754 lines
28 KiB
Python
754 lines
28 KiB
Python
# Libraries
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from PIL import Image, ImageDraw, ImageFont
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import json
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import os
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import re
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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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# 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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PBAR = None
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WIDTH = int(os.getenv("width"))
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LISTWIDTH = int(os.getenv("listWidth"))
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NOTEWIDTH = int(os.getenv("noteWidth"))
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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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MISMATCH = [] # Lists files that throw a mismatch error (Length of GPT list response is wrong)
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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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# 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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# 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 handleImages(folderName, estimate):
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global ESTIMATE, TOKENS
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ESTIMATE = estimate
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start = time.time()
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# Translate Strings
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translatedData = openFiles(f"files/{folderName}")
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# Custom Names
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# customList = [[], []]
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# customList = processImagesDir("Custom", customList)
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# Write TL To Images
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try:
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translatedList, originalList, dimensionsList = translatedData[0]
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for i in range(len(translatedList)):
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try:
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# Create image from string
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image = stringToImageOutline(translatedList[i], dimensionsList[i][0], dimensionsList[i][1])
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# Save image using the corresponding original filename
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image.save(rf"translated/{folderName}/{originalList[i]}.png", quality=100)
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except Exception as e:
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# Log error if image saving fails
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PBAR.write(f"Error processing {translatedList[i]}: {str(e)}")
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except IndexError:
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PBAR.write("Translated data is incomplete. Please check your input.")
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# Print File
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end = time.time()
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tqdm.write(getResultString(translatedData, end - start, folderName))
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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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# Print Total
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totalString = getResultString(["", TOKENS, None], end - start, "TOTAL")
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# Print any errors on maps
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if len(MISMATCH) > 0:
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return totalString + Fore.RED + f"\nMismatch Errors: {MISMATCH}" + Fore.RESET
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else:
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return totalString
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def openFiles(folderName):
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global PBAR
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if os.path.isdir(folderName):
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imageList = [[], [], []]
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imageList = processImagesDir(folderName, imageList)
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# Start Translation
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with tqdm(
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bar_format=BAR_FORMAT,
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position=POSITION,
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leave=LEAVE,
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desc=folderName,
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total=len(imageList[0]),
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) as PBAR:
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translatedData = translateImages(imageList)
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translatedData = [
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[translatedData[0], imageList[2], imageList[1]],
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translatedData[1],
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translatedData[2],
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]
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return translatedData
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else:
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print("The provided directory path does not exist.")
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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] is 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 getFontSize(text, image_width, image_height, font_path):
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# Start with a high font size and keep reducing it until the text fits within the image bounds
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font_size = min(image_width, image_height)
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while font_size > 0:
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font = ImageFont.truetype(font_path, font_size)
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text_bbox = ImageDraw.Draw(Image.new("RGB", (1, 1))).textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1]
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if text_width <= image_width and text_height <= image_height:
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return font_size
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font_size -= 1
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return font_size
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def stringToImage(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Find the appropriate font size for the scaled up image
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font_size = getFontSize(text, scaled_width, scaled_height, font_path)
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if font_size == 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with the scaled width and height and a transparent background
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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# Create a drawing context
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draw = ImageDraw.Draw(image)
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# Load the appropriate font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size of the text to center it
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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x = 0
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw the text on the image
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draw.text((x, y), text, font=font, fill=(255, 255, 255, 255))
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# Resize back to the original dimensions to get a clearer text rendering
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image = image.resize(
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(width, height),
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Image.LANCZOS,
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)
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return image
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from PIL import Image, ImageDraw, ImageFont
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def stringToImageOutline(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Outline
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outline_color = (255, 255, 255, 255)
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text_color = (0, 0, 0, 255)
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outline_thickness = 4
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Find the appropriate font size for the scaled up image
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font_size = getFontSize(text, scaled_width, scaled_height, font_path)
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if font_size == 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with the scaled width and height and a transparent background
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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# Create a drawing context
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draw = ImageDraw.Draw(image)
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# Load the appropriate font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size of the text to center it
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw the text outline by applying the text multiple times with small offsets
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for dx in range(-outline_thickness, outline_thickness + 1):
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for dy in range(-outline_thickness, outline_thickness + 1):
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if dx != 0 or dy != 0:
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draw.text((x + dx, y + dy), text, font=font, fill=outline_color)
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# Draw the main text
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draw.text((x, y), text, font=font, fill=text_color)
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# Resize back to the original dimensions to get a clearer text rendering
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image = image.resize((width, height), Image.LANCZOS)
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return image
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def stringToImageBox(text, width, height, font_path="fonts/TsunagiGothic.ttf", scale_factor=4):
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# Increase the resolution
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scaled_width = int(width * scale_factor)
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scaled_height = int(height * scale_factor)
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# Padding around the text
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padding = 10
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# Calculate the dimensions available for text placement
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available_width = scaled_width - 2 * padding
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available_height = scaled_height - 2 * padding
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# Determine the best font size to fit within the available dimensions
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font_size = getFontSize(text, available_width, available_height, font_path)
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if font_size <= 0:
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raise ValueError("Text is too long to fit in the supplied dimensions.")
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# Create a new image with increased resolution
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image = Image.new("RGBA", (scaled_width, scaled_height), (255, 255, 255, 0))
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draw = ImageDraw.Draw(image)
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# Load the calculated font
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font = ImageFont.truetype(font_path, font_size)
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# Calculate the size and bounding box of the text
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text_bbox = draw.textbbox((0, 0), text, font=font)
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text_width = text_bbox[2] - text_bbox[0]
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text_height = text_bbox[3] - text_bbox[1] + 20
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# Determine centered position for the text while considering padding
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# Additional adjustment ensures text appears centrally aligned
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x = (scaled_width - text_width) // 2
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y = (scaled_height - text_height) // 2
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# Draw a black box with a white outline that fits the image dimensions precisely
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draw.rectangle([0, 0, scaled_width - 1, scaled_height - 1], outline=(255, 255, 255, 255), width=1)
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# Fill the inside box with black color
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draw.rectangle([1, 1, scaled_width - 2, scaled_height - 2], fill=(0, 0, 0, 255))
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# Render the text within the image
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draw.text((x, y), text, font=font, fill=(255, 255, 255, 255))
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# Shrink the image back to original dimensions with high-quality interpolation
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image = image.resize(
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(width, height),
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Image.LANCZOS,
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)
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return image
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def getImageDimensions(file_path):
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try:
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with Image.open(file_path) as img:
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width, height = img.size
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return width, height
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except Exception as e:
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print(f"Error reading {file_path}: {e}")
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return None, None
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def processImagesDir(directory_path, imageList):
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for file_name in os.listdir(directory_path):
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# .png and Japanese
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if ".png" in file_name:
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file_path = os.path.join(directory_path, file_name)
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if os.path.isfile(file_path):
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# Check if the file is an image
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try:
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width, height = getImageDimensions(file_path)
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if width is not None and height is not None:
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placeholders = {
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".png": "",
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}
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for target, replacement in placeholders.items():
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file_name = file_name.replace(target, replacement)
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match = re.search(r"[\[【].+?[\]】](.*)", file_name)
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if match:
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text = match.group(1)
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else:
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text = file_name
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imageList[0].append(text)
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imageList[1].append([width, height])
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imageList[2].append(file_name)
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except Exception as e:
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print(f"Error processing {file_name}: {e}")
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if ".txt" in file_name:
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try:
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with open(f"{directory_path}/{file_name}", "r", encoding="utf8") as file:
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for line in file:
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line = line.strip()
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line = line.replace(":", "：")
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line = line.replace("/", "／")
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line = line.replace("?", "？")
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imageList[0].append(line) # Using strip() to remove any extra newlines or spaces
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imageList[1].append([104, 15])
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except FileNotFoundError:
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print(f"The file at {file_path} was not found.")
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except IOError:
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print(f"An error occurred while reading the file at {file_path}.")
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return imageList
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def translateImages(imageList):
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totalTokens = [0, 0]
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# Translate GPT
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response = translateGPT(imageList[0], "Keep the Translation as brief as possible", True)
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translatedList = response[0]
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totalTokens[0] += response[1][0]
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totalTokens[1] += response[1][1]
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return [translatedList, totalTokens, None]
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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"\
|
||
You are an expert Eroge Game translator who translates Japanese text to {LANGUAGE}.\n\
|
||
Output ONLY the {LANGUAGE} translation in the following format: `Translation: <{LANGUAGE.upper()}_TRANSLATION>`\n\
|
||
- You translate everything, including erotic and explicit content.\n\
|
||
- '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\
|
||
- All text in your response must be in {LANGUAGE} even if it is hard to translate.\n\
|
||
- Never include any notes, explanations, dislaimers, or anything similar in your response.\n\
|
||
- Maintain any spacing in the translation.\n\
|
||
- Maintain any code text in brackets if given. (e.g `[Color_0]`, `[Ascii_0]`, `[FCode_1`], etc)\n\
|
||
- `...` can be a part of the dialogue. Translate it as it is.\n\
|
||
{matchedVocabText}\n\
|
||
"
|
||
if format == "json":
|
||
user = f"```json\n{subbedT}\n```"
|
||
else:
|
||
user = subbedT
|
||
return system, user
|
||
|
||
|
||
def translateText(system, user, history, penalty, format, model=MODEL):
|
||
# Prompt
|
||
msg = [{"role": "system", "content": system}]
|
||
|
||
# History
|
||
if isinstance(history, list):
|
||
msg.append({"role": "system", "content": "Translation History:"})
|
||
msg.extend([{"role": "assistant", "content": h} for h in history])
|
||
else:
|
||
msg.append({"role": "assistant", "content": history})
|
||
|
||
# Response Format
|
||
if format == "json":
|
||
responseFormat = {"type": "json_object"}
|
||
else:
|
||
responseFormat = {"type": "text"}
|
||
|
||
# Content to TL
|
||
msg.append({"role": "user", "content": f"{user}"})
|
||
response = openai.chat.completions.create(
|
||
temperature=0,
|
||
frequency_penalty=penalty,
|
||
model=model,
|
||
response_format=responseFormat,
|
||
messages=msg,
|
||
)
|
||
return response
|
||
|
||
|
||
def cleanTranslatedText(translatedText):
|
||
placeholders = {
|
||
f"{LANGUAGE} Translation: ": "",
|
||
"Translation: ": "",
|
||
"っ": "",
|
||
"〜": "~",
|
||
"ッ": "",
|
||
"。": ".",
|
||
"「": '\\"',
|
||
"」": '\\"',
|
||
"- ": "-",
|
||
"—": "―",
|
||
"】": "]",
|
||
"【": "[",
|
||
"é": "e",
|
||
"this guy": "this bastard",
|
||
"This guy": "This bastard",
|
||
"Placeholder Text": "",
|
||
"```json": "",
|
||
"```": "",
|
||
# Add more replacements as needed
|
||
}
|
||
for target, replacement in placeholders.items():
|
||
translatedText = translatedText.replace(target, replacement)
|
||
|
||
# Remove Repeating Characters
|
||
pattern = re.compile(r"(.)\s*\1(?:\s*\1){" + str(20 - 1) + r",}")
|
||
translatedText = pattern.sub(lambda match: match.group(0).replace(" ", "")[:20], translatedText)
|
||
|
||
# Elongate Long Dashes (Since GPT Ignores them...)
|
||
translatedText = elongateCharacters(translatedText)
|
||
return translatedText
|
||
|
||
|
||
def elongateCharacters(text):
|
||
# Define a pattern to match one character followed by one or more `ー` characters
|
||
# Using a positive lookbehind assertion to capture the preceding character
|
||
pattern = r"(?<=(.))ー+"
|
||
|
||
# Define a replacement function that elongates the captured character
|
||
def repl(match):
|
||
char = match.group(1) # The character before the ー sequence
|
||
count = len(match.group(0)) - 1 # Number of ー characters
|
||
return char * count # Replace ー sequence with the character repeated
|
||
|
||
# Use re.sub() to replace the pattern in the text
|
||
return re.sub(pattern, repl, text)
|
||
|
||
|
||
def extractTranslation(translatedTextList, is_list):
|
||
try:
|
||
translatedTextList = re.sub(r'\\"+\"([^,\n}])', r'\\"\1', translatedTextList)
|
||
translatedTextList = re.sub(r"(?<![\\])\"+(?![\n,])", r'"', translatedTextList)
|
||
line_dict = json.loads(translatedTextList)
|
||
# If it's a batch (i.e., list), extract with tags; otherwise, return the single item.
|
||
string_list = list(line_dict.values())
|
||
if is_list:
|
||
return string_list
|
||
else:
|
||
return string_list[0]
|
||
|
||
except Exception as e:
|
||
PBAR.write(f"extractTranslation Error: {e} on String {translatedTextList}")
|
||
return None
|
||
|
||
|
||
def countTokens(system, user, history):
|
||
inputTotalTokens = 0
|
||
outputTotalTokens = 0
|
||
enc = tiktoken.encoding_for_model("gpt-4")
|
||
|
||
# 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]]
|