Build Industrial Equipment Twins with Siemens Composer and MLflow

                      
                     
"""
Production implementation for building industrial equipment twins using Siemens Composer and MLflow.
Provides secure, scalable operations for real-time data integration and processing.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
import pandas as pd
from sqlalchemy import create_engine, text

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')
    batch_size: int = int(os.getenv('BATCH_SIZE', 100))

# Validate input data to ensure required fields are present
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 'equipment_id' not in data:
        raise ValueError('Missing equipment_id')
    if 'data' not in data:
        raise ValueError('Missing data field')
    return True

# Sanitize fields to prevent SQL injection
async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    
    Args:
        data: Input data to sanitize
    Returns:
        Sanitized data
    """
    return {key: str(value).replace("'", "\'") for key, value in data.items()}

# Normalize data for standard format
async def normalize_data(data: List[Dict[str, Any]]) -> pd.DataFrame:
    """Normalize input data into a DataFrame.
    
    Args:
        data: List of input data
    Returns:
        Normalized DataFrame
    """
    return pd.DataFrame(data)

# Transform records for ML model compatibility
async def transform_records(data: pd.DataFrame) -> pd.DataFrame:
    """Transform DataFrame records for ML processing.
    
    Args:
        data: Input DataFrame
    Returns:
        Transformed DataFrame
    """
    # Example transformation
    data['normalized_value'] = data['value'] / data['value'].max()  # Normalize values
    return data

# Process batch of records and log metrics
async def process_batch(batch: pd.DataFrame) -> None:
    """Process a batch of records.
    
    Args:
        batch: DataFrame of batch records
    """
    # Simulate processing
    logger.info(f'Processing batch of size: {len(batch)}')
    # Log metrics here (e.g., save to MLflow)
    # For example:
    # mlflow.log_metric('batch_size', len(batch))

# Fetch data from external API
async def fetch_data(url: str) -> List[Dict[str, Any]]:
    """Fetch data from an external API.
    
    Args:
        url: API endpoint
    Returns:
        List of records
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.get(url)
        response.raise_for_status()  # Raise error for bad responses
        return response.json()  # Return JSON response
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise Exception('Failed to fetch data')

# Save processed data to the database
async def save_to_db(df: pd.DataFrame) -> None:
    """Save DataFrame to the database.
    
    Args:
        df: DataFrame to save
    Raises:
        Exception: If saving fails
    """
    try:
        engine = create_engine(Config.database_url)
        with engine.connect() as connection:
            df.to_sql('equipment_twins', con=connection, if_exists='append', index=False)
    except Exception as e:
        logger.error(f'Error saving to database: {e}')
        raise Exception('Failed to save data')

# Format output for user-friendly display
async def format_output(data: pd.DataFrame) -> str:
    """Format DataFrame for output.
    
    Args:
        data: DataFrame to format
    Returns:
        Formatted string output
    """
    return data.to_string(index=False)

# Main orchestrator class
class IndustrialTwinBuilder:
    """Orchestrates the process of building industrial twins.
    """
    def __init__(self, config: Config):
        self.config = config

    async def run(self, equipment_id: str) -> None:
        """Main entry point to run the twin builder.
        
        Args:
            equipment_id: ID of the equipment
        """
        try:
            # Fetch data for the given equipment ID
            url = f'https://api.example.com/equipment/{equipment_id}'
            raw_data = await fetch_data(url)
            # Validate and sanitize input data
            await validate_input(raw_data)
            sanitized_data = await sanitize_fields(raw_data)
            # Normalize and transform data
            df = await normalize_data(sanitized_data)
            transformed_df = await transform_records(df)
            # Process data in batches
            for i in range(0, len(transformed_df), self.config.batch_size):
                batch = transformed_df.iloc[i:i + self.config.batch_size]
                await process_batch(batch)
                await save_to_db(batch)
            output = await format_output(transformed_df)
            logger.info(f'Final output:
{output}')
        except Exception as e:
            logger.error(f'Error in processing: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    builder = IndustrialTwinBuilder(config)
    import asyncio
    asyncio.run(builder.run('equipment_123'))