from tweepy import OAuthHandler

from tweepy import API

from tweepy import Cursor

from datetime import datetime, date, time, timedelta

from collections import Counter

import sys

import re

import ast

import json

import matplotlib.pyplot as plt

from wordcloud import WordCloud, STOPWORDS

import random

from kivy.app import App

from kivy.uix.label import Label

#from kivy.core.window import Window

from nltk.corpus import PlaintextCorpusReader

import nltk.classify.util

from nltk.corpus import PlaintextCorpusReader

from nltk.classify import NaiveBayesClassifier

from nltk.tokenize import word_tokenize

class abc(username):

consumer_key = "D9idPsR9iCUbzQzlUIoOUlOjc"

consumer_secret = "VGLhthlGLxbJpPyu3WtTTz4oKLYYkJ5VHJIn94Azf0bDoeE7vm"

access_token = "798166878-qgZxk593TZpoSpaZyPBMI1wnOjkKH80AFGR0ZqAg"

access_token_secret = "DvQDsAEGm23yG6aRVoPhrVIeZINR6Y47jgjdXgdF7OaVH"

auth = OAuthHandler(consumer_key, consumer_secret)

auth.set_access_token(access_token, access_token_secret)

auth_api = API(auth)

target = username

rel = ['hate', 'left', 'bf','gf','girlfriend','boyfriend','heartbreak','alone', 'love','abuse']

edu = ['exam','test','assignment','school','studying']

mon = ['broke','money','cash','economy', 'finance','crisis']

cleantweet = []

clist = []

text = []

mylist = []

words = []

global dcount,Money

dcount = 0

Money = 0

global ccount,Academic

ccount = 0

Academic = 0

global tcount,Relationship

tcount = 0

Relationship = 0

long_stop_list = ['a','are','be','an','and','at','by','the','is','this','that','to','for','it','in','on']

def create_word_features(clist):

useful_words = [word for word in clist if word not in long_stop_list]

my_dict = dict([(word, True) for word in useful_words])

return my_dict

corpus_root = 'C:/Users/Bindu/Desktop/samp1/neg'

wordlists=PlaintextCorpusReader(corpus_root,'.*')

neg_re=[]

mylist=[]

x=[]

for fileids in wordlists.fileids():

words = wordlists.words(fileids)

neg_re.append((create_word_features(words),"Depressed"))

corpus_root='C:/Users/Bindu/Desktop/samp1/pos'

wordlists=PlaintextCorpusReader(corpus_root,'.*')

pos_re=[]

mylist=[]

x=[]

for fileids in wordlists.fileids():

words = wordlists.words(fileids)

pos_re.append((create_word_features(words),"Not depressed"))

train_set = neg_re[:45] + pos_re[:45]

random.shuffle(train_set)

test_set = neg_re[45:] + pos_re[45:]

random.shuffle(test_set)

#print(len(train_set),len(test_set))

classifier = NaiveBayesClassifier.train(train_set)

accuracy = nltk.classify.util.accuracy(classifier, test_set)

#print(accuracy * 100)

class Helpline(App):

def build(self):

#Window.clearcolor = (0.50,0.50,0.50,1)

return Label(text="Need to talk to someone? \n\nNational Suicide Helpline \n\nVisit: http://www.aasra.info/ \nCall: +912227546669 \n\nWant to chat with a counsellor? \nVisit:https://yourdost.com/")

hp=Helpline()

def get_info(target):

exit()

def get_tweets(target):

mylist.append(json.dumps(status._json['text']))

def get_cause(clist):

Money+=1

def get_classify(tweet):

ccount+=1

get_info(target)

try:

get_tweets(target)

except:

print("%s's account is set to private" % target)

exit()

for tweet in mylist:

tcount+=1

tweet = tweet.replace('"','')

tweet = tweet.lower()

tweet = re.sub('^rt','',tweet)#Remove RT if they appear at the beginning of a tweet

tweet = re.sub('((www\.[^\s]+)|(https?://[^\s]+))','',tweet)#Remove www.* or https?://*

tweet = re.sub('@[^\s]+','',tweet)#Remove @username

tweet = " ".join(word.strip() for word in re.split('#|_', tweet))#Convert #word into word

tweet = re.sub('[0-9]+','',tweet)#Remove numbers

tweet = re.sub(r'[^\w\s]','',tweet)#Remove punctuation

tweet = re.sub('[\s]+',' ', tweet)#Remove additional white spaces

cleantweet.append(tweet)

get_classify(tweet)

def biggest(a, y, z):

return Max

tot = dcount+ccount

#print(dcount,ccount)

rat = dcount/tot

print (rat)

if (rat < 0.25 ):

print (" %s is unlikely to be depressed" % target)

elif (rat > 0.25 and rat < 0.4):

print (" %s is likely to be moderately depressed" % target)

cause = biggest(Relationship,Money,Academic)

if (cause == Relationship):

print("Cause for depression is likely to be relationship troubles")

elif (cause == Money):

print("Cause for depression is likely to be money troubles")

elif (cause == Academic):

print ("Cause for depression is likely to be academic troubles")

else:

hp.run()

print ("%s is likely to be severely depressed" % target)

cause = biggest(Relationship,Money,Academic)

if (cause == Relationship):

print("Cause for depression is likely to be relationship troubles")

elif (cause == Money):

print("Cause for depression is likely to be money troubles")

elif (cause == Academic):

print ("Cause for depression is likely to be academic troubles")

with open("%s.txt" % target,'w',encoding = 'utf-8') as f:

f.write(str(cleantweet))

f.close()

def random_color_func(word=None, font_size=None, position=None, orientation=None, font_path=None, random_state=None):

return "hsl({}, {}%, {}%)".format(h, s, l)

file_content=open ("%s.txt" % target).read()

wordcloud = WordCloud(font_path = r'C:\Windows\WinSxS\amd64_microsoft-windows-font-truetype-verdana_31bf3856ad364e35_10.0.16299.15_none_e1654f127052576a\verdana.ttf', stopwords = STOPWORDS,

).generate(file_content)

plt.imshow(wordcloud)

plt.axis('off')

plt.show()

class LoginApp(App):

)

if __name__ == '__main__':

LoginApp().run()