from flask import Flask, request, render_template,jsonify,Response,redirect,url_for,session
import os
from Sastrawi.Stemmer.StemmerFactory import StemmerFactory
from Sastrawi.StopWordRemover.StopWordRemoverFactory import StopWordRemoverFactory
import pandas as pd
pd.set_option('display.max_colwidth', 3000)
pd.set_option('display.max_rows', 10)

import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.stem.snowball import SnowballStemmer
from nltk.stem import PorterStemmer
from nltk.corpus import stopwords
import json

import re
from sklearn.neighbors import KNeighborsClassifier

from sklearn import svm
from sklearn.svm import SVC
from sklearn import model_selection
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import Normalizer
import numpy as np
from ast import literal_eval
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix

app = Flask(__name__)
app.secret_key = "super secret key"

#Set Random seed
np.random.seed(400)
#import data csv
trainData = pd.read_csv('dataset.csv',encoding="ISO-8859-1")
test_pd = pd.DataFrame(trainData) #makes this into a panda data frame

content = test_pd['Content']

#CASE FOLDING & REMOVE REGEX
def preprocess(text):
    clean_data = []
    for x in (text[:]): #this is Df_pd for Df_np (text[:])
        new_text = re.sub('<.*?>', '', x)   # remove HTML tags
        new_text = re.sub(r'[^\w\s]', '', new_text) # remove punc.
        new_text = re.sub(r'\d+','',new_text)# remove numbers
        new_text = new_text.lower() # lower case, .upper() for upper
        if new_text != '':
            clean_data.append(new_text)
    return clean_data


caseFolding = preprocess(content)
test_pd['case_folding']=caseFolding
case_folding = test_pd['case_folding']
#print(case_folding)
#json = case_folding.to_json(orient='index')
#print(json)
#TOKENIZATION
def identify_tokens(row):
    review = row['case_folding']
    tokens = word_tokenize(review)
    # taken only words (not punctuation)
    token_words = [w for w in tokens if w.isalpha()]
    return token_words
test_pd['token'] = test_pd.apply(identify_tokens,axis=1)
tokens=test_pd['token']

#REMOVE STOPWORDS
stops = set(stopwords.words("indonesian"))
#stops = pd.read_csv('D:\Tugas\Skripsi\Data\stopwordbahasa.csv',encoding="ISO-8859-1")
def remove_stops(row):
    my_list = row['token']
    meaningful_words = [w for w in my_list if not w in stops]
    return (meaningful_words)
test_pd['stopwords'] = test_pd.apply(remove_stops, axis=1)
stopword=test_pd['stopwords']

#STEMMING
def stem_list(row):
    factory = StemmerFactory()
    stemmer = factory.create_stemmer()
    my_list = row['stopwords']
    stemmed_list = [stemmer.stem(word) for word in my_list]
    return (stemmed_list)
test_pd['stemming'] = test_pd.apply(stem_list, axis=1)
stem=test_pd['stemming']
test_pd['final']=stem.astype(str) #array list ke string
text_final = test_pd['final'] #final column

Train_X, Test_X, Train_Y, Test_Y = model_selection.train_test_split(text_final,test_pd['Label'],test_size=0.3)

Tfidf_vect = TfidfVectorizer(max_features=5000)
Tfidf_vect.fit(text_final)

Train_X_Tfidf = Tfidf_vect.transform(Train_X)
Test_X_Tfidf = Tfidf_vect.transform(Test_X)

modelknn = KNeighborsClassifier(n_neighbors=2)
modelknn.fit(Train_X_Tfidf,Train_Y)
predictions_KNN = modelknn.predict(Test_X_Tfidf)
print("KNN Accuracy Score -> ",round(accuracy_score(predictions_KNN, Test_Y)*100))

seriesA = pd.Series(Test_X,name='testing').reset_index()
seriesB = pd.Series(predictions_KNN,name='predict').reset_index()
gab = pd.concat([seriesA,seriesB],axis=1)


@app.route("/")
@app.route("/index")
def index():

	return render_template('index.html')

@app.route('/loginCheck',methods=['POST', 'GET'])
def loginCheck():
	username= request.form['username']
	password= request.form['password']
	if (username == "Admin" and password == "P@ssw0rd"):
		session['admin'] = True
	else:
		return redirect(url_for('login'))

	return redirect(url_for('dashboard'))

@app.route('/login')
def login():
	if not session.get('admin'):
		return render_template('login.html')
	else:
		return redirect(url_for('dashboard'))


@app.route('/logout')
def logout():
	session.pop('admin', None)
	return redirect(url_for('login'))

@app.route("/admin/dashboard")
def dashboard():
	if not session.get('admin'):
		return redirect(url_for('login'))
	else:
		trainNew = test_pd
		trainNew = trainNew.drop("Label", axis=1)
		trainNew = trainNew.drop("case_folding", axis=1)
		trainNew = trainNew.drop("token", axis=1)
		trainNew = trainNew.drop("stemming", axis=1)
		trainNew = trainNew.drop("final", axis=1)
		trainNew = trainNew.drop("stopwords", axis=1)

		return render_template('admin/dashboard.html')


@app.route("/admin/case")
def case():
	if not session.get('admin'):
		return redirect(url_for('login'))
	else:
		trainNew = test_pd
		trainNew = trainNew.drop("Label", axis=1)
		#trainNew = trainNew.drop("case_folding", axis=1)
		trainNew = trainNew.drop("token", axis=1)
		trainNew = trainNew.drop("stemming", axis=1)
		trainNew = trainNew.drop("final", axis=1)
		trainNew = trainNew.drop("stopwords", axis=1)

		return render_template('admin/casefolding.html',  tables=[trainNew.to_html(classes='table table-striped table-bordered table-hover',header='true',justify='justify',table_id='tabel')])

@app.route("/admin/token")
def token():
	if not session.get('admin'):
		return redirect(url_for('login'))
	else:
		trainNew = test_pd
		trainNew = trainNew.drop("Label", axis=1)
		trainNew = trainNew.drop("case_folding", axis=1)
		#trainNew = trainNew.drop("token", axis=1)
		trainNew = trainNew.drop("stemming", axis=1)
		trainNew = trainNew.drop("final", axis=1)
		trainNew = trainNew.drop("stopwords", axis=1)
		return render_template('admin/token.html',  tables=[trainNew.to_html(classes='table table-striped table-bordered table-hover',header='true',justify='justify',table_id='tabel')])

@app.route("/admin/stopword")
def stopword():
	if not session.get('admin'):
		return redirect(url_for('login'))
	else:
		trainNew = test_pd
		trainNew = trainNew.drop("Label", axis=1)
		trainNew = trainNew.drop("case_folding", axis=1)
		trainNew = trainNew.drop("token", axis=1)
		trainNew = trainNew.drop("stemming", axis=1)
		trainNew = trainNew.drop("final", axis=1)
		#trainNew = trainNew.drop("stopwords", axis=1)

		return render_template('admin/stopword.html',  tables=[trainNew.to_html(classes='table table-striped table-bordered table-hover',header='true',justify='justify',table_id='tabel')])

@app.route("/admin/stemming")
def stemming():
	if not session.get('admin'):
		return redirect(url_for('login'))
	else:
		trainNew = test_pd
		trainNew = trainNew.drop("Label", axis=1)
		trainNew = trainNew.drop("case_folding", axis=1)
		trainNew = trainNew.drop("token", axis=1)
		#trainNew = trainNew.drop("stemming", axis=1)
		trainNew = trainNew.drop("final", axis=1)
		trainNew = trainNew.drop("stopwords", axis=1)

		return render_template('admin/stemming.html',  tables=[trainNew.to_html(classes='table table-striped table-bordered table-hover',header='true',justify='justify',table_id='tabel')])

@app.route("/hasil")
def hasil():
	trainNew = test_pd
	trainNew = gab
	trainNew = trainNew.drop("index", axis=1)
	label  = test_pd['Label']
	pos = label[label=='pos']
	neg = label[label=='neg']
	jml_pos = pos.count()
	jml_neg = pos.count()
	jml_label = label.count()
	persen_pos = str(round((jml_pos/jml_label)*100,2))
	persen_neg = str(round((jml_neg/jml_label)*100,2))

	return render_template('hasil.html',  tables=[trainNew.to_html(classes='table table-striped table-bordered table-hover',header='true',justify='justify',table_id='tabel')])


#@app.route('/sentimenttest')
#def sentimentTest():
#	return render_template('sentimenttest.html')

#@app.route('/checksentiment', methods=['POST'])
#def checkSentiment():
#	text = request.form['text']
#	review_vector = Tfidf_vect.transform([text])
#	hasil = modelknn.predict(review_vector)
#	if hasil==['neg'] :
#		hasil = 'Negative'
#	else :
#		hasil = 'Positif'
#	return render_template('sentimenthasil.html',hasil=hasil,text=text)
#
if __name__ == '__main__':
    app.run(debug=True)