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import os
import sys
import pandas as pd
import joblib
from anime_recommender.loggers.logging import logging
from anime_recommender.exception.exception import AnimeRecommendorException
from anime_recommender.constant import *
from huggingface_hub import HfApi, HfFolder
def export_data_to_dataframe(dataframe: pd.DataFrame, file_path: str) -> pd.DataFrame:
"""
Saves a given Pandas DataFrame to a CSV file.
Args:
dataframe (pd.DataFrame): The DataFrame to be saved.
file_path (str): The file path where the DataFrame should be stored.
Returns:
pd.DataFrame: The same DataFrame that was saved.
"""
try:
logging.info(f"Saving DataFrame to file: {file_path}")
dir_path = os.path.dirname(file_path)
os.makedirs(dir_path, exist_ok=True)
dataframe.to_csv(file_path, index=False, header=True)
logging.info(f"DataFrame saved successfully to {file_path}.")
return dataframe
except Exception as e:
logging.error(f"Error saving DataFrame to {file_path}: {e}")
raise AnimeRecommendorException(e, sys)
def load_csv_data(file_path: str) -> pd.DataFrame:
"""
Loads a CSV file into a Pandas DataFrame.
Args:
file_path (str): The file path of the CSV file.
Returns:
pd.DataFrame: The loaded DataFrame.
"""
try:
logging.info(f"Loading CSV data from file: {file_path}")
df = pd.read_csv(file_path)
logging.info("CSV file loaded successfully.")
return df
except Exception as e:
logging.error(f"Error loading CSV file {file_path}: {e}")
raise AnimeRecommendorException(e, sys) from e
def save_model(model: object, file_path: str) -> None:
"""
Saves a machine learning model to a file using joblib.
Args:
model (object): The model object to be saved.
file_path (str): The file path where the model should be stored.
"""
try:
logging.info("Entered the save_model method.")
os.makedirs(os.path.dirname(file_path), exist_ok=True)
with open(file_path, "wb") as file_obj:
joblib.dump(model, file_obj)
logging.info(f"Model saved successfully to {file_path}.")
except Exception as e:
logging.error(f"Error saving model to {file_path}: {e}")
raise AnimeRecommendorException(e, sys) from e
def load_object(file_path: str) -> object:
"""
Loads a model or object from a file using joblib.
Args:
file_path (str): The file path of the saved model.
Returns:
object: The loaded model.
"""
try:
logging.info(f"Attempting to load object from {file_path}")
if not os.path.exists(file_path):
error_msg = f"The file: {file_path} does not exist."
logging.error(error_msg)
raise Exception(error_msg)
with open(file_path, "rb") as file_obj:
logging.info("Object loaded successfully.")
return joblib.load(file_obj)
except Exception as e:
logging.error(f"Error loading object from {file_path}: {e}")
raise AnimeRecommendorException(e, sys) from e
def upload_model_to_huggingface(model_path: str, repo_id: str, filename: str):
"""
Uploads a trained model file to the specified Hugging Face repository.
Args:
model_path (str): Local path of the trained model file.
repo_id (str): Hugging Face repository ID (e.g., 'krishnaveni76/anime-recommendation-models').
filename (str): Name of the file when uploaded to Hugging Face.
"""
try:
api = HfApi()
api.upload_file(
path_or_fileobj=model_path,
path_in_repo=filename,
repo_id=repo_id,
repo_type="model"
)
logging.info(f"Successfully uploaded {filename} to Hugging Face: {repo_id}")
except Exception as e:
logging.info(f"Error uploading model to Hugging Face: {str(e)}")
raise AnimeRecommendorException(e, sys) from e |