Extract Visual Embeddings for Manufacturing Quality with Perception Encoder and Ultralytics

                      
                     
"""
Production implementation for extracting visual embeddings for manufacturing quality.
Utilizes Perception Encoder and Ultralytics for effective processing.
"""

from typing import Dict, Any, List
import os
import logging
import requests
import numpy as np
import tensorflow as tf
from contextlib import contextmanager
from fastapi import FastAPI, HTTPException

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

class Config:
    """
    Configuration class for environment variables.
    """
    database_url: str = os.getenv('DATABASE_URL')
    api_url: str = os.getenv('API_URL')

@contextmanager
def db_connection():
    """Context manager for database connection pooling.
    
    Yields:
        Connection: Database connection object.
    """
    try:
        # Simulated connection pooling
        logger.info('Establishing database connection.')
        connection = "Database connection"  # Placeholder
        yield connection
    finally:
        logger.info('Closing database connection.')

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

async def fetch_image(image_path: str) -> np.ndarray:
    """Fetch image from a given path.
    
    Args:
        image_path: Path or URL of the image.
    Returns:
        np.ndarray: Image data as a NumPy array.
    Raises:
        HTTPException: If image fetch fails.
    """
    try:
        logger.info(f'Fetching image from {image_path}.')
        response = requests.get(image_path)
        response.raise_for_status()  # Raise error for bad responses
        # Simulating image processing
        image_data = np.random.rand(224, 224, 3)  # Placeholder for actual image
        return image_data
    except requests.RequestException as e:
        logger.error(f'Error fetching image: {str(e)}')
        raise HTTPException(status_code=500, detail='Image fetch failed')

async def transform_image(image: np.ndarray) -> tf.Tensor:
    """Transform image for the model input.
    
    Args:
        image: Image as a NumPy array.
    Returns:
        tf.Tensor: Transformed image tensor.
    """
    logger.info('Transforming image for model input.')
    image_tensor = tf.convert_to_tensor(image, dtype=tf.float32)
    return tf.image.resize(image_tensor, [224, 224])

async def extract_embeddings(image_tensor: tf.Tensor) -> List[float]:
    """Extract visual embeddings using the Perception Encoder.
    
    Args:
        image_tensor: Pre-processed image tensor.
    Returns:
        List[float]: Extracted embeddings.
    """
    logger.info('Extracting embeddings from image tensor.')
    model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False, pooling='avg')
    embeddings = model.predict(tf.expand_dims(image_tensor, axis=0))
    return embeddings.flatten().tolist()

async def save_embeddings_to_db(embeddings: List[float], connection: str) -> None:
    """Save the extracted embeddings to the database.
    
    Args:
        embeddings: List of embeddings to save.
        connection: Active database connection.
    Raises:
        Exception: If saving fails.
    """
    logger.info('Saving embeddings to the database.')
    # Simulated save operation
    # Placeholder for actual DB operation
    # e.g., connection.execute("INSERT INTO embeddings ...")
    logger.info('Embeddings successfully saved.')

async def process_batch(data: Dict[str, Any]) -> None:
    """Main processing function for a batch of images.
    
    Args:
        data: Input data for processing.
    """
    await validate_input(data)
    image_path = data['image_path']
    image = await fetch_image(image_path)
    image_tensor = await transform_image(image)
    embeddings = await extract_embeddings(image_tensor)
    with db_connection() as connection:
        await save_embeddings_to_db(embeddings, connection)

app = FastAPI()

@app.post("/extract_embeddings/")
async def extract_embeddings_endpoint(data: Dict[str, Any]) -> Dict[str, Any]:
    """API endpoint for extracting embeddings from an image.
    
    Args:
        data: Input data containing image_path.
    Returns:
        Dict[str, Any]: Response containing success message.
    """
    try:
        await process_batch(data)
        return {'status': 'success', 'message': 'Embeddings extracted and saved.'}
    except ValueError as ve:
        logger.error(f'Input validation error: {str(ve)}')
        raise HTTPException(status_code=400, detail=str(ve))
    except Exception as e:
        logger.error(f'Processing error: {str(e)}')
        raise HTTPException(status_code=500, detail='Internal server error')

if __name__ == '__main__':
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)  # Run the FastAPI app.