Main.py

import pandas as pd
from numpy import *
import numpy as np
import numpy
import pygal
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import io
import base64
import xlrd 				#To read Excel File
import csv					# importing csv module 
from pygal.style import Style
custom_style = Style(
  colors=('#E80080', '#404040', '#9BC850', '#E80017','#17E800'))
from xlwt import Workbook	#To perforn Excel file I/O operation

wb=Workbook()
# Give the location of the file 
loc = ("questions.xlsx") 
  
wb = xlrd.open_workbook(loc) 		#Opening Excel Workbook of Our Given Location
sheet = wb.sheet_by_index(0) 		#Select the Sheet Number(0,1,2....) 

#No. of Questions are = qnlenth
qnlenth=sheet.nrows	#Length of Sheet Questions

qnlist=[]	#Array of Questions
qnnumber=[]	#Array of Questions Number
optn1=[]	#Array of Option 1
optn2=[]	#Array of Option 2
optn3=[]	#Array of Option 3
optn4=[]	#Array of Option 4
optn5=[]	#Array of Option 5
trait=[]	#Array of Trait(Extraversion, Agreeableness, Conscientiousness, Emotional Stability, Intellect )

#Reading ALL Values of from file in array format:
for i in range (qnlenth):
	qnnumber=np.append(qnnumber,(sheet.cell_value(i, 0) ))
	qnlist=np.append(qnlist,(sheet.cell_value(i, 1) ))
	optn1=np.append(optn1,(sheet.cell_value(i, 2) ))
	optn2=np.append(optn2,(sheet.cell_value(i, 3) ))
	optn3=np.append(optn3,(sheet.cell_value(i, 4) ))
	optn4=np.append(optn4,(sheet.cell_value(i, 5) ))
	optn5=np.append(optn5,(sheet.cell_value(i, 6) ))
	trait=np.append(trait,(sheet.cell_value(i, 7) ))

from flask import Flask, redirect, url_for, request ,render_template
app = Flask(__name__) 

@app.route('/') 
def main(): 
	#return 'welcome to main page'
	
	return '<br><a href="http://127.0.0.1:5000/front"><button onclick= "" align="center"  >Welcome to Personality detection</button></a>'
@app.route('/front')
def front():
    return render_template('front.html')	
@app.route('/displaygen')
def displaygen():
    return render_template('diplaygen.html')
G=0
@app.route('/nxt',methods=['POST'])
def nxt():
    if request.method=='POST':
        G=int(request.form['g'])
        G=int(G)
    print(G)
    return redirect(url_for('displayqn'))
@app.route('/displayqn')        
def displayqn(): 	
	f = open("responce.csv", "w")
	f.truncate()
	
	
	"""
	if b is None:
		b = -1
	else:
		b = temp + 1
	print (b)
	"""
	b=0
	return render_template('main.html', qno = int(qnnumber[b]), qn = qnlist[b] ,op1 = optn1[b] , op2 = optn2[b], op3 = optn3[b],	op4 = optn4[b], op5 = optn5[b], trait = trait[b])	
	
@app.route('/next',methods = ['POST']) 
def next():
	
	if request.method == 'POST': 
		responceqno = request.form['qno']				#Question Number
		responceqno = int(responceqno)
		ans = float(request.form['q'])			#option selected/Value for question
		ans = float(ans)
		qtrait = request.form['trait']			#Trait for Question
	else:
		responceqno=0
		ans=0
	print (responceqno)
	print (ans)
	
	with open("responce.csv", "a") as recordbook:
		writer = csv.writer(recordbook)
		writer.writerow([responceqno,ans])
	
	if(responceqno == 40 ):		#40 is the number of questions
		return redirect(url_for('result'))
	else:
		b=responceqno
		return render_template('main.html', qno = int(qnnumber[b]), qn = qnlist[b] ,op1 = optn1[b] , op2 = optn2[b], op3 = optn3[b],	op4 = optn4[b], op5 = optn5[b], trait = trait[b])	
	#, trait = trait[b])	
ar=oro=er=nr=cr=e=a=n=c=o=E=N=A=O=C=0
@app.route('/result')
def result():
        

    data =pd.read_csv('train dataset.csv')
    array = data.values
    for i in range(len(array)):
        if(array[i][0]=="Male"):
            array[i][0]=1
        else:
            array[i][0]=0
        df=pd.DataFrame(array)
    maindf =df[[0,1,2,3,4,5]]
    mainarray=maindf.values

    temp=df[6]

    train_y =temp.values
    #print(train_y)
        
    train_y=temp.values

    for i in range(len(train_y)):
        train_y[i] =str(train_y[i])
    
    mul_lr =DecisionTreeClassifier()
    mul_lr.fit(mainarray, train_y)
    
    e=n=c=o=a=0
    File = open('responce.csv')
    Reader=csv.reader(File)
    Data=list(Reader)
    #print(Data)
    print(len(Data))
    Data1= [x for x in Data if x != []]
    for i in range (8):
        e=e+float(Data1[i][1]) #first list ka first element and so on
    er=round(e)
    E=(e/8)*100
    print(er)
    for i in range (8,16):
        a=a+float(Data1[i][1]) #first list ka first element and so on
    ar=round(a)
    A=(a/8)*100
    print(ar)
    for i in range (16,24):
        c=c+float(Data1[i][1]) #first list ka first element and so on
    C=(c/8)*100
    cr=round(c)
    print(cr)
    for i in range (24,32):
        o=o+float(Data1[i][1]) #first list ka first element and so on
    O=(o/8)*100
    oro=round(o)
    print(oro)
    for i in range (32,40):
        n=n+float(Data1[i][1]) #first list ka first element and so on
    N=(n/8)*100
    nr=round(n)
    print(nr)
    File.close()
    y_pred = mul_lr.predict([[0,oro,nr,cr,ar,er]])
    print(y_pred)
    f=open('responce.csv',"w")
    f.truncate()
    
    data =pd.read_csv('train dataset.csv')
    array = data.values
    for i in range(len(array)):
        if (array[i][0]=="Male"):
            array[i][0]=1
        else:
            array[i][0]=0


    df=pd.DataFrame(array)

    maindf =df[[0,1,2,3,4,5]]
    mainarray=maindf.values



    temp=df[6]

    train_y =temp.values
    #print(train_y)

    train_y=temp.values

    for i in range(len(train_y)):
        train_y[i] =str(train_y[i])



    mul_lr =DecisionTreeClassifier()
    mul_lr.fit(mainarray, train_y)


    y_pred = mul_lr.predict([[G,oro,nr,cr,ar,er]])
    


    line_chart = pygal.Bar(width=500, height=400, explicit_size=True, style=custom_style)
    line_chart.x_labels = map(str,['extraversion','agreeableness','conscientiousness','openness','neuroticism']  )
    line_chart.add('Extraversion', [{'value': E,'style': ' stroke_width: 20', 'label': 'Your Extraversion level is'}])
    line_chart.add('Agreeableness', [{'value': A,'style': ' stroke_width: 20', 'label': 'Your Agreeableness level is'}])
    line_chart.add('Conscientiousness', [{'value': C,'style': ' stroke_width: 20', 'label': 'Your Conscientiouness level is'}])
    line_chart.add('Openness', [{'value':O,'style': ' stroke_width: 20', 'label': 'Your Openness level is'}])	
    line_chart.add('Neuroticism', [{'value':N,'style': ' stroke_width: 20', 'label': 'Your Neuroticism level is'}])	
    
    line_chart = line_chart.render_data_uri()
    

    # x-coordinates of left sides of bars  
    left = [1,2,3,4,5] 

    # heights of bars 

    height= [E,A,C,O,N]


    #print ('Height is :')
    #print (height) 

    # labels for bars 
    tick_label = ['extraversion','agreeableness','conscientiousness','openness','neuroticism'] 
	  
    # plotting a bar chart 
    plt.bar(left, height, tick_label = tick_label,width = 20.0, color = ['red', 'green','yellow', 'blue','brown']) 
	  
    # naming the x-axis 
    plt.xlabel('x - axis') 
	
    # naming the y-axis 
    plt.ylabel('y - axis') 
	
    # plot title 
    plt.title('Personality Chart!') 
	  
    # function to show the plot 
    #plt.show() 
	
    s = pd.Series([er,ar,cr,oro,nr])
    fig, ax = plt.subplots()
    s.plot.bar()
    fig.savefig('my_plot.png')
    	

    def fig_to_base64(fig):
        img = io.BytesIO()
        fig.savefig(img, format='png',
                     x_inches='tight')
        img.seek(0)
		
        return base64.b64encode(img.getvalue())
	
    encoded = fig_to_base64(fig)

        
    return render_template('predict.html', var=y_pred[0],A=ar,O=oro,N=nr,C=cr,E=er,Ge=G, graph_data = line_chart )
    


    




#training data and prediction algorithm

if __name__ == '__main__': 
	app.run(debug = True)