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personality_detector.py
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personality_detector.py
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## Personality Detector Page ##
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 5 03:30:41 2021
@author: Сухас Дхолз
"""
import pandas as pd, numpy as np, re
from sklearn.metrics import classification_report, accuracy_score , confusion_matrix
from sklearn.model_selection import train_test_split
import tkinter as tk
from sklearn import svm
from PIL import Image, ImageTk
from tkinter import ttk
from joblib import dump , load
from sklearn.feature_extraction.text import TfidfVectorizer
from textblob import TextBlob
from nltk.corpus import stopwords
from sklearn import metrics
from sklearn.model_selection import GridSearchCV
import pickle
import nltk
nltk.download('stopwords')
stop = stopwords.words('english')
nltk.download('punkt')
nltk.download('averaged_perceptron_tagger')
root = tk.Tk()
root.title("Personality Prediction using Twitter")
w, h = root.winfo_screenwidth(), root.winfo_screenheight()
root.geometry("%dx%d+0+0" % (w, h))
# image2 =Image.open(r'C:/Users/GTekSD/Desktop/Personality_Prediction/Background_and_Images/BG.jpg')
# image2 =image2.resize((w,h), Image.ANTIALIAS)
# background_image=ImageTk.PhotoImage(image2)
# background_label = tk.Label(root, image=background_image)
# background_label.image = background_image
# background_label.place(x=0, y=0)
img=ImageTk.PhotoImage(Image.open("s1.jpg"))
img2=ImageTk.PhotoImage(Image.open("s2.jpg"))
img3=ImageTk.PhotoImage(Image.open("s3.jpg"))
logo_label=tk.Label()
logo_label.place(x=0,y=100)
x = 1
# function to change to next image
def move():
global x
if x == 4:
x = 1
if x == 1:
logo_label.config(image=img)
elif x == 2:
logo_label.config(image=img2)
elif x == 3:
logo_label.config(image=img3)
x = x+1
root.after(4000, move)
# calling the function
move()
w = tk.Label(root, text="Personality Prediction",width=40,background="#7D0552",height=2,font=("Times new roman",28,"bold"))
w.place(x=500,y=10)
w,h = root.winfo_screenwidth(),root.winfo_screenheight()
root.geometry("%dx%d+0+0"%(w,h))
root.configure(background="#7D0552")
def Train():
result = pd.read_csv(r"C:/Users/GTekSD/Desktop/personality_Prediction/new_dataset.csv",encoding = 'unicode_escape')
result.head()
result['posts'] = result['posts'].apply(lambda x: ' '.join([word for word in x.split() if word not in (stop)]))
def pos(review_without_stopwords):
return TextBlob(review_without_stopwords).tags
os = result.posts.apply(pos)
os1 = pd.DataFrame(os)
os1.head()
os1['pos'] = os1['posts'].map(lambda x: " ".join(["/".join(x) for x in x]))
result = result = pd.merge(result, os1, right_index=True, left_index=True)
result.head()
result['pos']
review_train, review_test, label_train, label_test = train_test_split(result['pos'], result['type'],
test_size=0.2, random_state=13)
tf_vect = TfidfVectorizer(lowercase=True, use_idf=True, smooth_idf=True, sublinear_tf=False)
X_train_tf = tf_vect.fit_transform(review_train)
X_test_tf = tf_vect.transform(review_test)
def svc_param_selection(X, y, nfolds):
Cs = [0.001, 0.01, 0.1, 1, 10]
gammas = [0.001, 0.01, 0.1, 1]
param_grid = {'C': Cs, 'gamma': gammas}
grid_search = GridSearchCV(svm.SVC(kernel='linear'), param_grid, cv=nfolds)
grid_search.fit(X, y)
return grid_search.best_params_
svc_param_selection(X_train_tf, label_train, 2)
clf = svm.SVC(C=10, gamma=0.001, kernel='linear')
clf.fit(X_train_tf, label_train)
pred = clf.predict(X_test_tf)
with open('vectorizer.pickle', 'wb') as fin:
pickle.dump(tf_vect, fin)
with open('mlmodel.pickle', 'wb') as f:
pickle.dump(clf, f)
pkl = open('mlmodel.pickle', 'rb')
clf = pickle.load(pkl)
vec = open('vectorizer.pickle', 'rb')
tf_vect = pickle.load(vec)
X_test_tf = tf_vect.transform(review_test)
pred = clf.predict(X_test_tf)
print(metrics.accuracy_score(label_test, pred))
print(confusion_matrix(label_test, pred))
print(classification_report(label_test, pred))
print("=" * 40)
print("==========")
print("Classification Report : ",(classification_report(label_test, pred)))
print("Accuracy : ",accuracy_score(label_test, pred)*100)
accuracy = accuracy_score(label_test, pred)
print("Accuracy: %.2f%%" % (accuracy * 100.0))
ACC = (accuracy_score(label_test, pred) * 100)
repo = (classification_report(label_test, pred))
label4 = tk.Label(root,text =str(repo),width=35,height=10,bg='khaki',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=205,y=100)
label5 = tk.Label(root,text ="Accracy : "+str(ACC)+"%\nModel saved as SVM_MODEL.joblib",width=35,height=3,bg='khaki',fg='black',font=("Tempus Sanc ITC",14))
label5.place(x=205,y=320)
dump (clf,"SVM_MODEL.joblib")
print("Model saved as SVM_MODEL.joblib")
frame = tk.LabelFrame(root,text="Control Panel",width=600,height=400,bd=3,background="#F67280",font=("Tempus Sanc ITC",15,"bold"))
frame.place(x=600,y=200)
entry = tk.Entry(frame,width=50)
entry.insert(0,"Enter text here...")
entry.place(x=25,y=150)
def Test():
predictor = load("SVM_MODEL.joblib")
Given_text = entry.get()
#Given_text = "the 'roseanne' revival catches up to our thorny po..."
vec = open('vectorizer.pickle', 'rb')
tf_vect = pickle.load(vec)
X_test_tf = tf_vect.transform([Given_text])
y_predict = predictor.predict(X_test_tf)
print(y_predict[0])
if y_predict[0]==0:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Feeling,Judging",width=70,height=2,bg='Green',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==1:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Intuition,Thinking,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==2:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Thinking,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==3:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==4:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Intuition,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==5:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Intuition,Feeling,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==6:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Feeling,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==7:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Intuition,Feeling,Perceiving",width=70,eight=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==8:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Intuition,Feeling,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==9:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Sensing,Thinking,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==10:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Sensing,Feeling,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==11:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Sensing,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==12:
label4 = tk.Label(root,text ="Personality Prediction is Extroversion,Sensing,Thinking,Perceiving",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==13:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==14:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
elif y_predict[0]==15:
label4 = tk.Label(root,text ="Personality Prediction is Introvrsion,Intuition,Thinking,Judging",width=70,height=2,bg='Red',fg='black',font=("Tempus Sanc ITC",14))
label4.place(x=600,y=800)
def window():
root.destroy()
# = tk.Button(frame,command=Train,text="Train",bg="red",fg="black",width=15,font=("Times New Roman",15,"italic"))
#button2.place(x=5,y=100)
button3 = tk.Button(frame,command=Test,text="Test",bg="green",fg="black",width=15,font=("Times New Roman",20,"italic"))
button3.place(x=50,y=250)
exit = tk.Button(root, text="Exit", command=window, width=15, height=2, font=('times', 15, ' bold '),bg="red",fg="white")
exit.place(x=800, y=650)
root.mainloop()