app.py

# Required Libraries

#Base and Cleaning
import json
import requests
import pandas as pd
import numpy as np
import emoji
import regex
import re
import string
from collections import Counter
import tqdm
from operator import itemgetter

#Visualizations
import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt
import pyLDAvis.gensim
import chart_studio
import chart_studio.plotly as py
import chart_studio.tools as tls

#Natural Language Processing (NLP)
import spacy
import gensim
import json
from spacy.tokenizer import Tokenizer
from gensim.corpora import Dictionary
from gensim.models.ldamulticore import LdaMulticore
from gensim.models.coherencemodel import CoherenceModel
from gensim.parsing.preprocessing import STOPWORDS as SW
from sklearn.decomposition import LatentDirichletAllocation, TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import GridSearchCV
from pprint import pprint
from wordcloud import STOPWORDS
from gensim.parsing.preprocessing import preprocess_string, strip_punctuation, strip_numeric

import gradio as gr

def give_emoji_free_text(text):
    """
    Removes emoji's from tweets
    Accepts:
        Text (tweets)
    Returns:
        Text (emoji free tweets)
    """
    emoji_list = [c for c in text if c in emoji.EMOJI_DATA]
    clean_text = ' '.join([str for str in text.split() if not any(i in str for i in emoji_list)])
    return clean_text

def url_free_text(text):
    '''
    Cleans text from urls
    '''
    text = re.sub(r'http\S+', '', text)
    return text

# Tokenizer function
def tokenize(text):
    """
    Parses a string into a list of semantic units (words)
    Args:
        text (str): The string that the function will tokenize.
    Returns:
        list: tokens parsed out
    """
    # Removing url's
    pattern = r"http\S+"

    tokens = re.sub(pattern, "", text) # https://www.youtube.com/watch?v=O2onA4r5UaY
    tokens = re.sub('[^a-zA-Z 0-9]', '', text)
    tokens = re.sub('[%s]' % re.escape(string.punctuation), '', text) # Remove punctuation
    tokens = re.sub('\w*\d\w*', '', text) # Remove words containing numbers
    # tokens = re.sub('@*!*$*', '', text) # Remove @ ! $
    tokens = tokens.strip(',') # TESTING THIS LINE
    tokens = tokens.strip('?') # TESTING THIS LINE
    tokens = tokens.strip('!') # TESTING THIS LINE
    tokens = tokens.strip("'") # TESTING THIS LINE
    tokens = tokens.strip(".") # TESTING THIS LINE

    tokens = tokens.lower().split() # Make text lowercase and split it

    return tokens

def compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=1):
  coherence_values = []
  model_list = []
  for num_topics in range(start, limit, step):
    model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                            num_topics=num_topics,
                                            random_state=100,
                                            chunksize=200,
                                            passes=10,
                                            per_word_topics=True,
                                            id2word=id2word)
    model_list.append(model)
    coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='c_v')
    coherence_values.append(coherencemodel.get_coherence())

  return model_list, coherence_values

def compute_coherence_values2(corpus, dictionary, k, a, b):
  lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                              id2word=id2word,
                                              num_topics=num_topics,
                                              random_state=100,
                                              chunksize=200,
                                              passes=10,
                                              alpha=a,
                                              eta=b,
                                              per_word_topics=True)
  coherence_model_lda = CoherenceModel(model=lda_model, texts=df['lemma_tokens'], dictionary=id2word, coherence='c_v')

  return coherence_model_lda.get_coherence()

def assignTopic(l):
  maxTopic = max(l,key=itemgetter(1))[0]
  return maxTopic

def get_topic_value(row, i):
  if len(row) == 1:
    return row[0][1]
  else:
    return row[i][1]


df = pd.DataFrame()

def dataframeProcessing(dataset):
    # Opening JSON file
    f = open('stopwords-tl.json')
    tlStopwords = json.loads(f.read())
    stopwords = set(STOPWORDS)
    stopwords.update(tlStopwords)
    stopwords.update(['na', 'sa', 'ko', 'ako', 'ng', 'mga', 'ba', 'ka', 'yung', 'lang', 'di', 'mo', 'kasi'])

    df = pd.read_csv('katip-december.csv')
    df.rename(columns = {'tweet':'original_tweets'}, inplace = True)
    df = df.apply(lambda row: row[df['language'].isin(['en'])])
    df.reset_index(inplace=True)

    # Apply the function above and get tweets free of emoji's
    call_emoji_free = lambda x: give_emoji_free_text(x)

    # Apply `call_emoji_free` which calls the function to remove all emoji's
    df['emoji_free_tweets'] = df['original_tweets'].apply(call_emoji_free)

    #Create a new column with url free tweets
    df['url_free_tweets'] = df['emoji_free_tweets'].apply(url_free_text)

    # Load spacy
    # Make sure to restart the runtime after running installations and libraries tab
    nlp = spacy.load('en_core_web_lg')

    # Tokenizer
    tokenizer = Tokenizer(nlp.vocab)


    # Custom stopwords
    custom_stopwords = ['hi','\n','\n\n', '&', ' ', '.', '-', 'got', "it's", 'it’s', "i'm", 'i’m', 'im', 'want', 'like', '$', '@']


    # Customize stop words by adding to the default list
    STOP_WORDS = nlp.Defaults.stop_words.union(custom_stopwords)

    # ALL_STOP_WORDS = spacy + gensim + wordcloud
    ALL_STOP_WORDS = STOP_WORDS.union(SW).union(stopwords)


    tokens = []
    STOP_WORDS.update(stopwords)

    for doc in tokenizer.pipe(df['url_free_tweets'], batch_size=500):
        doc_tokens = []
        for token in doc:
            if token.text.lower() not in STOP_WORDS:
                doc_tokens.append(token.text.lower())
        tokens.append(doc_tokens)

    # Makes tokens column
    df['tokens'] = tokens

    # Make tokens a string again
    df['tokens_back_to_text'] = [' '.join(map(str, l)) for l in df['tokens']]

    def get_lemmas(text):
        '''Used to lemmatize the processed tweets'''
        lemmas = []

        doc = nlp(text)

        # Something goes here :P
        for token in doc:
            if ((token.is_stop == False) and (token.is_punct == False)) and (token.pos_ != 'PRON'):
                lemmas.append(token.lemma_)

        return lemmas

    df['lemmas'] = df['tokens_back_to_text'].apply(get_lemmas)

    # Make lemmas a string again
    df['lemmas_back_to_text'] = [' '.join(map(str, l)) for l in df['lemmas']]

    # Apply tokenizer
    df['lemma_tokens'] = df['lemmas_back_to_text'].apply(tokenize)

    # Create a id2word dictionary
    id2word = Dictionary(df['lemma_tokens'])

    # Filtering Extremes
    id2word.filter_extremes(no_below=2, no_above=.99)
    print(len(id2word))

    # Creating a corpus object
    corpus = [id2word.doc2bow(d) for d in df['lemma_tokens']]

    lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                                id2word=id2word,
                                                num_topics=5,
                                                random_state=100,
                                                chunksize=200,
                                                passes=10,
                                                per_word_topics=True)

    pprint(lda_model.print_topics())
    doc_lda = lda_model[corpus]

    coherence_model_lda = CoherenceModel(model=lda_model, texts=df['lemma_tokens'], dictionary=id2word, coherence='c_v')
    coherence_lda = coherence_model_lda.get_coherence()

    model_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus,
                                                            texts=df['lemma_tokens'],
                                                            start=2,
                                                            limit=10,
                                                            step=1)

    k_max = max(coherence_values)
    num_topics = coherence_values.index(k_max) + 2

    lda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                                id2word=id2word,
                                                num_topics=num_topics,
                                                random_state=100,
                                                chunksize=200,
                                                passes=10,
                                                per_word_topics=True)

    grid = {}
    grid['Validation_Set'] = {}

    alpha = [0.05, 0.1, 0.5, 1, 5, 10]

    beta = [0.05, 0.1, 0.5, 1, 5, 10]

    num_of_docs = len(corpus)
    corpus_sets = [gensim.utils.ClippedCorpus(corpus, int(num_of_docs*0.75)),
                corpus]
    corpus_title = ['75% Corpus', '100% Corpus']
    model_results = {'Validation_Set': [],
                    'Alpha': [],
                    'Beta': [],
                    'Coherence': []
                    }
    if 1 == 1:
        pbar = tqdm.tqdm(total=540)

    for i in range(len(corpus_sets)):
        for a in alpha:
            for b in beta:
                cv = compute_coherence_values2(corpus=corpus_sets[i], dictionary=id2word, k=num_topics, a=a, b=b)
                model_results['Validation_Set'].append(corpus_title[i])
                model_results['Alpha'].append(a)
                model_results['Beta'].append(b)
                model_results['Coherence'].append(cv)

                pbar.update(1)
    pd.DataFrame(model_results).to_csv('lda_tuning_results_new.csv', index=False)
    pbar.close()

    params_df = pd.read_csv('lda_tuning_results_new.csv')
    params_df = params_df[params_df.Validation_Set == '100% Corpus']
    params_df.reset_index(inplace=True)

    max_params = params_df.loc[params_df['Coherence'].idxmax()]
    max_coherence = max_params['Coherence']
    max_alpha = max_params['Alpha']
    max_beta = max_params['Beta']

    lda_model_final = gensim.models.ldamodel.LdaModel(corpus=corpus,
                                                    id2word=id2word,
                                                    num_topics=7,
                                                    random_state=100,
                                                    chunksize=200,
                                                    passes=10,
                                                    alpha=max_alpha,
                                                    eta=max_beta,
                                                    per_word_topics=True)

    coherence_model_lda = CoherenceModel(model=lda_model_final, texts=df['lemma_tokens'], dictionary=id2word,
                                        coherence='c_v')
    coherence_lda = coherence_model_lda.get_coherence()

    lda_topics = lda_model_final.show_topics(num_words=10)

    topics = []
    filters = [lambda x: x.lower(), strip_punctuation, strip_numeric]

    for topic in lda_topics:
        print(topic)
        topics.append(preprocess_string(topic[1], filters))

    df['topic'] = [sorted(lda_model_final[corpus][text][0]) for text in range(len(df['original_tweets']))]

    df = df[df['topic'].map(lambda d: len(d)) > 0]
    df['topic'][0]

    df['max_topic'] = df['topic'].map(lambda row: assignTopic(row))

    topic_clusters = []
    for i in range(num_topics):
        topic_clusters.append(df[df['max_topic'].isin(([i]))])
        topic_clusters[i] = topic_clusters[i]['original_tweets'].tolist()

    for i in range(len(topic_clusters)):
        tweets = df.loc[df['max_topic'] == i]
        tweets['topic'] = tweets['topic'].apply(lambda x: get_topic_value(x, i))
        # tweets['topic'] = [row[i][1] for row in tweets['topic']]
        tweets_sorted = tweets.sort_values('topic', ascending=False)
        tweets_sorted.drop_duplicates(subset=['original_tweets'])
        rep_tweets = tweets_sorted['original_tweets']
        rep_tweets = [*set(rep_tweets)]
        print('Topic ', i)
        print(rep_tweets[:5])
    
    return df

def greet(name):
    return "Hello " + name + "!!"

iface = gr.Interface(fn=dataframeProcessing, inputs="text", outputs=gr.Dataframe(headers=['original_tweets', 'max_topic']))
iface.launch()