Accelerate Sensor Analytics with ONNX Runtime and vLLM

                      
                     
"""
Production implementation for Accelerate Sensor Analytics with ONNX Runtime and vLLM.
Provides secure, scalable operations for processing sensor data efficiently.
"""

from typing import Dict, Any, List
import os
import logging
import time
import onnxruntime as ort
from fastapi import FastAPI, HTTPException, Request

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

class Config:
    """
    Configuration class for environment variables.
    """  
    database_url: str = os.getenv('DATABASE_URL')
    model_path: str = os.getenv('MODEL_PATH')

# Initialize FastAPI app
app = FastAPI()

# Load ONNX model
try:
    session = ort.InferenceSession(Config.model_path)
except Exception as e:
    logger.error(f"Failed to load model: {e}")
    raise RuntimeError("Model loading failed")

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 'sensor_id' not in data:
        raise ValueError('Missing sensor_id')
    if 'values' not in data or not isinstance(data['values'], list):
        raise ValueError('Values must be a list')
    return True

async def sanitize_fields(data: Dict[str, Any]) -> Dict[str, Any]:
    """Sanitize input fields.
    Args:
        data: Input data
    Returns:
        Sanitized data
    """
    data['sensor_id'] = str(data['sensor_id']).strip()
    return data

async def normalize_data(values: List[float]) -> List[float]:
    """Normalize sensor data values.
    Args:
        values: List of sensor values
    Returns:
        Normalized values
    """
    min_val, max_val = min(values), max(values)
    return [(x - min_val) / (max_val - min_val) for x in values]

async def fetch_data(sensor_id: str) -> Dict[str, Any]:
    """Fetch data for a specific sensor.
    Args:
        sensor_id: Sensor identifier
    Returns:
        Sensor data
    Raises:
        ValueError: If sensor not found
    """
    logger.info(f"Fetching data for sensor_id: {sensor_id}")
    # Simulating DB fetch
    # In a real application, replace with actual DB call
    if sensor_id == '1':
        return {'sensor_id': '1', 'values': [1.0, 2.0, 3.0]}
    else:
        raise ValueError(f"Sensor {sensor_id} not found")

async def process_batch(sensor_data: Dict[str, Any]) -> List[float]:
    """Process a batch of sensor data.
    Args:
        sensor_data: Sensor data to process
    Returns:
        Processed results
    """
    normalized_values = await normalize_data(sensor_data['values'])
    return normalized_values

async def aggregate_metrics(results: List[float]) -> Dict[str, float]:
    """Aggregate metrics from processed results.
    Args:
        results: List of processed results
    Returns:
        Aggregated metrics
    """
    return {
        'mean': sum(results) / len(results),
        'max': max(results),
        'min': min(results),
    }

async def save_to_db(sensor_id: str, metrics: Dict[str, float]) -> None:
    """Save metrics to the database.
    Args:
        sensor_id: Sensor identifier
        metrics: Metrics to save
    """
    logger.info(f"Saving metrics for sensor_id: {sensor_id} to DB")
    # Simulating DB save
    # In a real application, replace with actual DB call

@app.post("/analyze")
async def analyze_sensor_data(request: Request) -> Dict[str, Any]:
    """Analyze sensor data from POST request.
    Args:
        request: FastAPI request object
    Returns:
        Analysis results
    Raises:
        HTTPException: If validation fails
    """
    try:
        data = await request.json()
        await validate_input(data)
        data = await sanitize_fields(data)
        sensor_data = await fetch_data(data['sensor_id'])
        results = await process_batch(sensor_data)
        metrics = await aggregate_metrics(results)
        await save_to_db(sensor_data['sensor_id'], metrics)
        return metrics
    except ValueError as e:
        logger.error(f"Validation error: {e}")
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        logger.error(f"Unexpected error: {e}")
        raise HTTPException(status_code=500, detail="Internal Server Error")

if __name__ == '__main__':
    # Example usage with FastAPI
    pass