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HyperAI.py
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# import copy #pip install deeppavlov,transformers,(fasttext для некоторых моделей)
# TORCH на GPU ПРИ ПОМОЩИ pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# pip install language-tool-python,
import multiprocessing
# pip install googletrans==3.1.0a0
# pip install nltk
# pip install cyrtranslit
# pip install pymorphy2
# pip install num2words
# pip install pygame==2.4.0 ##pip install pygame==2.0.0.dev8 ? не работает
import numpy as np
import random
from datetime import datetime, date, timedelta
import time
from multiprocessing import Process, Manager
import threading
import os, sys
import traceback
thisfolder = os.path.dirname(os.path.realpath(__file__))
class bcolors:
HEADER = '\033[95m'
OKBLUE = '\033[94m'
OKCYAN = '\033[96m'
OKGREEN = '\033[92m'
WARNING = '\033[93m'
FAIL = '\033[91m'
ENDC = '\033[0m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
def calcTime(time):
return bcolors.OKGREEN + str((datetime.now() - time).total_seconds()) + bcolors.ENDC
def TTS_PROCESS(ctx):
import torch
# from torch import package
print('[TTS INIT] Started load TTS model...')
device = torch.device("cpu") # 'cpu') # cuda
torch.set_num_threads(4)
t = datetime.now()
local_file = thisfolder + '/HyperAI_Models/TTS/variants/Silero/silero_tts.pt'
if not os.path.isfile(local_file):
torch.hub.download_url_to_file('https://models.silero.ai/models/tts/ru/v3_1_ru.pt',
local_file)
VoiceModel = torch.package.PackageImporter(local_file).load_pickle("tts_models", "model")
# VoiceModel, exampleText = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language='ru',
# speaker='v3_1_ru', trust_repo=True, cache_dir=) # v3_1_ru или ru_v3
VoiceModel.to(device) # gpu or cpu
print('[TTS INIT] время запуска VOICE TTS на', device, '=', calcTime(t))
# VoiceModel.eval() не работает
print('[TTS INIT 2 NEW] Started load ACCENTUATOR model...')
# from ruaccent import RUAccent
#
# accentizer = RUAccent()
# custom_words_accent_dict = {"бовдур":"б+овдур","бовдурус":"б+овдурус"}
# accentizer.load(omograph_model_size='big', use_dictionary=True, custom_dict=custom_words_accent_dict)
# https://huggingface.co/TeraTTS/accentuator
print('[TTS INIT 2 NEW] ENDED! load ACCENTUATOR model! time =', calcTime(t))
ctx.loading_flag.set()
while True:
try:
text = ctx.Queue.get()
print('[VOICE QUEUE] получена очередь', text)
# text = accentizer.process_all(text)
# print('[VOICE QUEUE DEBUG NEW] ТЕКСТ С УДАРЕНИЯМИ >>', text)
# VoiceModel, exampleText = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts',
# language='ru',
# speaker='v3_1_ru', trust_repo=True) # v3_1_ru или ru_v3
VoiceModel = torch.package.PackageImporter(local_file).load_pickle("tts_models", "model")
VoiceModel.to(device) # gpu or cpu
try:
audiowave = VoiceModel.apply_tts(ssml_text=text, speaker='baya', sample_rate=48000, put_accent=True,
put_yo=True)
except BaseException as err:
print("=== ПРОИЗОШЛА ОШИБКА", err, "В ГЕНЕРАЦИИ СИНТЕЗА РЕЧИ! ====\n")
print("=== ТЕКСТ>>" + text + "<< ====\n")
print('ТЕКСТ ОБ***Й ОШИБКИ', traceback.format_exc())
print("\n=== КОНЕЦ ОШИБКИ ====")
audiowave = VoiceModel.apply_tts(text=text, speaker='baya', sample_rate=48000, put_accent=True,
put_yo=True)
ctx.QueueOutput.put(audiowave)
except BaseException as err:
print('[TTS ERR] ОШИБКА ПРОЦЕССА В TTS: ', err)
print('[TTS ERR] ТЕКСТ ОБ***Й ОШИБКИ', traceback.format_exc())
print("\n[TTS ERR] === КОНЕЦ ОШИБКИ ====")
time.sleep(1)
if __name__ == "__main__":
try:
import psutil
os_used = sys.platform
process = psutil.Process(os.getpid()) # Set highest priority for the python script for the CPU
if os_used == "win32": # Windows (either 32-bit or 64-bit)
process.nice(psutil.HIGH_PRIORITY_CLASS) # REALTIME_PRIORITY_CLASS)
print('[MAIN] УСТАНОВЛЕН ВЫСОКИЙ ПРИОРИТЕТ ПРОЦЕССА PID =', os.getpid())
elif os_used == "linux": # linux
process.nice(psutil.IOPRIO_HIGH)
else: # MAC OS X or other
process.nice(20)
# https://github.com/mlo40/VsPyYt
def tm(x):
return datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
# python -m deeppavlov download rusentiment_bert
timeStartMetrics = []
timeStartMetrics.append(datetime.now())
manager = Manager()
speech_available_event = manager.Event()
speech_available_event.set()
ctx = manager.Namespace()
ctx.ThreadsActived = True
ctx.mood = 0.0
vtube_ctx = manager.Namespace()
vtube_ctx.NeedX = -0.5
vtube_ctx.NeedY = -1.0
ctx.state = "gaming"
ctx.SeparateEyes = True
ctx.MineEvent = manager.Event()
ctx.MineEventName = ""
vtube_ctx.eyeX = vtube_ctx.NeedX
vtube_ctx.eyeY = vtube_ctx.NeedY
ctx.AnimEvent = manager.Event()
ctx.AnimEventInfo = {"name": "SawChat.exp3.json", "type": "expression", "time": 0}
ctx.IsMCStarted = False
ctx.IsVtubeStarted = False
ctx.IsYTChatConnected = False
import re
import signal
import PySimpleGUI as sg
from rapidfuzz import fuzz
from pympler.tracker import SummaryTracker
# pip install pysimplegui
utils_folder = "HyperAI_Utils"
def start_bat(bat_path: str):
print("[MAIN SCRIPT] started bat file", bat_path)
os.startfile(bat_path)
def start_program(program: str):
bat_name = ""
if program == "VTUBE":
bat_name = "StartVTubeStudioNoSteam.bat"
elif program == "SheepChat":
bat_name = "StartSheepChat.bat"
else:
print("neizv programma")
return
start_bat(os.path.join(thisfolder, utils_folder, bat_name))
FisrtBuildingLanguageTool = True
# Для библиотеки dostoevskiy скачиваем Build Tools Visual studio 14.0+, идёт в комплекте с визуал студио
tool = []
ttsCtx = manager.Namespace()
ttsCtx.Queue = manager.Queue()
ttsCtx.QueueOutput = manager.Queue()
ttsCtx.loading_flag = manager.Event()
LargeTTSProc = Process(
target=TTS_PROCESS,
args=(ttsCtx,)) # Thread(target = a, kwargs={'c':True}).start()
LargeTTSProc.start()
from HyperAI_OBS import OBS_Websocket
obs_ka = OBS_Websocket()
def do_vtube_event(event_name):
ctx.AnimEventInfo = {"name": event_name, "type": "hotkey"}
ctx.AnimEvent.set()
ctx.AnimEvent.clear()
def obs_scene_check_sync():
obs_scene_name = ""
if obs_scene_name == "NetTyanChat":
do_vtube_event("MoveChat")
elif obs_scene_name == "NetTyan":
do_vtube_event("MoveGame")
def TTS_QUEUE(text):
max_numm = 990
put_text = (text[:max_numm] + '..') if len(text) > max_numm else text
ttsCtx.Queue.put(put_text)
return ttsCtx.QueueOutput.get()
def modifyMood(mood: float) -> None:
"""add to mood argument number"""
mood_now = ctx.mood
mood_now += mood
mood_now = clamp(mood_now, -10, 10)
if mood_now == -10:
ctx.mood = random.choice([1, 2, 3, 4])
return
elif mood_now == 10:
ctx.mood = random.choice([-3, -2, 0, 1, 2])
return
ctx.mood = mood_now
DATABASE.set_mood(mood_now)
def check_literacy(text):
# Initialize the LanguageTool object
# Check the text for errors
matches = tool.check(text)
# Build a list of errors and corrections
errors = []
corrections = {}
for match in matches:
if match.ruleId in ('UPPERCASE_SENTENCE_START', 'MORFOLOGIK_RULE_RU_RU'):
# Add the error to the errors list
error = match.message.replace('{', '').replace('}', '')
errors.append((text[match.offset:match.errorLength], error))
# Add the correction to the corrections dictionary
correction = match.replacements[0] if match.replacements else ''
corrections[(match.offset, match.offset + match.errorLength)] = correction
# Apply the corrections to the original text
corrected_text = text
for (offset, error_length), correction in sorted(corrections.items(), reverse=True):
corrected_text = corrected_text[:offset] + correction + corrected_text[offset + error_length:]
# Fix punctuation errors
# punctuation_regex = r'([\wа-яА-ЯёЁ]+)\s*([^\w\s]+)\s*(?=[\wа-яА-ЯёЁ]+)'
# punctuation_errors = re.findall(punctuation_regex, corrected_text)
# for match in punctuation_errors:
# error, punctuation = match
# corrected_text = corrected_text.replace(f'{error} {punctuation}', f'{error}{punctuation} ')
return [corrected_text, errors]
model = []
FisrtBuildingModel = True
# Для библиотеки dostoevskiy скачиваем Build Tools Visual studio 14.0+, идёт в комплекте с визуал студио
# def BuildSentimentModel():
# global model
# global FisrtBuildingModel
# # ИМПОРТИРОВАТЬ torch
# if FisrtBuildingModel:
# print('# СТРОИМ МОДЕЛЬ.....')
# from deeppavlov import build_model
# model = build_model('rusentiment_convers_bert', download=True)
# # model = build_model('sentiment_twitter', download=True) #очень неточное, постоянно негатив
# # model = build_model("rusentiment_bert") #среднее, такое -_- часто ошибается
# print('# МОДЕЛЬ ЗАГРУЖЕНА!')
# FisrtBuildingModel = False
def determine_tone(text):
if not text or not text.strip():
return "Input text is empty"
print('# Начата проверка... [' + text + ']')
predict = model([text])
print(predict)
sentiment_label = predict[0]
sentiment_score = 0
print('# Проверка окончена!')
if sentiment_label == "positive":
return 1
elif sentiment_label == "negative":
return -1
else:
return 0
# # TEXT TO SPEECH
###pip install -q torchaudio omegaconf
###pip install playsound playsound
# from playsound import playsound
# ОПИСАНИЕ SSML TEXT: + перед ударным слогом
# rate x-slow slow medium fast x-fast
# pitch x-low, low, medium, high, x-high
# <p> </p> параграфы, перед ними пауза 3 сек. <s> 1.5 сек. <break time = "?s">
# ПОДРОБНЕЕ https://github.com/snakers4/silero-models/wiki/SSML
def OformText(text="Привет школьник", rate="fast", pitch="medium"):
textStartet = '<speak><prosody rate="' + rate + '" pitch="' + pitch + '">'
text = textStartet + text + "</prosody></speak>"
return text
# FisrtBuildingVoice = True
# torch.set_num_threads(4)# если cuda, отрубаем это
from HyperAI_WEB import print_subtitles
# def LoadTTS():
# global VoiceModel,VoiceModelLoaded
# startTime = datetime.now()
# if not VoiceModelLoaded:
# print('Загрузка модели TTS...')
# VoiceModel, badtext = torch.hub.load(repo_or_dir='snakers4/silero-models', model='silero_tts', language='ru',
# speaker='v3_1_ru') # v3_1_ru или ru_v3
# VoiceModel.to(device) # gpu or cpu
# VoiceModelLoaded = True
# print('Загрузка модели TTS завершена!', calcTime(startTime))
###print('Загрузка подготовщика TTS...')
###TextEnhancerModel, example_texts, languages, punct, apply_te = torch.hub.load(repo_or_dir='snakers4/silero-models',
### model='silero_te')
#### see avaiable languages
###print(f'Available languages {languages}')
###print(f'Available punctuation marks {punct}')
###print('Загрузка подготовщика TTS завершена!', calcTime(startTime))
# LoadTTS()
###def apply_te(text, lan='ru'):
### global TextEnhancerModel
### return TextEnhancerModel.enhance_text(text, lan)
# print(f"Output:\n{apply_te('Перевод с английского, немецкого, французского, испанского, польского, турецкого и других языков на русский и обратно. Возможность переводить отдельные слова и фразы, а также целые тексты, фотографии, документы и веб-страницы.')}")
def textToSpeech(text, rate="fast", pitch="medium", seeChat=False):
"""В версии 0.0.4 добавили особый блок: ждем, только если есть другая речь"""
print('Запускаем модель...')
startTime = datetime.now()
text = PrepareToSpeech(text)
subtitles = PrepareToSpeech(text, subtitles=True)
text = OformText(text, rate, pitch)
ctx.isVoiceBusy = True
if seeChat:
ctx.AnimEventInfo = {"name": "SawChat.exp3.json", "type": "expression", "time": 0.01}
ctx.AnimEvent.set()
print("Генерация файла wav... время до этого шага:", calcTime(startTime)) # +text)
audio = TTS_QUEUE(text)
print("Играем звук wav...", calcTime(startTime))
if seeChat:
ctx.AnimEvent.clear()
speech_available_event.wait()
TextDisplaySpeed.value = rate
textSubtitlesHttp.value = subtitles
# Thread
threading.Thread(target=SoundToMicro,
kwargs={"audio": audio, "sleep": True, "smart_wait": True, "change_emotes": True},
daemon=True).start()
# SoundToMicro(audio=audio, sleep=True)
# print_subtitles(subtitles,rate)
print("Голосовой вывод отправлен! Затраченное на всё про всё время =",
calcTime(startTime)) # закончен! Затраченное на всё про всё время
# vtube_ctx.NeedX = -0.5
# vtube_ctx.NeedY = -1
# ctx.AnimEvent.clear()
# del wav_play
#### print_subtitles В ФАЙЛЕ WEB
# PYTTSX3
###pip install pyttsx3
##import pyttsx3
##engine = pyttsx3.init()
##voices = engine.getProperty('voices')
##engine.setProperty('voice', voices[1].id)
##engine.setProperty('rate', 100)
##engine.say('Privet ebanniy ti pidoras')
##engine.runAndWait()
###достаточно фигово только с англ, но робит оффлайн
# GTTS
# pip install gtts
# from gtts import gTTS
# tts = gTTS(text='Привет пошел **', lang='ru', tld='ru')
# похож на бота, работает быстро, через гугл переводчик
# tts.save('hello.mp3')
# from pymorphy2 import MorphAnalyzer
# import nltk
# from nltk import sent_tokenize, word_tokenize, regexp_tokenize
# from nltk.corpus import stopwords
# morph = MorphAnalyzer(lang='ru')
# nltk.download('stopwords')
# stopwords_ru = stopwords.words("russian")
# https://pymorphy2.readthedocs.io/en/stable/user/guide.html#normalization
def fixFemWords(doc):
# wordsmas = doc.split(" ")
# result = doc
# pat = r"(?u)\b\w\w+\b"
# for word in regexp_tokenize(doc, pat):
# if word not in stopwords_ru:
# p = morph.parse(word)[0]
# # МУЖСКИЕ ГЛАГОЛЫ в ЖЕНСКИЙ РОД))
# #print('Разбор '+str(p.tag.POS)+' ='+str(p.word)+' пол='+ str(p.tag.gender) +' число=' + str(p.tag.number)+' включенность='+str(p.tag.involvement)
# #+' наклонение='+str(p.tag.mood)+' ИТОГ: '+p.inflect({'femn'}).word)
# if (p.tag.POS == "PRTS" or p.tag.POS == 'ADJS' or p.tag.POS == 'VERB') and p.tag.gender == 'masc' and p.tag.number == 'sing' and p.tag.mood != "impr":
# #print('Разбор глагола ='+str(p.word)+' пол='+ str(p.tag.gender) +' число=' + str(p.tag.number)+' включенность='+str(p.tag.involvement)
# #+' наклонение='+str(p.tag.mood)+' ИТОГ: '+p.inflect({'femn'}).word)
# result = result.replace(word,p.inflect({'femn'}).word)
return doc
# print()
import cyrtranslit
def translit(x):
try:
result = x
defaultCyrReplaceMap = {"x": "Кс", "h": "Х"}
for symbol in defaultCyrReplaceMap:
result = result.replace(symbol, defaultCyrReplaceMap[symbol].lower())
result = result.replace(symbol.upper(), defaultCyrReplaceMap[symbol])
partCyrReplaceMap = {"sh": "щ", "Sh": "Щ", "sH": "щ", "SH": "Щ"}
for part in partCyrReplaceMap:
result = result.replace(part, partCyrReplaceMap[part])
result = cyrtranslit.to_cyrillic(result, "ru")
except BaseException as err:
print('ошибка ', err, ' при транслитерации =(')
result = x
return result
translitLatin = lambda x: cyrtranslit.to_latin(x, "ru")
from num2words import num2words
# print(num2words("543534", lang='ru'))
def NumbersToSpeech(inp):
numbers = re.findall(r'\b\d+\b', inp)
result = inp
for numb in numbers:
print(numb, num2words(numb, lang='ru'))
result = result.replace(numb, num2words(numb, lang='ru'))
return result
# print("N2WORDS>>",NumbersToSpeech("У меня в жопе 8 хромосом"))
# fixFemWords("уверен классный сделай сделал сделает сделаешь делаешь делает сделал сделают сделала сделали авылпов ьвао алв ле коцавы щзшеоку л уоц fgdj jw jwe skd")
# return str(regexp_tokenize(doc, pat))
# doc = re.sub(patterns, ' ', doc)
# tokens = []
# for token in doc.split():
# if token and token not in stopwords_ru:
# token = token.strip()
# token = morph.normal_forms(token)[0]
# tokens.append(token)
# if len(tokens) > 2:
# return tokens
# return None
# from googletrans import Translator
#
# translator = Translator()
ListForFemWords = "i' i'm im i self myself myself, myself.".split(' ')
# def translate(inp, destTrans="ru", femFix=True):
# if (destTrans == "ru"):
# if (femFix):
# result = inp
# splitter = " "
# resultmas = result.split(splitter)
# result = ""
# for i, sequence in enumerate(resultmas):
# if i + 1 == len(resultmas):
# splitter = ""
# for word in ListForFemWords:
# # print(">"+sequence+"<"+'\n'+">"+word+"<"+"\n"+str(sequence.lower()==word))
# if (sequence.lower() == word):
# find = sequence.lower().find(word.lower())
# if find != -1:
# sequence = sequence + " 🚌Lisa🚌" # sequence[:find]+" 🚌Lisa🚌" + sequence[find:]
# # sequence = sequence.lower().replace(word, word+" 🚌Lisa🚌")
# # if(sequence.lower().find(word) != -1):
# # sequence = sequence.replace(sequence, word+" <(Lisa)>")
# result += sequence + splitter
#
# # print(result)
# result = translator.translate(result, src='en', dest='ru').text
# # print(result)
# resultnn = result
# result = ""
# chetnost = False
# # def switch():
#
# for symbol in resultnn:
# if symbol == "🚌" or chetnost:
# if chetnost and symbol == "🚌":
# chetnost = False
# elif not chetnost:
# chetnost = True
# else:
# chetnost = False
# result += symbol
# result = result.replace(" ", " ")
# # result = result.replace(" 🚌Lisa🚌","")
# # result = result.replace(" 🚌Лиза🚌","")
# # result = result.replace(" 🚌Лизы🚌","")
# # result = result.replace(" 🚌Лизе🚌","")
# # result = result.replace(" 🚌Лизу🚌","")
# # result = result.replace(" 🚌Лизой🚌","")
# # print(result)
# # for item in resultPrev:
# # result = resultPrev.
# else:
# result = translator.translate(inp, src='en', dest='ru').text
# else:
# result = translator.translate(inp, src='ru', dest='en').text
# return result
# ###################################
# ### ПРАВКИ ПО ТЕКСТУ ВЫВОДИМОМУ ###
# ###################################
# # цвет горчичный
# # ниже
# # обрезание сверху
### zzfrf2 = translator.translate(zzfrf,dest='en').text
### zzfrf3=translator.translate(zzfrf2,dest='ru').text
###
### print("текст >>"+zzfrf)
### print("перевод текста >>"+zzfrf2)
### print("перевод текста Обратно >>"+zzfrf3)
### zzfrf4=fixFemWords(zzfrf3)
### print("перевод текста (ФИКС РОД) >>"+zzfrf4)
# src: Исходный язык
# dest: Язык назначения, установленный на английский (en)
# result.origin: Оригинальный текст, в нашем примере это Mitä sinä teet
# result.text: Переведенный текст, в нашем случае это будет «what are you doing?»
# result.pronunciation: Произношение переведенного текста
import json
def eztime():
return datetime.now().strftime('%Y-%m-%d %H:%M:%S')
def eztime_min():
return datetime.now().strftime('%M:%S')
def js_r(filename: str):
with open(filename, encoding='utf-8') as f_in:
return json.load(f_in)
def js_w(filename: str, data):
with open(filename, 'w+', encoding='utf-8') as f_out:
f_out.write(json.dumps(data, sort_keys=True, indent=4, separators=(',', ': '), ensure_ascii=False))
####js_w('usersDatabase.json', usersDatabase)
# js_w('evaLogs.json', LogChat)
def ConvertDiagFormat(record, fredFormat=False):
if fredFormat:
cmd = ""
emo = ""
if record["role"] == "assistant":
cmd = record.get("command", "")
if cmd != "":
cmd = " <команда=" + cmd + ">"
emo = record.get("emotion", "")
if emo != "":
emo = " [эмоция=" + emo + "]"
usr = record.get("user", "")
# rle = record["role"]
# if usr == "" or usr == "default":
if record["role"] == "assistant":
rle = "A"
else:
rle = "Q"
# else:
# rle = "A" # изначально это ник чела
return {"user": record.get("user", "noname"),
"command": record.get("command", ""),
"emotion": record.get("emotion", ""),
"role": record["role"],
"msg": record["content"],
"content": rle + ': ' + record["content"] + cmd + emo + '</s>\n'
}
else:
return {"role": record["role"], "content": record["content"]}
# @deprecated
def ConvertDiagFormatOld(record, fredFormat=False):
if fredFormat:
cmd = ""
emo = ""
if record["role"] == "assistant":
cmd = record.get("command", "")
if cmd != "":
cmd = " <команда=" + cmd + ">"
emo = record.get("emotion", "")
if emo != "":
emo = " [эмоция=" + emo + "]"
usr = record.get("user", "")
if usr == "" or usr == "default":
if record["role"] == "assistant":
record["user"] = "Ева"
else:
record["user"] = "Пользователь"
return {"role": record["role"],
"content": '- ' + record["user"] + ': ' + record["content"] + cmd + emo + '</s>\n'}
else:
return {"role": record["role"], "content": record["content"]}
def col(inp, color="green", limiter=False):
limiterstart = '>'
limiterend = '<'
if not limiter:
limiterstart = ''
limiterend = ''
if color == "green":
return limiterstart + bcolors.OKGREEN + str(inp) + bcolors.ENDC + limiterend
elif color == "yellow":
return limiterstart + bcolors.OKCYAN + str(inp) + bcolors.ENDC + limiterend
else:
return limiterstart + bcolors.OKGREEN + str(inp) + bcolors.ENDC + limiterend
def clamp(n, smallest, largest):
return max(smallest, min(n, largest))
def EmotionToRank(emotion):
# агрессия, скука, усталость, интерес, смущение, счастье, веселье, страх.
emotionMap = {"агрессия": -0.1, "скука": 0, "усталость": -0.1, "интерес": 0.1, "смущение": 0.1,
"счастье": 0.1, "веселье": 0.1, "страх": 0.01}
# global GlobalEvaMood #sox,terminator,android,..
# global GlobalEvaEmotion #annoyed angry bored tired interested embarrassed scared happy confused
emo = emotionMap.get(emotion, -999)
if emo == -999:
if emotion == "annoyed":
return -0.05
elif emotion == "angry":
return -0.1
elif emotion == "bored":
return -0.1
elif emotion == "tired":
return 0.1
elif emotion == "interested":
return 0.1
elif emotion == "embarrassed":
return 0.2
elif emotion == "scared":
return 0.1
elif emotion == "happy":
return 0.1
elif emotion == "confused":
return 0.1
else:
return random.uniform(-0.2, 0.2)
else:
return emo
def findBlocked(inpt, extended=False, blockedPhrasesMass=[], blockedPhrasesMassExtend=[]):
inp = inpt.lower()
if not extended:
for word in blockedPhrasesMass:
if inp.find(word) != -1:
return True
return False
else:
for word in blockedPhrasesMassExtend:
if inp.find(word) != -1:
return True
return False
def emotions_to_str(text: str) -> str:
emotions_str_map = {"<3": "сердешко",
"^_^": "няааааа",
"^^": "няа",
":)": "улыбка",
":')": "плак",
":-)": "улыбка",
":(": "грусть",
":'(": "плак",
":-(": "печалька",
"(": "то есть",
}
for emo in emotions_str_map:
text = text.replace(emo, emotions_str_map[emo])
return text
def PrepareToSpeech(ninp, subtitles=False):
inp = ninp
result = ""
# print('.', end='')
if subtitles:
result = inp
else:
result = NumbersToSpeech(translit(inp)) # добавить опред. смайлов;
result = emotions_to_str(result)
if len(ninp) > 900:
inp = ninp[0:899]
return result
# inputs = gr.inputs.Textbox(lines=7, label="Chat with AI")
# outputs = gr.outputs.Textbox(label="Reply")
# gr.Interface(fn=chatbot, inputs=inputs, outputs=outputs, title="AI Chatbot",
# description="Ask anything you want",
# theme="compact").launch(share=True)
# loop = asyncio.new_event_loop()
# tr = threading.Thread(target=start_tornado_loop, args = (loop,))
# tr.start()
print("\n[LODAING] часть 1 загружена, время: " + calcTime(timeStartMetrics[0]) + '\n')
# run_site()
# tornado.ioloop.IOLoop.current().start()
# threading.Thread(target=update_text).start()
# Run the Flask application
def isSimilar(inp1, inp2, val=75):
result = fuzz.ratio(inp1, inp2)
# print(f'DEBUG REPEAT Уникальность={str(result)}\n*{inp1}*\n*{inp2}**')
return result > val
def MasSimilarity(example, mas):
max = 0
for part in mas:
ratio = fuzz.ratio(example, part)
if ratio >= max:
max = ratio
return max
def isSimilarMas(example, mas, val=75):
for part in mas:
if (isSimilar(part, example, val)):
return True
return False
from HyperAI_WEB import HttpAppRun
textSubtitlesHttp = manager.Value('u', '')
TextDisplaySpeed = manager.Value('u', 'fast')
RefreshInterval = manager.Value('i', 3)
screenPrintMas = manager.list()
HttpProc = Process(
target=HttpAppRun,
args=(ctx, textSubtitlesHttp, TextDisplaySpeed, RefreshInterval,
screenPrintMas,)) # Thread(target = a, kwargs={'c':True}).start()
HttpProc.start()
#
import sys
sys.path.insert(0, 'HyperAI_VTube')
from vsnoyt import VtubeProcess
VtubeProc = Process(
target=VtubeProcess,
args=(vtube_ctx, ctx,)) # Thread(target = a, kwargs={'c':True}).start()
VtubeProc.start()
#####################
### FILTERS INIT ####
#####################
sys.path.insert(0, 'HyperAI_Models/Filters')
from Filters import wordtokenize
botNicknames = ['Net Tyan', 'NetTyan', 'NeTyan', 'NedoSama', 'Eva', 'Ева', 'НетТян', 'нет тян', 'Нетян',
'НетТуан', 'неттиан', 'Недосама', 'TyankaTashit', 'NeuroDeva', 'neurodeva', 'WebcamTyan',
'ChoDedyTyan',
'ChoBabkeTyan']
ctx.botNicknames = botNicknames
botRelativesL1 = ['ботиха', 'тянка', 'тян', 'нитан', 'нтян', 'бот', 'ии', 'bot', 'gpt', 'chatgpt', 'ai',
'chatbot']
botRelativesL2 = ['интеллект', 'разум', 'помощник']
ChooserPreviousUsers = [2]
from HyperAI_Database import HyperAIDatabase
DATABASE = HyperAIDatabase()
print('[DB INIT] DATABSE START INIT!')
ctx.mood = DATABASE.get_mood()
print('[DB INIT] LOADED MOOD:', str(ctx.mood))
def ChooseQuestion(questions, priority=""):
# {"user":"nickname","msg":"message","date":"25-03.245235","nicktype":"ytname"}
# PreviousUsers = ["lol",'4odedy']
ScoredQuestions = []
maxScore = -99999
bestQuestion = None
startTime = datetime.now()
for i, question in enumerate(questions):
if question.get("delete", False) == True:
print('пропускаем вопрос, он должен быть удален')
continue
user = question.get("user", "")
# DB_getUserNicknames()
msg = question.get("msg", "")
msg = msg.strip()
words = wordtokenize(msg)
q_date = question.get("date", "")
if isinstance(q_date, str):
if q_date != "":
q_date = tm(q_date)
else:
q_date = datetime.now()
LastInteract = (datetime.now() - q_date).total_seconds()
if (LastInteract > 300):
print('DELETE QUESTION reason=time ', ctx_chat[i])
question["delete"] = True
# questions[i] = question
# ctx_chat[i] = question
chats_replace(ctx_chat, processing_timestamp=question["processing_timestamp"],
new_chat_entry=question,
this_array=questions)
continue
score = 0
user_id = None
if user.strip():
db_data = DATABASE.get_or_create_user(data=question, return_nick_id_too=True)
if db_data:
user_id = db_data["user_id"]
question["nick_id"] = db_data["nick_id"]
if user_id:
if user_id in ChooserPreviousUsers: # or AuthorizedUsers (+ High ranked + donaters)
score += 5
question["last_interact"] = DATABASE.get_user_last_interact_time(user_id=user_id,
last_answered=True)
question["user_id"] = user_id
user_rank = DATABASE.get_user_rank(user_id=user_id)
question["user_rank"] = user_rank
score += user_rank / 2
diags_count = DATABASE.get_user_diag_count(user_id=user_id, real=True)
# DEBUG DISABLED!! ПОТОМ НАДО ДОПИЛИТЬ
# TODO TODO TODO
# collect_all_chat_user_msgs(ctx_chat,
# processing_timestamp=question["processing_timestamp"],
# this_chat_entry=question, this_array=questions)
if diags_count is not None:
if (diags_count > 10):
score += 1
ChooserPreviousUsers[0] = user_id
else:
print('[CHOOSER] CANT ADD OR GET USER!!!')
continue
q_changed = False
if question.get("filter_allowed", None) is None:
filter = FiltersQueue(msg) # filt.Filter(msg)
# print('filter,msg', filter,msg)
question["filter_score"] = filter["score"]
question["filter_allowed"] = filter["allow"]
question["filter_topics"] = filter["topics"]
# questions[i] = question
# ctx_chat[i] = question
q_changed = True
chats_replace(ctx_chat, processing_timestamp=question["processing_timestamp"],
new_chat_entry=question, this_array=questions)
###ctx_chat = list(questions)
if question.get("sentence_type", None) is None:
question_analysis = FiltersQueue(msg, filter_type="info") # filt.Filter(msg)
question["sentence_type"] = question_analysis["sentence_type"]
q_changed = True
if q_changed:
chats_replace(ctx_chat, processing_timestamp=question["processing_timestamp"],
new_chat_entry=question, this_array=questions)
filter_score = question["filter_score"]
filter_allowed = question["filter_allowed"]
filter_topics = question["filter_topics"]
# print("debug QUEST 0FilterResults>>>", question.get("FilterResults", ""))
# print("debug QUEST FilterResults>>>", questions[i].get("FilterResults", ""))
# print("debug QUEST CHAT FilterResults>>>",ctx_chat[i].get("FilterResults",""))
if (not question.get("env", "") in ["youtube", "twitch", "trovo"]) and priority == "youtube":
continue
# msg=""
# todo bypass filter for some users (devs?)
if not ((filter_score <= -10 and not filter_allowed) or not msg): # and LastInteract>120):
## ОПРЕДЕЛЕНИЕ СРЕДЫ, ЮТУБЕРАМ +10
if question.get("env", "") == "youtube":
score += 5
elif question.get("env", "") == "discord":
score += 10 # old 4 todo find premium and add +100
elif question.get("env", "") == "twitch" or question.get("env", "") == "trovo":
score += 3
if question.get("priority_group", "") == "max":
score += 100
## АВТОРИЗАЦИЯ ##
## ОПРЕДЕЛЕНИЕ ОБРАЩЕНИЯ ##
for word in words:
nickSim = MasSimilarity(word, botNicknames)
if nickSim > 95:
score += 20
break
elif nickSim > 75: # def isSimilarMas(example,mas,val=75):
score += 15
break
elif MasSimilarity(word, botRelativesL1) > 75:
score += 10
break
elif MasSimilarity(word, botRelativesL2) > 75:
score += 5
break
## КАЧЕСТВО ТЕКСТА ##
# первая буква маленькая
py_clip = lambda x, l, u: l if x < l else u if x > u else x
if msg[0].islower():
score -= 0.1
else:
score += 0.2
# сообщение 8 и более символов интереснее
if len(msg) >= 10:
score += 1
else:
score -= 1
# ранжировка по дате (предпочтительны более ранние но релевантные вопросы)
scored = question # copy.deepcopy(question)
if LastInteract < 10:
pass
# score+=LastInteract/50
else:
scored["processing"] = "queue"
# рассчитываем время последнего общения КОНКРЕТНО с данным пользователем
def calc_user_last_interact(last_answerred: bool = False) -> datetime.date:
if last_answerred:
last_interact_time_string = DATABASE.get_user_last_question_date(user_id)
if not last_interact_time_string:
last_interact_time_string = None
else:
last_interact_time_string = question.get("last_interact", None)
if last_interact_time_string:
last_interact_date = tm(last_interact_time_string)
else:
last_interact_date = datetime(2022, 12, 25)
return (datetime.now() - last_interact_date).total_seconds()
user_last_interact = calc_user_last_interact(False)
user_last_questioned = calc_user_last_interact(True)
# меньше 80 сек назад говорили с этим пользователем?
if user_last_interact < 80:
score += 3
print('[DEBUG NEW] Q chooser: SCORE+3 (недавний ответ)')
# спрашивали ли бы пользователя о чем-то последний раз
if user_last_questioned < 100:
print('[DEBUG NEW] Q chooser: SCORE+10 (БЫЛ НЕДАВНО СПРОШЕН!)')
score += 10