draft.py

import streamlit as st
import pandas as pd
import numpy as np
import nltk
import string
from nltk.classify import NaiveBayesClassifier

from personalityclassifier import IntuitionSensing, JudgingPercieiving, ThinkingFeeling

# Function to tokenize the words
useless_words = nltk.corpus.stopwords.words("english") + list(string.punctuation)
def build_bag_of_words_features_filtered(words):
    words = nltk.word_tokenize(words)
    return {word: 1 for word in words if not word in useless_words}

# Function to classify MBTI type
def MBTI(input):
    tokenize = build_bag_of_words_features_filtered(input)
    ie = IntroExtro.classify(tokenize)
    Is = IntuitionSensing.classify(tokenize)
    tf = ThinkingFeeling.classify(tokenize)
    jp = JudgingPercieiving.classify(tokenize)
    
    mbt = ''
    
    if(ie == 'introvert'):
        mbt+='I'
    if(ie == 'extrovert'):
        mbt+='E'
    if(Is == 'Intuition'):
        mbt+='N'
    if(Is == 'Sensing'):
        mbt+='S'
    if(tf == 'Thinking'):
        mbt+='T'
    if(tf == 'Feeling'):
        mbt+='F'
    if(jp == 'Judging'):
        mbt+='J'
    if(jp == 'Percieving'):
        mbt+='P'
    return(mbt)

# Function to predict MBTI type based on user input
def tell_me_my_MBTI(posts):
    result = tell_me_my_MBTI(posts, "Your Personality:")
    return result

# Streamlit app
def main():
    st.title("MBTI Personality Type Classifier")

    # Load the dataset (assuming it's already available)
    data_set = pd.read_csv("C:/Users/ADMIN/Desktop/project/mbti_1.csv")

    # Extract posts from the dataset
    all_posts = pd.DataFrame()
    types = np.unique(np.array(data_set['type']))
    for j in types:
        temp1 = data_set[data_set['type']==j]['posts']
        temp2 = []
        for i in temp1:
            temp2 += i.split('|||')
        temp3 = pd.Series(temp2)
        all_posts[j] = temp3

    # Features for the bag of words model
    features = []
    for j in types:
        temp1 = all_posts[j]
        temp1 = temp1.dropna()
        if('I' in j):
            features += [[(build_bag_of_words_features_filtered(i), 'introvert') for i in temp1]]
        if('E' in j):
            features += [[(build_bag_of_words_features_filtered(i), 'extrovert') for i in temp1]]

    # Data for training
    split = []
    for i in range(16):
        split += [len(features[i]) * 0.8]
    split = np.array(split, dtype=int)

    train = []
    for i in range(16):
        train += features[i][:split[i]]

    # Training the model
    global IntroExtro
    IntroExtro = NaiveBayesClassifier.train(train)

    # Build the Streamlit app
    st.sidebar.header("User Input")
    user_input = st.sidebar.text_area("Enter your posts (separated by |||):")
    if st.sidebar.button("Predict MBTI"):
        if user_input:
            result = tell_me_my_MBTI(user_input)
            st.success("Your predicted MBTI Personality Type is: " + result)
        else:
            st.warning("Please enter your posts to predict MBTI.")

if __name__ == "__main__":
    main()