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import sys
from anime_recommender.pipelines.training_pipeline import TrainingPipeline
from anime_recommender.loggers.logging import logging
from anime_recommender.exception.exception import AnimeRecommendorException
if __name__ == "__main__":
try:
logging.info("Starting the Anime Recommendation System Training Pipeline...")
pipeline = TrainingPipeline()
pipeline.run_pipeline()
except Exception as e:
logging.error(f"Pipeline execution failed: {str(e)}")
raise AnimeRecommendorException(e, sys)
# import sys
# from anime_recommender.loggers.logging import logging
# from anime_recommender.exception.exception import AnimeRecommendorException
# from anime_recommender.components.data_ingestion import DataIngestion
# from anime_recommender.entity.config_entity import TrainingPipelineConfig,DataIngestionConfig,DataTransformationConfig,CollaborativeModelConfig,ContentBasedModelConfig
# from anime_recommender.components.data_transformation import DataTransformation
# from anime_recommender.components.collaborative_recommender import CollaborativeModelTrainer
# from anime_recommender.components.content_based_recommender import ContentBasedModelTrainer
# from anime_recommender.components.top_anime_recommenders import PopularityBasedRecommendor
# if __name__ == "__main__":
# try:
# training_pipeline_config = TrainingPipelineConfig()
# data_ingestion_config = DataIngestionConfig(training_pipeline_config)
# data_ingestion = DataIngestion(data_ingestion_config)
# logging.info("Initiating Data Ingestion.")
# data_ingestion_artifact = data_ingestion.ingest_data()
# logging.info(f"Data ingestion completed.")
# print(data_ingestion_artifact)
# # Data Transformation
# data_transformation_config = DataTransformationConfig(training_pipeline_config)
# data_transformation = DataTransformation(data_ingestion_artifact,data_transformation_config)
# logging.info("Initiating Data Transformation.")
# data_transformation_artifact = data_transformation.initiate_data_transformation()
# logging.info("Data Transformation Completed.")
# print(data_transformation_artifact)
# # Collaborative Model Training
# collaborative_model_trainer_config = CollaborativeModelConfig(training_pipeline_config)
# collaborative_model_trainer = CollaborativeModelTrainer(collaborative_model_trainer_config= collaborative_model_trainer_config,data_transformation_artifact=data_transformation_artifact)
# logging.info("Initiating Collaborative Model training.")
# collaborative_model_trainer_artifact = collaborative_model_trainer.initiate_model_trainer()
# logging.info("Collaborative Model training completed.")
# print(collaborative_model_trainer_artifact)
# # Content Based Model Training
# content_based_model_trainer_config = ContentBasedModelConfig(training_pipeline_config)
# content_based_model_trainer = ContentBasedModelTrainer(content_based_model_trainer_config=content_based_model_trainer_config,data_ingestion_artifact=data_ingestion_artifact)
# logging.info("Initiating Content Based Model training.")
# content_based_model_trainer_artifact = content_based_model_trainer.initiate_model_trainer()
# logging.info("Content Based Model training completed.")
# print(content_based_model_trainer_artifact)
# # Popularity Based Filtering
# logging.info("Initiating Popularity based filtering.")
# filtering = PopularityBasedRecommendor(data_ingestion_artifact=data_ingestion_artifact)
# popularity_recommendations = filtering.initiate_model_trainer(filter_type='top_avg_rated')
# logging.info("Popularity based filtering completed.")
# except Exception as e:
# raise AnimeRecommendorException(e, sys) |