Fix section "3-Bad Word Service" in e2e tutorial (#6425)

* Fix section "3-Bad Word Service" in e2e tutorial

The latest func cli generates a different Python function template than
shown in the tutorial. There is no setup.py anymore and the project
uses a toml file for configuration. The entire section was updated and
code was corrected.

The required func cli version `v0.46.0` was added to the tutorial with a
link to the GitHub func repository tags page.

The `solution-create-bad-word-filter-service.md` was removed because it
was identical to the `create-bad-word-filter-service.md`. All references
to it were removed.

Issue #6420.

Signed-off-by: Stanislav Jakuschevskij <[email protected]>

* Remove func version

Signed-off-by: Stanislav Jakuschevskij <[email protected]>

---------

Signed-off-by: Stanislav Jakuschevskij <[email protected]>

Author: twoGiants

docs/versioned/bookstore/page-2/sentiment-analysis-service-for-bookstore-reviews.md

@@ -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.
 
@@ -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"}.
@@ -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
@@ -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:
 
@@ -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)
@@ -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
 ```
 
@@ -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
@@ -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 }

docs/versioned/bookstore/page-3/create-bad-word-filter-service.md

@@ -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?**
 
@@ -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="[email protected]"},
+    ]
+
+    [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**
@@ -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
     ```
@@ -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
@@ -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 }