Monitor AI Model Health with Prometheus Client and BentoML

                      
                     
from typing import Dict, Any
import os
from fastapi import FastAPI
from prometheus_client import start_http_server, Summary
from bentoml import Service, api
from bentoml.adapters import JsonInput

# Configuration
MODEL_NAME = os.getenv('MODEL_NAME', 'my_model')
HEALTH_CHECK_PORT = int(os.getenv('HEALTH_CHECK_PORT', 8000))

# Initialize Prometheus metrics
REQUEST_TIME = Summary('request_processing_seconds', 'Time spent processing request')

# Create FastAPI app
app = FastAPI()

# Start Prometheus HTTP server
start_http_server(8080)

# Define service for the AI model
service = Service(name=MODEL_NAME)

@REQUEST_TIME.time()
@app.get('/health')
async def health_check() -> Dict[str, Any]:
    try:
        # Here, you would implement your model health check logic
        # For example, check if the model is loaded and ready
        response = {'status': 'healthy', 'model_name': MODEL_NAME}
        return response
    except Exception as error:
        return {'status': 'unhealthy', 'error': str(error)}

@api(JsonInput)
async def predict(input_data: Dict[str, Any]) -> Dict[str, Any]:
    try:
        # Implement prediction logic
        result = {'prediction': 'dummy_prediction'}  # Replace with actual logic
        return result
    except Exception as error:
        return {'error': str(error)}

if __name__ == '__main__':
    service.run(port=HEALTH_CHECK_PORT)