import os
from pipelines.training_pipeline import training_pipeline
from zenml import pipeline
from zenml.integrations.mlflow.steps import mlflow_model_deployer_step

from steps.predictor import predictor
from steps.prediction_service_loader import prediction_service_loader
from steps.dynamic_importer import dynamic_importer

requirements_file= os.path.join(os.path.dirname(__file__),"requirements.txt")

@pipeline
def continuous_deployment_pipeline():
    """
    Run a training job and deploy an MLFlow Model deployment.
    """
    # Run the training pipeline
    trained_model = training_pipeline()
    
    # (Re)deploy the trained model
    mlflow_model_deployer_step(workers= 3,deploy_decision = True,model = trained_model)
    
    
@pipeline(enable_cache= False)
def inference_pipeline():
    """
    Run a batch inference job with data loade from an API
    """
    # Load batch data for inference
    batch_data = dynamic_importer()
    
    # load the deployed model service
    model_deployment_service = prediction_service_loader(
        pipeline_name = "continuous_deployment_pipeline",
        step_name = "mlflow_model_deployer_step",)
    
    
    # Run prediction on the batch data
    predictor(service = model_deployment_service,input_data = batch_data)