NLP with Attention Models/Text_Summarization/Summarization/tf/utils.py

import pandas as pd
import re


def get_train_test_data(data_dir):
    # Get the train data
    train_data = pd.read_json(f"{data_dir}/train.json")
    train_data.drop(['id'], axis=1, inplace=True)

    # Get the test data
    test_data = pd.read_json(f"{data_dir}/test.json")
    test_data.drop(['id'], axis=1, inplace=True)
    
    return train_data, test_data


def preprocess(input_data):
    # Define the custom preprocessing function
    def preprocess_util(input_data):
        # Convert all text to lowercase
        lowercase = input_data.lower()
        # Remove newlines and double spaces
        removed_newlines = re.sub("\n|\r|\t", " ",  lowercase)
        removed_double_spaces = ' '.join(removed_newlines.split(' '))
        # Add start of sentence and end of sentence tokens
        s = '[SOS] ' + removed_double_spaces + ' [EOS]'
        return s
    
    # Apply the preprocessing to the train and test datasets
    input_data['summary'] = input_data.apply(lambda row : preprocess_util(row['summary']), axis = 1)
    input_data['dialogue'] = input_data.apply(lambda row : preprocess_util(row['dialogue']), axis = 1)

    document = input_data['dialogue']
    summary = input_data['summary']
    
    return document, summary