Trace Digital Twin Model Predictions with Arize Phoenix and MLflow

"""
Production implementation for tracing digital twin model predictions.
Integrates Arize Phoenix and MLflow for effective data tracking.
"""

from typing import Dict, Any, List
import os
import logging
import requests
import pandas as pd
import time

# Set up logging for monitoring and debugging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class for environment variables.
    
    Attributes:
        arize_api_key: API key for Arize.
        mlflow_tracking_uri: URI for MLflow tracking.
    """
    arize_api_key: str = os.getenv('ARIZE_API_KEY')
    mlflow_tracking_uri: str = os.getenv('MLFLOW_TRACKING_URI')

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

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input data fields to ensure security.
    
    Args:
        data: Raw input data
    Returns:
        Cleaned data fields
    """  
    return {k: str(v).strip() for k, v in data.items()}  # Remove extra spaces

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

async def save_to_mlflow(data: pd.DataFrame) -> None:
    """Save model predictions to MLflow.
    
    Args:
        data: DataFrame containing model predictions
    Raises:
        Exception: If saving fails
    """  
    try:
        mlflow.set_tracking_uri(Config.mlflow_tracking_uri)  # Set MLflow URI
        with mlflow.start_run():
            mlflow.log_params(data.to_dict())  # Log parameters
            mlflow.log_artifact('predictions.csv')  # Log predictions
            logger.info('Saved predictions to MLflow.')  # Log success
    except Exception as e:
        logger.error(f'Error saving to MLflow: {e}')  # Log the error
        raise Exception('Failed to save data to MLflow')

async def aggregate_metrics(predictions: List[Dict[str, Any]]) -> Dict[str, float]:
    """Aggregate metrics from predictions.
    
    Args:
        predictions: List of prediction results
    Returns:
        Dictionary of aggregated metrics
    """  
    # Aggregate metrics as needed
    metrics = {'mean': sum(p['score'] for p in predictions) / len(predictions)}  # Calculate mean
    return metrics

async def transform_records(records: List[Dict[str, Any]]) -> pd.DataFrame:
    """Transform records into a DataFrame.
    
    Args:
        records: Raw records to transform
    Returns:
        DataFrame of transformed records
    """  
    return pd.DataFrame(records)  # Convert list to DataFrame

async def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of records for predictions.
    
    Args:
        data: Batch of records to process
    """  
    try:
        await validate_input(data)  # Validate input
        sanitized_data = await sanitize_fields(data)  # Sanitize input fields
        predictions = await fetch_data('http://api.example.com/predict', {'Authorization': f'Bearer {Config.arize_api_key}'})  # Fetch predictions
        aggregated_metrics = await aggregate_metrics(predictions)  # Aggregate metrics
        predictions_df = await transform_records(predictions)  # Transform to DataFrame
        await save_to_mlflow(predictions_df)  # Save to MLflow
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')  # Log validation errors
    except Exception as e:
        logger.error(f'Processing error: {e}')  # Log processing errors

class DigitalTwinModel:
    """Main orchestrator class for handling digital twin model predictions.
    
    Methods:
        execute: Run the end-to-end prediction process.
    """  
    async def execute(self, input_data: List[Dict[str, Any]]) -> None:
        """Execute the main workflow.
        
        Args:
            input_data: Data to process
        """  
        await process_batch(input_data)  # Process input data

if __name__ == '__main__':
    # Example usage
    model = DigitalTwinModel()
    example_data = [{'id': '1', 'feature': 'value1'}, {'id': '2', 'feature': 'value2'}]  # Example input data
    import asyncio
    asyncio.run(model.execute(example_data))  # Run the model execution