Step 6 Custom metric with Prometheus

step_6_custom_metric.py

%%writefile sentiment_analysis_service.py
# Now let's modify our service to use prometheus to measure custom metrics

import bentoml

from bentoml.frameworks.sklearn import SklearnModelArtifact
from bentoml.service.artifacts.common import PickleArtifact
from bentoml.adapters import JsonInput

import sentry_sdk
import logging
from sentry_sdk.integrations.logging import LoggingIntegration

# All of this is already happening by default!
sentry_logging = LoggingIntegration(
    level=logging.INFO,        # Capture info and above as breadcrumbs
    event_level=logging.ERROR  # Send errors as events
)
sentry_sdk.init(
    dsn="https://[email protected]/6197278",
    integrations=[sentry_logging]
)

from prometheus_client import Summary
REQUEST_TIME = Summary('request_processing_time', 'Time spend processing request')
# Edit 1: Create a custom metric
REQUEST_TEXT_LEN = Summary('request_text_len', 'Length of texts array for inference')


@bentoml.artifacts([PickleArtifact('model')])
@bentoml.env(infer_pip_packages=True)
class SKSentimentAnalysis(bentoml.BentoService):

    sentiment_names = {
        0: "very negative",
        1: "somewhat negative",
        2: "neutral",
        3: "somewhat positive",
        4: "very positive",
    }
    
    @REQUEST_TIME.time()
    @bentoml.api(input=JsonInput())
    def predict(self, parsed_json):
        """
        Sentiment prediction API service
        
        Expected input format:
        {"tweet": "Tweet text to predict the sentiment..."}

        Output format:
        {"sentiment_score": 4, "sentiment": "Very Positive", "tweet": "Tweet text to predict the sentiment..."}
        """
        try:
            texts = parsed_json
            predictions = self.artifacts.model.predict(texts)
            res = []
            # Edit 2: Monitor request text lengt on the API
            REQUEST_TEXT_LEN.observe(len(texts))
            for idx, pred in enumerate(predictions):
                res.append({
                    "sentiment_score": pred, 
                    "sentiment": self.sentiment_names[pred], 
                    "text": texts[idx]
                })

            return res        
        except:
            sentry_sdk.capture_exception()
            return "error"