Updates mainly to Scenario 5 docs (#20)

* Updates to docs

* Updates to docs

* Updates to docs

* Updates to scanario 5 docs

* Updates to scanario docs

data/hackathon/scenario4.mdx

@@ -50,7 +50,7 @@ Your newly imported image should be visible in the `Image selection` dropdown wi
 TODO Add screenshots
 
 ## 4.5 - Check your work
-If your ACME Financial Services custom image  `acme-workbench-ai-custom` is available in the dropdownplease post a screenshot of it to  #event-anz-ai-hackathon with the message:
+If your ACME Financial Services custom image  `acme-workbench-ai-custom` is available in the dropdown, please post a screenshot of it to  #event-anz-ai-hackathon with the message:
 
 Please review [team name] solution for exercise 4.
 
@@ -60,7 +60,7 @@ This exercise is worth 25 points. The event team will reply in slack to confirm
 TODO PULL all Check your work sections from https://github.com/jmhbnz/workshops/blob/main/data/workshop/scenario1.mdx
 
 
-HINTS
+# HINTS
 [4.2.1] TODO - move the contents of this repo to the final github location:
 Solution via Helm Chart is available here: https://github.com/butler54/rhoai-custom-image
 

data/hackathon/scenario5.mdx

@@ -8,10 +8,55 @@ authors: ['default']
 summary: "Let's show integration between AI and Intelligent Application workloads, and the effieciencies acheived by using one platform for both"
 ---
 
-The ACME Financial Services team are underway with the OpenShift AI Proof of Concept and are now in postition to integrate the AI model with a client applicaiton that uses the model for inference.
-The ACME team is using more traditional `Predictive AI` use case that uses an object detection model to determine what object appears in side a webpage that uses a webcam to detect certain objects.
+The ACME Financial Services team are underway with the OpenShift AI Proof of Concept and are now in position to integrate the AI model with a client application that uses the model for inference.
+The ACME team is using more traditional `Predictive AI` use case that uses an object detection AI model to determine what object appears inside a webpage that uses a webcam to detect certain objects.
+
+Their Data Science team have already deployed such a model, in a similar fashion to the model server you deployed earlier.
+
+You challenge is to integrate their web application with the AI model. You will need to a) deploy it, b) configure it to talk to the model server.
 
 ## 5.1 - Deploy Application
-Your first task 
+Your first task to deploy the web application inside a new OpenShift project to house this web app.
+
+## 5.1.1 - Deploy Application helper
+Prior to your challenge, there is a helper deployment, that you will require. The first thing you will need to do is deply that. Here it is:
+https://raw.githubusercontent.com/rh-aiservices-bu/mad_m6_workshop/main/deployment/pre_post_processor_deployment.yaml
+
+## 5.1.2 - Deploy the application.
+Here are some data point you will require
+- the container image for the web app is here: quay.io/rh-aiservices-bu/mad-m6-workshop-intelligent-application:latest
+- These Environment variables should be set:
+ - OBJECT_DETECTION_URL: The model server host with path `/predictions` appended
+ - DISPLAY_BOX: true
+
+
 Documentation you may find helpful is:
-- 
+- https://docs.redhat.com/en/documentation/openshift_container_platform/4.15/html/building_applications/creating-applications#odc-deploying-container-image_odc-creating-applications-using-developer-perspective
+- https://docs.openshift.com/container-platform/3.11/dev_guide/environment_variables.html
+
+
+## 5.2 - Test Application
+Once you have deployed your application, open its Route in the Developer > Topology view.
+You will need to allow webcam access to the application. Go ahead and take a photo. It should detect any of these:
+- bottles
+- caps/hats
+- tshirts
+The app should draw a bounding box around any of these - or indeed of anything you placed in front of the webcam and snapped a shot.
+
+
+## 5.3 - Check your work
+If your ACME Financial Services web app has successfully made an inference call to the Object Detection AI model server, it should have drawn a bounding box on the screen.
+Please post a screenshot of it to  #event-anz-ai-hackathon with the message:
+
+Please review [team name] solution for exercise 5.
+
+This exercise is worth 25 points. The event team will reply in slack to confirm your updated team total score.
+
+
+
+
+
+
+# HINTS
+- [5.1.2]: The actual web app yaml (already with the configuration to talk to the model server) is available here: https://raw.githubusercontent.com/rh-aiservices-bu/mad_m6_workshop/main/deployment/intelligent_application_deployment.yaml
+