pacnpal/ebooks_example
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move code that should be customized into constants for easier replacement

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Hubert Lee
2014-06-27 16:08:50 -04:00
parent 2cbc1910c2
commit fe5c765121
1 changed files with 15 additions and 7 deletions
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22
bots.rb
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@@ -5,8 +5,16 @@ include Ebooks
CONSUMER_KEY = "" CONSUMER_KEY = ""
CONSUMER_SECRET = "" CONSUMER_SECRET = ""
OATH_TOKEN = "" # oauth token for ebooks account
OAUTH_TOKEN_SECRET = "" # oauth secret for ebooks account
ROBOT_ID = "ebooks" # Avoid infinite reply chains
TWITTER_USERNAME = "ebooks_username" # Ebooks account username
TEXT_MODEL_NAME = "username" # This should be the name of the text model
DELAY = 2..30 # Simulated human reply delay range in seconds DELAY = 2..30 # Simulated human reply delay range in seconds
BLACKLIST = ['insomnius', 'upulie'] # Grumpy users to avoid interaction with BLACKLIST = ['insomnius', 'upulie'] # Grumpy users to avoid interaction with
SPECIAL_WORDS = ['ebooks', 'bot', 'bots', 'clone', 'singularity', 'world domination']
# Track who we've randomly interacted with globally # Track who we've randomly interacted with globally
$have_talked = {} $have_talked = {}
@@ -39,12 +47,12 @@ class GenBot
bot.on_mention do |tweet, meta| bot.on_mention do |tweet, meta|
# Avoid infinite reply chains (very small chance of crosstalk) # Avoid infinite reply chains (very small chance of crosstalk)
next if tweet[:user][:screen_name].include?('ebooks') && rand > 0.05 next if tweet[:user][:screen_name].include?(ROBOT_ID) && rand > 0.05
tokens = NLP.tokenize(tweet[:text]) tokens = NLP.tokenize(tweet[:text])
very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2 very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2
special = tokens.find { |t| ['ebooks', 'bot', 'bots', 'clone', 'singularity', 'world domination'].include?(t) } special = tokens.find { |t| SPECIAL_WORDS.include?(t) }
if very_interesting || special if very_interesting || special
favorite(tweet) favorite(tweet)
@@ -63,7 +71,7 @@ class GenBot
# tweet matches our keywords # tweet matches our keywords
interesting = tokens.find { |t| @top100.include?(t.downcase) } interesting = tokens.find { |t| @top100.include?(t.downcase) }
very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2 very_interesting = tokens.find_all { |t| @top50.include?(t.downcase) }.length > 2
special = tokens.find { |t| ['ebooks', 'bot', 'bots', 'clone', 'singularity', 'world domination'].include?(t) } special = tokens.find { |t| SPECIAL_WORDS.include?(t) }
if special if special
favorite(tweet) favorite(tweet)
@@ -122,9 +130,9 @@ def make_bot(bot, modelname)
GenBot.new(bot, modelname) GenBot.new(bot, modelname)
end end
Ebooks::Bot.new("username_ebooks") do |bot| # Ebooks account username Ebooks::Bot.new(TWITTER_USERNAME) do |bot|
bot.oauth_token = "" # oauth token for ebooks account bot.oauth_token = OATH_TOKEN
bot.oauth_token_secret = "" # oauth secret for ebooks account bot.oauth_token_secret = OAUTH_TOKEN_SECRET
make_bot(bot, "username") # This should be the name of the text model make_bot(bot, TEXT_MODEL_NAME)
end end