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bin/markovbot
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22
bin/markovbot
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#!/usr/bin/env python
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import markovbot
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import twitter
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import itertools
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import random
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# Command line parser
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args = markovbot.parser()
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# Set the coke binge parameter
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if args.coke_binge:
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coke_binge_num = random.randint(1, 6)
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else:
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coke_binge_num = 1
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text_model=markovbot.build_model(args.text)
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# Make the api keys
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api = markovbot.make_api_keys(args.filepath)
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markovbot.make_tweets(api, args.test, text_model, coke_binge_num)
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29
build/lib/markovbot/__init__.py
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build/lib/markovbot/__init__.py
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import argparse
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def parser() =
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--config', dest='filepath', metavar='CREDENTIALS', type=str, help='Path to config file')
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parser.add_argument('text', metavar='CORPUS', type=str, help='path to the text you wish to mimic') # filepath should be a flag; corpus an argument!
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parser.add_argument('--coke-binge', dest='coke_binge', action="store_true", default=False, help='Tweet excessively/emulate our POTUS') # instead of tweeting once, tweet a random number of times.
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parser.add_argument('-t', dest='test', action="store_true", default=False, help='Test text generation without tweeting.') # distance on markov chains?? distance on syntax trees? # default to test or default to tweet??
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return parser.parse_args()
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# Function to help process credentials file
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def drop_tag(str):
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parsed = itertools.dropwhile(lambda x: x!= ':', str)
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str2 = "".join(parsed)
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parsed2 = itertools.dropwhile(lambda x: x == ' ' or x == ':', str2)
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return "".join(parsed2)
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# Process api keys etc.
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def make_api_keys(filepath):
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# Open file with credentials
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with open(filepath) as f:
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content = f.readlines()
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# Strip irrelevant text
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let content = [drop_tag(x.strip()) for x in content]
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# create twitter api object
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return twitter.Api(consumer_key=content[0],
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consumer_secret=content[1],
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access_token_key=content[2],
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access_token_secret=content[3])
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41
build/scripts-3.5/markovbot
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41
build/scripts-3.5/markovbot
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#!python
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import markovify
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import markovbot
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import twitter
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import itertools
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import random
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# Command line parser
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args = parser()
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# Set the coke binge parameter
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if args.coke_binge:
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coke_binge_num = random.randint(1, 6)
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else:
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coke_binge_num = 1
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# Get raw text of markov chain as string.
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with open(args.text) as f:
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text = f.read()
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# Build the model.
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text_model = markovify.Text(text)
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api = make_api_keys(args.filepath)
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# Generate three randomly-generated sentences of no more than 140 characters and tweet them.
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selected = False
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for i in range(0, coke_binge_num):
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while selected is False:
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str_potentially = text_model.make_short_sentence(140)
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if not("http" in str_potentially):
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if not(args.test):
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# tweet the generated text
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status = api.PostUpdate(str_potentially)
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print(status.text) # verify it worked
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else:
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# test mode; just display it
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print(str_potentially)
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selected = True
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selected = False
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64
markov.py
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markov.py
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import markovify
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import twitter
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import itertools
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import argparse
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import random
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# Command line parser
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('filepath', metavar='CREDENTIALS', type=str, help='an integer for the accumulator')
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parser.add_argument('--text', dest='corpus', metavar='CORPUS', type=str, help='path to the text you wish to mimic') # filepath should be a flag; corpus an argument!
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parser.add_argument('--coke-binge', dest='coke_binge', action="store_true", default=False, help='path to the text you wish to mimic') # instead of tweeting once, tweet a random number of times.
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parser.add_argument('-t', dest='test', action="store_true", default=False, help='Test text generation without tweeting.') # distance on markov chains?? distance on syntax trees? # default to test or default to tweet??
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args = parser.parse_args()
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# Function to help process credentials file
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def drop_tag(str):
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parsed = itertools.dropwhile(lambda x: x!= ':', str)
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str2 = "".join(parsed)
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parsed2 = itertools.dropwhile(lambda x: x == ' ' or x == ':', str2)
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return "".join(parsed2)
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# Set the
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if args.coke_binge:
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coke_binge_num = random.randint(1, 6)
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else:
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coke_binge_num = 1
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# Open file with credentials
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with open(args.filepath) as f:
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content = f.readlines()
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# Process api keys etc.
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content = [drop_tag(x.strip()) for x in content]
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# Get raw text of markov chain as string.
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with open(args.corpus) as f:
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text = f.read()
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# Build the model.
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text_model = markovify.Text(text)
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# create twitter api object
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api = twitter.Api(consumer_key=content[0],
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consumer_secret=content[1],
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access_token_key=content[2],
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access_token_secret=content[3])
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# Generate three randomly-generated sentences of no more than 140 characters and tweet them.
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selected = False
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for i in range(0, coke_binge_num):
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while selected is False:
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str_potentially = text_model.make_short_sentence(140)
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if not("http" in str_potentially):
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if not(args.test):
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# tweet the generated text
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status = api.PostUpdate(str_potentially)
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print(status.text) # verify it worked
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else:
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# test mode; just display it
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print(str_potentially)
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selected = True
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selected = False
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57
markovbot/__init__.py
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57
markovbot/__init__.py
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import markovify
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import argparse
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import itertools
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import twitter
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def parser():
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parser = argparse.ArgumentParser(description='Process some integers.')
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parser.add_argument('--config', dest='filepath', metavar='CREDENTIALS', type=str, help='Path to config file')
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parser.add_argument('text', metavar='CORPUS', type=str, help='path to the text you wish to mimic') # filepath should be a flag; corpus an argument!
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parser.add_argument('--coke-binge', dest='coke_binge', action="store_true", default=False, help='Tweet excessively/emulate our POTUS') # instead of tweeting once, tweet a random number of times.
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parser.add_argument('-t', dest='test', action="store_true", default=False, help='Test text generation without tweeting.') # distance on markov chains?? distance on syntax trees?
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return parser.parse_args()
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def build_model(text):
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# Get raw text of markov chain as string.
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with open(text) as f:
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text = f.read()
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# Build the model.
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return markovify.Text(text)
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def make_tweets(api, test, model, coke_binge_num):
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# Generate three randomly-generated sentences of no more than 140 characters and tweet them.
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selected = False
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for i in range(0, coke_binge_num):
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while selected is False:
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str_potentially = model.make_short_sentence(140)
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if not("http" in str_potentially):
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if not(test):
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# tweet the generated text
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status = api.PostUpdate(str_potentially)
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print(status.text) # verify it worked
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else:
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# test mode; just display it
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print(str_potentially)
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selected = True
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selected = False
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# Function to help process credentials file
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def drop_tag(str):
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parsed = itertools.dropwhile(lambda x: x!= ':', str)
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str2 = "".join(parsed)
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parsed2 = itertools.dropwhile(lambda x: x == ' ' or x == ':', str2)
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return "".join(parsed2)
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# Process api keys etc.
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def make_api_keys(filepath):
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# Open file with credentials
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with open(filepath) as f:
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content = f.readlines()
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# Strip irrelevant text
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content = [drop_tag(x.strip()) for x in content]
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# create twitter api object
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return twitter.Api(consumer_key=content[0],
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consumer_secret=content[1],
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access_token_key=content[2],
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access_token_secret=content[3])
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BIN
markovbot/__pycache__/__init__.cpython-35.pyc
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BIN
markovbot/__pycache__/__init__.cpython-35.pyc
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17
setup.py
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17
setup.py
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from setuptools import setup
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setup(name='markovbot',
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version='0.1',
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description='Make a markov chain based twitter bot',
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url='http://github.com/vmchale/markov-bot',
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author='Vanessa McHale',
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author_email='tmchale@wisc.edu',
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license='BSD3',
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packages=['markovbot'],
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scripts=['bin/markovbot'],
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install_requires=[
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'gitpython',
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'markovify',
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'python-twitter',
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],
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zip_safe=False)
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3461
trumptweets.txt
3461
trumptweets.txt
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