Classify Factory Defects with Few-Shot DINOv2 Embeddings and Supervision

"""
Production implementation for Classifying Factory Defects Using Few-Shot DINOv2 Embeddings.
This module provides secure and scalable operations for defect classification.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
import numpy as np
from sklearn.metrics import accuracy_score

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

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_endpoint: str = os.getenv('API_ENDPOINT')

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for defect classification.
    
    Args:
        data: Input data to validate.
    Returns:
        bool: True if valid.
    Raises:
        ValueError: If validation fails.
    """
    if 'image' not in data:
        raise ValueError('Missing image in input data')
    if not isinstance(data['image'], str):
        raise ValueError('Image path must be a string')
    return True

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

async def fetch_data(api_url: str) -> List[Dict[str, Any]]:
    """Fetch data from the API.
    
    Args:
        api_url: URL of the API endpoint.
    Returns:
        List[Dict[str, Any]]: List of records fetched.
    Raises:
        requests.HTTPError: If the API request fails.
    """
    response = requests.get(api_url)
    response.raise_for_status()  # Raise an error for bad responses
    return response.json()

async def normalize_data(data: List[Dict[str, Any]]) -> np.ndarray:
    """Normalize the fetched data for model input.
    
    Args:
        data: List of records to normalize.
    Returns:
        np.ndarray: Normalized data.
    """
    # Assume data contains images for classification
    normalized_data = np.array([record['features'] for record in data])
    return normalized_data / 255.0  # Normalize pixel values

async def process_batch(data: np.ndarray) -> List[int]:
    """Process a batch of data through the classification model.
    
    Args:
        data: Batch data to process.
    Returns:
        List[int]: List of predicted classes.
    """
    logger.info('Processing batch of data for classification.')
    # Dummy model inference - replace with actual model call
    predictions = np.argmax(data, axis=1).tolist()  # Simulated predictions
    return predictions

async def aggregate_metrics(y_true: List[int], y_pred: List[int]) -> Dict[str, float]:
    """Aggregate performance metrics from model predictions.
    
    Args:
        y_true: True labels.
        y_pred: Predicted labels.
    Returns:
        Dict[str, float]: Dictionary of accuracy and other metrics.
    """
    accuracy = accuracy_score(y_true, y_pred)
    return {'accuracy': accuracy}

async def save_to_db(data: Dict[str, Any]) -> None:
    """Save classification results to the database.
    
    Args:
        data: Data to save.
    Raises:
        Exception: If database operation fails.
    """
    # Simulate DB save operation
    logger.info('Saving classification results to the database.')
    # Actual DB save logic would go here

async def call_api(data: Dict[str, Any]) -> None:
    """Call an external API with processed data.
    
    Args:
        data: Data to send to the API.
    """
    try:
        response = requests.post(Config.api_endpoint, json=data)
        response.raise_for_status()  # Raise an error for bad responses
    except requests.HTTPError as http_err:
        logger.error(f'HTTP error occurred: {http_err}')
    except Exception as err:
        logger.error(f'Other error occurred: {err}')

class DefectClassifier:
    """Main class for classifying defects using few-shot learning.
    """
    def __init__(self, config: Config):
        self.config = config

    async def classify(self, input_data: Dict[str, Any]) -> None:
        """Main workflow for classifying defects.
        
        Args:
            input_data: Input data for classification.
        """
        try:
            await validate_input(input_data)  # Validate input
            sanitized_data = await sanitize_fields(input_data)  # Sanitize input
            fetched_data = await fetch_data(self.config.api_endpoint)  # Fetch data
            normalized_data = await normalize_data(fetched_data)  # Normalize data
            predictions = await process_batch(normalized_data)  # Process data
            results = await aggregate_metrics(sanitized_data['true_labels'], predictions)  # Aggregate results
            await save_to_db(results)  # Save results
            await call_api(results)  # Call external API
        except ValueError as val_err:
            logger.error(f'Validation error: {val_err}')
        except Exception as e:
            logger.error(f'An exception occurred: {e}')

if __name__ == '__main__':
    # Example usage
    config = Config()
    classifier = DefectClassifier(config)
    sample_input = {'image': 'path/to/image.jpg', 'true_labels': [0]}  # Sample input
    import asyncio
    asyncio.run(classifier.classify(sample_input))