Deploying a Sentiment Analysis API with Nitric and Python

In this guide, you’ll build a serverless API using Nitric and Python that performs sentiment analysis on text input using a pre-trained machine learning model. You'll use the transformers library from Hugging Face and keep your project lightweight by installing dependencies directly with uv.

What You'll Build

  • A POST /analyze HTTP endpoint
  • A Python function that loads and uses an ML model
  • A serverless app you can run locally and deploy to the cloud

Prerequisites

Create a New Project

Create a new Nitric Python project:

nitric new ml-api py-starter
cd ml-api

Install Dependencies

Use uv to install your required libraries:

uv add torch transformers==4.39.3

Write the Sentiment API

Open or create the file functions/api.py and add the following:

from nitric.resources import api
from nitric.application import Nitric
from transformers import pipeline

# Load the model once globally
analyzer = pipeline("sentiment-analysis", model="ProsusAI/finbert")

# Define an API
ml_api = api("ml-api")

@ml_api.post("/analyze")
async def analyze(ctx):
    body = ctx.req.json
    text = body.get("text")

    if not text:
        ctx.res.status = 400
        ctx.res.body = {"error": "Missing 'text' field"}
        return

    result = analyzer(text)[0]

    ctx.res.body = {
        "sentiment": result["label"],
        "confidence": round(result["score"], 3)
    }

# Create the Nitric app and run it
Nitric.run()

This defines an API route that accepts a POST /analyze request and returns the sentiment and confidence score based on the input text.

Run Locally

Start your app locally using the Nitric CLI:

nitric start

Once running, test it using the dashboard - http://localhost:49152/

Image description

Or you can use cURL:

curl -X POST http://localhost:4001/analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "The market is showing strong signs of recovery."}'

Example response:

{
  "sentiment": "positive",
  "confidence": 0.996
}

⚠️ The first request may take a few seconds as the model is downloaded.

Deploy to the Cloud

Step 1: Create a Stack 

nitric stack new dev aws

Edit nitric.dev.yaml:

provider: nitric/aws@latest
region: us-east-1

You can also use gcp or azure depending on your cloud preference.

Step 2: Deploy 

nitric up

Nitric will:

  • Package your function (including installed ML libraries)
  • Provision the API Gateway and runtime
  • Deploy to your cloud account

The CLI will print the public URL when deployment is complete.

Test the Deployed API

Replace with the deployed API base URL:

curl -X POST https:///analyze \
  -H "Content-Type: application/json" \
  -d '{"text": "Interest rates are expected to rise."}'

Clean Up

To destroy the cloud stack and avoid charges:

nitric down

Summary

  • You built a serverless ML inference API
  • Ran it locally with Nitric
  • Deployed it to the cloud with one command

You can now adapt this pattern for other use cases like classification, moderation, or lightweight recommendations.