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Vanessa McHale
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Copyright Vanessa McHale (c) 2017
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above
copyright notice, this list of conditions and the following
disclaimer in the documentation and/or other materials provided
with the distribution.
* Neither the name of Vanessa McHale nor the names of other
contributors may be used to endorse or promote products derived
from this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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# Trump Markov Chain Twitter Bot
Written in python with the aid of markovify and python-twitter.
Put your API keys in a file (e.g. .api-keys-trump) like so:
```
api-key: API_KEY
api-sec: API_SECRET_KEY
tok: TOKEN
tok-sec: SECRET_TOKEN
```
Then run:
```
python markov.py PATH_TO_API_KEYS --text PATH_TO_CORPUS.txt
```
to send one tweet at a time.

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import markovify
import twitter
import itertools
import argparse
## Command line parser
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('filepath', metavar='CREDENTIALS', type=str, help='an integer for the accumulator')
parser.add_argument('--text', dest='corpus', metavar='CORPUS', type=str, help='path to the text you wish to analyze')
args = parser.parse_args()
## Function to help process credentials file
def drop_tag(str):
parsed = itertools.dropwhile(lambda x: x!= ':', str)
str2 = "".join(parsed)
parsed2 = itertools.dropwhile(lambda x: x == ' ' or x == ':', str2)
return "".join(parsed2)
## Open file with credentials
with open(args.filepath) as f:
content = f.readlines()
## Process api keys etc.
content = [drop_tag(x.strip()) for x in content]
# Get raw text of markov chain as string.
with open(args.corpus) as f:
text = f.read()
# Build the model.
text_model = markovify.Text(text)
# create twitter api object
api = twitter.Api(consumer_key=content[0],
consumer_secret=content[1],
access_token_key=content[2],
access_token_secret=content[3])
# Generate three randomly-generated sentences of no more than 140 characters and tweet them.
selected = False
while selected == False:
str_potentially = text_model.make_short_sentence(140)
if not("http" in str_potentially):
status = api.PostUpdate(str_potentially)
print(status.text) # verify it worked
selected = True

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