665 lines
24 KiB
Python
665 lines
24 KiB
Python
# Libraries
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import json
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import os
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import re
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import util.dazedwrap as dazedwrap
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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 openai
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from pathlib import Path
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from colorama import Fore
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from dotenv import load_dotenv
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from retry import retry
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from tqdm import tqdm
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# Open AI
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load_dotenv()
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if os.getenv("api").replace(" ", "") != "":
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openai.base_url = os.getenv("api")
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openai.organization = os.getenv("org")
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openai.api_key = os.getenv("key")
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# Globals
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MODEL = os.getenv("model")
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TIMEOUT = int(os.getenv("timeout"))
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LANGUAGE = os.getenv("language").capitalize()
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PROMPT = Path("prompt.txt").read_text(encoding="utf-8")
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VOCAB = Path("vocab.txt").read_text(encoding="utf-8")
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THREADS = int(os.getenv("threads"))
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LOCK = threading.Lock()
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WIDTH = int(os.getenv("width"))
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LISTWIDTH = int(os.getenv("listWidth"))
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NOTEWIDTH = 70
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MAXHISTORY = 10
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ESTIMATE = ""
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TOKENS = [0, 0]
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NAMESLIST = []
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NAMES = False # Output a list of all the character names found
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BRFLAG = False # If the game uses <br> instead
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FIXTEXTWRAP = True # Overwrites textwrap
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IGNORETLTEXT = False # Ignores all translated text.
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MISMATCH = [] # Lists files that throw a mismatch error (Length of GPT list response is wrong)
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PBAR = None
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FILENAME = None
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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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# Regex - Need to change this if you want to translate from/to other languages. Default is Japanese Regex
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LANGREGEX = r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+"
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# Pricing - Depends on the model https://openai.com/pricing
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# Batch Size - GPT 3.5 Struggles past 15 lines per request. GPT4 struggles past 50 lines per request
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# If you are getting a MISMATCH LENGTH error, lower the batch size.
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if "gpt-3.5" in MODEL:
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INPUTAPICOST = 3.00
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OUTPUTAPICOST = 5.00
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BATCHSIZE = 10
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FREQUENCY_PENALTY = 0.2
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elif "gpt-4" in MODEL:
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INPUTAPICOST = 2.0
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OUTPUTAPICOST = 8.00
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BATCHSIZE = 30
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FREQUENCY_PENALTY = 0.05
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elif "deepseek" in MODEL:
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INPUTAPICOST = 0.27
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OUTPUTAPICOST = 1.10
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BATCHSIZE = 30
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FREQUENCY_PENALTY = 0.05
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else:
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INPUTAPICOST = float(os.getenv("input_cost"))
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OUTPUTAPICOST = float(os.getenv("output_cost"))
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BATCHSIZE = int(os.getenv("batchsize"))
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FREQUENCY_PENALTY = float(os.getenv("frequency_penalty"))
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def handleLune(filename, estimate):
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global FILENAME, ESTIMATE, totalTokens
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ESTIMATE = estimate
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FILENAME = filename
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if estimate:
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start = time.time()
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translatedData = openFiles(filename)
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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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with LOCK:
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TOKENS[0] += translatedData[1][0]
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TOKENS[1] += translatedData[1][1]
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return getResultString(["", TOKENS, None], end - start, "TOTAL")
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else:
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try:
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with open("translated/" + filename, "w", encoding="utf-8", newline="\n") as outFile:
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start = time.time()
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translatedData = openFiles(filename)
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# Print Result
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end = time.time()
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json.dump(translatedData[0], outFile, ensure_ascii=False, indent=4)
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tqdm.write(getResultString(translatedData, end - start, filename))
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with LOCK:
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TOKENS[0] += translatedData[1][0]
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TOKENS[1] += translatedData[1][1]
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except Exception:
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return "Fail"
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return getResultString(["", TOKENS, None], end - start, "TOTAL")
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def openFiles(filename):
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with open("files/" + filename, "r", encoding="UTF-8-sig") as f:
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data = json.load(f)
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# Map Files
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if ".json" in filename:
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translatedData = parseJSON(data, filename)
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else:
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raise NameError(filename + " Not Supported")
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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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totalTokenstring = (
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Fore.YELLOW + "[Input: " + str(translatedData[1][0]) + "]"
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"[Output: "
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+ str(translatedData[1][1])
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+ "]" "[Cost: ${:,.4f}".format(((translatedData[1][0] / 1000000) * INPUTAPICOST) + ((translatedData[1][1] / 1000000) * OUTPUTAPICOST))
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+ "]"
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)
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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 + ": " + totalTokenstring + timeString + Fore.GREEN + " \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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traceback.print_exc()
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errorString = str(e) + Fore.RED
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return filename + ": " + totalTokenstring + timeString + Fore.RED + " \u2717 " + errorString + Fore.RESET
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def parseJSON(data, filename):
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totalTokens = [0, 0]
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totalLines = 0
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totalLines = len(data)
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global LOCK
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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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try:
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result = translateJSON(data, pbar)
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totalTokens[0] += result[0]
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totalTokens[1] += result[1]
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except Exception as e:
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return [data, totalTokens, e]
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return [data, totalTokens, None]
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def translateJSON(data, pbar):
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global PBAR
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PBAR = pbar
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textHistory = []
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batch = []
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maxHistory = MAXHISTORY
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tokens = [0, 0]
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speaker = "None"
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insertBool = False
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i = 0
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batchStartIndex = 0
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while i < len(data):
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item = data[i]
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# Speaker
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if "name" in item:
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if item["name"] not in [None, "-"]:
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response = getSpeaker(item["name"])
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speaker = response[0]
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tokens[0] += response[1][0]
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tokens[1] += response[1][1]
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item["name"] = speaker
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else:
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speaker = "None"
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# Text
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if "message" in item:
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for text in [
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"text",
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"text2",
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"help1",
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"help2",
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"help3",
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"like",
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"message",
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"me",
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]:
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if text in item:
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if item[text] != None:
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jaString = item[text]
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# Remove any textwrap
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if FIXTEXTWRAP == True:
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finalJAString = jaString.replace("\n", " ")
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# [Passthrough 1] Pulling From File
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if insertBool is False:
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# Append to List and Clear Values
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batch.append(finalJAString)
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speaker = ""
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# Translate Batch if Full
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if len(batch) == BATCHSIZE:
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# Translate
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response = translateGPT(batch, textHistory, True)
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tokens[0] += response[1][0]
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tokens[1] += response[1][1]
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translatedBatch = response[0]
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textHistory = translatedBatch[-10:]
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# Set Values
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if len(batch) == len(translatedBatch):
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i = batchStartIndex
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insertBool = True
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# Mismatch
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else:
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pbar.write(f"Mismatch: {batchStartIndex} - {i}")
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MISMATCH.append(batch)
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batchStartIndex = i
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batch.clear()
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if insertBool is False:
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pbar.update(1)
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i += 1
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currentGroup = []
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# [Passthrough 2] Setting Data
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else:
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# Get Text
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translatedText = translatedBatch[0]
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# Remove added speaker
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translatedText = re.sub(r"^.+?:\s", "", translatedText)
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# Textwrap
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translatedText = dazedwrap.wrapText(translatedText, width=WIDTH)
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# Set Text
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item[text] = translatedText
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translatedBatch.pop(0)
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speaker = ""
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currentGroup = []
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i += 1
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# If Batch is empty. Move on.
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if len(translatedBatch) == 0:
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insertBool = False
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batchStartIndex = i
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batch.clear()
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else:
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i += 1
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pbar.update(1)
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# Translate Batch if not empty and EOF
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if len(batch) != 0 and i >= len(data):
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# Translate
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response = translateGPT(batch, textHistory, True)
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tokens[0] += response[1][0]
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tokens[1] += response[1][1]
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translatedBatch = response[0]
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textHistory = translatedBatch[-10:]
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# Set Values
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if len(batch) == len(translatedBatch):
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i = batchStartIndex
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insertBool = True
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# Mismatch
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else:
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pbar.write(f"Mismatch: {batchStartIndex} - {i}")
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MISMATCH.append(batch)
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batchStartIndex = i
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batch.clear()
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currentGroup = []
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return tokens
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# Save some money and enter the character before translation
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def getSpeaker(speaker):
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match speaker:
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case "ファイン":
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return ["Fine", [0, 0]]
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case "":
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return ["", [0, 0]]
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case _:
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# Find Speaker
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for i in range(len(NAMESLIST)):
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if speaker == NAMESLIST[i][0]:
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return [NAMESLIST[i][1], [0, 0]]
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# Translate and Store Speaker
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response = translateGPT(
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f"{speaker}",
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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False,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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response[0] = response[0].replace("Speaker: ", "")
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# Retry if name doesn't translate for some reason
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if re.search(r"([a-zA-Z？?])", response[0]) == None:
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response = translateGPT(
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f"{speaker}",
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"Reply with the " + LANGUAGE + " translation of the NPC name.",
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False,
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)
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response[0] = response[0].title()
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response[0] = response[0].replace("'S", "'s")
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speakerList = [speaker, response[0]]
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NAMESLIST.append(speakerList)
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return response
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return [speaker, [0, 0]]
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def batchList(input_list, batch_size):
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if not isinstance(batch_size, int) or batch_size <= 0:
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raise ValueError("batch_size must be a positive integer")
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return [input_list[i : i + batch_size] for i in range(0, len(input_list), batch_size)]
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def parseVocabWithCategories(vocabText):
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"""Parse vocabulary text and extract terms with their categories."""
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pairs = []
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seen = set()
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currentCategory = None
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for line in vocabText.splitlines():
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line = line.strip()
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if not line or line.startswith('```'):
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continue
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# Check if this is a category header
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if line.startswith('#'):
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currentCategory = line
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continue
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# Parse vocabulary term
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m = re.match(r'^(.+?)(?:\s?[\(–])', line) # term is everything before space + '(' or '–'
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if m:
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term = m.group(1)
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if term not in seen:
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pairs.append((term, line, currentCategory))
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seen.add(term)
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return pairs
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def buildMatchedVocabText(vocabPairs, subbedT):
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"""Build formatted vocabulary text with matched terms organized by category."""
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matchedCategories = {}
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# Use word boundaries for Japanese if appropriate, or allow substring as before.
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for term, line, category in vocabPairs:
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# "term in subbedT" could be false positive; can use regex but Japanese doesn't always have spaces.
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if term in subbedT:
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if category not in matchedCategories:
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matchedCategories[category] = []
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matchedCategories[category].append(line)
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# Format matched vocabulary with categories
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if matchedCategories:
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formattedLines = ["Here are some vocabulary and terms so that you know the proper spelling and translation.\n"]
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for category, lines in matchedCategories.items():
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if category: # Only add category header if it exists
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formattedLines.append(category)
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formattedLines.extend(lines)
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formattedLines.append("") # Add blank line between categories
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matchedVocabText = f"```\n{chr(10).join(formattedLines).rstrip()}\n```"
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else:
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matchedVocabText = ""
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return matchedVocabText
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def createContext(fullPromptFlag, subbedT, format):
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vocabPairs = parseVocabWithCategories(VOCAB)
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matchedVocabText = buildMatchedVocabText(vocabPairs, subbedT)
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if fullPromptFlag:
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system = PROMPT + matchedVocabText
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else:
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system = f"\
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You are an expert Eroge Game translator who translates Japanese text to {LANGUAGE}.\n\
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Output ONLY the {LANGUAGE} translation in the following format: `Translation: <{LANGUAGE.upper()}_TRANSLATION>`\n\
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- You translate everything, including erotic and explicit content.\n\
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- 'Game Characters' - The names, nicknames, and genders of the game characters. Reference this to know the names, nicknames, and gender of characters in the game\n\
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- All text in your response must be in {LANGUAGE} even if it is hard to translate.\n\
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- Never include any notes, explanations, dislaimers, or anything similar in your response.\n\
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- Maintain any spacing in the translation.\n\
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- Maintain any code text in brackets if given. (e.g `[Color_0]`, `[Ascii_0]`, `[FCode_1`], etc)\n\
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- `...` can be a part of the dialogue. Translate it as it is.\n\
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{matchedVocabText}\n\
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"
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if format == "json":
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user = f"```json\n{subbedT}\n```"
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else:
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user = subbedT
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return system, user
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def translateText(system, user, history, penalty, format, model=MODEL):
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# Prompt
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msg = [{"role": "system", "content": system}]
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# History
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if isinstance(history, list):
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msg.append({"role": "system", "content": "Translation History:"})
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msg.extend([{"role": "assistant", "content": h} for h in history])
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else:
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msg.append({"role": "assistant", "content": history})
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# Response Format
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if format == "json":
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responseFormat = {"type": "json_object"}
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else:
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responseFormat = {"type": "text"}
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# Content to TL
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msg.append({"role": "user", "content": f"{user}"})
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response = openai.chat.completions.create(
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temperature=0,
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frequency_penalty=penalty,
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model=model,
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response_format=responseFormat,
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messages=msg,
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)
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return response
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def cleanTranslatedText(translatedText):
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placeholders = {
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f"{LANGUAGE} Translation: ": "",
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"Translation: ": "",
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"っ": "",
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"〜": "~",
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"ッ": "",
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"。": ".",
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"「": '\\"',
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"」": '\\"',
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"- ": "-",
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"—": "―",
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"】": "]",
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"【": "[",
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"é": "e",
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"this guy": "this bastard",
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"This guy": "This bastard",
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"Placeholder Text": "",
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"```json": "",
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"```": "",
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# Add more replacements as needed
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}
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for target, replacement in placeholders.items():
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translatedText = translatedText.replace(target, replacement)
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# Remove Repeating Characters
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pattern = re.compile(r"(.)\s*\1(?:\s*\1){" + str(20 - 1) + r",}")
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translatedText = pattern.sub(lambda match: match.group(0).replace(" ", "")[:20], translatedText)
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# Elongate Long Dashes (Since GPT Ignores them...)
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translatedText = elongateCharacters(translatedText)
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return translatedText
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def elongateCharacters(text):
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# Define a pattern to match one character followed by one or more `ー` characters
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# Using a positive lookbehind assertion to capture the preceding character
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pattern = r"(?<=(.))ー+"
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# Define a replacement function that elongates the captured character
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def repl(match):
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char = match.group(1) # The character before the ー sequence
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count = len(match.group(0)) - 1 # Number of ー characters
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return char * count # Replace ー sequence with the character repeated
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# Use re.sub() to replace the pattern in the text
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return re.sub(pattern, repl, text)
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def extractTranslation(translatedTextList, is_list):
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try:
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translatedTextList = re.sub(r'\\"+\"([^,\n}])', r'\\"\1', translatedTextList)
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translatedTextList = re.sub(r"(?<![\\])\"+(?![\n,])", r'"', translatedTextList)
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line_dict = json.loads(translatedTextList)
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# If it's a batch (i.e., list), extract with tags; otherwise, return the single item.
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string_list = list(line_dict.values())
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if is_list:
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return string_list
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else:
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return string_list[0]
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except Exception as e:
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PBAR.write(f"extractTranslation Error: {e} on String {translatedTextList}")
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return None
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def countTokens(system, user, history):
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inputTotalTokens = 0
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outputTotalTokens = 0
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enc = tiktoken.encoding_for_model("gpt-4")
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|
||
# Input
|
||
if isinstance(history, list):
|
||
for line in history:
|
||
inputTotalTokens += len(enc.encode(line))
|
||
else:
|
||
inputTotalTokens += len(enc.encode(history))
|
||
inputTotalTokens += len(enc.encode(system))
|
||
inputTotalTokens += len(enc.encode(user))
|
||
|
||
# Output
|
||
outputTotalTokens += round(len(enc.encode(user)) * 2.5)
|
||
|
||
return [inputTotalTokens, outputTotalTokens]
|
||
|
||
|
||
@retry(exceptions=Exception, tries=5, delay=5)
|
||
def translateGPT(text, history, fullPromptFlag):
|
||
global PBAR, MISMATCH, FILENAME
|
||
if text:
|
||
with open("log/translationHistory.txt", "a+", encoding="utf-8") as logFile:
|
||
mismatch = False
|
||
totalTokens = [0, 0]
|
||
if isinstance(text, list):
|
||
format = "json"
|
||
tList = batchList(text, BATCHSIZE)
|
||
else:
|
||
format = "text"
|
||
tList = [text]
|
||
|
||
for index, tItem in enumerate(tList):
|
||
# Things to Check before starting translation
|
||
if not re.search(LANGREGEX, str(tItem)):
|
||
if PBAR is not None:
|
||
PBAR.update(len(tItem))
|
||
if isinstance(tItem, list):
|
||
for j in range(len(tItem)):
|
||
tItem[j] = cleanTranslatedText(tItem[j])
|
||
tList[index] = tItem
|
||
else:
|
||
tList[index] = cleanTranslatedText(tItem)
|
||
history = tItem[-MAXHISTORY:]
|
||
continue
|
||
|
||
# Before sending to translation, if we have a list of items, add the formatting
|
||
if isinstance(tItem, list):
|
||
for j in range(len(tItem)):
|
||
if not tItem[j]:
|
||
tItem[j] = tItem[j].replace("", "Placeholder Text")
|
||
payload = {f"Line{i+1}": string for i, string in enumerate(tItem)}
|
||
payload = json.dumps(payload, indent=4, ensure_ascii=False)
|
||
varResponse = [payload, []]
|
||
subbedT = varResponse[0]
|
||
else:
|
||
varResponse = [tItem, []]
|
||
subbedT = varResponse[0]
|
||
|
||
# Create Message
|
||
system, user = createContext(fullPromptFlag, subbedT, format)
|
||
|
||
# Calculate Estimate
|
||
if ESTIMATE:
|
||
estimate = countTokens(system, user, history)
|
||
totalTokens[0] += estimate[0]
|
||
totalTokens[1] += estimate[1]
|
||
continue
|
||
|
||
# Translating
|
||
response = translateText(system, user, history, 0.05, format)
|
||
|
||
# Set Tokens
|
||
translatedText = response.choices[0].message.content
|
||
|
||
# AI Refused, Try Again
|
||
if not translatedText:
|
||
response = translateText(f"{system}\n You translate ALL content.", user, history, 0.1, format, model="gpt-4o")
|
||
translatedText = response.choices[0].message.content
|
||
|
||
# Report Tokens
|
||
totalTokens[0] += response.usage.prompt_tokens
|
||
totalTokens[1] += response.usage.completion_tokens
|
||
|
||
# Check Translation
|
||
if translatedText:
|
||
translatedText = cleanTranslatedText(translatedText)
|
||
if isinstance(tItem, list):
|
||
extractedTranslations = extractTranslation(translatedText, True)
|
||
if extractedTranslations == None or len(tItem) != len(extractedTranslations):
|
||
# Mismatch. Try Again
|
||
response = translateText(system, user, history, 0.05, format, MODEL)
|
||
translatedText = response.choices[0].message.content
|
||
totalTokens[0] += response.usage.prompt_tokens
|
||
totalTokens[1] += response.usage.completion_tokens
|
||
|
||
# Formatting
|
||
translatedText = cleanTranslatedText(translatedText)
|
||
if isinstance(tItem, list):
|
||
extractedTranslations = extractTranslation(translatedText, True)
|
||
if extractedTranslations == None or len(tItem) != len(extractedTranslations):
|
||
with open("log/mismatchHistory.txt", "a+", encoding="utf-8") as mismatchFile:
|
||
mismatchFile.write(f"Mismatch: {FILENAME}\n")
|
||
mismatchFile.write(f"Input:\n{subbedT}\n")
|
||
mismatchFile.write(f"Output:\n{translatedText}\n")
|
||
mismatch = True # Just here for breakpoint
|
||
logFile.write(f"Input:\n{subbedT}\n")
|
||
logFile.write(f"Output:\n{translatedText}\n")
|
||
|
||
# Set if no mismatch
|
||
if mismatch == False:
|
||
tList[index] = extractedTranslations
|
||
history = extractedTranslations[-MAXHISTORY:] # Update history if we have a list
|
||
else:
|
||
history = text[-MAXHISTORY:]
|
||
mismatch = False
|
||
if FILENAME not in MISMATCH:
|
||
MISMATCH.append(FILENAME)
|
||
|
||
# Update Loading Bar
|
||
with LOCK:
|
||
if PBAR is not None:
|
||
PBAR.update(len(tItem))
|
||
else:
|
||
# Ensure we're passing a single string to extractTranslation
|
||
tList[index] = translatedText.replace("Placeholder Text", "")
|
||
else:
|
||
PBAR.write(f"AI Refused:{tItem}\n")
|
||
|
||
# Combine if multilist
|
||
if isinstance(tList[0], list):
|
||
tList = [t for sublist in tList for t in sublist]
|
||
|
||
# Return
|
||
if format == "json":
|
||
return [tList, totalTokens]
|
||
else:
|
||
return [tList[0], totalTokens]
|
||
else:
|
||
return [text, [0, 0]]
|