Top anime recommender completed

anime_recommender/source/{collaborative_recommenders.py → collaborative_recommender.py}

@@ -4,7 +4,7 @@ from anime_recommender.exception.exception import AnimeRecommendorException
 from anime_recommender.entity.config_entity import CollaborativeModelConfig
 from anime_recommender.entity.artifact_entity import DataTransformationArtifact, CollaborativeModelArtifact
 from anime_recommender.utils.main_utils.utils import load_csv_data, save_model, load_object
-from anime_recommender.model_trainer.collaborative_filtering import CollaborativeAnimeRecommender
+from anime_recommender.model_trainer.collaborative_modelling import CollaborativeAnimeRecommender
 
 class CollaborativeModelTrainer:
     """

anime_recommender/source/content_based_recommender.py

@@ -4,7 +4,7 @@ from anime_recommender.exception.exception import AnimeRecommendorException
 from anime_recommender.entity.config_entity import ContentBasedModelConfig
 from anime_recommender.entity.artifact_entity import ContentBasedModelArtifact, DataIngestionArtifact
 from anime_recommender.utils.main_utils.utils import load_csv_data
-from anime_recommender.model_trainer.content_filtering import ContentBasedRecommender
+from anime_recommender.model_trainer.content_based_modelling import ContentBasedRecommender
 from anime_recommender.constant import *
  
 class ContentBasedModelTrainer:

anime_recommender/source/top_anime_recommenders.py

@@ -0,0 +1,53 @@
+import sys
+from anime_recommender.exception.exception import AnimeRecommendorException
+from anime_recommender.loggers.logging import logging
+from anime_recommender.utils.main_utils.utils import load_csv_data 
+from anime_recommender.entity.artifact_entity import DataIngestionArtifact
+from anime_recommender.model_trainer.top_anime_filtering import PopularityBasedFiltering 
+
+
+class PopularityBasedRecommendor: 
+
+    def __init__(self,data_ingestion_artifact = DataIngestionArtifact):
+        try:
+            self.data_ingestion_artifact = data_ingestion_artifact
+        except Exception as e:
+            raise AnimeRecommendorException(e,sys)
+        
+    def initiate_model_trainer(self,filter_type:str):
+        try:
+            logging.info("Loading transformed data...")
+            df = load_csv_data(self.data_ingestion_artifact.feature_store_anime_file_path)
+
+            recommender = PopularityBasedFiltering(df)
+
+            if filter_type == 'popular_animes': 
+                popular_animes = recommender.popular_animes(n =10) 
+                logging.info(f"Popular Anime recommendations: {popular_animes}") 
+
+            elif filter_type == 'top_ranked_animes': 
+                top_ranked_animes = recommender.top_ranked_animes(n =10) 
+                logging.info(f"top_ranked_animes recommendations: {top_ranked_animes}")  
+
+            elif filter_type == 'overall_top_rated_animes': 
+                overall_top_rated_animes = recommender.overall_top_rated_animes(n =10) 
+                logging.info(f"overall_top_rated_animes recommendations: {overall_top_rated_animes}") 
+
+            elif filter_type == 'favorite_animes': 
+                favorite_animes = recommender.favorite_animes(n =10) 
+                logging.info(f"favorite_animes recommendations: {favorite_animes}") 
+
+            elif filter_type == 'top_animes_members': 
+                top_animes_members = recommender.top_animes_members(n = 10) 
+                logging.info(f"top_animes_members recommendations: {top_animes_members}") 
+
+            elif filter_type == 'popular_anime_among_members': 
+                popular_anime_among_members = recommender.popular_anime_among_members(n =10) 
+                logging.info(f"popular_anime_among_members recommendations: {popular_anime_among_members}") 
+            
+            elif filter_type == 'top_avg_rated': 
+                top_avg_rated = recommender.top_avg_rated(n =10) 
+                logging.info(f"top_avg_rated recommendations: {top_avg_rated}") 
+
+        except Exception as e:
+            raise AnimeRecommendorException(e,sys)

requirements.txt

@@ -5,4 +5,5 @@ streamlit
 transformers
 huggingface_hub
 datasets
-scikit-surprise
+scikit-surprise
+-e .

run_pipeline.py

@@ -3,11 +3,10 @@ from anime_recommender.loggers.logging import logging
 from anime_recommender.exception.exception import AnimeRecommendorException
 from anime_recommender.source.data_ingestion import DataIngestion
 from anime_recommender.entity.config_entity import TrainingPipelineConfig,DataIngestionConfig,DataTransformationConfig,CollaborativeModelConfig,ContentBasedModelConfig
-# ,DataTransformationConfig
 from anime_recommender.source.data_transformation import DataTransformation
-from anime_recommender.source.collaborative_recommenders import CollaborativeModelTrainer
+from anime_recommender.source.collaborative_recommender import CollaborativeModelTrainer
 from anime_recommender.source.content_based_recommender import ContentBasedModelTrainer 
-# from anime_recommender.source.popularity_based_recommenders import PopularityBasedRecommendor
+from anime_recommender.source.top_anime_recommenders import PopularityBasedRecommendor
 
 if __name__ == "__main__":
     try:
@@ -36,18 +35,18 @@ if __name__ == "__main__":
         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)
+        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.") 
+        # 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)

setup.py

@@ -0,0 +1,29 @@
+from setuptools import find_packages, setup
+from typing import List
+
+def get_requirements() -> List[str] :
+    """
+    This function returns the list of requirements
+    """
+    requirements_lst:List[str] = []
+    try:
+        with open("requirements.txt", "r") as file:
+            lines = file.readlines()
+            for line in lines:
+                requirement = line.strip()
+                if requirement and requirement != "-e .":
+                    requirements_lst.append(requirement)
+    except FileNotFoundError:
+        print("requirements.txt file not found")
+    return requirements_lst
+
+print(get_requirements())
+
+setup(
+    name="AnimeRecommendationSystem",
+    version= "0.0.1",
+    author= "Krishnaveni Ponna",
+    author_email= "[email protected]",
+    packages= find_packages(),
+    install_requires = get_requirements()
+)