Fix bug showing app command

Signed-off-by: John Collier <[email protected]>
redhat-ai-dev · Aug 7, 2024 · 76fa5d5 · 76fa5d5
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diff --git a/skeleton/techdoc/docs/.assets/open-terminal.png b/skeleton/techdoc/docs/.assets/open-terminal.png
diff --git a/skeleton/techdoc/docs/.assets/workbench-name.png b/skeleton/techdoc/docs/.assets/workbench-name.png
diff --git a/skeleton/techdoc/docs/rhoai.md b/skeleton/techdoc/docs/rhoai.md
@@ -11,19 +11,25 @@ This document will outline how you can run build and run your sample application
 ## Running the Sample
 
 1) Navigate to the OpenShift AI workbench created for your sample application
+
 2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
 3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
 4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
-5) Run `${{ values.appRunCommand }}` to run the sample
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
 
 ## Accessing the Sample
 
 With the sample app now running, the following steps will allow you to access the sample app in your browser:
 
 1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
 
 2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
 
-3) Run `oc port-forward 
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
 
-4) Access `http://localhost:${{ values.appPort }}` in your browser to access the application
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/skeleton/template.yaml b/skeleton/template.yaml
@@ -205,6 +205,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else '${APP_INTERFACE_CONTAINER}' }}
           appPort: ${APP_PORT}
+          appRunCommand: "${APP_RUN_COMMAND}"
           modelServiceContainer: ${MODEL_SERVICE_CONTAINER}
           modelServicePort: ${MODEL_SERVICE_PORT}          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -225,7 +226,7 @@ spec:
           tags: 'sed.edit.APPTAGS'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain
@@ -298,7 +299,6 @@ spec:
           modelPath: "${MODEL_PATH}"
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else '${APP_INTERFACE_CONTAINER}' }}
           appPort: ${APP_PORT}
-          appRunCommand: ${APP_RUN_COMMAND}
           modelServiceContainer: ${MODEL_SERVICE_CONTAINER}
           modelServicePort: ${MODEL_SERVICE_PORT}
           # SED_LLM_SERVER_START

diff --git a/templates/audio-to-text/content/docs/.assets/open-terminal.png b/templates/audio-to-text/content/docs/.assets/open-terminal.png
diff --git a/templates/audio-to-text/content/docs/.assets/workbench-name.png b/templates/audio-to-text/content/docs/.assets/workbench-name.png
diff --git a/templates/audio-to-text/content/docs/rhoai.md b/templates/audio-to-text/content/docs/rhoai.md
@@ -11,19 +11,25 @@ This document will outline how you can run build and run your sample application
 ## Running the Sample
 
 1) Navigate to the OpenShift AI workbench created for your sample application
+
 2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
 3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
 4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
-5) Run `${{ values.appRunCommand }}` to run the sample
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
 
 ## Accessing the Sample
 
 With the sample app now running, the following steps will allow you to access the sample app in your browser:
 
 1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
 
 2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
 
-3) Run `oc port-forward 
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
 
-4) Access `http://localhost:${{ values.appPort }}` in your browser to access the application
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/audio-to-text/template.yaml b/templates/audio-to-text/template.yaml
@@ -165,6 +165,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/audio-to-text:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run whisper_client.py"
           modelServiceContainer: quay.io/redhat-ai-dev/whispercpp:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -185,7 +186,7 @@ spec:
           tags: '["ai", "whispercpp", "python", "asr"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain
@@ -258,7 +259,6 @@ spec:
           modelPath: "/model/model.file"
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/audio-to-text:latest' }}
           appPort: 8501
-          appRunCommand: streamlit run whisper_client.py
           modelServiceContainer: quay.io/redhat-ai-dev/whispercpp:latest
           modelServicePort: 8001
           existingModelServer: ${{ parameters.modelServer === 'Existing model server' }}

diff --git a/templates/chatbot/content/docs/.assets/open-terminal.png b/templates/chatbot/content/docs/.assets/open-terminal.png
diff --git a/templates/chatbot/content/docs/.assets/workbench-name.png b/templates/chatbot/content/docs/.assets/workbench-name.png
diff --git a/templates/chatbot/content/docs/rhoai.md b/templates/chatbot/content/docs/rhoai.md
@@ -11,19 +11,25 @@ This document will outline how you can run build and run your sample application
 ## Running the Sample
 
 1) Navigate to the OpenShift AI workbench created for your sample application
+
 2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
 3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
 4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
-5) Run `${{ values.appRunCommand }}` to run the sample
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
 
 ## Accessing the Sample
 
 With the sample app now running, the following steps will allow you to access the sample app in your browser:
 
 1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
 
 2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
 
-3) Run `oc port-forward 
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
 
-4) Access `http://localhost:${{ values.appPort }}` in your browser to access the application
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/chatbot/template.yaml b/templates/chatbot/template.yaml
@@ -179,6 +179,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/chatbot:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run chatbot_ui.py"
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -199,7 +200,7 @@ spec:
           tags: '["ai", "llamacpp", "vllm", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain
@@ -272,7 +273,6 @@ spec:
           modelPath: "/model/model.file"
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/chatbot:latest' }}
           appPort: 8501
-          appRunCommand: streamlit run chatbot_ui.py
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001
           # SED_LLM_SERVER_START

diff --git a/templates/codegen/content/docs/.assets/open-terminal.png b/templates/codegen/content/docs/.assets/open-terminal.png
diff --git a/templates/codegen/content/docs/.assets/workbench-name.png b/templates/codegen/content/docs/.assets/workbench-name.png
diff --git a/templates/codegen/content/docs/rhoai.md b/templates/codegen/content/docs/rhoai.md
@@ -11,19 +11,25 @@ This document will outline how you can run build and run your sample application
 ## Running the Sample
 
 1) Navigate to the OpenShift AI workbench created for your sample application
+
 2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
 3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
 4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
-5) Run `${{ values.appRunCommand }}` to run the sample
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
 
 ## Accessing the Sample
 
 With the sample app now running, the following steps will allow you to access the sample app in your browser:
 
 1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
 
 2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
 
-3) Run `oc port-forward 
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
 
-4) Access `http://localhost:${{ values.appPort }}` in your browser to access the application
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/codegen/template.yaml b/templates/codegen/template.yaml
@@ -179,6 +179,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/codegen:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run codegen-app.py"
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -199,7 +200,7 @@ spec:
           tags: '["ai", "llamacpp", "vllm", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain
@@ -272,7 +273,6 @@ spec:
           modelPath: "/model/model.file"
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/codegen:latest' }}
           appPort: 8501
-          appRunCommand: streamlit run codegen-app.py
           modelServiceContainer: quay.io/ai-lab/llamacpp_python:latest
           modelServicePort: 8001
           # SED_LLM_SERVER_START

diff --git a/templates/object-detection/content/docs/.assets/open-terminal.png b/templates/object-detection/content/docs/.assets/open-terminal.png
diff --git a/templates/object-detection/content/docs/.assets/workbench-name.png b/templates/object-detection/content/docs/.assets/workbench-name.png
diff --git a/templates/object-detection/content/docs/rhoai.md b/templates/object-detection/content/docs/rhoai.md
@@ -11,19 +11,25 @@ This document will outline how you can run build and run your sample application
 ## Running the Sample
 
 1) Navigate to the OpenShift AI workbench created for your sample application
+
 2) Go to `File->Open` and select `Terminal`
+![image](./.assets/open-terminal.png)
+
 3) In the terminal, run `cd ${{ values.name }}` to navigate to your sample app's directory
+
 4) Run `pip install --upgrade -r requirements.txt` to install the dependencies for your application
-5) Run `${{ values.appRunCommand }}` to run the sample
+
+5) Run `${{ values.appRunCommand }}` to run the sample in the workbench.
 
 ## Accessing the Sample
 
 With the sample app now running, the following steps will allow you to access the sample app in your browser:
 
 1) Navigate back to the OpenShift AI dashboard, and find the name of your workbench.
+![image](./.assets/workbench-name.png)
 
 2) In a terminal window on your machine, run `oc get pods -l app=<workbench-name>`. This will retrieve the name of the pod where the workbench is running.
 
-3) Run `oc port-forward 
+3) Run `oc port-forward <pod-name> ${{ values.appPort }}` to port forward the sample application's port to your local machine.
 
-4) Access `http://localhost:${{ values.appPort }}` in your browser to access the application
+4) Finally, visit `http://localhost:${{ values.appPort }}` in your browser to access the application.
diff --git a/templates/object-detection/template.yaml b/templates/object-detection/template.yaml
@@ -165,6 +165,7 @@ spec:
           srcRepoURL: https://${{ parameters.githubServer if parameters.hostType === 'GitHub' else parameters.gitlabServer }}/${{ parameters.repoOwner }}/${{ parameters.repoName }}
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/object_detection:latest' }}
           appPort: 8501
+          appRunCommand: "streamlit run object_detection_client.py"
           modelServiceContainer: quay.io/redhat-ai-dev/object_detection_python:latest
           modelServicePort: 8000          
     # Renders all the template variables into the files and directory names and content, and places the result in the workspace.
@@ -185,7 +186,7 @@ spec:
           tags: '["ai", "detr", "python"]'
           owner: ${{ parameters.owner }} 
           repoSlug: '${{ parameters.imageOrg }}/${{ parameters.imageName }}'
-          defaultBranch: ${{ parameters.branch }} 
+          defaultBranch: ${{ parameters.branch }}
     - id: fetch-github-action
       name: Fetch GitHub Action
       action: fetch:plain
@@ -258,7 +259,6 @@ spec:
           modelPath: "/model/detr-resnet-101"
           appContainer: ${{ 'quay.io/redhat-ai-dev/ai-template-bootstrap-app:latest' if parameters.hostType === 'GitHub' else 'quay.io/redhat-ai-dev/object_detection:latest' }}
           appPort: 8501
-          appRunCommand: streamlit run object_detection_client.py
           modelServiceContainer: quay.io/redhat-ai-dev/object_detection_python:latest
           modelServicePort: 8000
           existingModelServer: ${{ parameters.modelServer === 'Existing model server' }}