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   "source": [
    "## Demo Argo Workflow - Submit Custom\n",
    "\n",
    "First we need to provide some settings configuration in order to be able to interact with the service.\n",
    "\n",
    "1. FAAS_URL. This is the URL of the Argo Workflow service.\n",
    "2. NAMESPACE. This is the intended namespace in the kubernetes cluster for creating the Argo Workflows.\n",
    "3. ARGO_WORKFLOWS_TOKEN. This is the token that will be used to authenticate with the Argo Workflows server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import eodc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from eodc.settings import settings\n",
    "\n",
    "settings.FAAS_URL =\"https://services.eodc.eu/workflows/\"\n",
    "settings.NAMESPACE = \"default\"\n",
    "settings.ARGO_WORKFLOWS_TOKEN = \"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Within eodc.faas ther CustomWorkflow class makes use of the FaasProcessorBase that has a number of functions defined for interacting with Argo Workflow resources. We will use the eodc.faas.FaasProcessor.custom in order to instantiate a service connection that will allow us to submit an arbitrary Argo Workflow.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "service = eodc.faas.CustomWorkflow(\n",
    "    processor_details=eodc.faas.FaasProcessor.custom\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using [Hera](https://hera.readthedocs.io/en/stable/), it is possible to define Argo Workflow resources in code. The following is an example resource taken from their documentation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hera.workflows import Container, Parameter, Step, Steps, Workflow, WorkflowsService\n",
    "\n",
    "def hello(service: WorkflowsService):\n",
    "    with Workflow(\n",
    "        workflows_service=service,\n",
    "        namespace=service.namespace,\n",
    "        generate_name=\"steps-\",\n",
    "        entrypoint=\"hello-hello-hello\",\n",
    "    ) as w:\n",
    "        print_message = Container(\n",
    "            name=\"print-message\",\n",
    "            image=\"busybox\",\n",
    "            command=[\"echo\"],\n",
    "        )\n",
    "\n",
    "        with Steps(name=\"hello-hello-hello\") as s:\n",
    "            Step(\n",
    "                name=\"hello1\",\n",
    "                template=print_message,\n",
    "                arguments=[Parameter(name=\"message\", value=\"hello1\")],\n",
    "            )\n",
    "\n",
    "            with s.parallel():\n",
    "                Step(\n",
    "                    name=\"hello2a\",\n",
    "                    template=print_message,\n",
    "                    arguments=[Parameter(name=\"message\", value=\"hello2a\")],\n",
    "                )\n",
    "                Step(\n",
    "                    name=\"hello2b\",\n",
    "                    template=print_message,\n",
    "                    arguments=[Parameter(name=\"message\", value=\"hello2b\")],\n",
    "                )\n",
    "    return w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have an Argo Workflow resource that is has been wrapped in a function that will passes the workflow service on instantiation. You will be able to submit it to the Argo Workflow server in the following way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = service.submit_workflow(\n",
    "    workflow=hello(service.workflows_service)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A number of functions are available via the eodc-sdk to monitor and interact with Argo Workflows. To get the workflow logs, we can use the .get_logs() function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "service.get_logs(name)"
   ]
  }
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