Updating troubleshooting and images

Author: kquinn1204

content/patterns/medical-diagnosis/_index.adoc

@@ -94,19 +94,10 @@ The following diagram shows the components that are deployed with the the data f
 
 image::medical-edge/physical-dataflow.png[link="/images/medical-edge/physical-dataflow.png"]
 
-== Recorded demo
-
-link:/videos/xray-deployment.svg[image:/videos/xray-deployment.svg[Demo\]]
-
 == Presentation
 
 View presentation for the Medical Diagnosis Validated Pattern link:https://speakerdeck.com/rhvalidatedpatterns/md-speakerdeck[here]
 
-[id="demo-script"]
-== Demo Script
-
-Use this demo script to successfully complete the Medical Diagnosis pattern demo link:demo-script/#demo-intro[here]
-
 [id="next-steps_med-diag-index"]
 == Next steps
 

content/patterns/medical-diagnosis/demo-script.adoc

@@ -1,5 +1,5 @@
 ---
-title: Verifying the demo Medical diagnosis pattern
+title: Verifying the demo
 weight: 20
 aliases: /medical-diagnosis/demo/
 ---
@@ -27,23 +27,23 @@ We simulate the function of the remote medical facility with an application call
 [id="viewing-the-grafana-based-dashboard-getting-started"]
 == Enabling the Grafana based dashboard
 
-The Grafana dashboard is a visual representation of the AI/ML workflow. It provides the CPU and Memory metrics for the pod running the risk assessment application. The dashboard also provides visual representation of the AI/ML workflow from the images being generated at the remote medical facility. Carry out the following steps to view the dashboard:
+The Grafana dashboard offers a visual representation of the AI/ML workflow, including CPU and memory metrics for the pod running the risk assessment application. Additionally, it displays a graphical overview of the AI/ML workflow, illustrating the images being generated at the remote medical facility. 
 
-This showcase application is deployed using self-signed certificates that are untrusted by our browser. Unless you have provisioned valid certificates for your OpenShift cluster you must accept the invalid certificates for: 
+This showcase application is deployed with self-signed certificates, which are considered untrusted by most browsers. If valid certificates have not been provisioned for your OpenShift cluster, you will need to manually accept the untrusted certificates by following these steps:
 
-. Accept the SSL certificates on the browser for the dashboard. In the {ocp} web console,  go to the Routes for *All Projects*. Click the URL for the `s3-rgw`.
+. Accept the SSL certificates on the browser for the dashboard. In the {ocp} web console,  go to the *Netwoorking* > *Routes* for *All Projects*. Click the URL for the `s3-rgw`.
 +
 image::../../images/medical-edge/storage-route.png[s3-rgw route]
 +
 Ensure that you see some XML and not the access denied error message.
 +
 image::../../images/medical-edge/storage-rgw-route.png[link="/images/medical-edge/storage-rgw-route.png"]
 
-. While still looking at Routes, change the project to `xraylab-1`. Click the URL for the `image-server`. Ensure that you do not see an access denied error message. You must to see a `Hello world` message.
+. While still looking at *Routes*, change the project to `xraylab-1`. Click the URL for the `image-server`. Ensure that you do not see an access denied error message. You must to see a `Hello world` message.
 
 This showcase application does not have access to a x-ray machine hanging around that we can use for this demo, so one is emulated by creating an s3 bucket and hosting the x-ray images within it. In the "real world" an x-ray would be taken at an edge medical facility and then uploaded to an OpenShift Data Foundations (ODF) S3 compatible bucket in the Core Hospital, triggering the AI/ML workflow.
 
-To emulate the edge medical facility we use an application called `image-generator` which (when scaled up) will download the x-rays from s3 and put them in an ODF s3 bucket in the cluster, triggering the AI/ML workflow. 
+To emulate the edge medical facility we use an application called `image-generator` which when scaled up will download the x-rays from s3 and put them in an ODF s3 bucket in the cluster, triggering the AI/ML workflow. 
 
 Turn on the image file flow. There are couple of ways to go about this.
 
@@ -85,4 +85,4 @@ You did it! You have completed the deployment of the medical diagnosis pattern!
 
 The medical diagnosis pattern is more than just the identification and detection of pneumonia in x-ray images. It is an object detection and classification model built on top of Red Hat OpenShift and can be transformed to fit multiple use-cases within the object classification paradigm. Similar use-cases would be detecting contraband items in the Postal Service or even in luggage in an airport baggage scanner.
 
-For more information on Validated Patterns visit our link:https://validatedpatterns.io/[website]
+For more information about Validated Patterns, visit our link:https://validatedpatterns.io/[website]. 

content/patterns/medical-diagnosis/getting-started.adoc

@@ -130,7 +130,7 @@ $ python s3-create.py -b kevtest-bucket -r us-east-1 -p
 $ python s3-sync-buckets.py -s validated-patterns-md-xray -t kevtest-bucket -r us-east-1
 ----
 
-Note the name of the bucket for further pattern configuration. Later you will update the `bucketSource` in the `values-global.yaml` file, where there is a section for `s3:`
+Note the name of the bucket for further pattern configuration. Later you will update the `bucketSource` in the `values-global.yaml` file, where there is a section for `s3:`.
 
 [id="preparing-for-deployment"]
 == Preparing for deployment
@@ -145,6 +145,37 @@ Note the name of the bucket for further pattern configuration. Later you will up
 $ git clone [email protected]:<your-username>/medical-diagnosis.git
 ----
 
+. Go to your repository: Ensure you are in the root directory of your Git repository by using:
++
+[source,terminal]
+----
+$ cd /path/to/your/repository
+----
+
+. Run the following command to set the upstream repository:
++
+[source,terminal]
+----
+$ git remote add -f upstream [email protected]:validatedpatterns/medical-diagnosis.git
+----
+
+. Verify the setup of your remote repositories by running the following command:
++
+[source,terminal]
+----
+$ git remote -v
+----
++
+.Example output
++
+[source,terminal]
+----
+origin	[email protected]:kquinn1204/medical-diagnosis.git (fetch)
+origin	[email protected]:kquinn1204/medical-diagnosis.git (push)
+upstream	[email protected]:validatedpatterns/medical-diagnosis.git (fetch)
+upstream	[email protected]:validatedpatterns/medical-diagnosis.git (push)
+----
+
 . Create a local copy of the Helm values file that can safely include credentials.
 +
 [WARNING]
@@ -428,7 +459,7 @@ $ ./pattern.sh make install
  .. Check that the *Red Hat OpenShift GitOps Operator* is installed in the `openshift-operators` namespace and its status is `Succeeded`.
 . Wait some time for everything to deploy. You can track the progress through the `Hub ArgoCD` UI from the nines menu. 
 +
-image::multicloud-gitops/multicloud-gitops-argocd.png[Multicloud GitOps Hub]
+image::../../images/medical-edge/medical-diags-overview.png[link="/images/medical-edge/medical-diags-overview.png.png"]
 
 As part of installing by using the script `pattern.sh` pattern, HashiCorp Vault is installed. Running `./pattern.sh make install` also calls the `load-secrets` makefile target. This `load-secrets` target looks for a YAML file describing the secrets to be loaded into vault and in case it cannot find one it will use the `values-secret.yaml.template` file in the git repository to try to generate random secrets.
 
@@ -443,41 +474,4 @@ To check the various applications that are being deployed, you can view the prog
 Examine the `medical-diagnosis-hub` ArgoCD instance. You can track all the applications for the pattern in this instance.
 ====
 
-. Check that all applications are synchronized. There are thirteen different ArgoCD `applications` that are deployed as part of this pattern.
-
-
-
-
-
-//Module to be included
-//:_content-type: PROCEDURE
-//:imagesdir: ../../../images
-[id="making-some-changes-on-the-dashboard-getting-started"]
-=== Making some changes on the dashboard
-
-You can change some of the parameters and watch how the changes effect the dashboard.
-
-. You can increase or decrease the number of image generators.
-+
-[source,terminal]
-----
-$ oc scale deploymentconfig/image-generator --replicas=2
-----
-+
-Check the dashboard.
-+
-[source,terminal]
-----
-$ oc scale deploymentconfig/image-generator --replicas=0
-----
-+
-Watch the dashboard stop processing images.
-
-. You can also simulate the change of the AI model version - as it's only an environment variable in the Serverless Service configuration.
-+
-[source,terminal]
-----
-$ oc patch service.serving.knative.dev/risk-assessment --type=json -p '[{"op":"replace","path":"/spec/template/metadata/annotations/revisionTimestamp","value":"'"$(date +%F_%T)"'"},{"op":"replace","path":"/spec/template/spec/containers/0/env/0/value","value":"v2"}]'
-----
-+
-This changes the model version value, and the `revisionTimestamp` in the annotations, which triggers a redeployment of the service.
+. Check that all applications are synchronized. There are thirteen different ArgoCD `applications` that are deployed as part of this pattern.

content/patterns/medical-diagnosis/ideas-for-customization.adoc

@@ -30,3 +30,37 @@ These are just a few ideas to help you understand how you could use the {med-pat
 
 //We have relevant links on the patterns page
 //AI: Why does this point to AEG though? https://github.com/validatedpatterns/ansible-edge-gitops/issues[Report Bugs]
+
+
+//Module to be included
+//:_content-type: PROCEDURE
+//:imagesdir: ../../../images
+[id="making-some-changes-on-the-dashboard-getting-started"]
+=== Making some changes on the dashboard
+
+You can change some of the parameters and watch how the changes effect the dashboard.
+
+. You can increase or decrease the number of image generators.
++
+[source,terminal]
+----
+$ oc scale deploymentconfig/image-generator --replicas=2
+----
++
+Check the dashboard.
++
+[source,terminal]
+----
+$ oc scale deploymentconfig/image-generator --replicas=0
+----
++
+Watch the dashboard stop processing images.
+
+. You can also simulate the change of the AI model version - as it's only an environment variable in the Serverless Service configuration.
++
+[source,terminal]
+----
+$ oc patch service.serving.knative.dev/risk-assessment --type=json -p '[{"op":"replace","path":"/spec/template/metadata/annotations/revisionTimestamp","value":"'"$(date +%F_%T)"'"},{"op":"replace","path":"/spec/template/spec/containers/0/env/0/value","value":"v2"}]'
+----
++
+This changes the model version value, and the `revisionTimestamp` in the annotations, which triggers a redeployment of the service.

content/patterns/medical-diagnosis/troubleshooting.adoc

@@ -92,7 +92,7 @@ Ensure that the Prometheus data source exists and that the status is available.
 '''
 Problem:: The dashboard is showing red in the corners of the dashboard panes.
 +
-image::medical-edge/medDiag-noDB.png[link="/images/medical-edge/medDiag-noDB.png"]
+image::../../images/medical-edge/medDiag-noDB.png[link="/images/medical-edge/medDiag-noDB.png"]
 
 Solution:: This is most likely due to the *xraylab* database not being available or misconfigured. Please check the database and ensure that it is functioning properly.