Forecast Spare Parts Replenishment Cycles with NeuralForecast and Darts

"""
Production implementation for forecasting spare parts replenishment cycles using NeuralForecast and Darts.
Provides secure, scalable operations with effective error handling and logging.
"""
from typing import Dict, Any, List, Union
import os
import logging
import numpy as np
import pandas as pd
from neuralforecast import NeuralForecast
from darts import TimeSeries
from darts.models import ExponentialSmoothing
from darts.utils import timeseries_generation as tg
from sqlalchemy import create_engine, text
import time

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

# Configuration class to handle environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    neural_forecast_model: str = os.getenv('NEURAL_MODEL')

# Validate input data format
async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data format for forecasting.
    
    Args:
        data: A dictionary containing the input data.
    Returns:
        bool: True if the input is valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'time_series' not in data or 'spare_parts' not in data:
        raise ValueError('Input must contain time_series and spare_parts fields.')
    return True

# Sanitize input fields to prevent SQL injection
def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent SQL injection.
    
    Args:
        data: Input data dictionary.
    Returns:
        Dict[str, Any]: Sanitized data.
    """
    return {k: v.replace(';', '') for k, v in data.items()}  # Basic sanitization

# Fetch data from the database
async def fetch_data(query: str) -> List[Dict[str, Any]]:
    """Fetch data from the database using a SQL query.
    
    Args:
        query: SQL query string.
    Returns:
        List[Dict[str, Any]]: Result set from the database.
    Raises:
        Exception: If the database query fails.
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as conn:
            result = conn.execute(text(query))
            return [dict(row) for row in result]
    except Exception as e:
        logger.error(f"Database query failed: {e}")
        raise

# Save forecast results to the database
async def save_to_db(forecast_data: List[Dict[str, Any]]) -> None:
    """Save forecast data to the database.
    
    Args:
        forecast_data: List of forecast records to save.
    Raises:
        Exception: If the save operation fails.
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as conn:
            for record in forecast_data:
                conn.execute(
                    text("INSERT INTO forecasts (part_id, forecast_value) VALUES (:part_id, :forecast_value)"),
                    {'part_id': record['part_id'], 'forecast_value': record['forecast_value']}
                )
    except Exception as e:
        logger.error(f"Failed to save forecast data: {e}")
        raise

# Normalize data for model input
def normalize_data(data: List[Dict[str, Union[str, float]]]) -> pd.DataFrame:
    """Normalize input data for machine learning model.
    
    Args:
        data: Input data as a list of dictionaries.
    Returns:
        pd.DataFrame: Normalized data suitable for model training.
    """
    df = pd.DataFrame(data)
    return (df - df.mean()) / df.std()  # Normalize data

# Transform records into a time series format
def transform_records(data: pd.DataFrame) -> TimeSeries:
    """Transform records into a Darts TimeSeries object.
    
    Args:
        data: Input data as a DataFrame.
    Returns:
        TimeSeries: Darts TimeSeries object.
    """
    return TimeSeries.from_dataframe(data, time_col='timestamp', value_cols=['value'])

# Main class orchestrating the forecasting process
class SparePartsForecaster:
    def __init__(self, model_name: str):
        self.model = NeuralForecast(model_name)

    async def forecast_spare_parts(self, input_data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """Main workflow for forecasting spare parts.
        
        Args:
            input_data: Input data for forecasting.
        Returns:
            List[Dict[str, Any]]: Forecast results.
        """        
        try:
            await validate_input(input_data)  # Validate input
            sanitized_data = sanitize_fields(input_data)
            data = await fetch_data(sanitized_data['query'])
            normalized_data = normalize_data(data)
            time_series = transform_records(normalized_data)
            # Fit model
            self.model.fit(time_series)
            forecast = self.model.predict()  # Generate forecast
            await save_to_db(forecast)
            return forecast
        except ValueError as ve:
            logger.warning(f"Validation error: {ve}")
            return []
        except Exception as e:
            logger.error(f"Error during forecasting: {e}")
            return []

# Main block for execution
if __name__ == '__main__':
    # Example usage of the forecasting system
    forecaster = SparePartsForecaster(model_name=Config.neural_forecast_model)
    sample_input = {
        'time_series': '2023-01-01',
        'spare_parts': 100,
        'query': 'SELECT * FROM spare_parts'  # Example query
    }
    result = forecaster.forecast_spare_parts(sample_input)
    logger.info(f"Forecast result: {result}")