import requests
import pandas as pd
from bs4 import BeautifulSoup
import pandas as pd
from datetime import datetime


def extract_div_contents_from_url(url):
    response = requests.get(url)
    if response.status_code != 200:
        return pd.DataFrame(columns=['title', 'text_url', 'deletion_discussion', 'label', 'confirmation', 'discussion', 'verdict'])

    soup = BeautifulSoup(response.content, 'html.parser')
    divs = soup.find_all('div', class_='boilerplate afd vfd xfd-closed archived mw-archivedtalk')

    # Extract the title fragment from the URL
    url_fragment = url.split('#')[-1].replace('_', ' ')

    data = []
    for div in divs:
        try:
            title = None
            text_url = None

            # Extract title and text_url
            title_tag = div.find('a')
            if title_tag:
                title_span = div.find('span', {'data-mw-comment-start': True})
                if title_span:
                    title_anchor = title_span.find_next_sibling('a')
                    if title_anchor:
                        title = title_anchor.text
                        text_url = 'https://en.wikipedia.org' + title_anchor['href']
                else:
                    title = title_tag.text
                    text_url = 'https://en.wikipedia.org' + title_tag['href']

            if title == 'talk page' or title is None:
                heading_tag = div.find('div', class_='mw-heading mw-heading3')
                if heading_tag:
                    title_tag = heading_tag.find('a')
                    if title_tag:
                        title = title_tag.text
                        text_url = 'https://en.wikipedia.org' + title_tag['href']

            if title != url_fragment:
                continue  # Skip if the title does not match the URL fragment

            deletion_discussion = div.prettify()

            # Extract label
            label = ''
            verdict_tag = div.find('p')
            if verdict_tag:
                label_b_tag = verdict_tag.find('b')
                if label_b_tag:
                    label = label_b_tag.text.strip()

            # Extract confirmation
            confirmation = ''
            discussion_tag = div.find('dd').find('i')
            if discussion_tag:
                confirmation_b_tag = discussion_tag.find('b')
                if confirmation_b_tag:
                    confirmation = confirmation_b_tag.text.strip()

            # Split deletion_discussion into discussion and verdict
            parts = deletion_discussion.split('<div class="mw-heading mw-heading3">')
            discussion = parts[0] if len(parts) > 0 else ''
            verdict = '<div class="mw-heading mw-heading3">' + parts[1] if len(parts) > 1 else ''

            data.append([title, text_url, deletion_discussion, label, confirmation, verdict, discussion])
        except Exception as e:
            print(f"Error processing div: {e}")
            continue

    df = pd.DataFrame(data, columns=['title', 'text_url', 'deletion_discussion', 'label', 'confirmation', 'verdict', 'discussion'])
    df = df[['title','discussion','verdict','label']]
    return df


def extract_post_links_text(discussion_html):
    split_point = '<span class="plainlinks">'
    if split_point in discussion_html:
        parts = discussion_html.split(split_point)
        if len(parts) > 1:
            return parts[1]
    return discussion_html

def process_discussion(df):
    df['discussion_cleaned'] = df['verdict'].apply(extract_post_links_text)
    return df

def html_to_plaintext(html_content):
    soup = BeautifulSoup(html_content, 'html.parser')
    for tag in soup.find_all(['p', 'li', 'dd', 'dl']):
        tag.insert_before('\n')
        tag.insert_after('\n')
    for br in soup.find_all('br'):
        br.replace_with('\n')

    text = soup.get_text(separator=' ', strip=True)
    text = '\n'.join([line.strip() for line in text.splitlines() if line.strip() != ''])

    return text
def process_html_to_plaintext(df):
    df['discussion_cleaned'] = df['discussion_cleaned'].apply(html_to_plaintext)
    df = df[['title', 'discussion_cleaned', 'label']]
    return df

import pysbd
def split_text_into_sentences(text):
    seg = pysbd.Segmenter(language="en", clean=False)
    sentences = seg.segment(text)
    return ' '.join(sentences[1:])
def process_split_text_into_sentences(df):
    df['discussion_cleaned'] = df['discussion_cleaned'].apply(split_text_into_sentences)
    return df



def process_data(url):
    df = extract_div_contents_from_url(url)
    df = process_discussion(df)
    df = process_html_to_plaintext(df)
    df = process_split_text_into_sentences(df)
    #if not empty
    if not df.empty:
        return df.at[0,'title']+ ' : '+df.at[0, 'discussion_cleaned']
    else:
        return 'Empty DataFrame'