import json
from tqdm import tqdm
#data processing
import string
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import re
import numpy as np
import pickle
import os
import time
from sklearn.decomposition import NMF

#data cleaning methods
stopword = nltk.corpus.stopwords.words('english')
wordnet_lemmatizer = WordNetLemmatizer()

def remove_punctuation(text):
    no_punct=[words for words in text if words not in string.punctuation]
    words_wo_punct=''.join(no_punct)
    return words_wo_punct

def tokenize(text):
    split=re.split("\W+",text) 
    return split

def remove_stopwords(text):
    text=[word for word in text if word not in stopword and word.isnumeric()==False]
    return text

def Lemmatization(text):
    text=[wordnet_lemmatizer.lemmatize(word) for word in text]
    return text

def process_data(request):
    print("this is called")
    #recieve data from frontend
    title_list = request.POST.getlist("titles[]")
    content_list = request.POST.getlist("contents[]")
    SIZE = len(title_list)
    #store in complete doc dic
    Complete_doc = {}
    Allword = set() #contains all unique words

    for i in tqdm(range(SIZE)):
        Complete_doc[i] = {'title': title_list[i]}
        Complete_doc[i]['body'] = content_list[i]

    #start to process the data
    #process each document by iterations
    for i in tqdm(range(SIZE)):
        #remove puntuation
        doc_wo_punct = remove_punctuation(content_list[i])
        #tokenization and convert whole string to lower case
        tokenized_doc = tokenize(doc_wo_punct.lower())
        #remove all stop words from tokenized_doc
        cleaned_tokenized_doc = remove_stopwords(tokenized_doc)
        #Lemmatizatize the data
        content_list[i] = Lemmatization(cleaned_tokenized_doc)
        #add new words in collection for later use
        for word in content_list[i]:
            Allword.add(word)
    #All words data ready
    NUM_WORDS = len(Allword)
    Allword_numpy = np.asarray(list(Allword))
    
    print('constructing frequency matrix...')
    DocWord = []
    for doc in tqdm(content_list):
        layer = [0 for _ in range(NUM_WORDS)]
        x = np.array(doc)
        #construct frequency matrix
        unique, counts = np.unique(x, return_counts=True)
        temp_frequency = np.asarray((unique, counts)).T
        for i in temp_frequency:
            index = np.where(Allword_numpy==i[0])[0][0]
            layer[index] = int(i[1])
        DocWord.append(layer)


import json
from tqdm import tqdm
#data processing
import string
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer
import re
import numpy as np
import pickle
import os
import time
from sklearn.decomposition import NMF

#data cleaning methods
stopword = nltk.corpus.stopwords.words('english')
wordnet_lemmatizer = WordNetLemmatizer()

def remove_punctuation(text):
    no_punct=[words for words in text if words not in string.punctuation]
    words_wo_punct=''.join(no_punct)
    return words_wo_punct

def tokenize(text):
    split=re.split("\W+",text) 
    return split

def remove_stopwords(text):
    text=[word for word in text if word not in stopword and word.isnumeric()==False]
    return text

def Lemmatization(text):
    text=[wordnet_lemmatizer.lemmatize(word) for word in text]
    return text

def process_data(request):
    print("this is called")
    #recieve data from frontend
    title_list = request.POST.getlist("titles[]")
    content_list = request.POST.getlist("contents[]")
    SIZE = len(title_list)
    #store in complete doc dic
    Complete_doc = {}
    Allword = set() #contains all unique words

    for i in tqdm(range(SIZE)):
        Complete_doc[i] = {'title': title_list[i]}
        Complete_doc[i]['body'] = content_list[i]

    #start to process the data
    #process each document by iterations
    for i in tqdm(range(SIZE)):
        #remove puntuation
        doc_wo_punct = remove_punctuation(content_list[i])
        #tokenization and convert whole string to lower case
        tokenized_doc = tokenize(doc_wo_punct.lower())
        #remove all stop words from tokenized_doc
        cleaned_tokenized_doc = remove_stopwords(tokenized_doc)
        #Lemmatizatize the data
        content_list[i] = Lemmatization(cleaned_tokenized_doc)
        #add new words in collection for later use
        for word in content_list[i]:
            Allword.add(word)
    #All words data ready
    NUM_WORDS = len(Allword)
    Allword_numpy = np.asarray(list(Allword))
    
    print('constructing frequency matrix...')
    DocWord = []
    for doc in tqdm(content_list):
        layer = [0 for _ in range(NUM_WORDS)]
        x = np.array(doc)
        #construct frequency matrix
        unique, counts = np.unique(x, return_counts=True)
        temp_frequency = np.asarray((unique, counts)).T
        for i in temp_frequency:
            index = np.where(Allword_numpy==i[0])[0][0]
            layer[index] = int(i[1])
        DocWord.append(layer)


#data processing #data cleaning methods #recieve data from frontend #store in complete doc dic #contains all unique words #start to process the data #process each document by iterations #remove puntuation #tokenization and convert whole string to lower case #remove all stop words from tokenized_doc #Lemmatizatize the data #add new words in collection for later use #All words data ready #construct frequency matrix