def markov():
tweetbot = MarkovBot()
dirname = os.path.dirname(os.path.abspath(__file__))
data1 = os.path.join(dirname, 'positiveTweets.txt')
data2 = os.path.join(dirname, '1liner.txt')
data3 = os.path.join(dirname, '10ondate.txt')
data4 = os.path.join(dirname, 'forCachetes.txt')
#data5 = os.path.join(dirname, 'book3.txt')
#data6 = os.path.join(dirname, 'book4.txt')
data7 = os.path.join(dirname, 'jokes.txt')
#data8 = os.path.join(dirname, 'book4.txt')
tweetbot.read(data1)
tweetbot.read(data2)
tweetbot.read(data3)
tweetbot.read(data4)
#tweetbot.read(data5)
#tweetbot.read(data6)
tweetbot.read(data7)
#tweetbot.read(data8)
my_first_text = tweetbot.generate_text(25, seedword=['life', 'motivation', 'happy', 'bullying'])
print("tweetbot says:")
print(my_first_text)
import os
from markovbot import MarkovBot
# # # # #
# INITIALISE
# Initialise a MarkovBot instance
tweetbot = MarkovBot()
# Get the current directory's path
dirname = os.path.dirname(os.path.abspath(__file__))
# Construct the path to the book
book = os.path.join(dirname, u'Freud_Dream_Psychology.txt')
# Make your bot read the book!
tweetbot.read(book)
# # # # #
# TEXT GENERATION
# Generate text by using the generate_text method:
# The first argument is the length of your text, in number of words
# The 'seedword' argument allows you to feed the bot some words that it
# should attempt to use to start its text. It's nothing fancy: the bot will
# simply try the first, and move on to the next if he can't find something
# that works.
my_first_text = tweetbot.generate_text(25,
seedword=[u'dream', u'psychoanalysis'])
# Print your text to the console
print(u'\ntweetbot says: "%s"' % (my_first_text))
from markovbot import MarkovBot # # # # # # INITIALISE # Initialise a MarkovBot instance tweetbot = MarkovBot() # Get the current directory's path dirname = os.path.dirname(os.path.abspath(__file__)) # Construct the path to the book book = os.path.join(dirname, u'Freud_Dream_Psychology.txt') # Make your bot read the book! tweetbot.read(book) # # # # # # TEXT GENERATION # Generate text by using the generate_text method: # The first argument is the length of your text, in number of words # The 'seedword' argument allows you to feed the bot some words that it # should attempt to use to start its text. It's nothing fancy: the bot will # simply try the first, and move on to the next if he can't find something # that works. my_first_text = tweetbot.generate_text(25, seedword=[u'dream', u'psychoanalysis']) # Print your text to the console print(u'\ntweetbot says: "%s"' % (my_first_text))
from random import randint
from markovbot import MarkovBot
import os
bot = MarkovBot()
book = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'realdonaldtrump.txt')
# Make your bot read the text!
bot.read(book)
# Choose how many tweets you want to generate
tweets_to_generate = 150
for x in range(0, tweets_to_generate):
# len of the new tweets that will be generated, in words
tweet_len = randint(10, 25)
# generating tweet, based on its len and seedwords!
tweet = bot.generate_text(tweet_len, seedword=[])
# writting to our generated sentences to the file the poster will use later
sentences_list = open("generated_sentences.txt", "a")
sentences_list.write(tweet + "\n")
sentences_list.close()
fThree = (fTwo.replace("SCENE", " "))
fFour = (fThree.replace("ACT", " "))
finalDataShake = fFour
'''
'''freud'''
'''
i need to clean this up a lot and add organon
- make methods more recycle --> code is stale
- many authors at once with func
- add learning method --> possible neural net? I want to make training worthwhile....
- add a score to output? compare results to text --> give score and only release the 'good' ones -> but also tell the model that it did a good or bad job
'''
dirname = os.path.dirname(os.path.abspath(__file__))
freudText = os.path.join(dirname, 'training_txt/freudCompleteWorks.txt')
bot.read(freudText)
freudTweets = bot.generate_text(25)
'''
fTwo = (re.sub('[-,_[@?#*"%;()}0-9]', " ", data))
fThree = (fTwo.replace("SCENE" "ACT", " "))
finalSText = fThree'''
''' really????? this is no good, going to fix this week '''
shakeText = os.path.join(dirname, 'training_txt/shakeComplete.txt')
bot2.read(shakeText)
shakeTweets = bot2.generate_text(25)
west_phil_text = os.path.join(dirname, 'training_txt/westPhil.txt')
bot4.read(west_phil_text)
westp_text = bot4.generate_text(25)
russellText = os.path.join(dirname, 'rtraining_txt/russelMath.txt')
with open('%s_tweets.txt' % screen_name, 'wb') as f:
for tweet in alltweets:
f.write(tweet.text.encode("utf-8") + ' '.encode("utf-8"))
pass
get_all_tweets("kanyewest")
kanyebot = MarkovBot()
# Get the current directory's path
dirname = os.path.dirname(os.path.abspath(__file__))
# Construct the path to the book
tweets = os.path.join(dirname, 'kanyewest_tweets.txt')
# Make your bot read the book!
kanyebot.read(tweets)
kanyebot.twitter_login(consumer_key, consumer_secret, access_key,
access_secret)
# Set some parameters for your bot
targetstring = 'KanyeToTheBot'
keywords = ['kim', 'pablo', 'wavy', 'bill', 'cosby']
prefix = None
suffix = '#KanyeToTheBot'
maxconvdepth = None
# Start periodically tweeting
kanyebot.twitter_tweeting_start(days=0,
hours=0,
minutes=15,
# import config # contains the API keys and access tokens
import twitter
import os
import time
from markovbot import MarkovBot
# Initialise the MarkovBot instance
tweetbot = MarkovBot()
# Get the seinfeld transcripts and read
dirname = os.path.dirname(os.path.abspath(__file__))
textFile = os.path.join(dirname, 'seinfelf.txt')
tweetbot.read(textFile)
# Text Generations
# Log in to Twitter
consumer_key = os.environ.get('cons_key')
consumer_key_secret = os.environ.get('cons_secret')
access_token = os.environ.get('access_token')
access_token_secret = os.environ.get('access_token_secret')
tweetbot.twitter_login(consumer_key, consumer_key_secret, access_token,
access_token_secret)
# Start periodically tweeting
while True:
minutesToWait = 120
secondsToWait = 60 * minutesToWait
tweetbot.twitter_tweeting_start(days=0,
hours=0,
import os
from markovbot import MarkovBot
tweetbot = MarkovBot()
dirname = os.path.dirname(os.path.abspath(__file__))
headline = os.path.join(dirname, 'hosking_headlines.txt')
comment = os.path.join(dirname, 'hosking_comments.txt')
gen = input('Generate headline? Y/N\n')
while True:
if gen == 'Y' or gen == 'y':
tweetbot.read(headline)
gen_head = tweetbot.generate_text(10)
print(u'\nMike Hosking: %s' % (gen_head))
tweetbot.clear_data()
tweetbot.read(comment)
gen_comm = tweetbot.generate_text(30)
print('COMMENT: %s' % (gen_comm))
gen = input(u'\nGenerate headline? Y/N\n')
elif gen == 'N' or gen == 'n':
exit()
elif gen != 'Y' and gen != 'N' and gen != 'y' and gen != 'n':
print(u'\nInput was not Y or N')
gen = input(u'\nGenerate headline? Y/N\n')
# -*- coding: utf-8 -*-
import os
import time
import sys
from markovbot import MarkovBot
reload(sys)
sys.setdefaultencoding('utf-8')
# Initialise a MarkovBot instance
tweetbot = MarkovBot()
# Make your bot read the book!
tweetbot.read(
'https://github.com/gini10/akelarreciberfeminista/blob/master/bots/ZorraMutante.py'
)
my_first_text = tweetbot.generate_text(
10, seedword=[u'código', u'lÃmite', u'hay'])
print(u'\ntweetbot says: "%s"' % (my_first_text))
# ALL YOUR SECRET STUFF!
# Consumer Key (API Key)
cons_key = '[ copy this value from Consumer Settings ]'
# Consumer Secret (API Secret)
cons_secret = '[ copy this value from Consumer Settings ]'
# Access Tokenf
access_token = '[ copy this value from Your Access Token ]'
# Access Token Secret
access_token_secret = '[ copy this value from Your Access Token ]'
def getBot(filePath):
tweetbot = MarkovBot()
tweetbot.read(filePath)
return tweetbot