of aggregated metrics
    """
    mean_demand = ts.mean().values()[0]
    logger.info(f'Aggregated mean demand: {mean_demand}')
    return {'mean_demand': mean_demand}

def save_to_db(metrics: Dict[str, float], product_id: str) -> None:
    """Save aggregated metrics to the database.
    
    Args:
        metrics: Metrics to save
        product_id: The ID of the product
    Raises:
        Exception: If saving fails
    """
    logger.info(f'Saving metrics for product {product_id}: {metrics}')
    # Simulate DB save
    pass  # Replace with actual DB save logic

class DemandForecast:
    def __init__(self, product_id: str, date_range: List[str]):
        self.product_id = product_id
        self.date_range = date_range
        self.data: pd.DataFrame = pd.DataFrame()
        self.ts: TimeSeries = TimeSeries()

    def execute(self) -> None:
        """Main orchestration method.
        
        Fetch, transform, and save demand forecast metrics.
        """
        try:
            logger.info(f'Starting demand forecast for {self.product_id}')
            self.data = fetch_data(self.product_id, self.date_range)
            self.ts = transform_records(self.data)
            metrics = aggregate_metrics(self.ts)
            save_to_db(metrics, self.product_id)
        except ValueError as ve:
            logger.error(f'Validation error: {ve}')  # Handle validation errors
        except Exception as e:
            logger.error(f'An error occurred: {e}')  # Handle general exceptions

if __name__ == '__main__':
    # Example usage
    product_id = 'P1234'
    date_range = ['2023-01-01', '2023-12-31']
    demand_forecast = DemandForecast(product_id, date_range)
    demand_forecast.execute()  # Run the forecasting process