Gate Digital Twin Retraining on Data Quality with Evidently and Weights and Biases

"""
Production implementation for Gate Digital Twin Retraining on Data Quality.
Provides secure, scalable operations using Evidently and Weights and Biases.
"""
from typing import Dict, Any, List
import os
import logging
import time
import requests
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker

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

class Config:
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

# Create a database engine with connection pooling
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
Session = sessionmaker(bind=engine)

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        bool: True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_id' not in data:
        raise ValueError('Missing model_id')
    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 for security.
    
    Args:
        data: Input data to sanitize
    Returns:
        Dict[str, Any]: Sanitized data
    """
    sanitized_data = {k: v.strip() for k, v in data.items()}
    return sanitized_data

async def fetch_data(model_id: str) -> List[Dict[str, Any]]:
    """Fetch data from the API for the given model_id.
    
    Args:
        model_id: Identifier for the model
    Returns:
        List[Dict[str, Any]]: Fetched records
    Raises:
        RuntimeError: If API call fails
    """
    try:
        response = requests.get(f'{Config.api_url}/models/{model_id}/data')
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.RequestException as e:
        logger.error(f'Error fetching data: {e}')
        raise RuntimeError('Failed to fetch data')

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform raw records into a suitable format.
    
    Args:
        records: Raw input records
    Returns:
        List[Dict[str, Any]]: Transformed records
    """
    transformed = []
    for record in records:
        normalized = {
            'feature_1': record['feature_1'],
            'feature_2': record['feature_2'],
            'label': record.get('label', None)
        }
        transformed.append(normalized)
    return transformed

async def save_to_db(records: List[Dict[str, Any]]) -> None:
    """Save transformed records to the database.
    
    Args:
        records: Records to save
    Raises:
        RuntimeError: If database operation fails
    """
    session = Session()
    try:
        for record in records:
            stmt = text("INSERT INTO model_data (feature_1, feature_2, label) VALUES (:feature_1, :feature_2, :label)")
            session.execute(stmt, record)
        session.commit()
    except Exception as e:
        session.rollback()  # Rollback if an error occurs
        logger.error(f'Error saving to database: {e}')
        raise RuntimeError('Database operation failed')
    finally:
        session.close()  # Ensure session is closed

async def aggregate_metrics() -> Dict[str, Any]:
    """Aggregate metrics from the data.
    
    Returns:
        Dict[str, Any]: Aggregated metrics
    """
    session = Session()
    try:
        result = session.execute(text("SELECT AVG(feature_1) as avg_feature_1, COUNT(*) as total FROM model_data"))
        metrics = result.fetchone()
        return {'avg_feature_1': metrics['avg_feature_1'], 'total': metrics['total']}
    finally:
        session.close()

async def process_batch(data: Dict[str, Any]) -> None:
    """Main function to process a batch of data.
    
    Args:
        data: Input data for processing
    """
    try:
        await validate_input(data)
        sanitized_data = await sanitize_fields(data)
        raw_records = await fetch_data(sanitized_data['model_id'])
        transformed_records = await transform_records(raw_records)
        await save_to_db(transformed_records)
        logger.info('Batch processed successfully')
    except ValueError as ve:
        logger.warning(f'Validation error: {ve}')
    except RuntimeError as re:
        logger.error(f'Processing error: {re}')

if __name__ == '__main__':
    # Example usage
    example_data = {'model_id': '12345', 'data': 'sample_data'}
    import asyncio
    asyncio.run(process_batch(example_data))