Build a REST API Using Python and Deploy it to Microsoft Azure/

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Implement Azure Text Analytics Service - 2

Implement Azure Text Analytics Service - 2

Create your REST API using the Azure Text Analytics Service.

We'll cover the following...

Define the analyze_text() function

In the previous lesson, we have already created the structure of the API. We are only left with writing the function that will handle the POST route. Let us see the code now.

from fastapi import FastAPI
from pydantic import BaseModel
import utils

app = FastAPI()

# headers = {
#     "Ocp-Apim-Subscription-Key": <SUBSCRIPTION_KEY>,
#     "Content-Type": "application/json",
#     "Accept": "application/json"
# }

class Model(BaseModel):
    text_to_analyze: list

@ app.post("/")
def analyze_text(text: Model):
    response = {"sentiment": [], "keyphrases": []}
    no_of_text = len(text.text_to_analyze)
    for i in range(no_of_text):
        document = {"documents": [{"id": i+1, "language": "en", "text": text.text_to_analyze[i]}]}
        
        sentiment = utils.call_text_analytics_api(headers, document, endpoint='sentiment')
        keyphrases = utils.call_text_analytics_api(headers, document, endpoint='keyPhrases')
        
        response["sentiment"].append(sentiment["documents"][0])
        response["keyphrases"].append(keyphrases["documents"][0])
    return response
Define the analyze_text() function

Explanation

  • All the code is the same. We will only understand the function analyze_text().

  • On line 18, we define the structure of the response. The structure of the response is going to be: ...