import pandas as pd
import numpy as np
import nltk
from nltk.tokenize import word_tokenize, RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
import textacy

from sklearn.feature_extraction.text import CountVectorizer
import csv
import re

nltk.download('stopwords')
nltk.download('punkt')
stopwords_es = stopwords.words('spanish')
spanish_stemmer = SnowballStemmer('spanish')




def remove_html_markup(s):
    tag = False
    quote = False
    out = ""

    for c in s:
            if c == '<' and not quote:
                tag = True
            elif c == '>' and not quote:
                tag = False
            elif (c == '"' or c == "'") and tag:
                quote = not quote
            elif not tag:
                out = out + c

    return out

def remove_URL(s):
    """Remove URLs from a sample string"""
    return re.sub(r"http\S+", "", s)    
    
def eliminar_puntuacion(articulo):
    deletetion_symbols = ['!','(',')',"'",'-','[',']','{','}',';',':','"','“','’','”',"'",'`','‘','``','\\' ,'/','|',',','|','<','>','.','..','...','?','@',"#",'$','^','&','*','_','~','+','%','=','¿','¡',"''"]
    new_articulo = ""
    for x in articulo:
        if x not in deletetion_symbols:
            new_articulo += x
    return new_articulo  

def remove_emoji(s):
    regrex_pattern = re.compile(pattern = "["
        u"\U0001F600-\U0001F64F"  # emoticons
        u"\U0001F300-\U0001F5FF"  # symbols & pictographs
        u"\U0001F680-\U0001F6FF"  # transport & map symbols
        u"\U0001F1E0-\U0001F1FF"  # flags (iOS)
                           "]+", flags = re.UNICODE)
    return regrex_pattern.sub(r'',s)

def remover_casos_especiales(s):
    #Removiendo texto que termina con .-, ya que usualmente es un texto que se usa como inicio de algunos articulos
    s= re.sub(r'^\w+(,)*([\s]\w+)*([\s]\(\w+\))*.-','',s)    
    return s

def frases_remover(s):
    lista_frases_remover=['La entrada', 'la entrada', '(Seguir leyendo…)', 'se publicó primero en', 'Remolacha - Noticias Republica Dominicana', 'Read more ›', 'Read more','[…]', 'RELACIONADAS']
    for l in lista_frases_remover:
        s = s.replace(l, '')
    return s            

def eliminar_stopwords(articulo):

    articulo_splitted=articulo.split()
    new_articulo = ""
    for x in articulo_splitted:
        if x not in stopwords_es:
            new_articulo += " " + x
    return new_articulo  

def obtener_raices(articulo):

    articulo_splitted=articulo.split()
    new_articulo = ""
    for x in articulo_splitted:
        x_new = spanish_stemmer.stem(x)
        new_articulo += " " + x_new
    return new_articulo 
    
def limpieza_articulos(df):
    
    df_titulos=pd.DataFrame(df['titulo'], columns=['titulo'])
    # Colocando texto en minusculas
    df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: x.lower())
    # Eliminando signos de puntuacion
    df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: eliminar_puntuacion(x))
    # Eliminando palabras vacias (stopwords) utilizando el corpus para estos fines que tiene nltk
    df_titulos['titulo']= df_titulos['titulo'].apply(lambda x: eliminar_stopwords(x))
    all_text = ' '. join(df_titulos['titulo'])
    vocab= np.unique(word_tokenize(all_text))
    return vocab

def obtener_kpes(df):
    df_titulos=pd.DataFrame(df['titulo'], columns=['titulo'])
    all_text = '. '. join(df_titulos['titulo'])
    titulos=textacy.make_spacy_doc(all_text, lang='es_core_news_sm')
    return textacy.extract.keyterms.textrank(titulos,normalize='lower',topn=10)