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30
LICENSE
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30
LICENSE
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Copyright Vanessa McHale (c) 2017
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All rights reserved.
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions are met:
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* Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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* Redistributions in binary form must reproduce the above
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copyright notice, this list of conditions and the following
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disclaimer in the documentation and/or other materials provided
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with the distribution.
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* Neither the name of Vanessa McHale nor the names of other
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contributors may be used to endorse or promote products derived
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from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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20
README.md
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20
README.md
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# Trump Markov Chain Twitter Bot
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Written in python with the aid of markovify and python-twitter.
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Put your API keys in a file (e.g. .api-keys-trump) like so:
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```
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api-key: API_KEY
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api-sec: API_SECRET_KEY
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tok: TOKEN
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tok-sec: SECRET_TOKEN
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```
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Then run:
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```
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python markov.py PATH_TO_API_KEYS --text PATH_TO_CORPUS.txt
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```
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to send one tweet at a time.
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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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## 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 analyze')
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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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## 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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while selected == 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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status = api.PostUpdate(str_potentially)
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print(status.text) # verify it worked
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selected = True
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3461
trumptweets.txt
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3461
trumptweets.txt
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