Run Compact Vision-Language Models for Industrial Inspection with Ollama and Supervision

                      
                     
"""
Production implementation for running compact vision-language models for industrial inspection.
Provides secure, scalable operations with efficient data handling.
"""

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

# Setting up logging for the application
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Database connection pooling setup
DATABASE_URL = os.getenv('DATABASE_URL', 'sqlite:///./test.db')
engine = create_engine(DATABASE_URL, pool_size=5, max_overflow=10)
SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)

class Config:
    """
    Configuration class to manage environment variables.
    """
    database_url: str = DATABASE_URL

def validate_input(data: Dict[str, Any]) -> bool:
    """Validate request data for industrial inspection.
    
    Args:
        data: Input data to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    if 'image_path' not in data:
        raise ValueError('Missing image_path')
    if not isinstance(data['image_path'], str):
        raise ValueError('image_path must be a string')
    return True

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

def normalize_data(data: Dict[str, Any]) -> Dict[str, Any]:
    """Normalize data for processing.
    
    Args:
        data: Input data to normalize
    Returns:
        Normalized data
    """
    return {key: value.lower() for key, value in data.items()}

def transform_records(records: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    """Transform records for saving to the database.
    
    Args:
        records: List of records to transform
    Returns:
        Transformed records
    """
    return [{'image_path': record['image_path'], 'processed': True} for record in records]

def fetch_data(session) -> List[Dict[str, Any]]:
    """Fetch data from the database.
    
    Args:
        session: Database session
    Returns:
        List of records
    """
    result = session.execute(text("SELECT * FROM inspections WHERE processed = false"))
    return [dict(row) for row in result]

def save_to_db(session, records: List[Dict[str, Any]]) -> None:
    """Save processed records to the database.
    
    Args:
        session: Database session
        records: Records to save
    """
    for record in records:
        session.execute(text("INSERT INTO processed_inspections (image_path) VALUES (:image_path)"), {'image_path': record['image_path']})
    session.commit()

def call_api(image_path: str) -> Dict[str, Any]:
    """Call external API for model inference.
    
    Args:
        image_path: Path to the image
    Returns:
        API response
    Raises:
        Exception: If API call fails
    """
    try:
        response = requests.post('http://api.example.com/inference', json={'image_path': image_path})
        response.raise_for_status()  # Raise an error for bad responses
        return response.json()
    except requests.exceptions.RequestException as e:
        logger.error(f'API call failed: {e}')
        raise Exception('API call failed')

def process_batch(session, batch: List[Dict[str, Any]]) -> None:
    """Process a batch of images for inspection.
    
    Args:
        session: Database session
        batch: List of images to process
    """
    for record in batch:
        try:
            result = call_api(record['image_path'])  # Call the API for inference
            logger.info(f'Processed {record['image_path']}: {result}')
        except Exception as e:
            logger.error(f'Error processing {record['image_path']}: {e}')

class InspectionOrchestrator:
    """Main orchestrator for the inspection process."""

    def __init__(self):
        self.session = SessionLocal()

    def run_inspection(self) -> None:
        """Run the inspection workflow."""
        try:
            raw_data = fetch_data(self.session)  # Fetch unprocessed data
            sanitized_data = [sanitize_fields(record) for record in raw_data]  # Sanitize input
            validated_data = [record for record in sanitized_data if validate_input(record)]  # Validate input
            normalized_data = [normalize_data(record) for record in validated_data]  # Normalize data
            processed_records = transform_records(normalized_data)  # Transform for DB
            save_to_db(self.session, processed_records)  # Save processed data
        except Exception as e:
            logger.error(f'Error in inspection workflow: {e}')
        finally:
            self.session.close()  # Clean up the session

if __name__ == '__main__':
    orchestrator = InspectionOrchestrator()  # Create orchestrator instance
    orchestrator.run_inspection()  # Run the inspection process