End-to-End-Chest-Cancer-Classification-using-MLflow/main.py

from cnnClassifier import logger
from cnnClassifier.pipeline.stage_01_data_ingestion import DataIngestionTrainingPipeline
from cnnClassifier.pipeline.stage_02_prepare_base_model  import PrepareBaseModelTrainingPipeline
from cnnClassifier.pipeline.stage_03_model_trainer import ModelTrainingPipeline
from cnnClassifier.pipeline.stage_04_model_evaluation import EvaluationPipeline

STAGE_NAME = "Data Ingestion Stage"

try:
    logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
    obj = DataIngestionTrainingPipeline()
    obj.main()
    logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
    logger.exception(e)
    raise e


STAGE_NAME = "Prepare Base Model"
try:
    logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
    obj = PrepareBaseModelTrainingPipeline()
    obj.main()
    logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
    logger.exception(e)
    raise e


STAGE_NAME = "Training"
try:
    logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
    obj = ModelTrainingPipeline()
    obj.main()
    logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")
except Exception as e:
    logger.exception(e)
    raise e


STAGE_NAME = "Evaluation stage"
try:
   logger.info(f"*******************")
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
   model_evalution = EvaluationPipeline()
   model_evalution.main()
   logger.info(f">>>>>> stage {STAGE_NAME} completed <<<<<<\n\nx==========x")

except Exception as e:
        logger.exception(e)
        raise e