Optimize Cross-Platform NLP Inference for Industrial Gateways with CTranslate2 and ONNX Runtime

                      
                     
"""
Production implementation for optimizing cross-platform NLP inference
for industrial gateways using CTranslate2 and ONNX Runtime.
Provides secure, scalable operations for NLP tasks.
"""

from typing import Dict, Any, List, Tuple, Optional
import os
import logging
import onnxruntime
import numpy as np
from fastapi import FastAPI, HTTPException, Request
from pydantic import BaseModel, constr
from contextlib import asynccontextmanager
from sqlalchemy import create_engine, text
from sqlalchemy.orm import sessionmaker
import time

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

# Configuration class to hold environment variables
class Config:
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH')

# Initialize the FastAPI app
app = FastAPI()

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

# Define the input data model using Pydantic
class InferenceRequest(BaseModel):
    text: constr(min_length=1)

@asynccontextmanager
async def get_db():
    """Context manager for database session.
    
    Yields:
        session: Database session
    """
    session = Session()
    try:
        yield session
    finally:
        session.close()  # Close session after use

def validate_input(data: InferenceRequest) -> None:
    """Validates the input data.
    
    Args:
        data: Input data to validate
    Raises:
        ValueError: If validation fails
    """
    if not data.text:
        raise ValueError('Text input is required.')  # Raise error if text is empty

def normalize_data(text: str) -> List[str]:
    """Normalizes the input text for processing.
    
    Args:
        text: Raw input text
    Returns:
        List of normalized tokens
    """
    tokens = text.lower().split()  # Simple tokenization - can be enhanced
    return tokens

def transform_records(tokens: List[str]) -> np.ndarray:
    """Transforms normalized tokens into model input format.
    
    Args:
        tokens: List of tokens
    Returns:
        Model input as a numpy array
    """
    return np.array(tokens).reshape(1, -1)  # Reshape for model input

def process_batch(inputs: np.ndarray) -> Any:
    """Processes a batch of inputs using ONNX Runtime.
    
    Args:
        inputs: Numpy array of model inputs
    Returns:
        Model predictions
    """
    session = onnxruntime.InferenceSession(Config.model_path)
    outputs = session.run(None, {session.get_inputs()[0].name: inputs})
    return outputs

def aggregate_metrics(results: Any) -> Dict[str, Any]:
    """Aggregates metrics from the model's output.
    
    Args:
        results: Model output
    Returns:
        Dictionary of aggregated metrics
    """
    # Placeholder for actual metric aggregation logic
    return {'predictions': results}

@app.post('/infer', response_model=Dict[str, Any])
async def infer(request: Request, data: InferenceRequest):
    """Endpoint for NLP inference.
    
    Args:
        request: HTTP request object
        data: InferenceRequest model data
    Returns:
        Dictionary with inference results
    Raises:
        HTTPException: If an error occurs during inference
    """  
    try:
        validate_input(data)  # Validate input data
        tokens = normalize_data(data.text)  # Normalize input text
        model_input = transform_records(tokens)  # Transform for model
        results = process_batch(model_input)  # Get model predictions
        output = aggregate_metrics(results)  # Aggregate results
        return output
    except ValueError as ve:
        logger.error(f'Input validation error: {ve}')
        raise HTTPException(status_code=400, detail=str(ve))  # Raise HTTP error for client
    except Exception as e:
        logger.error(f'Unexpected error: {e}')
        raise HTTPException(status_code=500, detail='Internal Server Error')  # Handle unexpected errors

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