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For our second plugin, let's create a bot that randomly replaces a noun in a random message with the word "banana".
You can read more about NLTK on it's official site. You should also check out NounHound on PyPI.
You may find that you need to install some additional NLTK libraries before you can use the tokenizers and other parts of the library. You can do this in your Python shell with the following commands:
>>> import nltk
>>> nltk.download()
And then choosing the appropriate components from the window that opens.
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This last plugin that I
want to make is simpler,
0:00
as in it has less code than the motivation
one, but it uses some more powerful tools.
0:03
So, six of one,
half a dozen of the other, I guess.
0:09
I want to have the bot randomly choose
a message, break it into sentences,
0:12
pick a random sentence from all of them,
and then break that sentence into words.
0:15
Then I'm gonna use a part of speech
library to find all of the nouns and
0:20
pick a random noun and
replace it with a word.
0:25
I think I'm gonna use banana,
cuz I find that funny.
0:27
Then patch everything back up and
post that message.
0:30
You could, of course,
0:33
use a word other than banana if you
want to, but I'll leave that up to you.
0:34
So first thing's first.
0:38
I need to install a couple of libraries.
0:40
I need to install the NLTK library and
the noun-hound library.
0:42
Now, if you're not familiar with NLTK,
it's the natural language toolkit.
0:48
And it has tons of tools,
who'd have thunk, for working with text,
0:52
including finding common word pairings,
parts of speech and all.
0:56
Tons and tons of other stuff.
1:00
Noun Hound is a specialized library
that sits on top of NLTK and
1:02
it finds individual nouns and
noun phrases in a bit of text.
1:07
Now I could just use
NLTK to find the nouns.
1:11
But that's a bit more fiddly than I
want to deal with in a simple Slack bot
1:15
when I can just use an already available
library that does exactly what I want.
1:18
So I'm gonna pip install NLTK and
noun-hound.
1:22
I love the new pip with the progress bars,
it's so nice.
1:34
Okay, so then of course I need to get my
file and everything ready for the plugins.
1:37
So I'm gonna do a new folder
that I'm gonna call banana.
1:42
And inside of there, a new file banana.py
partly cuz I like the joke there.
1:47
One of my first [LAUGH] open source
libraries was named banana pie.
1:54
And also because I'm
putting in the word banana.
1:57
If you wanted to make this to where
you could put in a random word or
2:00
you could configure the word
with like a variable in here,
2:04
word_to_use = 'banana' kind of thing.
2:09
Then you could totally call this
wordswap.py or something like that.
2:12
Call it whatever makes the most sense for
you.
2:16
So, I'm gonna start with the crontable and
2:20
outputs variables again because
it's good to have those.
2:23
It's good practice just to always put
those in there for these plugins.
2:28
And again, you don't need the crontable
one so feel free to leave that out.
2:31
But I like following formats.
2:34
So the process message function is
still going to receive data and
2:38
the first thing I want to do is I
want to get the text of the message.
2:43
So let's just say message equals data, so
2:47
that way I have it in
a variable I can hold on to it.
2:51
This is where I need to start doing
all the textual processing on all of
2:54
this stuff.
2:58
So I need to bring in
a couple of other tools.
2:59
Since I want random words and
sentences, I need to import random.
3:02
And then, I need to import stuff for
doing the tokenizing and the noun finding.
3:09
So from nltk.tokenize
3:16
import sent_tokenize and
3:20
wordpunct_tokenize.
3:25
And then from noun_hound,
import NounHound.
3:30
And then I'll make a new variable
right down here that is nh=NounHound.
3:35
So that way, I just have it available.
3:41
So now, these nltk_tokenizers
these two right here,
3:43
will let us break up
text on specific tokens.
3:47
So, in this case the sent tokenize,
tokenizer, breaks up text on sentences so
3:51
it knows how to find sentences no matter
what the ending punctuation is and
3:56
break your text apart on those sentences.
4:01
The wordpunct tokenizer breaks up a bit
of text on its words and its punctuation.
4:04
So you can get words without like
periods or commas after them and
4:11
if a word has a apostrophe in it or
something like that,
4:16
a hyphen or whatever, then it'll
break that up into multiple words so
4:21
that you can get both sides of the hyphen.
4:25
They're both pretty fun to play with and
4:27
there's a couple of
the tokenizers in NLTK.
4:28
So go check those out, read the docs,
they're linked in the teacher's notes.
4:31
There's some neat stuff in there.
4:35
For now though, I need to break
up the text into sentences and
4:37
get a random sentence.
4:40
So let's do sentences = sent_tokenize and
4:42
I'm gonna pass in message.
4:47
And then I'm gonna get a number that is
4:50
random.randint(0, len(sentences)).
4:54
Now why get a number instead of
just getting a random sentence?
4:59
Because I need to know where it
is in the list of sentences so
5:05
that I can replace that sentence, so
the message still makes sense, right?
5:08
And then finally let's get out
that sentence which will be
5:12
sentences(sentence_num).
5:17
Okay, so grabbing a random sentence and
just holding on to that sentence.
5:20
Okay, so now I need to do something
similar to this with the words that are in
5:27
that chosen sentence.
5:32
So let's do words =
wordpunct_tokenize with the sentence.
5:34
So break up that sentence into words,
and then I want to find all the nouns,
5:40
so I'm gonna do nh.process(sentence).
5:47
So what I've done here is I've created
a list of all the words and punctuation.
5:51
And then here, I've gone and
found all the nouns and noun phrases.
5:55
So nh.process will return
a dictionary that has two keys,
5:59
one is noun phrases and one is nouns.
6:05
And the values for each of those keys is
a list of words that are noun phrases or
6:08
nouns.
6:13
So a noun phrase would be the adjectives
that surround a noun or refer to a noun,
6:13
and the nouns would be
the individual nouns, right?
6:18
So quick brown fox, you'd get quick
brown fox as the noun phrase and
6:21
you'd get fox as the noun.
6:26
So now I need to get a random noun,
and replace that.
6:29
Let's find replacement random.choice
6:33
from the nouns key of
the nouns dictionary.
6:37
And then words and I want to do
[words.index(replacement)] = 'banana'.
6:43
Now, here's a good spot
to extend this script.
6:52
What I'm using is only going to
replace the first instance of the noun
6:55
in the sentence.
6:58
It might make more sense to replace all of
the instances of that particular noun, but
6:59
I'm gonna leave that up to you.
7:03
This is also where you could change
the replacement word if you want to use
7:05
something other than banana or have it use
a random replacement word or whatever.
7:09
Finally, I need to put the whole
thing back together, so
7:14
sentences[sentence_num] =
space .join(words).
7:19
And then I need to send that back out, so
7:26
outputs.append[data("channel")] and
7:30
space .join[sentences].
7:35
I'm a firm believer in
one space after periods.
7:38
Now one thing I will point out is that
this right here, this space .join(words),
7:42
you will end up getting spaces
before apostrophes inside of words.
7:47
You can make this a bit more
intelligent in how it rejoins these
7:52
sentences if you want but I didn't feel
like that was necessary for this example.
7:55
So now let's try restarting the bot and
see what happens.
8:00
Okay, and now lets send a message,
8:08
this message has some nouns in it.
8:12
And you can see here we've got a thing
about the NLTK is missing a library.
8:16
So we need to install that
through the NLTK Downloader.
8:21
So let's go ahead and hop into Python,
8:24
import nltk and do nltk.download.
8:30
And you can see this is looking for
english.pickle.
8:36
So let's come here to All Packages and,
Scroll down here.
8:39
I think it's in Models.
8:53
Punkt Tokenizer Models,
I think that's the one we want to install.
8:59
So I'll hit Download and
then we can quit Python and
9:02
let's try, Try running the bot.
9:07
The quick brown fox
jumped over the lazy dog.
9:14
And we've got a thing here where
it tried to pick the last word.
9:27
So we actually need to do
len(sentences)-1), and
9:30
then let's try running this again and
we'll send the same message.
9:35
And this right here, this nltk_data thing,
9:51
this is noun-hound downloading
things that it needs.
9:54
And then we get the quick brown
banana jumped over the lazy dog.
9:57
So that's cool but that's gonna
run on every single message and
10:01
I probably don't want that to
happen on every single message.
10:05
So what I'm gonna do is add
in a bit of randomness.
10:08
So let's add a new variable
up here that we'll call odds.
10:11
And it's gonna be 1 and
then nine 0s inside of a list.
10:16
This is kind of a cool way to make
a list that has multiple pieces to it.
10:21
And then, let's add a new thing in here.
10:25
And we'll say if random.choice(odds),
and then we'll do all of this stuff.
10:28
So now it should only happen about
once every ten messages, on average.
10:37
You can adjust this frequency of course by
changing how many items are in this list
10:41
but that's it.
10:45
You should now have a bot plugin that
in real time replaces a random word in
10:46
a random sentence in a random
message with the word banana.
10:50
Nothing more important
has ever been coded but
10:54
now you can build all sorts of
great Slack bots with the RTM API.
10:56
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