def getGenerator():
colPrint(
"\nInitializing AI Engine! (This might take a few minutes)\n",
colors["loading-message"],
)
models = [x for x in Path('models').iterdir() if x.is_dir()]
if not models:
raise FileNotFoundError(
'There are no models in the models directory! You must download a pytorch compatible model!'
)
elif len(models) > 1:
colPrint(
"You have multiple models in your models folder. Please select one to load:",
colors['message'])
for n, model_path in enumerate(models):
colPrint("{}: {}".format(n, model_path.name), colors['menu'])
model = models[getNumberInput(len(models) - 1)]
else:
model = models[0]
logger.info("Using model: " + str(model))
return GPT2Generator(
model_path=model,
generate_num=settings.getint("generate-num"),
temperature=settings.getfloat("temp"),
top_k=settings.getint("top-keks"),
top_p=settings.getfloat("top-p"),
repetition_penalty=settings.getfloat("rep-pen"),
)
def instructions():
if settings.getboolean("console-bell"):
bell = "on"
else:
bell = "off"
if settings.getboolean("action-d20"):
d20 = "on"
else:
d20 = "off"
print(
'\033[' + colors["instructions"] + 'm' +
'AID2: Clover Edition Instructions: \n Enter actions starting with a verb ex. "go to the tavern" or "attack the orc."\n To speak enter say "(thing you want to say)" or just "(thing you want to say)"'
)
print('The following commands can be entered for any action:')
print(
' "/revert" Reverts the last action allowing you to pick a different action.'
)
print(' "/quit" Quits the game and saves')
print(
' "/menu" Starts a new game and saves your current one')
print(' "/retry" Retries the last action')
print(' "/restart" Restarts the current story')
print(
' "/print" Prints a transcript of your adventure (without extra newline formatting)'
)
print(' "/help" Prints these instructions again')
print(
' "/set SETTING VALUE" Sets the specified setting to the specified value.:'
)
print(
' temp Higher values make the AI more random. Default: 0.4 | Current:',
settings.getfloat("temp"))
print(
' rep-pen Controls how repetitive the AI is allowed to be. Default: 1.2 | Current:',
settings.getfloat("rep-pen"))
print(
' text-wrap-width Maximum width of lines printed by computer. Default: 80 | Current:',
settings.getint("text-wrap-width"))
print(
' console-bell Beep after AI generates text? Default: on | Current:',
bell)
print(
' top-keks Number of words the AI can randomly choose. Default: 20 | Current:',
settings.getint("top-keks"))
print(' generate-num Default: 60 | Current:',
settings.getint("generate-num"))
print(' top-p Default: 0.9 | Current:',
settings.getfloat("top-p"))
print(' log-level Default: 3 | Current:',
settings.getint("log-level"))
print(
' action-sugg How many actions to generate, 0 is off. Default: 4 | Current:',
settings.getint("action-sugg"))
print(
' action-d20 Make actions difficult. Default: on | Current:',
d20)
print(
' action-temp How random the suggested actions are. Default: 1 | Current:',
settings.getfloat("action-temp"), '\033[39m')
def get_suggestion(self):
return re.sub('\n.*', '',
self.generator.generate_raw(
self.get_story() + "\n\n> You",
self.context,
temperature=settings.getfloat('action-temp'),
top_p=settings.getfloat('top-p'),
top_k=settings.getint('top-keks'),
repetition_penalty=1))
def getSuggestion(self):
#temporary fix (TODO)
return re.sub(
'\n.*', '',
self.generator.generate_raw(
self.getStory() + "\n\n> You",
self.prompt,
temperature=settings.getfloat('action-temp'),
top_p=settings.getfloat('top-p'),
top_k=settings.getint('top-keks'),
repetition_penalty=1))
def to_dict(self):
res = {}
res["temp"] = settings.getfloat('temp')
res["top-p"] = settings.getfloat("top-p")
res["top-keks"] = settings.getint("top-keks")
res["rep-pen"] = settings.getfloat("rep-pen")
res["context"] = self.context
res["memory"] = self.memory
res["actions"] = self.actions
res["results"] = self.results
return res
def act(self, action):
assert (self.context.strip() + action.strip())
assert (settings.getint('top-keks') is not None)
self.actions.append(format_result(action))
result = self.generator.generate(
self.get_story() + action,
self.context + ' '.join(self.memory),
temperature=settings.getfloat('temp'),
top_p=settings.getfloat('top-p'),
top_k=settings.getint('top-keks'),
repetition_penalty=settings.getfloat('rep-pen'))
self.results.append(format_result(result))
return self.results[-1]
def act(self, action):
assert (self.prompt + action)
results = []
for i in range(self.numResults):
assert (settings.getint('top-keks') is not None)
results.append(
self.generator.generate(
self.getStory() + action,
self.prompt,
temperature=settings.getfloat('temp'),
top_p=settings.getfloat('top-p'),
top_k=settings.getint('top-keks'),
repetition_penalty=settings.getfloat('rep-pen')))
#self.longTermMemory.join('\n\n'), self.prompt))
self.story.append([action, results])
return results
def get_action(self):
# While we want the story to be on track, but not to on track that it loops
# the actions can be quite random, and this helps inject some user curated randomness
# and prevent loops. So lets make the actions quite random, and prevent duplicates while we are at it
# what to feed to model?
mem_ind = random.randint(1, 6) # How many steps to include
sample = random.randint(0, 1) # Random steps from history?
include_prompt = random.randint(0, 1) # Include the initial promts
predicates = ['You try to ', 'You say "', 'You start to ',
'"'] # The model has to continue from here
predicate = random.sample(predicates, 1)[0]
action_prompt = self.story_manager.story_context(
mem_ind, sample, include_prompt)
action_prompt[-1] = action_prompt[-1].strip() + "\n> " + predicate
result_raw = self.story_manager.generator.generate_raw(
action_prompt,
generate_num=settings.getint("action-generate-num"),
temperature=settings.getfloat("action-temp"),
stop_tokens=self.story_manager.generator.tokenizer.encode(
["<|endoftext|>", "\n", ">"])
# stop_tokens=self.generator.tokenizer.encode(['>', '<|endoftext|>'])
)
logger.info(
"get_action (mem_ind=%s, sample=%s, include_prompt=%s, predicate=`%r`) -> %r",
mem_ind, sample, include_prompt, predicate, result_raw)
result = predicate + result_raw.lstrip()
result = clean_suggested_action(
result, min_length=settings.getint("action-min-length"))
# Sometimes the suggestion start with "You" we will add that on later anyway so remove it here
result = re.sub("^ ?[Yy]ou try to ?", "You ", result)
result = re.sub("^ ?[Yy]ou start to ?", "You ", result)
result = re.sub("^ ?[Yy]ou say \"", "\"", result)
result = re.sub("^ ?[Yy]ou ?", "", result)
return result
