Inspect Industrial Parts with Zero-Shot Multimodal Vision using Gemma 4 and Supervision

                      
                     
"""
Production implementation for Inspecting Industrial Parts with Zero-Shot Multimodal Vision using Gemma 4.
Provides secure, scalable operations.
"""

from typing import Dict, Any, List, Optional
import os
import logging
import httpx
import asyncio
from pydantic import BaseModel, ValidationError

# Logger configuration for tracking application logs
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class Config:
    """
    Configuration class to manage environment variables.
    """
    gemma_api_url: str = os.getenv('GEMMA_API_URL', 'http://localhost:8000/api')
    database_url: str = os.getenv('DATABASE_URL')

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 'image_url' not in data:
        raise ValueError('Missing image_url')  # Ensure image_url is provided
    return True

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

async def fetch_data(image_url: str) -> Dict[str, Any]:
    """Fetch data from the Gemma API.
    
    Args:
        image_url: URL of the image to inspect
    Returns:
        Response data from the API
    Raises:
        Exception: If an error occurs during the request
    """
    try:
        async with httpx.AsyncClient() as client:
            response = await client.post(f"{Config.gemma_api_url}/inspect", json={'image_url': image_url})
            response.raise_for_status()
            return response.json()
    except httpx.HTTPStatusError as exc:
        logger.error(f'HTTP error occurred: {exc}')
        raise Exception('Error fetching data from API')
    except Exception as e:
        logger.error(f'Unexpected error: {e}')
        raise Exception('An unexpected error occurred')

async def transform_records(data: Dict[str, Any]) -> Dict[str, Any]:
    """Transform response data into a usable format.
    
    Args:
        data: Raw data from the API
    Returns:
        Transformed data
    """
    # Example transformation logic
    return {
        'status': data.get('status'),
        'results': data.get('results', []),
        'timestamp': data.get('timestamp')
    }

async def aggregate_metrics(results: List[Dict[str, Any]]) -> Dict[str, Any]:
    """Aggregate metrics from the results.
    
    Args:
        results: List of inspection results
    Returns:
        Aggregated metrics
    """
    metrics = {'total': len(results), 'success': sum(1 for r in results if r['status'] == 'success')}
    return metrics

async def save_to_db(metrics: Dict[str, Any]) -> None:
    """Save metrics to the database.
    
    Args:
        metrics: Metrics to save
    Raises:
        Exception: If database save fails
    """
    # Here we would implement database logic, e.g., using an ORM
    logger.info(f'Saving metrics: {metrics}')
    # Simulate save operation
    await asyncio.sleep(1)  # Simulate async DB call

async def handle_errors(error: Exception) -> None:
    """Handle errors gracefully.
    
    Args:
        error: Exception to handle
    """
    logger.error(f'Handling error: {error}')

class InspectionService:
    """Main orchestrator for the inspection workflow.
    """
    async def inspect_part(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Main inspection method.
        
        Args:
            data: Input data for inspection
        Returns:
            Inspection results
        """
        try:
            await validate_input(data)  # Validate input
            sanitized_data = await sanitize_fields(data)  # Sanitize data
            api_response = await fetch_data(sanitized_data['image_url'])  # Fetch data from API
            transformed_data = await transform_records(api_response)  # Transform response
            metrics = await aggregate_metrics(transformed_data['results'])  # Aggregate metrics
            await save_to_db(metrics)  # Save metrics to the database
            return transformed_data  # Return transformed data
        except ValueError as ve:
            await handle_errors(ve)  # Handle input validation errors
            raise
        except Exception as e:
            await handle_errors(e)  # Handle general errors
            raise

if __name__ == '__main__':
    # Example usage
    service = InspectionService()
    sample_data = {'image_url': 'http://example.com/image.jpg'}
    result = asyncio.run(service.inspect_part(sample_data))
    logger.info(f'Inspection result: {result}')