Create Bad Word Filter Service¶

As a bookstore owner, you aim to receive instant notifications in a Slack channel whenever a customer submits a new negative review comment. By leveraging Knative Function, you can set up a serverless function that contains a simple bad word filter service to tell whether the text contains any hateful/insultive speech.

If you ever get stuck, check the solution here.

Solution - Go to Deploy ML workflow: Bad word filter

What Knative features will we learn about?¶

• The easiness to use Knative Function to deploy your service, and make it be managed by Knative Serving, which gives you the ability to auto-scale your service to zero, and scale up to handle the demand.

What does the final deliverable look like?¶

A running serverless Knative Function that contains a python application that receives the new review comments as CloudEvent and returns the result that tells your input text contains any inappropriate languages or not. The result is sent back as CloudEvent.

The function's output will be only from:

• good
• bad

Implementation¶

The process is straightforward:

1. Begin by utilizing the func create command to generate your code template.
2. Next, incorporate your unique code into this template.
3. Finally, execute func deploy to deploy your application seamlessly to the Kubernetes cluster.

This workflow ensures a smooth transition from development to deployment within the Knative Functions ecosystem.

Step 1: Create a Knative Function template¶

func deploy -b=s2i -v

/start/bad-word-filter
├── func.yaml
├── .funcignore
├── .gitignore
├── requirements.txt
├── app.sh
├── test_func.py
├── README.md
└── Procfile
└── func.py

Step 2: Replace the generated code with the bad word filter logic¶

bad-word-filter/func.py is the file that contains the code for the function. You can replace the generated code with the bad word filter logic. You can use the following code as a starting point:

from parliament import Context
from profanity_check import predict
from cloudevents.http import CloudEvent

# The function to convert the bad word filter result into a CloudEvent
def create_cloud_event(inputText, data):
    attributes = {
        "type": "new-review-comment",
        "source": "book-review-broker",
        "datacontenttype": "application/json",
        "badwordfilter": data,
    }

    # Put the bad word filter result into a dictionary
    data = {"reviewText": inputText, "badWordResult": data}

    # Create a CloudEvent object
    event = CloudEvent(attributes, data)
    return event

def inappropriate_language_filter(text):
    profanity_result = predict([text["reviewText"]])
    result = "good"
    if profanity_result[0] == 1:
        result = "bad"

    profanity_event = create_cloud_event(text["reviewText"], result)
    return profanity_event

def main(context: Context):
    """
    Function template
    The context parameter contains the Flask request object and any
    CloudEvent received with the request.
    """
    print("Received CloudEvent: ", context.cloud_event)

    # Add your business logic here
    return inappropriate_language_filter(context.cloud_event.data)

Step 3: Configure the dependencies¶

The content of bad-word-filter/requirements.txt:

parliament-functions==0.1.0
alt-profanity-check==1.4.1.post1
cloudevents==1.10.1

Step 4: Deploy the function to the cluster¶

func deploy -b=s2i -v

Function deployed in namespace "default" and exposed at URL:
http://bad-word-filter.default.svc.cluster.local

Verify¶

func invoke -f=cloudevent --data='{"reviewText":"I love Knative so much"}' -v

Context Attributes,
specversion: 1.0
type: new-review-comment
source: book-review-broker
id: ebbcd761-3a78-4c44-92e3-de575d1f2d38
time: 2024-05-27T04:44:07.549303Z
datacontenttype: application/json
Extensions,
badwordfilter: good
Data,
{
"reviewText": "I love Knative so much",
"badWordResult": "good"
}

If you see the response, it means that the function is running successfully.

Next Step¶

In this tutorial, you learned how to create a serverless function for a simple service that can detect inappropriate languages in text with Knative.

Next, we'll be learning how to use Knative Sequence to connect the 2 ML workflows and make sure they are executed in the order you want.

Go to Create Knative Sequence