# from kubernetes import client, config

# # Load the kubeconfig file
# config.load_kube_config()

# # Create an instance of the Kubernetes API client
# api_instance = client.BatchV1Api()

# # Define the job specification
# job_spec = {
#     "template": {
#         "metadata": {"labels": {"app": "python-pipeline"}},
#         "spec": {
#             "containers": [
#                 {
#                     "name": "python-pipeline",
#                     "image": "dummypipe",
#                     "resources": {
#                         "requests": {"memory": "512Mi"},
#                         "limits": {"memory": "1Gi"},
#                     },
#                     "cpu": {"requests": "0.5", "limits": "1"},
#                     "command": ["python", "pipeline.py"],
#                 }
#             ]
#         },
#     },
# }

# # Create the job object
# job = client.V1Job(
#     api_version="apps/v1",
#     kind="Job",
#     metadata=client.V1ObjectMeta(name="python-pipeline"),
#     spec=client.V1JobSpec(**job_spec),
# )

# # Create the job in the jobrunner namespace
# api_instance.create_namespaced_job(namespace="jobrunner", body=job)

from kubernetes import client, config
import string
import random

# Load the default kubeconfig
config.load_kube_config()

# Define the API client for batch jobs
api_instance = client.BatchV1Api()


def get_random_string(length):
    # choose from all lowercase letter
    letters = string.ascii_lowercase
    result_str = "".join(random.choice(letters) for i in range(length))
    return result_str


# Create a new job object
job = client.V1Job(
    api_version="batch/v1",
    kind="Job",
    metadata=client.V1ObjectMeta(name=f"myjob{get_random_string(5)}"),
    spec=client.V1JobSpec(
        ttl_seconds_after_finished=100,
        template=client.V1PodTemplateSpec(
            metadata=client.V1ObjectMeta(
                labels={"app": f"myjobspod{get_random_string(5)}"}
            ),
            spec=client.V1PodSpec(
                containers=[
                    client.V1Container(
                        name=f"myjobrunnercontainer{get_random_string(5)}",
                        image="registry.wayl.one/dummypipe:alpine",
                        command=["python", "pipeline.py"],
                        image_pull_policy="Always",
                    ),
                ],
                restart_policy="Never",
                image_pull_secrets=[client.V1LocalObjectReference(name="regcred")],
            ),
        ),
        backoff_limit=1,
    ),
)

# Call the Kubernetes API to create the job
api_instance.create_namespaced_job(namespace="jobrunner", body=job)