preprocessor.py

import re
import pandas as pd
import emoji
from urlextract import URLExtract


def preprocess(data):
    pattern = r'(\d{1,2}/\d{1,2}/\d{2,4},\s\d{1,2}:\d{2}\s[apAP][mM]\s-\s)'
    parts = re.split(pattern, data)
    messages = [part for part in parts if part and not re.match(pattern, part)]
    dates = [date.replace('\u202f', ' ') for date in re.findall(pattern, data)]

    df = pd.DataFrame({'user_message': messages, 'date': dates})
    df['date'] = pd.to_datetime(df['date'], format='%d/%m/%Y, %I:%M %p - ')

    pattern_for_username = r'([^:]+):'
    df['username'] = df['user_message'].str.extract(pattern_for_username)
    df['message'] = df['user_message'].str.replace(pattern_for_username, '', regex=True).str.strip()
    df.drop(columns=['user_message'], inplace=True)

    df['year'] = df['date'].dt.year
    df['month'] = df['date'].dt.month_name()
    df['day'] = df['date'].dt.day_name()
    df['hour'] = df['date'].dt.hour
    df['minute'] = df['date'].dt.minute
    df['time'] = df['date'].dt.strftime('%I:%M %p')

    df['total_word'] = df['message'].apply(lambda x: len(x.split()))

    extractor = URLExtract()
    df['url_count'] = df['message'].apply(lambda x: len(extractor.find_urls(x)))
    df['emoji_count'] = df['message'].apply(emoji.emoji_count)

    df['period'] = df['hour'].apply(lambda x: 'Night' if 0 <= x < 6 else (
        'Morning' if 6 <= x < 12 else ('Afternoon' if 12 <= x < 18 else 'Evening')))

    df = df.dropna(subset=['username'])
    return df