import os import pandas as pd from huggingface_hub import HfApi, Repository import shutil def merge_and_sort(tables, output_files, output_folder, columns): frames = [] for table in tables: try: frames.append(pd.read_csv(table, sep=";")) print(f"File '{table}' successfully prepared for merging.") except: try: frames.append(pd.read_csv(table, sep=",")) print(f"File '{table}' successfully prepared for merging.") except: try: frames.append(pd.read_csv(table, sep="\t")) print(f"File '{table}' successfully prepared for merging.") except Exception as e: print(f"Unable to merge file '{table}': {e}") full_df = pd.concat(frames).filter(items=columns,axis=1) zero = full_df.loc[(full_df['type'] == 0)] zero = zero.sample(frac = 1) # shuffling examples zero.to_csv(f"{output_folder}/{output_files[0]}", index=False) one = full_df.loc[(full_df['type'] == 1)] one = one.sample(frac = 1) # shuffling examples one.to_csv(f"{output_folder}/{output_files[1]}", index=False) def validate_csv(file_path: str, columns): """ Validates the structure of the CSV file to ensure it contains valid columns. """ df = pd.read_csv(file_path, sep=',') required_columns = set(columns) if not required_columns.issubset(df.columns): raise ValueError(f"The TSV file must contain the following columns: {required_columns}") print(f"CSV file '{file_path}' is valid with {len(df)} rows.") def create_splits(output_folder, output_files, dataset_name, dataset_structure): zero = pd.read_csv(f"{output_folder}/{output_files[0]}") one = pd.read_csv(f"{output_folder}/{output_files[1]}") for split, structure in dataset_structure.items(): try: for key, value in structure.items(): if key=="zero": rows_zero = zero.iloc[:value] zero.drop(rows_zero.index, inplace=True) elif key=="one": rows_one = one.iloc[:value] one.drop(rows_one.index, inplace=True) else: print(f"Invalid key in dataset structure: {key} in f{split} part.") df = pd.concat([rows_zero, rows_one]) df = df.sample(frac = 1) # shuffling examples print(df) df.to_csv(f"{output_folder}/{dataset_name}/{split}.csv", index=False) print(f"Created {split} split.") except Exception as e: print(f"Failure while creating the {split} splt: {e}") def push_dataset_to_HF(folder, dataset_name, user): try: # Initialize Hugging Face Hub API api = HfApi() repo_id = f"{user}/{dataset_name}" # Specify repo_type="dataset" here api.create_repo( repo_id=repo_id, exist_ok=True, repo_type="dataset" ) # Push files to Hub api.upload_folder( folder_path=folder, repo_id=repo_id, repo_type="dataset", commit_message=f"Add {dataset_name} dataset." ) print(f"Dataset '{folder}/{dataset_name}' has been uploaded to {user}'s HuggingFace repo") except Exception as e: print(f"Error occurred during upload: {str(e)}") raise if __name__=="__main__": os.chdir="/Users/arieldrozd/Downloads/IMLLA-FinalProject" tables = ["./examples_monika/final_table_together.csv", "./cleaned_examples_ariel/nkjp_ariel.csv", "./cleaned_examples_ariel/wikipedia.csv", "cleaned_examples_ariel/nowela.csv"] output_folder = "./dataset" output_files = ["zero.csv", "one.csv"] dataset_name = "jobtitles" columns=['type', 'source_sentence', 'target_sentence'] merge_and_sort(tables, output_files, output_folder, columns) for file in output_files: validate_csv(f"{output_folder}/{file}", columns) #test split -> zero: 250, one: 250 #validation split -> zero: 500, one: 50 #training split -> zero: 4221, one: 610 -> actually: all that is left dataset_structure = {"test":{"zero":250,"one":250}, "validation":{"zero":500,"one":50}, "train":{"zero":4221,"one":610}} final_dataset = create_splits(output_folder, output_files, dataset_name, dataset_structure) user = "ArielUW" push_dataset_to_HF(output_folder, dataset_name, user)