def create_passive(doc, sub_idx, root_idx, obj_index, obj_start, obj_end, negation_availability):
# if str(doc[obj_start:obj_end]
if len(doc) > obj_end + 2:
if negation_availability:
if inflect.singular_noun(str(doc[obj_index])) is False:
# sentence[root_verb].lemma_
print(str(doc[obj_start:obj_end]) + " is not " + str(
getInflection(doc[root_idx].lemma_, tag='VBN')[0]) + " " + str(doc[obj_end:]))
else:
print(str(doc[obj_start:obj_end]) + " are not " + str(
getInflection(doc[root_idx].lemma_, tag='VBN')[0]) + " " + str(doc[obj_end:]))
else:
if inflect.singular_noun(str(doc[obj_index])) is False:
print(str(doc[obj_start:obj_end]) + " is " + str(
getInflection(doc[root_idx].lemma_, tag='VBN')[0]) + " " + str(doc[obj_end:]))
else:
print(str(doc[obj_start:obj_end]) + " are " + str(
getInflection(doc[root_idx].lemma_, tag='VBN')[0]) + " " + str(doc[obj_end:]))
# print(negation_availability)
else:
if negation_availability:
if inflect.singular_noun(str(doc[obj_index])) is False:
print(str(doc[obj_start:obj_end]) + " is not " + str(
getInflection(doc[root_idx].lemma_, tag='VBN')[0]) + ".")
else:
print(str(doc[obj_start:obj_end]) + " are not" + str(getInflection(doc[root_idx].lemma_, tag='VBN')[0])+".")
else:
if inflect.singular_noun(str(doc[obj_index])) is False:
print(str(doc[obj_start:obj_end]) + " is " + str(getInflection(doc[root_idx].lemma_, tag='VBN')[0])+".")
else:
print(str(doc[obj_start:obj_end]) + " are " + str(getInflection(doc[root_idx].lemma_, tag='VBN')[0])+".")
# print(negation_availability)
print(" ")
def testGetInflection(self):
self.assertEqual(
pyinflect.getInflection('xxfocus', 'NN', inflect_oov=False), None)
self.assertEqual(
pyinflect.getInflection('xxfocus', 'NN', inflect_oov=True),
('xxfocus', ))
self.assertEqual(
pyinflect.getInflection('xxfocus', 'NNS', inflect_oov=True),
('xxfocuses', 'xxfoci'))
self.assertEqual(
pyinflect.getInflection('xxban', 'VBG', inflect_oov=True),
('xxbaning', 'xxbanning'))
self.assertEqual(
pyinflect.getInflection('xxdim', 'JJR', inflect_oov=True),
('xxdimer', 'xxdimmer'))
def makethird(string, male=True):
string = string.replace('i ', '', 1)
for i in string.split(' '):
if pyinflect.getInflection(i, tag='VBZ'):
if male:
return string.replace(
i,
pyinflect.getInflection(i, tag='VBZ')[0]).replace(
'my', 'his').replace('mine', 'his').replace(
' i ', ' he ').replace('am', 'is')
else:
return string.replace(
i,
pyinflect.getInflection(i, tag='VBZ')[0]).replace(
'my', 'her').replace('mine', 'her').replace(
' i ', ' she ').replace('am', 'is')
def create_passive(doc, root_idx, obj_index, obj_start, obj_end,
negation_availability):
# 'obj_end + 2' check whether sent ends with fullstop or not. If end with '.' need not to keep space in-between
print(doc)
try:
if len(doc) > obj_end + 2:
if negation_availability:
if inflect.singular_noun(str(doc[obj_index])) is False:
return str(doc[obj_start:obj_end]) + " is not " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + " " + str(
doc[obj_end:])
else:
return str(doc[obj_start:obj_end]) + " are not " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + " " + str(
doc[obj_end:])
else:
if inflect.singular_noun(str(doc[obj_index])) is False:
return str(doc[obj_start:obj_end]) + " is " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + " " + str(
doc[obj_end:])
else:
return str(doc[obj_start:obj_end]) + " are " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + " " + str(
doc[obj_end:])
# print(negation_availability)
else:
# sentence ending with object ;need keep a fullstop just after word without a space
if negation_availability:
if inflect.singular_noun(str(doc[obj_index])) is False:
return str(doc[obj_start:obj_end]) + " is not " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + "."
else:
return str(doc[obj_start:obj_end]) + " are not" + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + "."
else:
if inflect.singular_noun(str(doc[obj_index])) is False:
return str(doc[obj_start:obj_end]) + " is " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + "."
else:
return str(doc[obj_start:obj_end]) + " are " + str(
getInflection(doc[root_idx].lemma_,
tag='VBN')[0]) + "."
# print(negation_availability)
except:
return str(doc)
def process(self, statement, additional_response_selection_parameters=None):
confidence = 0
dis_word = False
if any_in(['distinguish', 'disfigure', 'distinct', 'distinction', 'distant',
'distance', 'distribution', 'distilled'], self.normalized):
confidence = 0
else:
logging.info(
"DisAdapter: Starting Advanced scan. dis_word == {}".format(self.dis)[0])
dis_word = self.dis[3:]
logging.info("DisAdapter: Distilled word == {}".format(dis_word))
sia = SentimentIntensityAnalyzer().polarity_scores(dis_word)
if dis_word[0] in ['a', 'e', 'i', 'o', 'u', 'g', 'm', 'p']:
confidence += 0.4
if 'infect' in dis_word:
confidence -= 0.3
if 'spirit' in dis_word:
confidence += 0.2
if any_in(['play', 'pensary', 'pense', 'patch', 'port',
'persal', 'perse', 'persion', 'praise'], dis_word):
confidence -= 0.2
confidence += sia['neg']
inflection = getInflection(
self.chatbot.lp.tokenize(self.dis)[0].lemma_, 'VBD')
if inflection is None:
past_participle_form_of_verb = self.dis
else:
past_participle_form_of_verb = inflection[0]
if 'you' in self.normalized:
response = random_response(DIS_RESPONSES_YOU).format(
past_participle_form_of_verb)
emotion = Emotion.angry_non_expressive
elif 'I' in self.normalized:
response = "{} {}".format(random_response(
DIS_RESPONSES_I), random_response(CONSOLATION))
emotion = Emotion.angel
else:
nn = None
pn = None
tokenized = spac_token(statement, chatbot=self.chatbot)
for i in tokenized:
if (i.pos_ == "NOUN") or (i.pos_ == 'PROPN'):
nn = i.text
elif i.pos_ == 'PRON':
pn = i.text
if not (nn or pn):
response = 'Lol. What?'
emotion = Emotion.seriously
else:
response = random_response(DIS_RESPONSES_HIM).format(nn or pn)
emotion = Emotion.cry_overflow
selected_statement = SugaroidStatement(response, chatbot=True)
selected_statement.confidence = confidence
selected_statement.emotion = emotion
selected_statement.adapter = None
return selected_statement
def testGetInflection03(self):
self.assertEqual(pyinflect.getAllInflections('watch'),
{'NN': ('watch',), 'NNS': ('watches',), 'VB': ('watch',), 'VBP': ('watch',), 'VBD': ('watched',),
'VBN': ('watched',), 'VBG': ('watching',), 'VBZ': ('watches',)})
self.assertEqual(pyinflect.getAllInflections('watch', 'V'),
{'VB': ('watch',), 'VBP': ('watch',), 'VBD': ('watched',), 'VBN': ('watched',),
'VBG': ('watching',), 'VBZ': ('watches',)})
self.assertEqual(pyinflect.getInflection('watch', 'VBD'), ('watched',))
self.assertEqual(pyinflect.getAllInflections('watch', 'A'), {})
def singular_sent(negation_availability, sentense, aux_idx, root_verb, base_verb):
"""conversion of singular sent """
if negation_availability:
return str(sentense[:aux_idx]).strip() + " does " + str(
sentense[aux_idx + 1:root_verb]).strip() + " " + base_verb + " " + str(
sentense[root_verb + 1:]).strip()
# VBZ - verb, 3rd person singular present
return str(sentense[:aux_idx]).strip() + " " + str(
sentense[aux_idx + 1:root_verb]).strip() + getInflection(base_verb, tag='VBZ')[0] + " " + str(
sentense[root_verb + 1:]).strip()
def generate(N, j, word, pos, wordseq, replacements):
alternatives = [[] for i in range(N)]
for i in range(0, N):
alternatives[i] = wordseq[:]
assert isinstance(replacements, set)
rep_lemma, *rep_lemmas = random.sample(replacements, 1)
rep = str(rep_lemma)
# Inflect to get the right form of the word
if not (word.xpos == "JJ" or word.xpos == "NN"):
if not (getInflection(rep_lemma, tag=word.xpos) == None):
rep, *rep_list = getInflection(rep_lemma, tag=word.xpos)
# Mimic capitalization and plurality
rep_words = rep.split("_")
l = len(rep_words)
if (pos == 'n'):
rep_words[l - 1] = mimic_plurality(rep_words[l - 1], word.text)
rep_words[0] = mimic_capitalization(rep_words[0], word.text)
# Fix the form of the determinier, i.e., a or an
last_index = j - 1
if (last_index >= 0):
last_word = alternatives[i][last_index]
if (last_word.lower() == "a" or last_word.lower() == "an"):
if starts_with_vowel_sound(rep_words[0].lower()):
alternatives[i][last_index] = mimic_capitalization(
"an", last_word)
else:
alternatives[i][last_index] = mimic_capitalization(
"a", last_word)
alternatives[i][j] = " ".join(rep_words)
# for i in range(0, N):
# print(" ".join(alternatives[i]))
return alternatives
def inflect_sing_plural(self, noun: str) -> (str, str):
'''
Inflect plural and singular for word.
:param noun: noun to inflect
:return: sing and plural of noun
'''
if noun in self.inflection_dict:
sing = noun
plu = self.inflection_dict[noun]
else:
lemma = LANG_MODEL(noun)[0].lemma_
sing = getInflection(lemma, 'NN')
plu = getInflection(lemma, 'NNS')
if sing is not None and plu is not None:
sing = sing[0]
plu = plu[0]
else:
raise WordNotSupportedError(
'Pyinflect could not inflect {}. Please add word '
'to inflection dict.'.format(noun))
return sing, plu
def disambiguate_verb_kgtk() -> Any:
"""Disambiguates verbs from event description and return candidate qnodes.
Returns:
A JSON response.
"""
if not request.json:
abort(HTTPStatus.BAD_REQUEST)
event_description = request.json["event_description"]
cleaned_description = event_description.replace("/", " ").replace("_", " ")
event_verb = get_verb_lemma(nlp, cleaned_description)
cached_file = KGTK_EVENT_CACHE / f"{event_verb}.json"
if cached_file.is_file():
with open(cached_file) as f:
return json.load(f)
kgtk_json = get_request_kgtk(event_verb)
event_verb_participle = getInflection(event_verb, tag="VBG")
if event_verb_participle and event_verb_participle != event_verb:
kgtk_json += get_request_kgtk(event_verb_participle[0])
if not kgtk_json:
return {"event_verb": kgtk_json, "options": []}
unique_candidates = filter_duplicate_candidates(kgtk_json)
options = []
top3 = wikidata_topk(SS_MODEL, cleaned_description, unique_candidates, k=3)
for candidate in top3:
option = {
"qnode": candidate["qnode"],
"rawName": candidate["label"][0],
"definition": candidate["description"][0],
}
if option not in options:
options.append(option)
response = {"event_verb": event_verb, "options": options}
with open(cached_file, "w") as f:
json.dump(response, f)
return response
def testGetInflection04(self):
self.assertEqual(pyinflect.getAllInflections('watch', 'A'), {})
self.assertEqual(pyinflect.getInflection('watch', 'JJ'), None)
self.assertEqual(pyinflect.getInflection('watch', 'VBD'),
('watched', ))
def testGetInflection02(self):
self.assertEqual(pyinflect.getInflection('squirrel', 'NN'),
('squirrel', ))
self.assertEqual(pyinflect.getInflection('squirrel', 'NNS'),
('squirrels', 'squirrel'))
def singular_sent(sentence, root_verb, base_verb):
"""conversion of singular sent """
# get the singular present tense verb form
return str(sentence[:root_verb]).strip() + " " + getInflection(
base_verb, tag='VBZ')[0] + " " + str(
sentence[root_verb + 1:]).strip()
#!/usr/bin/python3
import sys
sys.path.insert(0, '..') # make '..' first in the lib search path
from pyinflect import getAllInflections, getInflection
if __name__ == '__main__':
print(getAllInflections('be'))
print(getInflection('be', tag='VBD'))
print()
print(getAllInflections('watch'))
print(getAllInflections('watch', pos_type='V'))
print(getInflection('watch', tag='VBD'))
print()
def interact_model(
#model_name='1558M',
model_name='fanfic',
seed=None,
nsamples=1,
batch_size=1,
length=150,
temperature=1,
top_k=0,
):
os.environ['KMP_WARNINGS'] = 'off'
inventory = set()
if batch_size is None:
batch_size = 1
assert nsamples % batch_size == 0
f = open("carrying.txt", "r", errors='ignore')
if f.mode == 'r':
carrying_prompt = f.read()
f = open("items.txt", "r", errors='ignore')
if f.mode == 'r':
items_prompt = f.read()
f = open("animate.txt", "r", errors='ignore')
if f.mode == 'r':
animate_prompt = f.read()
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained("model_v5")
while True:
np.random.seed(seed)
print("***")
msg = (
"\n\n\n\n\n\n\n\n\n\n\nINFINITE ADVENTURE\n\n\n\n"
"INSTRUCTIONS: Infinite Adventure is primarily an exploration "
"game. You can type anything you want at the prompt, as "
"long as it starts with a verb. A few verbs have special effects:\n\n"
" * go LOCATION -- takes you to that place. If the place already has a "
"description, it appears down below. If not, you can still go "
"there if it appears in the description but it takes a minute "
"to generate the new description.\n\n"
" * get OBJECT -- allows you to pick up some objects that appear in the "
"description. If they are too big or can't be picked up, the "
"system will tell you.\n\n"
" * use OBJECT -- will only work with items in your inventory.\n\n"
" * drop OBJECT -- drops an object from your inventory.\n\n"
" * talk PERSON -- talk to a person in the current room. \n\n"
" * inventory -- prints your inventory.\n\n"
" * observe -- shows a list of items in the room. Useful if you "
" want to pick something up not explicitly in the description.\n\n"
" * fight -- asks opponent, what you want to do, and what weapon "
"from your inventory (or fists, foot, etc...) you want to use.\n\n"
" * save -- saves the game.\n\n"
" * regenerate -- changes the description of the current room to a "
"new one. This is mainly used for fixing descriptions that don't "
"make sense.\n\n"
" * quit -- quits the game.\n\n"
"Each location records what has happened at that location and uses "
"that record to decide what happens next. You can, however, bring "
"objects from one location to another.\n\n\n\n")
print(msg)
input_load_flag = input("Would you like to load a game? (Y or N) >>>")
if input_load_flag == "Y" or input_load_flag == "y":
### load game
interactive_flag = 1
print("please choose:")
files = []
for file in os.listdir("./"):
if file.endswith(".pkl"):
print(file[:-4])
files.append(file)
filename = ""
while not (filename in files):
filename = input("please check spelling >>> ")
if filename[-4:] != ".pkl":
filename = filename + ".pkl"
file = open("./" + filename, 'rb')
data = pickle.load(file)
descriptions = data[0]
rooms = data[1]
room_connections = data[2]
input_persona = data[3]
input_location = data[4]
input_atmosphere = data[5]
current_room = 0
inventory = set()
if len(data) > 6:
inventory = set(data[6])
current_room = data[7]
else:
### start a new game without loading
precalculate = input(
"Would you like to pregenerate room descriptions? It takes some time upfront, but gameplay is faster. (Y or N) >>> "
)
if precalculate in {"y", "Y", "yes"}:
interactive_flag = 0
else:
interactive_flag = 1
input_persona = input(
"Describe your character in a few words. You are >>> ")
input_location = input(
"Describe where the adventure takes place in a few words.\n You would like to explore the >>> "
)
input_atmosphere = input(
"Describe the feeling of the place in a few adjectives, separated by commas. The "
+ input_location + " is >>> ")
if input_location.startswith('the '):
input_location = input_location[4:]
if input_location.startswith('The '):
input_location = input_location[4:]
f = open("rooms.txt", "r", errors='ignore')
if f.mode == 'r':
contents = f.read()
raw_text = contents + "\r\n" + input_atmosphere + " " + input_location + ":"
print('generating places in the ' + input_location + '...')
rooms = []
for _ in range(4 // batch_size):
print("*", end=" ")
text = generate(tokenizer, model, raw_text, 70, 30)
rooms = rooms + rooms_cleanup(text)
#remove duplicates from the list of rooms
set_rooms = set(rooms)
rooms = list(set_rooms)
print(rooms)
room_connections = create_graph(len(rooms), len(rooms) * 3)
print(room_connections)
descriptions = ['' for i in range(0, len(rooms) + 1)]
current_room = 0
generate_more_flag = 0
describe_flag = 1
while True:
### main loop
if "".__eq__(descriptions[current_room]):
# print("\n" + rooms[current_room] + "\n")
description_prompt = 'The following excerpt from a novel is a long and detailed description of the ' + input_atmosphere + ' things found in the ' + rooms[
current_room] + ':\nYou were ' + input_persona + '. You were in the ' + rooms[
current_room] + ' within the ' + input_location + '. Here is what you saw there:'
#description_prompt = 'You were ' + input_persona + '. Previously, you were within a small stone room. Here is what you beheld: There were two chairs and a small table covered with various odds and ends. There was one small window and the floors were unswept. Later, you were in the ' + rooms[current_room] + ' of a ' + input_atmosphere + ' ' + input_location + '. You looked about at the furnishings of the ' + rooms[current_room] +'.'
print("running description generator")
text = generate(tokenizer, model, description_prompt, 120, 30)
descriptions[current_room] = description_cleanup(text)
if describe_flag == 1:
print("\n" + rooms[current_room] + "\n")
wrap_print(descriptions[current_room])
print("\n other nearby areas:", end=" ")
describe_flag = 0
for index in room_connections[current_room]:
print(rooms[index], end=" | ")
if interactive_flag == 0:
filename = './' + input_location + '.pkl'
afile = open(filename, 'wb')
pickle_file = (descriptions, rooms, room_connections,
input_persona, input_location, input_atmosphere,
list(inventory), current_room)
pickle.dump(pickle_file, afile)
afile.close()
describe_flag = 1
print("\n saved game.")
if interactive_flag == 1:
next_command = input("\n >>>")
next_command_split = next_command.split(" ", 1)
next_verb = next_command_split[0]
next_object = 'none'
if len(next_command_split) > 1:
next_object = next_command_split[1]
next_verb_past = getInflection(next_verb.strip(",.:-"),
tag='VBD')
if next_verb_past is None:
next_verb_past = "went"
next_verb = "go"
next_object = "ERROR"
else:
next_verb_past = next_verb_past[0]
### verb handling
if next_verb == "go":
describe_flag = 0
for i in range(0, len(rooms)):
if rooms[i].lower() == next_object.lower():
current_room = i
describe_flag = 1
if describe_flag == 0:
if next_object in descriptions[current_room]:
rooms.append(next_object)
room_list_length = len(rooms)
room_connections.append({current_room})
room_connections[current_room].add(
room_list_length - 1)
current_room = room_list_length - 1
descriptions.append("")
describe_flag = 1
else:
wrap_print(
"Sorry, I don't recognize that. Please choose a place using a word from the description."
)
elif next_verb in {"get", "grab"}:
if next_object in inventory:
print("You already have that.")
elif next_object in descriptions[current_room]:
carrying_generator = carrying_prompt + "\n" + next_object + ":"
print("checking to see if you can get the object... ")
text = generate(tokenizer, model, carrying_generator,
10, 1)
answer = text.split(" ")
if answer[1] == "okay":
print("You pick up the " + next_object + ".")
descriptions[current_room] = descriptions[
current_room] + "You picked up the " + next_object + ". "
inventory.add(next_object)
print("inventory: ")
print(inventory)
elif answer[1] == "too":
print("that's too big to carry.")
else:
print("I don't know how to do that.")
else:
wrap_print(
"Sorry, I don't recognize that. Please choose an object to get using a word from the description."
)
elif next_verb == "save":
filename = './' + input_location + '.pkl'
afile = open(filename, 'wb')
pickle_file = (descriptions, rooms, room_connections,
input_persona, input_location,
input_atmosphere, inventory, current_room)
pickle.dump(pickle_file, afile)
afile.close()
print("\n saved game.")
elif next_verb == "observe":
print(
"generating list of some objects found in this room..."
)
items = []
for _ in range(3 // batch_size):
text = generate(
tokenizer, model, items_prompt + "\n" +
rooms[current_room] + "of" + input_location + ":",
40, 30)
items = items + rooms_cleanup(text)
#remove duplicates from the list of items
set_items = set(items)
items = list(set_items)
comma_separated = ', '.join(items)
outtext = "\nThe following objects are also in the room: " + comma_separated
wrap_print(outtext)
descriptions[
current_room] = descriptions[current_room] + outtext
elif next_verb == "talk":
partner = next_object
if partner in descriptions[current_room]:
is_animate = animate_prompt + "\n" + partner + ":"
text = generate(tokenizer, model, is_animate, 4, 1)
animate_split = text.split("\n", 1)
if animate_split[0] == " inanimate":
print("The " + partner + " just sit/s there.\n")
continue_chat = "n"
else:
print("Talking to " + next_object)
continue_chat = "y"
random_room = random.choice(rooms)
full_talk_prompt = 'If the ' + partner + ' wants ' + input_persona + ' to go to the library, the ' + partner + ' might say, "You really need to the library." If ' + input_persona + ' is supposed to go to the beach, the ' + partner + ' could say, "It is important for you to find the beach." If ' + input_persona + ' has to go to the ' + random_room + ', the ' + partner + ' would say something like, "'
text = generate(tokenizer, model, full_talk_prompt,
50, 30)
split_text = text.split('"')
response = split_text[0]
full_talk_prompt = partner + ' says, "' + response + '"\n'
wrap_print(full_talk_prompt)
while continue_chat == "y":
you_say = input(
"What do you say? (Just press enter to quit chat mode.) >>>"
)
if you_say == '':
continue_chat = "n"
descriptions[current_room] = descriptions[
current_room] + '\nYou spoke with ' + partner + '.\n'
else:
talk_prompt = full_talk_prompt + input_persona + ' says, "' + you_say + '"\n' + partner + ', still trying to persuade ' + input_persona + ' to go to the ' + random_room + ' says: "'
#print("talk_prompt = " + talk_prompt)
text = generate(tokenizer, model,
talk_prompt, 50, 30)
#print("text =" + text)
split_text = text.split('"')
#print(split_text)
response = split_text[0]
printed = partner + ' says, "' + response + '"'
wrap_print(printed)
full_talk_prompt = talk_prompt + response + '"\n'
#print("full_talk_prompt = " + full_talk_prompt)
else:
print(
'try "talk PERSON" where "PERSON" is in the room description.'
)
elif next_verb == "regenerate":
descriptions[current_room] = ''
print("\n regenerating room description...")
describe_flag = 1
elif next_verb == "drop":
if next_object in inventory:
print("You drop the " + next_object + ".")
inventory.remove(next_object)
descriptions[current_room] = descriptions[
current_room] + "You dropped the " + next_object + " here. "
else:
print("That item isn't in your inventory.")
elif next_verb == "use":
for item in inventory:
if next_object.startswith(item):
print("you use the " + next_object + ".")
descriptions[current_room] = descriptions[
current_room] + "You used the " + next_object + ". "
generate_more_flag = 1
if generate_more_flag == 0:
print("You don't have that in your inventory.")
elif next_verb in {"fight", "punch", "stab", "attack", "kill"}:
#check if the enemy is in the description
hp = [0, 10, 10]
enemy = input("opponent >>>")
if enemy in descriptions[current_room]:
continue_fight = "y"
else:
continue_fight = "n"
print(
"That opponent doesn't appear in the room description."
)
if continue_fight == "y":
is_animate = animate_prompt + "\n" + enemy + ":"
text = generate(tokenizer, model, is_animate, 4, 1)
animate_split = text.split("\n", 1)
if animate_split[0] == " inanimate":
print("The " + enemy + " just sit/s there.\n")
continue_fight = "n"
#start the "continue fight" loop
weapon = 'fists'
while continue_fight == "y":
#get the action
raw_action = input("action (e.g. stab) >>> ")
action_split = raw_action.split(" ", 1)
action_present = action_split[0]
action = getInflection(action_present, tag='VBD')
if action is None:
action = "hit"
else:
if action[0] is None:
action = "hit"
else:
action = action[0]
if action in {
"quit", "stopped", "surrendered", "hid",
"escaped", "ran", "fled"
}:
print("You got away.")
continue_fight = "n"
break
#get the weapon and check it is in inventory
new_weapon = input(
"weapon (or press enter to use the same weapon) >>> "
)
if new_weapon == '':
pass
else:
weapon = new_weapon
possible_weapons = inventory.union({
"fists", "fist", "knee", "foot", "elbow", "head",
"forehead", "finger", "fingers", "teeth", "voice",
"hands", "hand", "feet", "knees", "elbows"
})
for possible in possible_weapons:
if possible.endswith(weapon):
weapon = possible
if weapon in possible_weapons:
#generate response
start_sentence = "You " + action + " " + enemy + " with your " + weapon
prompt = "You are " + input_persona + ". Your adversary, " + enemy + ", faced off agaist you. You attacked with a mighty stroke, slicing " + enemy + "'s arm.\n" + enemy + " fought back, wounding your shoulder.\n You pressed your attack, wounding " + enemy + "'s leg.\n" + enemy + " tried again, but missed.\nYou pressed forward and took a mighty swing, but " + enemy + " escaped.\n" + enemy + " charged, dealing a heavy wound.\nYou managed to deal a nearly fatal blow, almost killing " + enemy + ".\n" + enemy + " let loose an onslaught, lightly wounding your arm.\nYou struck, but " + enemy + " got away unscathed.\n" + enemy + " retaliated with a barrage, doing heavy damage.\nYou fought back, rushing " + enemy + " and knocking " + enemy + " to the ground.\nYou rallied and caught " + enemy + " offguard.\n" + enemy + " blocked and returned the attack with a vicious strike.\nYou managed to get past " + enemy + "'s defenses and dealt a wound.\n" + enemy + " lunged, but missed by a mile.\nYou feinted to the left and struck to the right, but missed doing any damage.\n" + enemy + " knocked you off your feet with a heavy blow.\nYou fired away, successfully penetrating " + enemy + "'s defense.\n" + start_sentence
text = generate(tokenizer, model, prompt, 40, 30)
text = other_cleanup(text)
text = start_sentence + text
sentences = text.split("\n")
paragraph_length = len(sentences)
sentences = sentences[0:min(paragraph_length, 3)]
for sentence in sentences:
sentence = sentence.strip()
if sentence == '':
sentences.remove(sentence)
# parse the response
for sentence in sentences:
#default is that no one is damaged
damaged = 0
# if the sentence starts with "you", then the enemy is the one damaged
yous = {'You', 'you'}
for term in yous:
if sentence.startswith(term):
damaged = 1
for term in yous:
# if the word "you" occurs somewhere else in the sentence, the player is the one damaged
if term in sentence:
if damaged == 0:
damaged = 2
wases = {'was', 'were'}
swapflag = 0
# if the sentence is a passive sentence, then who is damaged gets swapped
for term in wases:
if term in sentence:
swapflag = 1
if swapflag == 1:
if damaged == 2:
damaged = 1
else:
damaged = 2
# damage_flag=1
# if someone is killed, the damage brings hitpoints to zero
kills = {
"kills", "killed", "slay", "slayed",
"slays"
}
for term in kills:
if term in sentence:
hp[damaged] = 0
continue_fight = "n"
you_die = {
"you die", "you are killed",
"you are slain", "you are dead", "You die",
"You are killed", "You are slain",
"You are dead"
}
for term in you_die:
if term in sentence:
hp[2] = 0
continue_fight = "n"
dies = {"dies", "died", "die"}
for term in dies:
if term in sentence:
if hp[2] > 0:
hp[1] = 0
continue_fight = "n"
#if a miss is mentioned, assume no damage was done
misses = {
"escape", "escaped", "escapes", "try",
"tried", "tries", "miss", "missing",
"missed", "misses", "dodge", "dodges",
"dodged", "dodging", "block", "blocks",
"blocked", "blocking", "save", "saved",
"saving"
}
for term in misses:
if term in sentence:
damaged = 0
#if the player or enemy are damaged, subtract one from their hitpoints
if damaged in {1, 2}:
hp[damaged] = hp[damaged] - 1
print(sentence)
print("enemy hp:", end=" ")
print(hp[1])
print("your hp:", end=" ")
print(hp[2])
if hp[1] < 1:
print("ENEMY KILLED")
continue_fight = "n"
descriptions[current_room] = descriptions[
current_room] + "\nOn the ground you see the remains of the " + enemy + "."
break
if hp[2] < 1:
print(
"YOU WERE KILLED (but we'll pretend you weren't so you can keep playing if you want)"
)
continue_fight = "n"
break
else:
print(
"You don't seem to have that weapon in your inventory."
)
elif next_verb == "inventory":
print("inventory:")
print(inventory)
elif next_verb == "quit":
raise SystemExit
else:
generate_more_flag = 1
#other verbs
if generate_more_flag == 1:
generate_more_flag = 0
prompt = descriptions[
current_room] + '\nYou ' + next_verb_past + " " + next_object
text = generate(tokenizer, model, prompt, 50, 30)
text = other_cleanup(text)
wrapped = '\nYou ' + next_verb_past + " " + next_object + text
wrap_print(wrapped)
descriptions[current_room] = prompt + text
next_verb = ""
### this steps through all the rooms generating them
if interactive_flag == 0:
current_room = current_room + 1
if current_room > len(rooms) - 1:
current_room = 0
print(
"\n\n\n\n***** Pregeneration finished. Have fun! *****\n\n\n\n"
)
interactive_flag = 1
def process(self,
statement,
additional_response_selection_parameters=None):
confidence = 0
dis_word = False
if any_in(
[
"distinguish",
"disfigure",
"distinct",
"distinction",
"distant",
"distance",
"distribution",
"distilled",
],
self.normalized,
):
confidence = 0
else:
logging.info(
"DisAdapter: Starting Advanced scan. dis_word == {}".format(
self.dis)[0])
dis_word = self.dis[3:]
logging.info("DisAdapter: Distilled word == {}".format(dis_word))
sia = SentimentIntensityAnalyzer().polarity_scores(dis_word)
if dis_word[0] in ["a", "e", "i", "o", "u", "g", "m", "p"]:
confidence += 0.4
if "infect" in dis_word:
confidence -= 0.3
if "spirit" in dis_word:
confidence += 0.2
if any_in(
[
"play",
"pensary",
"pense",
"patch",
"port",
"persal",
"perse",
"persion",
"praise",
],
dis_word,
):
confidence -= 0.2
confidence += sia["neg"]
inflection = getInflection(
self.chatbot.lp.tokenize(self.dis)[0].lemma_, "VBD")
if inflection is None:
past_participle_form_of_verb = self.dis
else:
past_participle_form_of_verb = inflection[0]
if "you" in self.normalized:
response = random_response(DIS_RESPONSES_YOU).format(
past_participle_form_of_verb)
emotion = Emotion.angry_non_expressive
elif "I" in self.normalized:
response = "{} {}".format(random_response(DIS_RESPONSES_I),
random_response(CONSOLATION))
emotion = Emotion.angel
else:
nn = None
pn = None
tokenized = spac_token(statement, chatbot=self.chatbot)
for i in tokenized:
if (i.pos_ == "NOUN") or (i.pos_ == "PROPN"):
nn = i.text
elif i.pos_ == "PRON":
pn = i.text
if not (nn or pn):
response = "Lol. What?"
emotion = Emotion.seriously
else:
response = random_response(DIS_RESPONSES_HIM).format(nn or pn)
emotion = Emotion.cry_overflow
selected_statement = SugaroidStatement(response, chatbot=True)
selected_statement.confidence = confidence
selected_statement.emotion = emotion
selected_statement.adapter = None
return selected_statement
if len(mappings) == 1:
best_infl_word = mappings[0][
0] # mappings is a list of (word, count)
best_infl_count = mappings[0][1]
elif len(mappings) > 1:
# Choose the one with the highest count. If equal, choose alphabetically.
# Note that counts are rarely equal when using the entire corpus. This mostly occurs
# for mispellings that only show-up once and these will get filtered out by "req_count".
# So, we won't be too concerned if alphabetical isn't the perfect fall-back heuristic.
mappings = sorted(mappings,
key=lambda x: x[0]) # sort alphabetically
mappings = sorted(mappings, key=lambda x: x[1],
reverse=True) # sort highest count first
best_infl_word = mappings[0][0]
best_infl_count = mappings[0][1]
# Write out for info / debug
multiples_f.write(' %s/%s -> %s\n' % (lemma, tag, str(mappings)))
# Skip overrides for cases where there's only a few instances in the corpus
if best_infl_count < req_count:
continue
# Now that we know what we want the lemma/tag to inflect to, check with pyinflect to see
# what it's actually doing and if it's different, write an override.
infl_list = pyinflect.getInflection(lemma, tag)
infl = infl_list[0] if infl_list else '' # choose form 0, the default
if infl != best_infl_word:
overrides_f.write('%s,%s,%s\n' % (lemma, tag, best_infl_word))
multiples_f.close()
overrides_f.close()
print('Overrides file saved to: ', overrides_fn)
print('Multiple entries saved to: ', multiples_fn)
def inflect_verb(self, verb, tag='VBG'):
try:
return pyinflect.getInflection(verb, tag=tag)[0]
except:
raise 'Couldn\'t inflect verb "{}"'.format(verb)
def informal_word_detection(self, sent_list):
"""detection and replacement of informal words with formal words"""
# get the punctuations for the manipulation
punctuation_list = string.punctuation
# define matchers used for replacement purpose
matcher_rule = Matcher(nlp.vocab)
matcher_phrase = PhraseMatcher(nlp.vocab)
# define different types of verbs
verb_types = ["VB", "VBD", "VBG", "VBN", "VBP", "VBZ"]
# get the list of informal word list
with open('Model/informal_word_list.txt', 'r') as file:
informal_word_list = ["" + line.strip() + "" for line in file]
# get the list of formal word list
with open('Model/formal_word_list.txt', 'r') as file:
formal_word_list = ["" + line.strip() + "" for line in file]
phrase_list = list()
for i in range(len(informal_word_list)):
try:
# get the words that matcher informal word list
word = informal_word_list[i]
# check whether the word length is 1 and it's a verb
if len(word.split()) == 1 and str(
nlp(word)[0].tag_) in verb_types:
# apply the rule base matching
# get the base verb of the selected verb
pattern = [{'LEMMA': word}, {'IS_PUNCT': True, 'OP': '?'}]
# match with according to matcher_rule
matcher_rule.add(str(i), None, pattern)
else:
# assign the words to the list(phrase_list) that need to formalize with phrase matching technique
phrase_list.append(word)
except Exception:
continue
# tokenize the phrases
phrase_patterns = [nlp(text) for text in phrase_list]
# match with according to matcher_phrase concept - direct phrase replacement
matcher_phrase.add('Informal word matcher', None, *phrase_patterns)
for i in range(len(sent_list)):
# sentence tokenized
sentense = nlp(sent_list[i])
# check for matching with respect to rule base technique in the sentence
matches_1 = matcher_rule(sentense)
# check for matching with respect to phrase base technique in the sentence
matches_2 = matcher_phrase(sentense)
# unit the two matches into a single
matches = matches_1 + matches_2
# sort the matches according to the occurrence of words in the original sentence
# with the aim of preventing the complication due to availability of two matches
matches.sort(key=lambda x: x[1])
if len(matches) != 0:
try:
new_sent = ""
# declare variable for later use
previous_end = None
# get match the informal word with formal word
for match in matches:
# get the informal word of the related match in sentence
informal_word = str(sentense[match[1]:match[2]])
# get the tag as word type - of single word match
word_type = str(sentense[match[1]:match[2]][0].tag_)
# as the informal word list is in base for check for the other possibilities of occurrence
# (verb types)
# if these conditions as match get them
if not informal_word_list.__contains__(
informal_word) and word_type in verb_types:
# get the index of the base form of those words in informal list
index = informal_word_list.index(
sentense[match[1]:match[2]][0].lemma_)
# get the respective formal word using index.
# convert that formal word into initial word_type as detected(tenses)
formal_word = getInflection(
formal_word_list[index], tag=str(word_type))[0]
# applies for the phrase base direct replacement
else:
index = informal_word_list.index(informal_word)
formal_word = formal_word_list[index]
# get the respective formal word upon the index
# if it indicates a new sentence.
if previous_end is None:
new_sent = new_sent + str(
sentense[:match[1]]).strip(
) + " " + formal_word
# if next character is not a punctuation need to put a space
if len(sentense) != match[2] and str(sentense[
match[2]]) not in punctuation_list:
new_sent = new_sent + " "
previous_end = match[2]
else:
previous_end = match[2]
else:
# continuation of sentence
new_sent = new_sent + str(
sentense[previous_end:match[1]]).strip(
) + " " + formal_word
# if next character is not a punctuation need to put a space
if len(sentense) != match[2] and str(sentense[
match[2]]) not in punctuation_list:
new_sent = new_sent + " "
previous_end = match[2]
else:
previous_end = match[2]
new_sent = new_sent + str(sentense[previous_end:]).strip()
sent_list[i] = new_sent.strip()
except Exception:
sent_list[i] = str(sentense)
# for sent in sent_list:
# print(sent)
self.tense_conversion_obj.future_tense_det(sent_list)
def get_gerund_word(self, word):
gerund = getInflection(word, 'VBG')
return [gerund[0]] if gerund is not None else ''
def inflect(word, target): inflected = getInflection(word, target, inflect_oov=True) return inflected[0] if inflected != None else word
