DeepScan-Automated-Chest-Cancer-Detection-Using-MLflow-and-DVC/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 = "Prepare base model"
try: 
   logger.info(f"*******************")
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
   prepare_base_model = DataIngestionTrainingPipeline()
   prepare_base_model.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"*******************")
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
   prepare_base_model = PrepareBaseModelTrainingPipeline()
   prepare_base_model.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"*******************")
   logger.info(f">>>>>> stage {STAGE_NAME} started <<<<<<")
   model_trainer = ModelTrainingPipeline()
   model_trainer.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