374 lines
No EOL
13 KiB
Python
374 lines
No EOL
13 KiB
Python
from concurrent.futures import ThreadPoolExecutor, as_completed
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import os
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from pathlib import Path
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import re
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import sys
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import textwrap
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import threading
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import time
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import traceback
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import tiktoken
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import csv
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from colorama import Fore
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from dotenv import load_dotenv
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import openai
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from retry import retry
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from tqdm import tqdm
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#Globals
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load_dotenv()
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openai.organization = os.getenv('org')
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openai.api_key = os.getenv('key')
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APICOST = .002 # Depends on the model https://openai.com/pricing
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PROMPT = Path('prompt.txt').read_text(encoding='utf-8')
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THREADS = 20
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LOCK = threading.Lock()
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WIDTH = 60
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MAXHISTORY = 10
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ESTIMATE = ''
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TOTALCOST = 0
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TOKENS = 0
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TOTALTOKENS = 0
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#tqdm Globals
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BAR_FORMAT='{l_bar}{bar:10}{r_bar}{bar:-10b}'
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POSITION=0
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LEAVE=False
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def handleCSV(filename, estimate):
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global ESTIMATE, TOKENS, TOTALTOKENS, TOTALCOST
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ESTIMATE = estimate
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with open('translated/' + filename, 'w+t', newline='', encoding='utf-8') as writeFile:
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start = time.time()
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translatedData = openFiles(filename, writeFile)
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# Print Result
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end = time.time()
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tqdm.write(getResultString(translatedData, end - start, filename))
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TOTALCOST += translatedData[1] * .001 * APICOST
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TOTALTOKENS += translatedData[1]
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return getResultString(['', TOTALTOKENS, None], end - start, 'TOTAL')
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def openFiles(filename, writeFile):
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with open('files/' + filename, 'r', encoding='utf-8') as readFile, writeFile:
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translatedData = parseCSV(readFile, writeFile, filename)
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return translatedData
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def getResultString(translatedData, translationTime, filename):
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# File Print String
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tokenString = Fore.YELLOW + '[' + str(translatedData[1]) + \
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' Tokens/${:,.4f}'.format(translatedData[1] * .001 * APICOST) + ']'
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timeString = Fore.BLUE + '[' + str(round(translationTime, 1)) + 's]'
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if translatedData[2] == None:
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# Success
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return filename + ': ' + tokenString + timeString + Fore.GREEN + u' \u2713 ' + Fore.RESET
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else:
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# Fail
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try:
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raise translatedData[2]
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except Exception as e:
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errorString = str(e) + '|' + translatedData[3] + Fore.RED
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return filename + ': ' + tokenString + timeString + Fore.RED + u' \u2717 ' +\
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errorString + Fore.RESET
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def parseCSV(readFile, writeFile, filename):
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totalTokens = 0
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totalLines = 0
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textHistory = []
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global LOCK
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format = ''
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while format == '':
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format = input('\n\nSelect the CSV Format:\n\n1. Translator++\n2. Translate All\n')
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match format:
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case '1':
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format = '1'
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case '2':
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format = '2'
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# Get total for progress bar
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totalLines = len(readFile.readlines())
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readFile.seek(0)
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reader = csv.reader(readFile, delimiter=',',)
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writer = csv.writer(writeFile, delimiter=',', quotechar='\"')
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with tqdm(bar_format=BAR_FORMAT, position=POSITION, total=totalLines, leave=LEAVE) as pbar:
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pbar.desc=filename
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pbar.total=totalLines
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for row in reader:
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try:
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totalTokens += translateCSV(row, pbar, writer, textHistory, format)
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except Exception as e:
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tracebackLineNo = str(traceback.extract_tb(sys.exc_info()[2])[-1].lineno)
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return [reader, totalTokens, e, tracebackLineNo]
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return [reader, totalTokens, None]
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def translateCSV(row, pbar, writer, textHistory, format):
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translatedText = ''
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maxHistory = MAXHISTORY
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tokens = 0
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global LOCK, ESTIMATE
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try:
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match format:
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# Japanese Text on column 1. English on Column 2
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case '1':
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# Skip already translated lines
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if row[1] == '' or re.search(r'[一-龠]+|[ぁ-ゔ]+|[ァ-ヴ]+|[\uFF00-\uFFEF]', row[1]):
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jaString = row[0]
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# Remove repeating characters because it confuses ChatGPT
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jaString = re.sub(r'([\u3000-\uffef])\1{2,}', r'\1\1', jaString)
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# Translate
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response = translateGPT(jaString, 'Previous text for context: ' + ' '.join(textHistory), True)
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# Check if there is an actual difference first
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if response[0] != row[0]:
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translatedText = response[0]
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else:
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translatedText = row[1]
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tokens += response[1]
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# Textwrap
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translatedText = textwrap.fill(translatedText, width=WIDTH)
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# Set Data
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row[1] = translatedText
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# Keep textHistory list at length maxHistory
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with LOCK:
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if len(textHistory) > maxHistory:
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textHistory.pop(0)
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if not ESTIMATE:
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writer.writerow(row)
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pbar.update(1)
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# TextHistory is what we use to give GPT Context, so thats appended here.
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textHistory.append('\"' + translatedText + '\"')
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# Translate Everything
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case '2':
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for i in range(len(row)):
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# This will allow you to ignore certain columns
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if i not in [1]:
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continue
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jaString = row[i]
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matchList = re.findall(r':name\[(.+?),.+?\](.+?[」）\"。]+)', jaString)
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# Start Translation
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for match in matchList:
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speaker = match[0]
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text = match[1]
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# Translate Speaker
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response = translateGPT (speaker, 'Reply with the English translation of the NPC name.', True)
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translatedSpeaker = response[0]
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tokens += response[1]
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# Translate Line
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jaText = re.sub(r'([\u3000-\uffef])\1{3,}', r'\1\1\1', text)
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response = translateGPT(translatedSpeaker + ': ' + jaText, 'Previous Translated Text: ' + '|'.join(textHistory), True)
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translatedText = response[0]
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tokens += response[1]
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# TextHistory is what we use to give GPT Context, so thats appended here.
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textHistory.append(translatedText)
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# Remove Speaker from translated text
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translatedText = re.sub(r'.+?: ', '', translatedText)
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# Set Data
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translatedSpeaker = translatedSpeaker.replace('\"', '')
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translatedText = translatedText.replace('\"', '')
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translatedText = translatedText.replace('「', '')
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translatedText = translatedText.replace('」', '')
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row[i] = row[i].replace('\n', ' ')
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# Textwrap
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translatedText = textwrap.fill(translatedText, width=WIDTH)
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translatedText = '「' + translatedText + '」'
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row[i] = re.sub(rf':name\[({re.escape(speaker)}),', f':name[{translatedSpeaker},', row[i])
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row[i] = row[i].replace(text, translatedText)
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# Keep History at fixed length.
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with LOCK:
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if len(textHistory) > maxHistory:
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textHistory.pop(0)
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with LOCK:
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if not ESTIMATE:
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writer.writerow(row)
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pbar.update(1)
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except Exception as e:
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traceback.print_exc()
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tracebackLineNo = str(traceback.extract_tb(sys.exc_info()[2])[-1].lineno)
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raise Exception(str(e) + '|Line:' + tracebackLineNo + '| Failed to translate: ' + text)
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return tokens
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def subVars(jaString):
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jaString = jaString.replace('\u3000', ' ')
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# Icons
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count = 0
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iconList = re.findall(r'[\\]+[iI]\[[0-9]+\]', jaString)
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iconList = set(iconList)
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if len(iconList) != 0:
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for icon in iconList:
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jaString = jaString.replace(icon, '<I' + str(count) + '>')
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count += 1
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# Colors
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count = 0
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colorList = re.findall(r'[\\]+[cC]\[[0-9]+\]', jaString)
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colorList = set(colorList)
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if len(colorList) != 0:
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for color in colorList:
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jaString = jaString.replace(color, '<C' + str(count) + '>')
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count += 1
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# Names
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count = 0
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nameList = re.findall(r'[\\]+[nN]\[[0-9]+\]', jaString)
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nameList = set(nameList)
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if len(nameList) != 0:
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for name in nameList:
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jaString = jaString.replace(name, '<N' + str(count) + '>')
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count += 1
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# Variables
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count = 0
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varList = re.findall(r'[\\]+[vV]\[[0-9]+\]', jaString)
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varList = set(varList)
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if len(varList) != 0:
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for var in varList:
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jaString = jaString.replace(var, '<V' + str(count) + '>')
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count += 1
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# Formatting
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count = 0
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formatList = re.findall(r'[\\]+[!.]', jaString)
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formatList = set(formatList)
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if len(formatList) != 0:
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for format in formatList:
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jaString = jaString.replace(format, '<F' + str(count) + '>')
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count += 1
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# Put all lists in list and return
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allList = [iconList, colorList, nameList, varList, formatList]
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return [jaString, allList]
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def resubVars(translatedText, allList):
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# Fix Spacing and ChatGPT Nonsense
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matchList = re.findall(r'<\s?.+?\s?>', translatedText)
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if len(matchList) > 0:
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for match in matchList:
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text = match.replace(' ', '')
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translatedText = translatedText.replace(match, text)
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# Icons
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count = 0
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if len(allList[0]) != 0:
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for var in allList[0]:
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translatedText = translatedText.replace('<I' + str(count) + '>', var)
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count += 1
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# Colors
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count = 0
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if len(allList[1]) != 0:
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for var in allList[1]:
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translatedText = translatedText.replace('<C' + str(count) + '>', var)
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count += 1
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# Names
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count = 0
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if len(allList[2]) != 0:
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for var in allList[2]:
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translatedText = translatedText.replace('<N' + str(count) + '>', var)
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count += 1
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# Vars
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count = 0
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if len(allList[3]) != 0:
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for var in allList[3]:
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translatedText = translatedText.replace('<V' + str(count) + '>', var)
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count += 1
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# Formatting
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count = 0
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if len(allList[4]) != 0:
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for var in allList[4]:
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translatedText = translatedText.replace('<F' + str(count) + '>', var)
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count += 1
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return translatedText
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@retry(exceptions=Exception, tries=5, delay=5)
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def translateGPT(t, history, fullPromptFlag):
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# If ESTIMATE is True just count this as an execution and return.
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if ESTIMATE:
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enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
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tokens = len(enc.encode(t)) * 2 + len(enc.encode(history)) + len(enc.encode(PROMPT))
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return (t, tokens)
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# Sub Vars
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varResponse = subVars(t)
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subbedT = varResponse[0]
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# If there isn't any Japanese in the text just skip
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if not re.search(r'[一-龠]+|[ぁ-ゔ]+|[ァ-ヴー]+', subbedT):
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return(t, 0)
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"""Translate text using GPT"""
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context = 'Eroge Names Context: ミカエル == Mikael | Female, ミカ == Mika | Female, ベルゼビュート == Beelzebuth | Female, ベル == Bel | Female, アズラエル == Azriel | Female, アズ == Az | Female, フレイア == Freya | Female'
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if fullPromptFlag:
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system = PROMPT
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user = 'Current Text to Translate: ' + subbedT
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else:
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system = 'You are an expert translator who translates everything to English. Reply with only the English Translation of the text.'
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user = 'Current Text to Translate: ' + subbedT
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response = openai.ChatCompletion.create(
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temperature=0,
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model="gpt-3.5-turbo",
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messages=[
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{"role": "system", "content": system},
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{"role": "user", "content": context},
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{"role": "user", "content": history},
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{"role": "user", "content": user}
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],
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request_timeout=30,
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)
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# Save Translated Text
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translatedText = response.choices[0].message.content
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tokens = response.usage.total_tokens
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# Resub Vars
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translatedText = resubVars(translatedText, varResponse[1])
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# Remove Placeholder Text
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translatedText = translatedText.replace('English Translation: ', '')
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translatedText = translatedText.replace('Translation: ', '')
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translatedText = translatedText.replace('Current Text to Translate: ', '')
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translatedText = translatedText.replace('English Translation:', '')
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translatedText = translatedText.replace('Translation:', '')
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translatedText = translatedText.replace('Current Text to Translate:', '')
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# Return Translation
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if len(translatedText) > 15 * len(t) or "I'm sorry, but I'm unable to assist with that translation" in translatedText:
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return [t, response.usage.total_tokens]
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else:
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return [translatedText, tokens] |