Detect Industrial Surface Anomalies by Latent Embedding Distance with FiftyOne and Anomalib

"""
Production implementation for detecting industrial surface anomalies using latent embedding distance.
Provides secure, scalable operations for anomaly detection in images.
"""
from typing import Dict, Any, List, Tuple
import os
import logging
import time
import fiftyone as fo
import fiftyone.zoo as foz
from anomalib import AnomalyDetector

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

class Config:
    """
    Configuration class to store environment variables.
    """  
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH', 'path/to/model')

async 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 'images' not in data:
        raise ValueError('Missing images key in input data')
    return True

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

async def fetch_data(image_paths: List[str]) -> List[fo.Dataset]:
    """Fetch data from the given image paths.
    
    Args:
        image_paths: List of image paths to fetch
    Returns:
        List of FiftyOne datasets
    """  
    datasets = []
    for path in image_paths:
        dataset = fo.Dataset(path)
        datasets.append(dataset)
    return datasets

async def normalize_data(data: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Normalize input data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    normalized_data = []
    for item in data:
        normalized_item = {key: value / 255.0 for key, value in item.items()}
        normalized_data.append(normalized_item)
    return normalized_data

async def process_batch(datasets: List[fo.Dataset], model: AnomalyDetector) -> List[Tuple[str, float]]:
    """Process a batch of datasets to detect anomalies.
    
    Args:
        datasets: List of datasets to process
        model: Anomaly detection model
    Returns:
        List of tuples containing image path and anomaly score
    """  
    results = []
    for dataset in datasets:
        for sample in dataset:
            score = model.predict(sample)
            results.append((sample.filepath, score))
    return results

async def save_to_db(results: List[Tuple[str, float]]) -> None:
    """Save anomaly detection results to the database.
    
    Args:
        results: List of tuples containing image paths and scores
    """  
    # Simulate database save with logging
    for image_path, score in results:
        logger.info(f'Saving {image_path} with score {score} to database.')

async def call_api(data: Dict[str, Any]) -> None:
    """Call external API with the provided data.
    
    Args:
        data: Data to send to API
    """  
    # Simulate API call
    logger.info('Calling external API with data: %s', data)

class AnomalyDetectorService:
    """
    Service class for anomaly detection workflow.
    """
    def __init__(self, model_path: str) -> None:
        self.model = AnomalyDetector.load(model_path)

    async def run_detection(self, input_data: Dict[str, Any]) -> None:
        """Run the complete anomaly detection workflow.
        
        Args:
            input_data: Input data for detection
        """
        try:
            await validate_input(input_data)  # Validate input data
            sanitized_data = await sanitize_fields(input_data)  # Sanitize fields
            datasets = await fetch_data(sanitized_data['images'])  # Fetch datasets
            normalized_data = await normalize_data(datasets)  # Normalize data
            results = await process_batch(datasets, self.model)  # Process batch
            await save_to_db(results)  # Save results to database
            await call_api({'results': results})  # Call external API
        except ValueError as e:
            logger.error('Validation error: %s', e)
            raise  # Rethrow error for further handling
        except Exception as e:
            logger.exception('Error during anomaly detection: %s', e)
            raise  # Rethrow error for further handling

if __name__ == '__main__':
    # Example usage
    anomaly_service = AnomalyDetectorService(Config.model_path)
    sample_input = {'images': ['path/to/image1.jpg', 'path/to/image2.jpg']}
    try:
        anomaly_service.run_detection(sample_input)
    except Exception as e:
        logger.error('Failed to run detection: %s', e)