Build Industrial Equipment Twins with Siemens Composer and MLflow

                      
                     
from typing import Dict, Any
import os
import mlflow
from mlflow import log_metric, log_param
import pandas as pd

# Configuration
MLFLOW_TRACKING_URI = os.getenv('MLFLOW_TRACKING_URI', 'http://localhost:5000')
mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

# Initialize MLflow
class EquipmentTwin:
    def __init__(self, equipment_id: str):
        self.equipment_id = equipment_id
        self.model = None

    def load_model(self):
        try:
            self.model = mlflow.pyfunc.load_model(f'models/{self.equipment_id}')
        except Exception as e:
            print(f'Error loading model: {e}')

    def predict(self, input_data: Dict[str, Any]) -> Any:
        if not self.model:
            self.load_model()
        try:
            result = self.model.predict(pd.DataFrame([input_data]))
            return result
        except Exception as e:
            print(f'Prediction error: {e}')

if __name__ == '__main__':
    equipment_id = os.getenv('EQUIPMENT_ID', 'default_equipment')
    twin = EquipmentTwin(equipment_id)
    input_data = {'temperature': 70, 'pressure': 30}  # Sample input
    prediction = twin.predict(input_data)
    print(f'Prediction for {equipment_id}: {prediction}')