import dash_html_components as html
import plotly.express as px
from dash.dependencies import Input, Output
import plotly.graph_objects as go
import plotly.figure_factory as ff
from statsmodels.graphics.gofplots import qqplot
import pandas as pd
import numpy as np
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
corpus = Corpus() # khởi tạo
dirr = "resources/vn_express.txt" # chọn đường dẫn corpus
corpus.read(dirr) # đọc đường dẫn
corpus.preprocess() # Tiền xử lí
corpus.read_word2vec() # Đọc model word2vec để tìm từ đồng nghĩa
corpus.read_ner() # Đọc dữ liệu đã xử lí tên thực thể và từ loại
def genResult(res):
result = [
html.P(children=sen,
style={
'backgroundColor': 'white',
'borderBottom': '2px solid #4F2992',
'margin': '30px',
'padding': '10px'
}) for sen in res
]
res.append(html.Br())
print "\nLoading %i files"%nb_files
print "-------------------------------------------"
docs = []
# Load the files
for i, f in enumerate(files):
sys.stdout.write( "\r%3i/%i %s"%( i+1, nb_files, '{:<70}'.format(f) ) )
sys.stdout.flush()
docs.append(Document(f))
corpus = Corpus(docs, termino)
print "\n\nCorpus preprocessing"
print "-------------------------------------------"
corpus.preprocess()
print "\n\nExtracting the keywords"
print "-------------------------------------------"
corpus.process()
if Config().testing:
print "\n\nResults (%s average)"%("Macro" if config.macro_average else "Micro")
print "-------------------------------------------"
corpus.results()
else:
print "\n"
if config.save_file != "":
print "\nResults saved in %s"%config.save_file
corpus.save(config.save_file)
import sys
from Corpus import Corpus
corpus = Corpus()
dirr = input("Input directory of corpus:")
# dirr = "resources/corpus_mini.txt"
corpus.read(dirr)
corpus.preprocess()
corpus.ner()
corpus.train_word2vec()
