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bss:v3.1.0
bss:v3.0.0
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compare: bss:1cf0364268452ccc8470d082de3abd87eda337b1
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bss:master
bss:v3.1.0
bss:v3.0.0

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4 changed files with 37 additions and 42 deletions
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18
ircbot/migrations/0019_ircchannel_discord_bridge.py
View File

@ -1,18 +0,0 @@
# Generated by Django 3.2.18 on 2023-02-16 22:38
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('ircbot', '0018_ircserver_replace_irc_control_with_markdown'),
]
operations = [
migrations.AddField(
model_name='ircchannel',
name='discord_bridge',
field=models.CharField(blank=True, default='', max_length=32),
),
]

2
ircbot/models.py
View File

@ -104,8 +104,6 @@ class IrcChannel(models.Model):
markov_learn_from_channel = models.BooleanField(default=True)
discord_bridge = models.CharField(default='', max_length=32, blank=True)
class Meta:
"""Settings for the model."""

36
markov/lib.py
View File

@ -1,16 +1,17 @@
"""Provide methods for manipulating markov chain processing."""
import logging
from random import SystemRandom as sysrand
import random
from django.db.models import Sum
from markov.models import MarkovContext, MarkovState, MarkovTarget
log = logging.getLogger(__name__)
log = logging.getLogger('markov.lib')
def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bias=2, max_tries=5):
"""Combine multiple sentences together into a coherent sentence."""
"""String multiple sentences together into a coherent sentence."""
tries = 0
line = []
min_words_per_sentence = min_words / sentence_bias
@ -22,7 +23,7 @@ def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bia
else:
if len(line) > 0:
if line[-1][-1] not in [',', '.', '!', '?', ':']:
line[-1] += sysrand.choice(['?', '.', '!'])
line[-1] += random.choice(['?', '.', '!'])
tries += 1
@ -32,6 +33,7 @@ def generate_line(context, topics=None, min_words=15, max_words=30, sentence_bia
def generate_longish_sentence(context, topics=None, min_words=15, max_words=30, max_tries=100):
"""Generate a Markov chain, but throw away the short ones unless we get desperate."""
sent = ""
tries = 0
while tries < max_tries:
@ -50,19 +52,20 @@ def generate_longish_sentence(context, topics=None, min_words=15, max_words=30,
def generate_sentence(context, topics=None, min_words=15, max_words=30):
"""Generate a Markov chain."""
words = []
# if we have topics, try to work from it and work backwards
if topics:
topic_word = sysrand.choice(topics)
topic_word = random.choice(topics)
topics.remove(topic_word)
log.debug("looking for topic '%s'", topic_word)
log.debug("looking for topic '{0:s}'".format(topic_word))
new_states = MarkovState.objects.filter(context=context, v=topic_word)
if len(new_states) > 0:
log.debug("found '%s', starting backwards", topic_word)
log.debug("found '{0:s}', starting backwards".format(topic_word))
words.insert(0, topic_word)
while len(words) <= max_words and words[0] != MarkovState._start2:
log.debug("looking backwards for '%s'", words[0])
log.debug("looking backwards for '{0:s}'".format(words[0]))
new_states = MarkovState.objects.filter(context=context, v=words[0])
# if we find a start, use it
if MarkovState._start2 in new_states:
@ -84,7 +87,7 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
i = len(words)
while words[-1] != MarkovState._stop:
log.debug("looking for '%s','%s'", words[i-2], words[i-1])
log.debug("looking for '{0:s}','{1:s}'".format(words[i-2], words[i-1]))
new_states = MarkovState.objects.filter(context=context, k1=words[i-2], k2=words[i-1])
log.debug("states retrieved")
@ -100,7 +103,7 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
words.append(MarkovState._stop)
elif len(target_hits) > 0:
# if there's a target word in the states, pick it
target_hit = sysrand.choice(target_hits)
target_hit = random.choice(target_hits)
log.debug("found a topic hit %s, using it", target_hit)
topics.remove(target_hit)
words.append(target_hit)
@ -126,6 +129,7 @@ def generate_sentence(context, topics=None, min_words=15, max_words=30):
def get_or_create_target_context(target_name):
"""Return the context for a provided nick/channel, creating missing ones."""
target_name = target_name.lower()
# find the stuff, or create it
@ -152,6 +156,7 @@ def get_or_create_target_context(target_name):
def get_word_out_of_states(states, backwards=False):
"""Pick one random word out of the given states."""
# work around possible broken data, where a k1,k2 should have a value but doesn't
if len(states) == 0:
states = MarkovState.objects.filter(v=MarkovState._stop)
@ -163,9 +168,9 @@ def get_word_out_of_states(states, backwards=False):
# this being None probably means there's no data for this context
raise ValueError("no markov states to generate from")
hit = sysrand.randint(0, count_sum)
hit = random.randint(0, count_sum)
log.debug("sum: %s hit: %s", count_sum, hit)
log.debug("sum: {0:d} hit: {1:d}".format(count_sum, hit))
states_itr = states.iterator()
for state in states_itr:
@ -178,12 +183,13 @@ def get_word_out_of_states(states, backwards=False):
break
log.debug("found '%s'", new_word)
log.debug("found '{0:s}'".format(new_word))
return new_word
def learn_line(line, context):
"""Create a bunch of MarkovStates for a given line of text."""
log.debug("learning %s...", line[:40])
words = line.split()
@ -194,7 +200,7 @@ def learn_line(line, context):
return
for i, word in enumerate(words):
log.debug("'%s','%s' -> '%s'", words[i], words[i+1], words[i+2])
log.debug("'{0:s}','{1:s}' -> '{2:s}'".format(words[i], words[i+1], words[i+2]))
state, created = MarkovState.objects.get_or_create(context=context,
k1=words[i],
k2=words[i+1],

23
markov/models.py
View File

@ -1,22 +1,30 @@
"""Save brain pieces as markov chains for chaining."""
"""
markov/models.py --- save brain pieces for chaining
"""
import logging
from django.db import models
log = logging.getLogger(__name__)
log = logging.getLogger('markov.models')
class MarkovContext(models.Model):
"""Define contexts for Markov chains."""
name = models.CharField(max_length=200, unique=True)
def __str__(self):
"""Provide string representation."""
"""String representation."""
return "{0:s}".format(self.name)
class MarkovTarget(models.Model):
"""Define IRC targets that relate to a context, and can occasionally be talked to."""
name = models.CharField(max_length=200, unique=True)
@ -25,11 +33,13 @@ class MarkovTarget(models.Model):
chatter_chance = models.IntegerField(default=0)
def __str__(self):
"""Provide string representation."""
"""String representation."""
return "{0:s} -> {1:s}".format(self.name, self.context.name)
class MarkovState(models.Model):
"""One element in a Markov chain, some text or something."""
_start1 = '__start1'
@ -44,8 +54,6 @@ class MarkovState(models.Model):
context = models.ForeignKey(MarkovContext, on_delete=models.CASCADE)
class Meta:
"""Options for the model itself."""
index_together = [
['context', 'k1', 'k2'],
['context', 'v'],
@ -57,5 +65,6 @@ class MarkovState(models.Model):
unique_together = ('context', 'k1', 'k2', 'v')
def __str__(self):
"""Provide string representation."""
"""String representation."""
return "{0:s},{1:s} -> {2:s} (count: {3:d})".format(self.k1, self.k2, self.v, self.count)