* machine-learning-apps#57 is tracking setting up new staging and prod environments

  * This PR sets up a new staging (or dev environment)
  * We create a kustomize manifest for deploying the front end into that
    namespace
  * The staging environment is configured to use the dev instance of the
    issue label bot backend microservice (i.e the pubsub workers)
  * I created some python scripts to make it easier to setup the secrets.
  * The motivation for doing this was to test the changes to the front end

* Front end now forwards all issues for the kubeflow org to the backend

  * This is needed because we want to use multiple models for all Kubeflow
    repos kubeflow/code-intelligence#70

  * The backend should also be configured with logging to measure the impact
    of the predictions.

kubeflow/code-intelligence#104 is an a test issue showing that the bot is
working.

* Fix how keys are handled

  * For GOOGLE_APPLICATION_CREDENTIALS; depend on that environment variable
    being set and pointing to the file containing the private key;
    don't get the private key from an environment variable and then write it
    to a file.

* For the GitHub App private key; use an environment variable to point to
  the file containing the PEM key.

* Create a script to create the secrets.

* Flask app is running in dev namespace

  * create_secrets.py creates secrets needed for dev instance