import re
import requests
import wikipedia
import numpy as np
import pandas as pd
import streamlit as st
from bs4 import BeautifulSoup
from transformers import AutoModel
from transformers import BertTokenizer


def clean_page_text(text):
    text = re.sub(r'[^\w\s]', ' ', text) #Replaces the non-alphanumeric characters with spaces.
    text = re.sub(r'[^\u0600-\u06FF]', ' ', text) #Replaces the non-Arabic characters with spaces.
    text = re.sub(r'\s+', ' ', text) #Replaces extra spaces with a single space.
    return text


@st.cache_resource
def encode_page_text(page_text):
    tokenizer = BertTokenizer.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy')
    model = AutoModel.from_pretrained('CAMeL-Lab/bert-base-arabic-camelbert-mix-pos-egy')

    tokenized_page_text  = tokenizer(page_text, return_tensors='pt', max_length=512, truncation=True)
    encoded_page_text = model(**tokenized_page_text)[0][0][0].tolist()

    return encoded_page_text


@st.cache_data
def get_page_info(title):
    page_info = f"https://xtools.wmcloud.org/api/page/articleinfo/arz.wikipedia.org/{title}?format=json"

    creation_date = eval(str(BeautifulSoup(requests.get(page_info).content, "html.parser")).replace('null', 'None'))['created_at']
    creator_name = eval(str(BeautifulSoup(requests.get(page_info).content, "html.parser")).replace('null', 'None'))['author']
    total_edits = eval(str(BeautifulSoup(requests.get(page_info).content, "html.parser")).replace('null', 'None'))['revisions']
    total_editors = eval(str(BeautifulSoup(requests.get(page_info).content, "html.parser")).replace('null', 'None'))['editors']

    return creation_date, creator_name, total_edits, total_editors


@st.cache_data
def get_page_prose(title):
    page_prose = f"https://xtools.wmcloud.org/api/page/prose/arz.wikipedia.org/{title}?format=json"

    total_bytes = eval(str(BeautifulSoup(requests.get(page_prose).content, "html.parser")).replace('null', 'None'))['bytes']
    total_words = eval(str(BeautifulSoup(requests.get(page_prose).content, "html.parser")).replace('null', 'None'))['words']
    total_chars = eval(str(BeautifulSoup(requests.get(page_prose).content, "html.parser")).replace('null', 'None'))['characters']

    return total_bytes, total_words, total_chars


@st.cache_data
def prepare_features(selected_title):
    dataframe = get_metadata_features(selected_title)
    
    try:
        article = wikipedia.page(selected_title)
        full_article_text = clean_page_text(article.content)
    
    except wikipedia.exceptions.DisambiguationError as e:
        selected_title = e.options[0]
        article = wikipedia.page(selected_title)
        full_article_text = clean_page_text(article.content)

    encode_full_article_text = encode_page_text(full_article_text)

    X = []
    
    for i in range(dataframe.shape[0]):
        x = [] 
        x.append(dataframe['Total Edits'][i])
        x.append(dataframe['Total Editors'][i])
        x.append(dataframe['Total Bytes'][i])
        x.append(dataframe['Total Characters'][i])
        x.append(dataframe['Total Words'][i])
        
        # Both page_metadata + page_text_embeddings
        X.append(np.hstack([x, list(encode_full_article_text)])) 

    return X, article, dataframe, selected_title


@st.cache_data
def get_metadata_features(selected_title):
    creation_date, creator_name, total_edits, total_editors = get_page_info(selected_title)
    total_bytes, total_words, total_chars = get_page_prose(selected_title)
    
    data = {'Total Edits':[total_edits], 'Total Editors':[total_editors], 'Total Bytes':[total_bytes],
            'Total Characters':[total_chars], 'Total Words':[total_words], 'Creator Name':[creator_name],
            'Creation Date':[creation_date]}  
    
    dataframe = pd.DataFrame(data)

    return dataframe