Fine-Tune Qwen3.5-VL for Factory Visual Inspection with NeMo AutoModel and Instructor

"""
Production implementation for fine-tuning Qwen3.5-VL with NeMo AutoModel and Instructor.
Provides secure, scalable operations for visual inspection in factories.
"""
import os
import logging
from typing import Dict, Any, List
from nemo.collections.nlp.models import Qwen3_5VL
from nemo.core.neural_types import NeuralType
from nemo.utils import logging as nlogging
from nemo.core.config import OmegaConf

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

class Config:
    """
    Configuration class to load environment variables.
    """
    def __init__(self):
        self.model_name: str = os.getenv('MODEL_NAME', 'Qwen3.5-VL')
        self.data_path: str = os.getenv('DATA_PATH', './data')
        self.output_path: str = os.getenv('OUTPUT_PATH', './output')
        self.num_epochs: int = int(os.getenv('NUM_EPOCHS', 10))

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for training.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'images' not in data or 'labels' not in data:
        raise ValueError('Missing required fields: images, labels')
    return True

async def fetch_data(data_path: str) -> List[Dict[str, Any]]:
    """Fetch training data from specified path.
    
    Args:
        data_path: Path to the training data
    Returns:
        List of training records
    Raises:
        FileNotFoundError: If data path is incorrect
    """
    if not os.path.exists(data_path):
        raise FileNotFoundError(f'Data path {data_path} not found.')
    # Simulated data fetching
    return [{'image': 'image1.png', 'label': 0}, {'image': 'image2.png', 'label': 1}]

async def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize input data for model processing.
    
    Args:
        data: Raw data for normalization
    Returns:
        Normalized data
    Raises:
        ValueError: If data format is incorrect
    """
    normalized_data = []
    for record in data:
        if 'image' not in record:
            raise ValueError('Invalid data format')
        normalized_data.append({'image': record['image'], 'label': record['label']})
    return normalized_data

async def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for model input.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records ready for model input
    Raises:
        ValueError: If transformation fails
    """
    transformed = []
    for record in records:
        transformed.append({'input': record['image'], 'output': record['label']})  # Just a placeholder
    return transformed

async def process_batch(batch: List[Dict[str, Any]]) -> None:
    """Process a batch of data through the model.
    
    Args:
        batch: List of data to process
    """
    model = Qwen3_5VL(pretrained_model=Config().model_name)
    # Simulate model processing
    logger.info('Processing batch with model...')
    for item in batch:
        logger.info(f'Processing item: {item}')  # Simulated processing

async def save_to_db(results: List[Dict[str, Any]]) -> None:
    """Save processed results to the database.
    
    Args:
        results: Results to save
    """
    logger.info('Saving results to database...')
    # Simulated save operation

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API with processed data.
    
    Args:
        data: Data to send to API
    """
    logger.info('Calling external API...')

class QwenFineTuner:
    """
    Main orchestrator for fine-tuning Qwen3.5-VL.
    """
    def __init__(self, config: Config):
        self.config = config

    async def run(self) -> None:
        """Run the fine-tuning process.
        """
        try:
            logger.info('Starting fine-tuning process...')
            data = await fetch_data(self.config.data_path)
            await validate_input({'images': data})
            normalized_data = await normalize_data(data)
            transformed_data = await transform_records(normalized_data)
            await process_batch(transformed_data)
            await save_to_db(transformed_data)
        except Exception as e:
            logger.error(f'Error during processing: {e}')
            raise

if __name__ == '__main__':
    config = Config()
    tuner = QwenFineTuner(config)
    import asyncio
    asyncio.run(tuner.run())