Forecast Equipment Failure Windows with Chronos-2 and Prophet

                      
                     
"""
Production implementation for forecasting equipment failure windows using Chronos-2 and Prophet.
Provides secure, scalable operations with robust error handling and logging.
"""

import os
import logging
import numpy as np
import pandas as pd
from typing import Dict, Any, List
from prophet import Prophet
from sqlalchemy import create_engine, text
from contextlib import contextmanager
import time

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

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

@contextmanager
def db_connection() -> None:
    """Context manager for database connection pooling.
    
    Yields:
        Connection object
    """
    engine = create_engine(Config.database_url)
    connection = engine.connect()  # Establish connection
    try:
        yield connection
    finally:
        connection.close()  # Close connection

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for forecasting.
    
    Args:
        data: Input data for validation
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'data' not in data:
        raise ValueError('Missing data field')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injections.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    sanitized_data = {k: str(v).strip() for k, v in data.items()}
    logger.info('Sanitized input fields')  # Log sanitization
    return sanitized_data

async def fetch_data(query: str) -> pd.DataFrame:
    """Fetch data from the database using the given query.
    
    Args:
        query: SQL query to execute
    Returns:
        DataFrame containing the results
    Raises:
        Exception: On database errors
    """
    try:
        with db_connection() as conn:
            data = pd.read_sql(query, conn)
            logger.info('Data fetched successfully')  # Log fetch success
            return data
    except Exception as e:
        logger.error(f'Error fetching data: {e}')  # Log error
        raise

async def normalize_data(data: pd.DataFrame) -> pd.DataFrame:
    """Normalize the data for processing.
    
    Args:
        data: DataFrame to normalize
    Returns:
        Normalized DataFrame
    """
    # Normalization logic
    normalized_data = (data - data.mean()) / data.std()
    logger.info('Data normalized')  # Log normalization
    return normalized_data

async def transform_records(data: pd.DataFrame) -> List[Dict[str, Any]]:
    """Transform records for Prophet model.
    
    Args:
        data: DataFrame to transform
    Returns:
        List of records for modeling
    """
    records = data.to_dict(orient='records')
    logger.info('Records transformed for Prophet')  # Log transformation
    return records

async def call_forecasting_model(records: List[Dict[str, Any]]) -> pd.DataFrame:
    """Call the Prophet forecasting model on transformed records.
    
    Args:
        records: Transformed records
    Returns:
        DataFrame containing forecasted results
    """
    model = Prophet()  # Initialize Prophet model
    model.fit(pd.DataFrame(records))  # Fit model
    future = model.make_future_dataframe(periods=30)  # Create future dataframe
    forecast = model.predict(future)  # Generate forecast
    logger.info('Forecasting model called successfully')  # Log model call
    return forecast

async def save_to_db(data: pd.DataFrame) -> None:
    """Save forecasted results to the database.
    
    Args:
        data: DataFrame containing forecasted data
    Raises:
        Exception: On database errors
    """
    try:
        with db_connection() as conn:
            data.to_sql('forecast_results', conn, if_exists='replace', index=False)
            logger.info('Data saved to database successfully')  # Log save success
    except Exception as e:
        logger.error(f'Error saving data: {e}')  # Log error
        raise

async def process_batch(data: Dict[str, Any]) -> None:
    """Process a batch of data for forecasting.
    
    Args:
        data: Input data for processing
    Raises:
        Exception: On processing errors
    """
    try:
        await validate_input(data)  # Validate input
        sanitized_data = await sanitize_fields(data)  # Sanitize fields
        raw_data = await fetch_data(sanitized_data['data'])  # Fetch raw data
        normalized_data = await normalize_data(raw_data)  # Normalize data
        records = await transform_records(normalized_data)  # Transform data
        forecast = await call_forecasting_model(records)  # Call model
        await save_to_db(forecast)  # Save results
    except Exception as e:
        logger.error(f'Error processing batch: {e}')  # Log error
        raise

if __name__ == '__main__':
    # Example usage
    example_data = {'data': 'SELECT * FROM equipment_data'}
    try:
        await process_batch(example_data)  # Process batch
    except Exception as e:
        logger.error(f'Failed to process batch: {e}')  # Log failure