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[release-1.19] Fix section "3-Bad Word Service" in e2e tutorial #6432

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5b2aab1
Fix section "3-Bad Word Service" in e2e tutorial
twoGiants Oct 7, 2025
61c9911
Remove func version
twoGiants Oct 8, 2025
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28 changes: 10 additions & 18 deletions .../versioned/bookstore/page-2/sentiment-analysis-service-for-bookstore-reviews.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,7 @@ In order to do so, you need to install the func CLI. You can follow the [officia
???+ success "Verify"
Running `func version` in your terminal to verify the installation, and you should see the version of the func CLI you installed.


???+ bug "Troubleshooting"
If you see `command not found`, you may need to add the func CLI to your PATH.

Expand Down Expand Up @@ -206,9 +207,9 @@ The `sentiment-analysis-app/pyproject.toml` file contains the project configurat
Knative Function will automatically install the dependencies listed here when you build the function.

### **Step 4: Build and run your Knative Function locally (Optional)**

??? info "Click here to expand"



![Image4](images/image4.png)

In Knative Function, there are two ways to build: using the [pack build](https://github.com/knative/func/blob/8f3f718a5a036aa6b6eaa9f70c03aeea740015b9/docs/reference/func_build.md?plain=1#L46){:target="_blank"} or using the [source-to-image (s2i) build](https://github.com/knative/func/blob/4f48549c8ad4dad34bf750db243d81d503f0090f/docs/reference/func_build.md?plain=1#L43){:target="_blank"}.
Expand Down Expand Up @@ -276,7 +277,7 @@ Knative Function will automatically install the dependencies listed here when yo

In this case, you will get the full CloudEvent response:

```
```sh
Context Attributes,
specversion: 1.0
type: new-review-comment
Expand Down Expand Up @@ -319,7 +320,7 @@ func deploy -b=s2i -v
Function deployed in namespace "default" and exposed at URL:
http://sentiment-analysis-app.default.svc.cluster.local
```

!!! tip
You can find the URL of the Knative Function (Knative Service) by running the following command:

Expand All @@ -334,7 +335,6 @@ func deploy -b=s2i -v
sentiment-analysis-app http://sentiment-analysis-app.default.svc.cluster.local sentiment-analysis-app-00001 sentiment-analysis-app-00001 True
```


## **Knative Serving: scale down to zero**

![Image13](images/image13.png)
Expand Down Expand Up @@ -368,7 +368,7 @@ After deployment, the `func` CLI provides a URL to access your function. You can

Simply use Knative Function's command `func invoke` to directly send a CloudEvent to the function on your cluster:

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

Expand All @@ -380,7 +380,7 @@ func invoke -f=cloudevent --data='{"reviewText":"I love Knative so much"}' -v

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

```
```sh
Context Attributes,
specversion: 1.0
type: moderated-comment
Expand All @@ -399,23 +399,15 @@ func invoke -f=cloudevent --data='{"reviewText":"I love Knative so much"}' -v
}
```

## **Next Step**

![Image16](images/image16.png)

In this tutorial, you learned how to create a serverless function for a simple sentiment analysis service with Knative.

## **Next Step**

![Image5](images/image5.png)

Next, we'll deploy another ML service following the same procedure. We encourage you to try it yourself!
Next, we'll deploy another ML service following the same procedure.

!!! tip
Don't forget to `cd` into the root directory `/start` before proceeding.

If you feel comfortable deploying the other ML service yourself, follow this **simplified guide**:

[Go to Deploy ML workflow: Bad word filter :fontawesome-solid-paper-plane:](../page-3/create-bad-word-filter-service.md){ .md-button .md-button--primary }

If you encounter any issues, don't worry—we have a detailed tutorial ready for you.

[Solution - Go to Deploy ML workflow: Bad word filter :fontawesome-solid-paper-plane:](../page-3/solution-create-bad-word-filter-service.md){ .md-button .md-button--primary }
156 changes: 91 additions & 65 deletions docs/versioned/bookstore/page-3/create-bad-word-filter-service.md
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Expand Up @@ -10,11 +10,7 @@ function: tutorial

![Image 4](images/image4.png)

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 :fontawesome-solid-paper-plane:](../page-3/solution-create-bad-word-filter-service.md){ .md-button .md-button--primary }
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/insulting speech.

## **What Knative features will we learn about?**

Expand Down Expand Up @@ -59,78 +55,106 @@ func create -l python bad-word-filter

The file tree will look like this:

```
/start/bad-word-filter
├── func.yaml
```txt
start/bad-word-filter
├── .funcignore
├── function
│   ├── func.py
│   └── __init__.py
├── func.yaml
├── .gitignore
├── requirements.txt
├── app.sh
├── test_func.py
├── pyproject.toml
├── README.md
└── Procfile
└── func.py
└── tests
└── test_func.py
```

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

![Image 5](images/image5.png)

`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:
`bad-word-filter/function/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:

???+ abstract "_bad-word-filter/function/func.py_"

???+ abstract "_bad-word-filter/func.py_"
```python
from parliament import Context
from profanity_check import predict
import logging
from cloudevents.http import CloudEvent
from profanity_check import predict

def new():
return Function()

class Function:
async def handle(self, scope, receive, send):
""" Handle all HTTP requests to this Function. The incoming CloudEvent is in scope["event"]. """
logging.info("Request Received")

# 1. Extract the CloudEvent from the scope
request_event = scope["event"]

# 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)
# 2. Extract the data payload from the event, analyze and create CloudEvent
response_event = self.inappropriate_language_filter(request_event.data)

# 3. Send the response
logging.info(f"Sending response: {response_event.data}")
await send(response_event)

def create_cloud_event(self, inputText, data):
attributes = {
"type": "new-review-comment",
"source": "book-review-broker",
"datacontenttype": "application/json",
"badwordfilter": data,
}

data = {"reviewText": inputText, "badWordResult": data}

return CloudEvent(attributes, data)

def inappropriate_language_filter(self, text):
review_text = text.get("reviewText", "")
profanity_result = predict([review_text])
result = "good"
if profanity_result[0] == 1:
result = "bad"

return self.create_cloud_event(review_text, result)
```

### **Step 3: Configure the dependencies**

![Image 8](images/image8.png)
The content of `bad-word-filter/requirements.txt`:

???+ abstract "_bad-word-filter/requirements.txt_"
```plaintext
parliament-functions==0.1.0
alt-profanity-check==1.4.1.post1
cloudevents==1.10.1
The content of `bad-word-filter/pyproject.toml`:

???+ abstract "_bad-word-filter/pyproject.toml_"

```toml
[project]
name = "function"
description = ""
version = "0.1.0"
requires-python = ">=3.9"
readme = "README.md"
license = "MIT"
dependencies = [
"httpx",
"cloudevents",
"pytest",
"pytest-asyncio",
"alt-profanity-check==1.4.1.post1" # <-- add this dependency
]
authors = [
{ name="Your Name", email="you@example.com"},
]

[build-system]
requires = ["hatchling"]
build-backend = "hatchling.build"

[tool.pytest.ini_options]
asyncio_mode = "strict"
asyncio_default_fixture_loop_scope = "function"
```

### **Step 4: Deploy the function to the cluster**
Expand All @@ -139,12 +163,14 @@ The content of `bad-word-filter/requirements.txt`:
!!! note
Please enter `/bad-word-filter` when you are executing the following commands.

```plaintext
```sh
func deploy -b=s2i -v
```

???+ success "Verify"
Expect to see the following message:
```

```sh
Function deployed in namespace "default" and exposed at URL:
http://bad-word-filter.default.svc.cluster.local
```
Expand All @@ -153,14 +179,14 @@ func deploy -b=s2i -v

![Image 7](images/image7.png)

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

???+ success "Verify"
Expect to receive a CloudEvent response:

```plaintext
```sh
Context Attributes,
specversion: 1.0
type: new-review-comment
Expand All @@ -183,8 +209,8 @@ If you see the response, it means that the function is running successfully.

![Image 9](images/image9.png)

In this tutorial, you learned how to create a serverless function for a simple service that can detect inappropriate languages in text with Knative.
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.
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 :fontawesome-solid-paper-plane:](../page-4/create-sequence-to-streamline-ML-workflows.md){ .md-button .md-button--primary }
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