237 lines
7.4 KiB
Python
237 lines
7.4 KiB
Python
from concurrent.futures import ThreadPoolExecutor, as_completed
|
|
import json
|
|
import os
|
|
from pathlib import Path
|
|
import re
|
|
import sys
|
|
import textwrap
|
|
import threading
|
|
import time
|
|
import traceback
|
|
import tiktoken
|
|
|
|
from colorama import Fore
|
|
from dotenv import load_dotenv
|
|
import openai
|
|
from retry import retry
|
|
from tqdm import tqdm
|
|
|
|
#Globals
|
|
load_dotenv()
|
|
openai.organization = os.getenv('org')
|
|
openai.api_key = os.getenv('key')
|
|
|
|
APICOST = .002 # Depends on the model https://openai.com/pricing
|
|
PROMPT = Path('prompt.txt').read_text(encoding='utf-8')
|
|
THREADS = 20
|
|
LOCK = threading.Lock()
|
|
WIDTH = 72
|
|
LISTWIDTH = 75
|
|
MAXHISTORY = 10
|
|
ESTIMATE = ''
|
|
TOTALCOST = 0
|
|
TOKENS = 0
|
|
TOTALTOKENS = 0
|
|
|
|
#tqdm Globals
|
|
BAR_FORMAT='{l_bar}{bar:10}{r_bar}{bar:-10b}'
|
|
POSITION=0
|
|
LEAVE=False
|
|
|
|
# Flags
|
|
CODE401 = True
|
|
CODE102 = True
|
|
CODE122 = False
|
|
CODE101 = False
|
|
CODE355655 = False
|
|
CODE357 = False
|
|
CODE356 = False
|
|
CODE320 = False
|
|
CODE111 = False
|
|
|
|
def handleTextfile(filename, estimate):
|
|
global ESTIMATE, TOKENS, TOTALTOKENS, TOTALCOST
|
|
ESTIMATE = estimate
|
|
|
|
if estimate:
|
|
start = time.time()
|
|
translatedData = openFiles(filename)
|
|
|
|
# Print Result
|
|
end = time.time()
|
|
tqdm.write(getResultString(['', TOKENS, None], end - start, filename))
|
|
with LOCK:
|
|
TOTALCOST += TOKENS * .001 * APICOST
|
|
TOTALTOKENS += TOKENS
|
|
TOKENS = 0
|
|
|
|
return getResultString(['', TOTALTOKENS, None], end - start, 'TOTAL')
|
|
|
|
else:
|
|
with open('translated/' + filename, 'w', encoding='UTF-8') as outFile:
|
|
start = time.time()
|
|
translatedData = openFiles(filename)
|
|
|
|
# Print Result
|
|
end = time.time()
|
|
outFile.writelines(translatedData[0])
|
|
tqdm.write(getResultString(translatedData, end - start, filename))
|
|
with LOCK:
|
|
TOTALCOST += translatedData[1] * .001 * APICOST
|
|
TOTALTOKENS += translatedData[1]
|
|
|
|
return getResultString(['', TOTALTOKENS, None], end - start, 'TOTAL')
|
|
|
|
def openFiles(filename):
|
|
with open('files/' + filename, 'r', encoding='UTF-8') as f:
|
|
translatedData = parseText(f, filename)
|
|
|
|
return translatedData
|
|
|
|
def getResultString(translatedData, translationTime, filename):
|
|
# File Print String
|
|
tokenString = Fore.YELLOW + '[' + str(translatedData[1]) + \
|
|
' Tokens/${:,.4f}'.format(translatedData[1] * .001 * APICOST) + ']'
|
|
timeString = Fore.BLUE + '[' + str(round(translationTime, 1)) + 's]'
|
|
|
|
if translatedData[2] == None:
|
|
# Success
|
|
return filename + ': ' + tokenString + timeString + Fore.GREEN + u' \u2713 ' + Fore.RESET
|
|
|
|
else:
|
|
# Fail
|
|
try:
|
|
raise translatedData[2]
|
|
except Exception as e:
|
|
errorString = str(e) + Fore.RED
|
|
return filename + ': ' + tokenString + timeString + Fore.RED + u' \u2717 ' +\
|
|
errorString + Fore.RESET
|
|
|
|
def parseText(data, filename):
|
|
totalTokens = 0
|
|
totalLines = 0
|
|
global LOCK
|
|
|
|
# Get total for progress bar
|
|
linesList = data.readlines()
|
|
totalLines = len(linesList)
|
|
|
|
with tqdm(bar_format=BAR_FORMAT, position=POSITION, total=totalLines, leave=LEAVE) as pbar:
|
|
pbar.desc=filename
|
|
pbar.total=totalLines
|
|
try:
|
|
response = translateText(linesList, pbar)
|
|
except Exception as e:
|
|
return [linesList, 0, e]
|
|
return [response[0], response[1], None]
|
|
|
|
def translateText(data, pbar):
|
|
textHistory = []
|
|
maxHistory = MAXHISTORY
|
|
tokens = 0
|
|
speaker = ''
|
|
speakerFlag = False
|
|
|
|
for i in range(len(data)):
|
|
if '◆' in data[i]:
|
|
jaString = data[i]
|
|
|
|
### Translate
|
|
# Remove any textwrap
|
|
jaString = re.sub(r'\\n', ' ', jaString)
|
|
|
|
# Check if speaker
|
|
if '◆A' in jaString:
|
|
speakerFlag = True
|
|
|
|
# Need to remove outside code and put it back later
|
|
startString = re.search(r'^◆[a-zA-Z0-9]+◆', jaString)
|
|
jaString = re.sub(r'^◆[a-zA-Z0-9]+◆', '', jaString)
|
|
endString = re.search(r'\n$', jaString)
|
|
jaString = re.sub(r'\n$', '', jaString)
|
|
if startString is None: startString = ''
|
|
else: startString = startString.group()
|
|
if endString is None: endString = ''
|
|
else: endString = endString.group()
|
|
|
|
# Remove Repeating Chars
|
|
jaString = re.sub(r'([\u3000-\uffef])\1{1,}', r'\1', jaString)
|
|
|
|
# Translate
|
|
if speaker != '':
|
|
response = translateGPT(jaString, 'Previous Text for Context: ' + ' '.join(textHistory) \
|
|
+ '\n\n\n###\n\n\nCurrent Speaker: ' + speaker, True)
|
|
else:
|
|
response = translateGPT(jaString, 'Previous Text for Context: ' + ' '.join(textHistory), True)
|
|
tokens += response[1]
|
|
translatedText = response[0]
|
|
|
|
# TextHistory is what we use to give GPT Context, so thats appended here.
|
|
# rawTranslatedText = re.sub(r'[\\<>]+[a-zA-Z]+\[[a-zA-Z0-9]+\]', '', translatedText)
|
|
if speaker != '':
|
|
textHistory.append(speaker + ': ' + translatedText)
|
|
elif speakerFlag == False:
|
|
textHistory.append('\"' + translatedText + '\"')
|
|
|
|
# Keep textHistory list at length maxHistory
|
|
if len(textHistory) > maxHistory:
|
|
textHistory.pop(0)
|
|
|
|
# Textwrap
|
|
translatedText = textwrap.fill(translatedText, width=WIDTH)
|
|
translatedText = translatedText.replace('\n','\\n')
|
|
speaker = ''
|
|
|
|
# Setup Speaker
|
|
if speakerFlag == True:
|
|
# Remove characters that may break scripts
|
|
charList = ['.', '\"']
|
|
for char in charList:
|
|
translatedText = translatedText.replace(char, '')
|
|
|
|
speaker = translatedText
|
|
speakerFlag = False
|
|
|
|
# Write
|
|
data[i] = startString + translatedText + endString
|
|
pbar.update()
|
|
return [data, tokens]
|
|
|
|
@retry(exceptions=Exception, tries=5, delay=5)
|
|
def translateGPT(t, history, fullPromptFlag):
|
|
with LOCK:
|
|
# If ESTIMATE is True just count this as an execution and return.
|
|
if ESTIMATE:
|
|
global TOKENS
|
|
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
|
|
TOKENS += len(enc.encode(t)) * 2 + len(enc.encode(history)) + len(enc.encode(PROMPT))
|
|
return (t, 0)
|
|
|
|
# If there isn't any Japanese in the text just skip
|
|
if not re.search(r'[一-龠]+|[ぁ-ゔ]+|[ァ-ヴ]+', t):
|
|
return(t, 0)
|
|
|
|
"""Translate text using GPT"""
|
|
if fullPromptFlag:
|
|
system = PROMPT + history
|
|
else:
|
|
system = 'You are going to pretend to be Japanese visual novel translator, \
|
|
editor, and localizer. ' + history
|
|
response = openai.ChatCompletion.create(
|
|
temperature=0,
|
|
model="gpt-3.5-turbo",
|
|
messages=[
|
|
{"role": "system", "content": system},
|
|
{"role": "user", "content": "Text to Translate: " + t}
|
|
],
|
|
request_timeout=30,
|
|
)
|
|
|
|
# Make sure translation didn't wonk out
|
|
mlen=len(response.choices[0].message.content)
|
|
elnt=10*len(t)
|
|
if len(response.choices[0].message.content) > 9 * len(t):
|
|
return [t, response.usage.total_tokens]
|
|
else:
|
|
return [response.choices[0].message.content, response.usage.total_tokens]
|
|
|