Filter Production Line Detections by Semantic Similarity with CLIP and Supervision

                      
                     
"""
Production implementation for filtering production line detections by semantic similarity using CLIP and supervision.
Provides secure, scalable operations with robust error handling and logging.
"""

from typing import Dict, Any, List, Tuple
import os
import logging
import requests
from contextlib import contextmanager
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker
import time

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

class Config:
    """Configuration class to hold environment variables and app settings."""
    database_url: str = os.getenv('DATABASE_URL')
    clip_model_url: str = os.getenv('CLIP_MODEL_URL')

# Database connection pooling setup
engine = create_engine(Config.database_url, pool_size=10, max_overflow=20)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

@contextmanager
def get_db() -> Any:
    """Context manager for database sessions.
    Returns:
        Session object for database operations.
    """  
    db = SessionLocal()  # Create a new database session
    try:
        yield db  # Provide the session to the caller
    finally:
        db.close()  # Close the session upon completion

async def validate_input(data: Dict[str, Any]) -> bool:
    """Validate the input data for filtering detections.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'detections' not in data:
        raise ValueError('Missing detections in input data')
    if not isinstance(data['detections'], list):
        raise ValueError('Detections should be a list')
    return True

async def fetch_clip_embeddings(detections: List[str]) -> List[float]:
    """Fetch embeddings from the CLIP model for semantic similarity.
    
    Args:
        detections: List of detection labels
    Returns:
        List of embeddings from CLIP model
    Raises:
        ConnectionError: If CLIP model request fails
    """
    try:
        response = requests.post(Config.clip_model_url, json={'text': detections})
        response.raise_for_status()  # Raise error for bad responses
        return response.json()['embeddings']
    except requests.RequestException as e:
        logger.error(f'Error fetching CLIP embeddings: {e}')
        raise ConnectionError('Failed to fetch embeddings from CLIP model')

async def compute_similarity(embeddings: List[float]) -> List[Tuple[int, int, float]]:
    """Compute similarity between detection embeddings.
    
    Args:
        embeddings: List of embeddings to compare
    Returns:
        List of tuples containing pairs of indices and their similarity scores
    """
    similarity_scores = []
    for i in range(len(embeddings)):
        for j in range(i + 1, len(embeddings)):
            score = cosine_similarity(embeddings[i], embeddings[j])  # Replace with actual similarity calculation
            similarity_scores.append((i, j, score))
    return similarity_scores

async def filter_detections(data: Dict[str, Any]) -> List[Dict[str, Any]]:
    """Filter detections based on semantic similarity scores.
    
    Args:
        data: Input data containing detections
    Returns:
        Filtered list of detections
    Raises:
        ValueError: If input validation fails
    """
    await validate_input(data)  # Validate input
    embeddings = await fetch_clip_embeddings(data['detections'])  # Fetch embeddings
    similarity_scores = await compute_similarity(embeddings)  # Compute similarity
    # Filter logic based on similarity (threshold can be defined)
    filtered_detections = [data['detections'][i] for i, j, score in similarity_scores if score > 0.7]
    return filtered_detections  # Return filtered detections

async def save_filtered_detections(db: Any, detections: List[Dict[str, Any]]) -> None:
    """Save filtered detections to the database.
    
    Args:
        db: Database session
        detections: List of filtered detections to save
    Returns:
        None
    """
    # Insert filtered detections into the database
    for detection in detections:
        db.execute(text("INSERT INTO filtered_detections (label) VALUES (:label)"), {'label': detection})
    db.commit()  # Commit changes to the database

async def main(data: Dict[str, Any]) -> None:
    """Main orchestrator function to execute the workflow.
    
    Args:
        data: Input data for filtering detections
    Returns:
        None
    """
    try:
        async with get_db() as db:  # Use context manager for DB session
            filtered_detections = await filter_detections(data)  # Filter detections
            await save_filtered_detections(db, filtered_detections)  # Save to database
    except Exception as e:
        logger.error(f'Error during processing: {e}')  # Log any errors

if __name__ == '__main__':
    # Example usage
    sample_data = {'detections': ['object1', 'object2', 'object3']}
    import asyncio
    asyncio.run(main(sample_data))  # Run main function asynchronously