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# creature script that simulates a conversation with a creature
# configuration:
## name attached to the creature's messages
NAME = 'summer'
## name attached to the user's messages
MYNAME = 'you'
## "corpus file" containing examples to learn from
CORPUS = 'summer.corpus.txt'
# end of configuration
import random
import re
filters = list()
bigrams = dict()
trigrams = dict()
with open(CORPUS, 'r') as f:
corpus = '\n' + f.read() + '\nUSERTEXT'
tokens = ['\nUSERTEXT', ' CREATURETEXT', ' ENDCONVO']
def tokenise(tokens, text):
result = list()
for word in re.split('(?=[^A-Za-z:0-9<>])', text):
if word not in tokens:
tokens.append(word)
result.append(tokens.index(word))
return result
def detokenise(tokens, stuff):
return ''.join([tokens[t] for t in stuff])
def add_filters(filters, material, window):
start = 0
while start + window < len(material):
filt = set()
after = set()
for t in material[start:start+window]:
if t > 2: filt.add(t)
for t in material[start+window:start+int(window*1.5)]:
after.add(t)
if len(filt) > 0: filters.append((filt, after))
start += window // 2
def add_ngrams(ngrams, material, window):
for i in range(len(material) - window):
key = tuple(material[i:i+window])
if key not in ngrams:
ngrams[key] = dict()
gram = material[i+window]
if gram not in ngrams[key]:
ngrams[key][gram] = 0
ngrams[key][gram] += 1
def add_new_to_corpus(question):
print(f" * {NAME} didn't know what to say. Please suggest something appropriate:")
newanswer = input('> ')
newstuff = tokenise(tokens, newanswer)
with open(CORPUS, 'a') as f:
f.write(f'USERTEXT {question} CREATURETEXT {newanswer}\n')
return newstuff
def infer(filters, bigrams, trigrams, context):
scores = dict()
nkey = tuple(context[-2:])
if nkey in trigrams:
for a, m in trigrams[nkey].items():
if a not in scores:
scores[a] = 0.001
#scores[a] += m * 0.2 * (n+1)
permit_bullshit = len(scores) <= 0
bigram = bigrams[(context[-1],)]
for f, a in filters:
m = 0
for t in context[-len(f):]:
if t in f:
m += 0.1 if t <= 2 else 1
for n in a:
if n in scores:
scores[n] += m / len(f)
elif permit_bullshit:
scores[n] = m / len(f) + (bigram[n] if n in bigram else 0)
choices = random.choices(list(scores.items()), list(scores.values()), k=1)
choice = max(choices, key=lambda c: c[1])[0]
maxv = max([c[1] for c in choices])
#print(f'confidence: {maxv}')
if maxv < 1: return -1
return choice
material = tokenise(tokens, corpus)
add_filters(filters, material, 2)
add_filters(filters, material, 4)
add_filters(filters, material, 8)
add_filters(filters, material, 16)
add_filters(filters, material, 32)
add_ngrams(bigrams, material, 1)
add_ngrams(trigrams, material, 2)
newstuff = []
shouldask = True
while True:
if shouldask:
try:
question = input(f'<{MYNAME}> ')
except EOFError:
exit()
newstuff += tokenise(tokens, '\nUSERTEXT ' + question + ' CREATURETEXT')
promptlength = len(newstuff)
done = False
shouldquit = False
maxn = 5000
while not done and maxn > 0:
nexttoken = infer(filters, bigrams, trigrams, newstuff)
if (nexttoken == -1):
answertokens = add_new_to_corpus(question)
newstuff += tokenise(tokens, ' ') + answertokens + [0]
nexttoken = 0
done = True
question = detokenise(tokens, answertokens)
continue
done = nexttoken <= 2
shouldquit = nexttoken == 2
shouldask = nexttoken == 0
newstuff.append(nexttoken)
maxn -= 1
answer = detokenise(tokens, newstuff[promptlength:-1])[1:].strip()
if len(answer) > 0:
print(f'<{NAME}> ', end='')
print(answer)
if shouldquit: break
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