import tweepy
import codecs
from tweepy import Stream
from tweepy import OAuthHandler
from tweepy.streaming import StreamListener
from nltk.corpus import twitter_samples
from nltk.tokenize import TweetTokenizer
import json
import string
import re
import botometer
from nltk.corpus import stopwords
stopwords_english = stopwords.words('english')
from nltk.stem import PorterStemmer
stemmer = PorterStemmer()
from nltk.tokenize import TweetTokenizer
from nltk import classify
from nltk import NaiveBayesClassifier

# Happy Emoticons
emoticons_happy = set([
    ':-)', ':)', ';)', ':o)', ':]', ':3', ':c)', ':>', '=]', '8)', '=)', ':}',
    ':^)', ':-D', ':D', '8-D', '8D', 'x-D', 'xD', 'X-D', 'XD', '=-D', '=D',
    '=-3', '=3', ':-))', ":'-)", ":')", ':*', ':^*', '>:P', ':-P', ':P', 'X-P',
    'x-p', 'xp', 'XP', ':-p', ':p', '=p', ':-b', ':b', '>:)', '>;)', '>:-)',
    '<3'
    ])

# Sad Emoticons
emoticons_sad = set([
    ':L', ':-/', '>:/', ':S', '>:[', ':@', ':-(', ':[', ':-||', '=L', ':<',
    ':-[', ':-<', '=\\', '=/', '>:(', ':(', '>.<', ":'-(", ":'(", ':\\', ':-c',
    ':c', ':{', '>:\\', ';('
    ])
# all emoticons (happy + sad)
emoticons = emoticons_happy.union(emoticons_sad)

#consumer key, consumer secret, access token, access secret.
#ckey="mzhns6Ra7P1e1aO06yMVAmrAA"
#csecret="6wheiTnv9ACqdpEOG3q2YVijuXLlCD1njWZlyQJy3ky4XKrymj"
#atoken="366417856-tttnd9Eng3qyOx4yvzxO4ZuePy2qs1dus5ByYXbo"
#asecret="pjvo9pkLljPmANPat9ALmXfBzu6rmFR3eFcjPDyatshT"
ckey="ZUKS9ElXbYO4UBHnHBkhSLPdW"
csecret="Ww7YKAvf4LMSyffxmYSdkauu3JUhcKOo9XxLpnCPWAPx3ksCvg"
atoken="92337145-mI3PSDTdjAw38ld3kBqPnDavytDiSsFdve0DHd0jA"
asecret="6ATbGCPfsey6LYsfTKNVX8m8nT9QThvhzTQ50WcJX6nZd"
auth = OAuthHandler(ckey, csecret)
auth.set_access_token(atoken, asecret)
api = tweepy.API(auth)

mashape_key = "ad092870a1msh558166c739f9457p12bddfjsn39f1129751f5"
twitter_app_auth = {
    'consumer_key': 'ZUKS9ElXbYO4UBHnHBkhSLPdW',
    'consumer_secret': 'Ww7YKAvf4LMSyffxmYSdkauu3JUhcKOo9XxLpnCPWAPx3ksCvg',
    'access_token': '92337145-mI3PSDTdjAw38ld3kBqPnDavytDiSsFdve0DHd0jA',
    'access_token_secret': '6ATbGCPfsey6LYsfTKNVX8m8nT9QThvhzTQ50WcJX6nZd',
  }




query="ExpressVPN" #search word OR for a OR b, AND for tweets with a AND b
max_tweets=500 #number of tweets
n=1
#tweet_mode=extended for entire tweets
#tweets = open("tweetset","w", encoding="utf-8")
retweet = " -filter:retweets"
query = f"{query}{retweet}"
#for tweet in tweepy.Cursor(api.search, q=query, tweet_mode='extended', lang='en').items(max_tweets):
#	with open('tweetset.json', 'a') as f:
#		f.write(json.dumps(tweet._json))
#		f.write("\n")
#	f.close()
#	print (n,tweet.full_text)
#	n=n+1

#data tweetset
data = []
with open('tweetset.json') as  f:
	for line in f:
		data.append(json.loads(line))

posdata = []
with open('onlypostweet.json') as  f:
	for line in f:
		posdata.append(json.loads(line))


negdata = []
with open('onlynegtweet.json') as  f:
	for line in f:
		negdata.append(json.loads(line))

print(negdata)

#skriv ut pdata
#for pdata in posdata:
	#print(str(n), pdata['full_text'])
	#n = n+1
	#print(pdata['user']['screen_name'])

#skriv positiva tweets till en ny json med bara texten
#for ndata in negdata:
#	with open('onlynegtweet.json', 'a') as f:
#		f.write(json.dumps(ndata['full_text']))
#		f.write("\n")
#	f.close()


#for tweet in tweepy.Cursor(api.search, q=query, tweet_mode='extended', lang='en').items(max_tweets):
#	with open('tweetset.json', 'a') as f:
#		f.write(json.dumps(tweet._json))
#		f.write("\n")
#	f.close()
#	print (n,tweet.full_text)
#	n=n+1
#for tweetset in data:
#	print(str(n), tweetset['full_text'])
#	n = n+1
#	print(tweetset['user']['screen_name'])

bom = botometer.Botometer(wait_on_ratelimit=True,
                          mashape_key=mashape_key,
                          **twitter_app_auth)


#data = []
#with open('tweetset.json') as f:
#	for line in f:
#		data.append(json.loads(line))
#check accounts
#accounts = []
#for row in data:
#	name=row['user']['screen_name']
#	result=bom.check_account(name)
#	print(n,result['user']['screen_name'] + " %.2f%%" % (100*result['scores']['universal']))
#	n=n+1


#clean tweets
def clean_tweets(tweet):
    # remove stock market tickers like $GE
    tweet = re.sub(r'\$\w*', '', tweet)

    # remove old style retweet text "RT"
    tweet = re.sub(r'^RT[\s]+', '', tweet)

    # remove hyperlinks
    tweet = re.sub(r'https?:\/\/.*[\r\n]*', '', tweet)

    # remove hashtags
    # only removing the hash # sign from the word
    tweet = re.sub(r'#', '', tweet)
    tokenizer = TweetTokenizer(preserve_case=False, strip_handles=True, reduce_len=True)
    tweet_tokens = tokenizer.tokenize(tweet)

    tweets_clean = []
    for word in tweet_tokens:
        if (word not in stopwords_english and # remove stopwords
              word not in emoticons and # remove emoticons
                word not in string.punctuation): # remove punctuation
            tweets_clean.append(word)

    return tweets_clean

#print (clean_tweets(custom_tweet))

#for row in data:
#	name=row['user']['screen_name']
#	result=bom.check_account(name)
#	print(n,result['user']['screen_name'] + " %.2f%%" % (100*result['scores']['universal']))
#	n=n+1
#gettweets
#def get_tweets(tweetset):





#twitter samples
pos_tweets = twitter_samples.strings('positive_tweets.json')
print ("onlypostweets = ",len(pos_tweets)) # Output: 5000

neg_tweets = twitter_samples.strings('negative_tweets.json')
print (len(neg_tweets)) # Output: 5000

#all_tweets = twitter_samples.strings('tweetset.json')

#print (clean_tweets(pos_tweets[5]))
#print (pos_tweets[5])
#print (len(all_tweets)) # Output: 20000

    # tokenize tweets

#for tweet in pos_tweets[:5]:
#    print (tweet_tokenizer.tokenize(tweet))



#nästa steg skriva tweets till text.doc

# feature extractor function
def bag_of_words(tweet):
    words = clean_tweets(tweet)
    words_dictionary = dict([word, True] for word in words)
    return words_dictionary
#print (bag_of_words(custom_tweet))

# positive tweets feature set
pos_tweets_set = []
for tweet in posdata:
    pos_tweets_set.append((bag_of_words(tweet), 'pos'))

# negative tweets feature set
neg_tweets_set = []
for tweet in negdata:
    neg_tweets_set.append((bag_of_words(tweet), 'neg'))

#print (len(pos_tweets_set), len(neg_tweets_set)) # Output: (5000, 5000)



# radomize pos_reviews_set and neg_reviews_set
# doing so will output different accuracy result everytime we run the program
from random import shuffle
shuffle(pos_tweets_set)
shuffle(neg_tweets_set)

test_set = pos_tweets_set[:1000] + neg_tweets_set[:1000]
train_set = pos_tweets_set[1000:] + neg_tweets_set[1000:]

print(len(test_set),  len(train_set)) # Output: (2000, 8000)


classifier = NaiveBayesClassifier.train(train_set)

accuracy = classify.accuracy(classifier, test_set)
#print(accuracy) # Output: 0.765

#print (classifier.show_most_informative_features(10))

custom_tweet = "I hated the film. It was a disaster. Poor direction, bad acting."
custom_tweet_set = bag_of_words(custom_tweet)
#print (classifier.classify(custom_tweet_set)) # Output: neg

# probability result
prob_result = classifier.prob_classify(custom_tweet_set)
print (prob_result) # Output: <ProbDist with 2 samples>
print (prob_result.max()) # Output: neg
print ("neg probability:", prob_result.prob("neg")) # Output: 0.941844352481
print ("pos probability:", prob_result.prob("pos")) # Output: R0.0581556475194

custom_tweet = "It was a wonderful and amazing movie. I loved it. Best direction, good acting."
custom_tweet_set = bag_of_words(custom_tweet)

#print (classifier.classify(custom_tweet_set)) # Output: pos
# Positive tweet correctly classified as positive

# probability result
prob_result = classifier.prob_classify(custom_tweet_set)
#print (prob_result) # Output: <ProbDist with 2 samples>
#print (prob_result.max()) # Output: pos
#print (prob_result.prob("neg")) # Output: 0.00131055449755
#print (prob_result.prob("pos")) # Output: 0.998689445502


#bot träning
my_tweets = 'tweetset.json'
my_tweets_set = bag_of_words(my_tweets)

#classifier = NaiveBayesClassifier.train()