Let’s create a really simple FastAPI with, create yourself our app file (app.py) and requirements file (requirements.txt).

We’re going to use TextBlob to process our text.

In the requirements.txt add the following

fastapi
uvicorn
textblob

In the app.py add the following imports

from fastapi import FastAPI
from textblob import TextBlob

Now let’s create the FastAPI and a POST endpoint named sentiment, the code should look like this

@app.post("/sentiment")
def analyze_sentiment(payload: dict):
    text = payload["text"]
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    subjectivity = blob.sentiment.subjectivity
    return {
        "polarity": polarity,
        "subjectivity": subjectivity
    }

Don’t forget to run pip install

pip install -r requirements.txt

or if you’re using PyCharm, let this install the dependencies.

Run the app using

uvicorn app:app --reload

Note: as we’re using FastAPI we can access the OpenAPI interface using http://localhost:8000/docs

Now from curl run

curl -X POST http://localhost:8000/sentiment -H "Content-Type: application/json" -d '{"text": "I absolutely love this!"}'

and you’ve see a result along the following lines

{"polarity":0.625,"subjectivity":0.6}

Polarity is within the range [-1.0, 1.0], where -1.0 is a very negative sentiment, 0, neutral sentiment and 1.0 very positive sentiment. Subjectivity is in the range [0.0. 1.0] where 0.0 is very objective (i.e. facts or neutral statements) and 1.0 is very subjective (i.e. opinions, feelings or personal judgement).