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#Import all neccessary libraries
import streamlit as st
import re
import wikipediaapi
import malaya
import torch
import tensorflow
import pandas as pd
from sklearn.preprocessing import OneHotEncoder, LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.multioutput import MultiOutputClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score
import numpy as np
import matplotlib.pyplot as plt
import os
import psutil
#LOAD PAGE AND GET TEXT
st.cache(suppress_st_warning=True)
def find_text():
global article, link, page
mwiki = wikipediaapi.Wikipedia(language = 'ms', extract_format = wikipediaapi.ExtractFormat.WIKI)
page = mwiki.page("Pemahsyuran Kemerdekaan Tanah Melayu")
link = page.fullurl
article = page.text
namefile = "malaytext.txt"
return article, page, link
#CLEAN DATA
st.cache(suppress_st_warning=True)
def clean_data():
global clean_file
file = article
file1 = file.strip("\n")
file1 = re.sub("[=(),:;.]", "", file1)
file1 = file1.strip()
file1 = re.sub("[-']", " ", file1)
file1 = file1.strip()
file1 = file1.replace("\n", " ")
clean_file = file1
return clean_file
#USE MALAYA MODULE
st.cache(allow_output_mutation=True)
def use_malaya():
global malay_pred
q_model = malaya.entity.transformer(model1 = 'bert', quantized = True)
malay_pred = q_model.predict(clean_file)
return malay_pred
#ORGANISE DATAFRAME MODEL (NO ST.COLUMNS)
st.cache(allow_output_mutation=True)
def data_model():
global df4 #Start as LABELENCODER
df = pd.DataFrame(malay_pred)
df.columns = ['kata', 'entiti'] #1, #2
df['kata'].astype('str') #KIV
df['entiti'].astype('str')
df['nombor'] = df.reset_index().index #3
df = df.reindex(['nombor', 'kata', 'entiti'], axis = 1)
#shift(1) moves backward by 1
df['SEBELUM'] = df['kata'].shift(1) #4
#shift(-1) moves forward by 1
df['SELEPAS'] = df['kata'].shift(-1) #5
df['TAGSEBELUM'] = df['entiti'].shift(1) #6
df['TAGSELEPAS'] = df['entiti'].shift(-1) #7
df.fillna("null", inplace=True)
#Observe entity LAIN-LAIN if it is a nuisance or otherwise
df1 = df.copy()
df1.replace("time", "OTHER", inplace=True)
df1.replace("event", "OTHER", inplace=True)
df1.replace("law", "OTHER", inplace=True)
df1.replace("quantity", "OTHER", inplace=True)
df1.replace("location", "lokasi", inplace=True)
df1.replace("organization", "organisasi", inplace=True)
df1.replace("person", "manusia", inplace=True)
df1.replace("OTHER", "LAIN-LAIN", inplace=True)
#ONE HOT ENCODER for LOKASI, MANUSIA dan ORGANISASI
ohe = OneHotEncoder()
ohe_entity = ohe.fit_transform(df1[['entiti']]).toarray() #8, 9, 10, 11 Expected 4 entity type
ohe_entity1 = pd.DataFrame(ohe_entity)
df2 = df1.join(ohe_entity1)
df2.columns = ['nombor', 'kata', 'entiti', 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS', 'LAIN-LAIN', 'LOKASI', 'MANUSIA', 'ORGANISASI']
#LABEL ENCODER for 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS',
le = LabelEncoder()
le_word = le.fit_transform(df1['kata'])
le_word1 = pd.DataFrame(le_word)
df3 = df2.join(le_word1) #COLUMNS OVERLAPPED
df3.columns = ['nombor', 'kata', 'entiti', 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS','LAIN-LAIN', 'LOKASI', 'MANUSIA', 'ORGANISASI', 'LKATA']
le_before = le.fit_transform(df1['SEBELUM'])
le_before1 = pd.DataFrame(le_before)
df3 = df3.join(le_before1)
df3.columns = ['nombor', 'kata', 'entiti', 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS', 'LAIN-LAIN', 'LOKASI', 'MANUSIA', 'ORGANISASI', 'LKATA', 'LSEBELUM']
le_after = le.fit_transform(df1['SELEPAS'])
le_after1 = pd.DataFrame(le_after)
df4 = df3.join(le_after1)
df4.columns = ['nombor', 'kata', 'entiti', 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS', 'LAIN-LAIN', 'LOKASI', 'MANUSIA', 'ORGANISASI', 'LKATA', 'LSEBELUM', 'LSELEPAS']
le_entity = le.fit_transform(df1['entiti'])
le_entity1 = pd.DataFrame(le_entity)
df4 = df4.join(le_entity1)
df4.columns = ['nombor', 'kata', 'entiti', 'SEBELUM', 'SELEPAS', 'TAGSEBELUM', 'TAGSELEPAS', 'LAIN-LAIN', 'LOKASI', 'MANUSIA', 'ORGANISASI', 'LKATA', 'LSEBELUM', 'LSELEPAS', 'LENTITI']
df4['LKATA'] = df4['LKATA'].astype(str)
df4['LSEBELUM'] = df4['LSEBELUM'].astype(str)
df4['LSELEPAS'] = df4['LSELEPAS'].astype(str)
df4['LAIN-LAIN'] = df4['LAIN-LAIN'].astype(int)
df4['LOKASI'] = df4['LOKASI'].astype(int)
df4['ORGANISASI'] = df4['ORGANISASI'].astype(int)
df4['MANUSIA'] = df4['MANUSIA'].astype(int)
return df4
#TRAIN MODEL USING KNN, MULTIOUTPUTCLASSIFIER
st.cache(allow_output_mutation=True)
def train_model():
global x, y, y_test, y_pred, knn, classifier, model_score
x = df4.iloc[:, [11,12,13]]
y = df4.iloc[:,[8,9,10]]
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state = 42, stratify = y)
knn = KNeighborsClassifier(n_neighbors= 3) #default 1st time k = 3, but entity type = 4
knn.fit(x_train, y_train)
classifier = MultiOutputClassifier(knn, n_jobs = -1)
classifier.fit(x_train, y_train)
#datax_test = x_test.values
datay_test = y_test.values
y_pred = classifier.predict(x_test)
model_score = classifier.score(datay_test, y_pred)
return x, y, y_test, y_pred, classifier, model_score
#EVALUATE MODEL
st.cache(allow_output_mutation=True)
def evaluate_model():
global cm, cr, accuracy
y_test1 = y_test.to_numpy().flatten()
y_pred1 = y_pred.flatten()
cm = confusion_matrix(y_test1, y_pred1)
cr = classification_report(y_test1, y_pred1)
accuracy = accuracy_score(y_test1, y_pred1)
return cm, cr, accuracy
#LOAD MODEL
st.cache(allow_output_mutation=True)
def knn_model():
result1 = find_text()
result2 = clean_data()
result3 = use_malaya()
result4 = data_model()
result5 = train_model()
result6 = evaluate_model()
return result1, result2, result3, result4, result5, result6
#PREDICT WORD OUTSIDE DATA
st.cache(allow_output_mutation=True)
def ramal_kata(kata):
string = re.sub("[=(),:;.]", "", kata)
string1 = string.split(" ")
string2 = pd.DataFrame(string1, columns = ["LKATA"])
string2['LSEBELUM'] = string2['LKATA'].shift(1)
string2['LSELEPAS'] = string2['LKATA'].shift(-1)
string2.fillna("null", inplace=True)
#string1
#st.table(string1[:10])
lbl = LabelEncoder()
lbl_sen = lbl.fit_transform(string2['LKATA'])
lbl_bef = lbl.fit_transform(string2['LSEBELUM'])
lbl_aft = lbl.fit_transform(string2['LSELEPAS'])
string2 = pd.DataFrame({'LKATA':lbl_sen, 'LSEBELUM': lbl_bef, 'LSELEPAS' : lbl_aft})
#st.dataframe(string2.head())
#Train, test model
pred_outdata = knn_model()
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size= 0.2, random_state = 42, stratify = y)
pred_knn = KNeighborsClassifier(n_neighbors= 3)
#"classifier" VARIABLE from "TEST MODEL USING TESTING DATA"
kelas = MultiOutputClassifier(pred_knn, n_jobs = -1)
kelas.fit(x_train, y_train)
hasil = kelas.predict(string2)
#st.write(hasil)
fin = []
for z in hasil:
if (z == [1, 0, 0]).all():
fin.append("LOKASI")
elif (z == [0, 1, 0]).all():
fin.append("MANUSIA")
elif (z == [0, 0, 1]).all():
fin.append("ORGANISASI")
else:
fin.append("LAIN-LAIN")
#st.write(fin)
global perkata, output
perkata = [(key, value) for i, (key, value) in enumerate(zip(string1, fin))]
output = pd.DataFrame({"kata" : string1, "entiti" : fin})
#st.dataframe(output.transpose())
return output
def get_data():
ts = output
return ts
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