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Sentiment analysis with Python Azure Functions

In this lab, you will build a serverless HTTP API with Azure Functions that takes a sentence as an input and returns the sentiment of the sentence.

You will learn to:

  • Build a serverless HTTP API with Azure Functions
  • Run and debug the API locally on your machine
  • Deploy the API to Azure Functions

Prerequisites

Create a local Azure Functions app and a new function

Azure Functions allows you to build and deploy your code as functions, without worrying about managing servers and other infrastructure. Your functions are triggered by events such as: items added on a queue, a document updated in a database, or an HTTP request.

You will build an HTTP API using an Azure Function. An Azure Functions app can contain one or more Azure Functions.

  1. Open a new VS Code window.

  2. Open the command palette by pressing F1, Ctrl-Shift-P (Windows or Linux), Cmd-Shift-P (macOS).

  3. Search for and select Azure Functions: Create new project....

  4. Browse to a location where you want to create your project. Create a new empty folder and click Select to open it.

  5. A series of prompts will appear. Enter the following values:

    Prompt Value Description
    Language for your project Python
    Python interpreter Select one Azure Functions requires Python 3.7, 3.8, 3.9
    Template for your first function HTTP trigger
    Function name sentiment
    Authorization level anonymous Allow function to be accessed anonymously
    How to open new project Open in current window

VS Code reopens in the function app. Here are some important files and folders that you'll need to know for this lab:

  • .venv/ - A virtual environment is automatically created for you using the version of Python you selected. When running your function app, it runs in this virtual environment.
  • .vscode/ - VS Code settings, including the tasks needed to run and debug the function app.
  • sentiment/
    • __init__.py - Your new function
    • function.json - Metadata for your function
  • requirements.txt - Contains Python dependencies for your app

Modify the function to perform sentiment analysis

  1. From the VS Code explorer, open requirements.txt.

  2. You will use the VADER Sentiment Analysis library in your app. On a new line in requirements.txt, add vaderSentiment.

    This will cause VS Code to install the package when you run the function app in the next section.

  3. Open the function at sentiment/__init__.py.

  4. Replace the body of the file with the following code that uses the VADER library to perform sentiment analysis on some input text that is passed to the function and returns the result:

    import azure.functions as func
from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer


def main(req: func.HttpRequest) -> func.HttpResponse:
    analyzer = SentimentIntensityAnalyzer()
    text = req.params.get("text")
    scores = analyzer.polarity_scores(text)
    sentiment = "positive" if scores["compound"] > 0 else "negative"
    return func.HttpResponse(sentiment)

Run and debug the app

  1. Start debugging in VS Code by pressing F5 or by selecting Debug: Start debugging from the command palette.

  2. If you don't have the Azure Functions Core Tools installed, VS Code will prompt you to install it. Select Install.

    If the installation fails, follow these instructions to install it.

  3. Once the function app has started, open your web browser and enter the following in the address bar to run your function:

    http://localhost:7071/api/sentiment?text=I+love+PyCon

    It should return a result of positive.

  4. In sentiment/__init__.py, set a breakpoint by placing your cursor on the first line in the function and pressing F9.

  5. Return to your browser and enter the above URL again (or you can replace the text query string with a sentence of your own).

    VS Code should stop on the breakpoint. You can step through the code with F10 and inspect the values of variables.

  6. Stop debugging by pressing Shift-F5.

Create Azure resources for your function

Before you can deploy your function code to Azure, you need to create several supporting resources inside a resource group, such as a Function App itself, a Storage account (for storing the code), and an Application Insights instance (to provide monitoring).

  1. In the command palette, search for and select Azure Functions: Create new Function App in Azure (Advanced).

  2. When prompted to select a subscription, select Sign in to Azure.

  3. The browser should open to the Azure sign in page. Log in with your Azure credentials.

  4. Enter the following values in the prompts:

    Prompt Description
    Enter a globally unique name for the new function app Type something like yourname-sentiment-analyzer-function
    Select a runtime stack Select the same version as you used to create your local function app
    Select a resource group If you were provided lab credentials, you must select the existing resource group from the dropdown
    Select a location for new resources Select whatever location is closest to you geographically
    Select a hosting plan Select Consumption
    Select a storage account Select Create new storage account and accept suggested name
    Select an Application Insights resource for your app Select Create new Application Insights resource and accept suggested name

  5. You should see both a notification and a message in the Azure: Activity Log panel once the Function App resources are successfully created.

Deploy the app to Azure

You will now use VS Code to actually deploy the local function app to the Azure resources you just created.

  1. In the command palette, search for and select Azure Functions: Deploy to Function App.

  2. When it prompts you to "Select a resource", select the name of the resource created in the previous step. In the dialog box that pops up, confirm that you do intend to overwrite the previously deployed version. Since this is your first time deploying, that shouldn't be a concern.

    Watch the deployment progress in the notification.

  3. When the deployment is complete, A prompt should appear. Click on View output.

  4. In the output, locate the HTTP trigger URL, it should look like https://<function-app-name>.azurewebsites.net/api/sentiment.

    Copy it and paste it into your browser's address bar. Append ?text=I+love+PyCon to the end of the URL and press enter.

    You should see the sentiment returned.

Congratulations! You have deployed an HTTP API using Azure Functions! 🎉