Fine-Tune Factory VLMs Efficiently on Apple Silicon with Unsloth and LlamaIndex

"""
Production implementation for Fine-Tune Factory VLMs using Unsloth and LlamaIndex.
Provides secure, scalable operations for fine-tuning VLMs on Apple Silicon.
"""
import os
import logging
import time
from typing import Dict, Any, List
from sqlalchemy import create_engine, Column, Integer, String
from sqlalchemy.orm import sessionmaker, declarative_base

# Logging configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Database configuration
Base = declarative_base()

# Config class for environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///fine_tune.db')  # Default to SQLite

# SQLAlchemy models
class FineTuneData(Base):
    __tablename__ = 'fine_tune_data'
    id = Column(Integer, primary_key=True)
    model_name = Column(String, nullable=False)
    training_data = Column(String, nullable=False)

# Create a database session
engine = create_engine(Config.database_url)
Base.metadata.create_all(engine)
Session = sessionmaker(bind=engine)


def validate_input(data: Dict[str, Any]) -> bool:
    """Validate input data for fine-tuning.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'model_name' not in data:
        raise ValueError('Missing model_name')
    if 'training_data' not in data:
        raise ValueError('Missing training_data')
    return True


def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields to prevent injection attacks.
    
    Args:
        data: Data to sanitize
    Returns:
        Sanitized data
    """
    return {key: value.strip() for key, value in data.items()}


def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize training data for consistency.
    
    Args:
        data: Raw input data
    Returns:
        Normalized data ready for processing
    """
    # Assuming training_data is a string of comma-separated values
    data['training_data'] = data['training_data'].split(',')
    return data


def fetch_data(session, model_name: str) -> List[FineTuneData]:
    """Fetch existing fine-tune data from the database.
    
    Args:
        session: Database session
        model_name: Name of the model to fetch data for
    Returns:
        List of FineTuneData objects
    """
    return session.query(FineTuneData).filter_by(model_name=model_name).all()


def save_to_db(session, model_name: str, training_data: str) -> None:
    """Save fine-tune data to the database.
    
    Args:
        session: Database session
        model_name: Name of the model
        training_data: Training data to store
    """ 
    fine_tune_record = FineTuneData(model_name=model_name, training_data=training_data)
    session.add(fine_tune_record)
    session.commit()


def process_batch(data: List[Dict[str, Any]]) -> None:
    """Process a batch of fine-tune requests.
    
    Args:
        data: List of input data dictionaries
    """  
    for record in data:
        try:
            validate_input(record)
            sanitized_data = sanitize_fields(record)
            normalized_data = normalize_data(sanitized_data)
            with Session() as session:
                save_to_db(session, normalized_data['model_name'], normalized_data['training_data'])
        except ValueError as ve:
            logger.error(f'Validation Error: {ve}')  # Log validation errors
        except Exception as e:
            logger.error(f'Error processing record {record}: {str(e)}')  # Log unexpected errors


def aggregate_metrics(session) -> Dict[str, Any]:
    """Aggregate metrics from the fine-tuning data.
    
    Args:
        session: Database session
    Returns:
        Dictionary of aggregated metrics
    """  
    total_records = session.query(FineTuneData).count()
    return {'total_records': total_records}


def handle_errors(func):
    """Decorator to handle errors for functions.
    
    Args:
        func: Function to wrap
    Returns:
        Wrapped function
    """  
    def wrapper(*args, **kwargs):
        try:
            return func(*args, **kwargs)
        except Exception as e:
            logger.error(f'Error in {func.__name__}: {str(e)}')
            return None
    return wrapper

class FineTuneOrchestrator:
    def __init__(self):
        self.session = Session()

    def run(self, data: List[Dict[str, Any]]) -> None:
        """Main orchestrator method to execute fine-tuning.
        
        Args:
            data: List of input data for fine-tuning
        """  
        process_batch(data)
        metrics = aggregate_metrics(self.session)
        logger.info(f'Aggregated Metrics: {metrics}')  # Log aggregated metrics

if __name__ == '__main__':
    # Example usage
    orchestrator = FineTuneOrchestrator()
    sample_data = [
        {'model_name': 'model1', 'training_data': 'data1,data2'},
        {'model_name': 'model2', 'training_data': 'data3,data4'},
    ]
    orchestrator.run(sample_data)  # Run the fine-tuning process