Compress and Serve Industrial Vision-Language Models with Optimum Quantization and vLLM

"""
Production implementation for Compressing and Serving Industrial Vision-Language Models with Optimum Quantization and vLLM.
Provides secure, scalable operations for deploying optimized models.
"""

from typing import Dict, Any, List
import os
import logging
import asyncio
import aiohttp
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, validator

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

# Configuration class to handle environment variables
class Config:
    model_url: str = os.getenv('MODEL_URL')
    database_url: str = os.getenv('DATABASE_URL')

# Input data model for requests
class ModelRequest(BaseModel):
    image: str  # Base64 encoded image data
    parameters: Dict[str, Any]  # Model parameters

    @validator('image')
    def validate_image(cls, v):
        if len(v) == 0:
            raise ValueError('Image data must not be empty')
        return v

async def validate_input(data: ModelRequest) -> bool:
    """Validate request data.
    
    Args:
        data: Input to validate
    Returns:
        True if valid
    Raises:
        ValueError: If validation fails
    """
    return True  # Validation passed

async def fetch_model_response(data: ModelRequest) -> Dict[str, Any]:
    """Fetch response from the model API.
    
    Args:
        data: The model request data
    Returns:
        Response from the model API
    Raises:
        HTTPException: If the request fails
    """
    async with aiohttp.ClientSession() as session:
        async with session.post(Config.model_url, json=data.dict()) as response:
            if response.status != 200:
                raise HTTPException(status_code=response.status, detail='Model API request failed')
            return await response.json()

async def save_to_db(result: Dict[str, Any]) -> None:
    """Save the model response to the database.
    
    Args:
        result: The model response data
    Raises:
        Exception: If database saving fails
    """
    # Simulate DB save operation
    logger.info('Saving result to database...')
    # Here, you would implement the actual database operation
    await asyncio.sleep(1)  # Simulating delay
    logger.info('Result saved successfully!')

async def compress_and_quantize(data: Dict[str, Any]) -> Dict[str, Any]:
    """Compress and quantize model outputs.
    
    Args:
        data: The raw output from the model
    Returns:
        Compressed and quantized output
    Raises:
        ValueError: If compression fails
    """
    # Simulate compression
    logger.info('Compressing and quantizing data...')
    compressed_data = {'compressed': True, 'data': data}
    return compressed_data

app = FastAPI()

@app.post('/model/serve', response_model=Dict[str, Any])
async def serve_model(request: ModelRequest) -> Dict[str, Any]:
    """Endpoint to serve the model and return results.
    
    Args:
        request: ModelRequest object containing image data
    Returns:
        Dictionary with model response
    Raises:
        HTTPException: If any errors occur during processing
    """
    try:
        await validate_input(request)  # Validate input data
        model_response = await fetch_model_response(request)  # Fetch model response
        compressed_response = await compress_and_quantize(model_response)  # Compress response
        await save_to_db(compressed_response)  # Save result to DB
        return compressed_response  # Return the result
    except Exception as e:
        logger.error(f'Error processing request: {e}')  # Log error
        raise HTTPException(status_code=500, detail=str(e))  # Raise server error

if __name__ == '__main__':
    # Example usage
    import uvicorn
    uvicorn.run(app, host='0.0.0.0', port=8000)