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Project description
Ingest corpuses of text and output a sentence generated from markov chains
Installation
After installing and configuring helga, use:
pip install helga-markovify
Add ‘markovify’ to your settings and restart helga.
Usage
Note: Please use punctuation in your text. This is a tough sticking point in practice, but it is helpful.
Command syntax:
ingest <topic> <learning_type> <learning_type_source> generate <topic>
Arguments:
topic: like tagging, so helga can respond in different ways learning_type: how helga is going to ingest. Can be text, a url to raw data, a relatively pathed (relative to plugin folder) file, or a twitter account. learning_type_source: the corresponding data e.g. plaintext if learning_type is "text", a url if "url", twitter id if "twitter"
Examples
Some example commands (file):
!markovify ingest zen file zen.txt !markovify generate zen helga> If the implementation is hard to explain, it may be a good idea.
Alternate example (text):
!markovify ingest hitler text "Mein Kampf is the best Kampf." !markovify ingest hitler text "Don't be stupid, be a smarty. Come and join the nazi party." !markovify ingest hitler text "Make America hate again." !markovify ingest hitler text "Kampf America is hate nazi smarty. Hate party again filler sentence. America is the best at being terrible." !markovify generate hitler helga> Mein Kampf is the best at being terrible.
TODO
Tweets
Generate default data from channel
Add settings for max corpus count, max corpus length
Travis
Talk about specific topics
Keep history aka conversations
Weighted round-robin type conversation, e.g. trump vs jesus vs samuel l jackson vs kim jong un
License
Copyright (c) 2016 Jon Robison
See included LICENSE for licensing information
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