Train Edge Vision Models with Qwen2.5-VL and ZenML

                      
                     
"""
Production implementation for training edge vision models with Qwen2.5-VL and ZenML.
Provides secure, scalable operations for model training and data processing.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import time
import requests
from zenml import pipeline
from zenml.steps import step
from sqlalchemy import create_engine, text
from sqlalchemy.exc import SQLAlchemyError
from contextlib import contextmanager

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

class Config:
    database_url: str = os.getenv('DATABASE_URL', 'sqlite:///models.db')  # Fallback to SQLite
    retry_count: int = int(os.getenv('RETRY_COUNT', 3))  # Number of retries for failed requests
    backoff_factor: float = float(os.getenv('BACKOFF_FACTOR', 1.5))  # Exponential backoff factor

@contextmanager
def get_db_connection() -> Any:
    """Context manager for database connection.
    
    Yields:
        Connection: Database connection object
    """
    try:
        engine = create_engine(Config.database_url)
        connection = engine.connect()  # Establish a connection
        yield connection
    except SQLAlchemyError as e:
        logger.error(f"Database connection error: {e}")
        raise
    finally:
        connection.close()  # Ensure the connection is closed

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_urls' not in data or not isinstance(data['image_urls'], list):
        raise ValueError('Missing or invalid image_urls')
    return True  # Validation passed

def fetch_data(image_urls: List[str]) -> List[Dict[str, Any]]:
    """Fetch images from provided URLs.
    
    Args:
        image_urls: List of image URLs to fetch
    Returns:
        List of fetched image data
    Raises:
        Exception: If fetching fails
    """
    images = []
    for url in image_urls:
        try:
            response = requests.get(url, timeout=5)
            response.raise_for_status()  # Check for HTTP errors
            images.append(response.json())  # Assuming the response is in JSON format
        except requests.RequestException as e:
            logger.error(f"Failed to fetch {url}: {e}")
            raise Exception(f"Failed to fetch data from {url}")
    return images

def normalize_data(images: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize image data for model training.
    
    Args:
        images: List of image data to normalize
    Returns:
        List of normalized image data
    """
    return [{'image': img['data'], 'label': img.get('label', None)} for img in images]  # Normalize structure

def save_to_db(data: List[Dict[str, Any]]) -> None:
    """Save processed data to the database.
    
    Args:
        data: List of data to save
    Raises:
        Exception: If saving fails
    """
    with get_db_connection() as conn:
        try:
            for record in data:
                conn.execute(text("INSERT INTO images (image, label) VALUES (:image, :label)"), 
                             {'image': record['image'], 'label': record['label']})  # Save each record
            conn.commit()  # Commit the transaction
        except SQLAlchemyError as e:
            logger.error(f"Failed to save data: {e}")
            raise Exception("Failed to save data to the database")

def call_api(model_data: Dict[str, Any]) -> None:
    """Call the model API for training.
    
    Args:
        model_data: Data to send for training
    Raises:
        Exception: If API call fails
    """
    url = os.getenv('MODEL_API_URL', 'http://localhost:8000/train')
    for attempt in range(Config.retry_count):
        try:
            response = requests.post(url, json=model_data)
            response.raise_for_status()  # Check for HTTP errors
            logger.info(f"Training response: {response.json()}")  # Log the response
            return  # Exit if successful
        except requests.RequestException as e:
            logger.warning(f"API call failed: {e}. Retrying... ({attempt + 1}/{Config.retry_count})")
            time.sleep(Config.backoff_factor ** attempt)  # Exponential backoff
    raise Exception("Failed to call the model API after retries")

@step
def process_batch(image_urls: List[str]) -> None:
    """Process a batch of image URLs for training.
    
    Args:
        image_urls: List of image URLs
    """
    try:
        validate_input({'image_urls': image_urls})  # Validate input
        images = fetch_data(image_urls)  # Fetch image data
        normalized_data = normalize_data(images)  # Normalize data
        save_to_db(normalized_data)  # Save to database
        logger.info(f"Batch processed successfully for {len(image_urls)} images.")  # Log success
    except Exception as e:
        logger.error(f"Error processing batch: {e}")

@pipeline
def train_model_pipeline(image_urls: List[str]) -> None:
    """Define the model training pipeline.
    
    Args:
        image_urls: List of image URLs
    """
    process_batch(image_urls)  # Execute the batch processing step

if __name__ == '__main__':
    # Example usage with hardcoded URLs
    urls = ['http://example.com/image1.jpg', 'http://example.com/image2.jpg']
    train_model_pipeline(urls)  # Trigger the training pipeline