Main1.py

import pandas as pd
from numpy import *
import numpy as np
import numpy
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import io
import base64

def pluskey(r):
    v=0
    if(r==1):
        v=v+0.2
    elif(r==2):
        v=v+0.4
    elif(r==3):
        v=v+0.6
    elif(r==4):
        v=v+0.8
    elif(r==5):
        v=v+1
    return v

def negkey(r):
    v=0
    if(r==5):
        v=v+0.2
    elif(r==4):
        v=v+0.4
    elif(r==3):
        v=v+0.6
    elif(r==2):
        v=v+0.8
    elif(r==2):
        v=v+1
    return v

    
#questions and response recording
g=int(input("Are you a male or a female(1 for male 0 for female)"))

print("Enter choices for below questions as 1 for strongly disagree, 2 for disagree, 3 for moderate, 4 for agree, 5 for Strongly disagree" )
#Extraversion
e=0
r=int(input("Q.1 Do you feel comforatble around people?"))
e=pluskey(r)
    
r=int(input("Q.2 At parties, do you often talk to different people?"))
e=e+pluskey(r)

r=int(input("Q.3 Do you mind being center of attention"))
e=e+pluskey(r)
      
r=int(input("Q.4 Are you the life of the party?(Party animal)"))
e=e+pluskey(r)   

r=int(input("Q.5 Do you talk a lot"))
e=e+negkey(r)

r=int(input("Q.6 Do you like to draw attention to yourself"))
e=e+negkey(r)

r=int(input("Q.7 Are you quite around strangers"))
e=e+negkey(r)

r=int(input("Q.8 Do you wait for others to lead the way"))
e=e+negkey(r)

E=(e/8)*100   

#Agreeableness
a=0
r=int(input("Q.9 Do you think you sympathise with others easily?"))
a=pluskey(r)
    
r=int(input("Q.10 Do you take out some of your time for others"))
a=a+pluskey(r)

r=int(input("Q.11 You feel others emotions?"))
a=a+pluskey(r)
      
r=int(input("Q.12 Can you make others feel at ease"))
a=a+pluskey(r)   

r=int(input("Q.13 Are you interested in other people's life?"))
a=a+negkey(r)

r=int(input("Q.14 Do you insult others?"))
a=a+negkey(r)

r=int(input("Q.15 Are you interested in other peoples' problem"))
a=a+negkey(r)

r=int(input("Q.16 Are you hard to get to know"))
a=a+negkey(r)

A=(a/8)*100  

#Conscientiousness
c=0
r=int(input("Q.17 Do you follow a regular schedule?"))
c=pluskey(r)
    
r=int(input("Q.18 Are you always prepared ?"))
c=c+pluskey(r)

r=int(input("Q.19 Are you exacting in your work?"))
c=c+pluskey(r)
      
r=int(input("Q.20 Do you play attention to details?"))
c=c+pluskey(r)   

r=int(input("Q.21 Do you waste time?"))
c=c+negkey(r)

r=int(input("Q.22 Often forget to put back things in their proper place"))
c=c+negkey(r)

r=int(input("Q.23 Do you neglect your duties?"))
c=c+negkey(r)

r=int(input("Q.24 Do things in half-way manner"))
c=c+negkey(r)

C=(c/8)*100 

#Openness
o=0
r=int(input("Q.25 Do you enjoy wild flights of fantasy?"))
o=pluskey(r)
    
r=int(input("Q.26 Enjoy thinking about things?"))
o=o+pluskey(r)

r=int(input("Q.27 Believe in importance of art?"))
o=o+pluskey(r)
      
r=int(input("Q.28 Tend to vote for liberal political candidates"))
o=o+pluskey(r)   

r=int(input("Q.29 Avoid philosophical discussions"))
o=o+negkey(r)

r=int(input("Q.30 Do not like poetry?"))
o=o+negkey(r)

r=int(input("Q.31 Rarely look for deeper meaning in things."))
o=o+negkey(r)

r=int(input("Q.32 Have difficulty understanding abstract ideas."))
o=o+negkey(r)

O=(o/8)*100   

#Neuroticism
n=0
r=int(input("Q.33 Dislike yourself?"))
n=pluskey(r)
    
r=int(input("Q.34 Are you filled with doubt about things?"))
n=n+pluskey(r)

r=int(input("Q.35 Are you relaxed most of the time?"))
n=n+pluskey(r)
      
r=int(input("Q.36 Seldom get mad?"))
n=n+pluskey(r)   

r=int(input("Q.37 Get upset eaily"))
n=n+negkey(r)

r=int(input("Q.38 Have frequent mood swings"))
n=n+negkey(r)

r=int(input("Q.39 Remain calm under pressure"))
n=n+negkey(r)

r=int(input("Q.40 Get stressed out easily"))
n=n+negkey(r)

N=(n/8)*100 
    




#training data and prediction algorithm
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,o,n,c,a,e]])
print(y_pred)


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

# heights of bars 

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


#print ('Height is :')
#print (height) 
# labels for bars 
tick_label = ['openness','neuroticism','conscientiousness','agreeableness','extraversion'] 
  
# plotting a bar chart 
plt.bar(left, height, tick_label = tick_label, 
        width = 0.5, color = ['red', 'green']) 
  
# 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()

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

encoded = fig_to_base64(fig)