""" Shared translation utilities for DazedMTLTool This module provides a centralized translation function that can be used across all modules without relying on global variables. """ import os import re import json import tiktoken import openai from pathlib import Path from retry import retry class TranslationConfig: """Configuration class to hold all translation settings""" def __init__(self, model=None, language=None, prompt=None, vocab=None, langRegex=None, batchSize=None, maxHistory=10, estimateMode=False, logFilePath="log/translationHistory.txt", mismatchLogPath="log/mismatchHistory.txt"): # Load from environment if not provided self.model = model or os.getenv("model") self.language = (language or os.getenv("language", "english")).capitalize() # Load prompt and vocab files if not provided if prompt is None: try: self.prompt = Path("prompt.txt").read_text(encoding="utf-8") except FileNotFoundError: self.prompt = "" else: self.prompt = prompt if vocab is None: try: self.vocab = Path("vocab.txt").read_text(encoding="utf-8") except FileNotFoundError: self.vocab = "" else: self.vocab = vocab # Set language regex (default is Japanese) self.langRegex = langRegex or r"[一-龠ぁ-ゔァ-ヴーａ-ｚＡ-Ｚ０-９\uFF61-\uFF9F]+" # Set batch size based on model if not provided if batchSize is None: if "gpt-3.5" in self.model: self.batchSize = 10 elif "gpt-4" in self.model: self.batchSize = 30 elif "deepseek" in self.model: self.batchSize = 30 else: # Try to get from environment, fallback to 10 try: self.batchSize = int(os.getenv("batchsize", 10)) except (ValueError, TypeError): self.batchSize = 10 else: self.batchSize = batchSize self.maxHistory = maxHistory self.estimateMode = estimateMode self.logFilePath = logFilePath self.mismatchLogPath = mismatchLogPath def getPricingConfig(model): """ Get pricing configuration for a given model. Args: model: The model name string Returns: dict: Dictionary containing inputAPICost, outputAPICost, batchSize, and frequencyPenalty """ # Pricing - Depends on the model https://openai.com/pricing ($ Price Per 1M) # Batch Size - GPT 3.5 Struggles past 15 lines per request. GPT4 struggles past 50 lines per request # If you are getting a MISMATCH LENGTH error, lower the batch size. if "gpt-3.5" in model: return { "inputAPICost": 3.00, "outputAPICost": 5.00, "batchSize": 10, "frequencyPenalty": 0.2 } elif "gpt-4.1-mini" in model: return { "inputAPICost": 0.40, "outputAPICost": 1.60, "batchSize": 30, "frequencyPenalty": 0.05 } elif "gpt-4.1" in model: return { "inputAPICost": 2.00, "outputAPICost": 8.00, "batchSize": 30, "frequencyPenalty": 0.05 } elif "gpt-5" in model: return { "inputAPICost": 1.25, "outputAPICost": 10.00, "batchSize": 30, "frequencyPenalty": 0.05 } elif "deepseek" in model: return { "inputAPICost": 0.27, "outputAPICost": 1.10, "batchSize": 30, "frequencyPenalty": 0.05 } elif "sonnet" in model: return { "inputAPICost": 3.00, "outputAPICost": 15.00, "batchSize": 30, "frequencyPenalty": 0.05 } elif "gemini-2.0-flash-lite" in model: return { "inputAPICost": 0.075, "outputAPICost": 0.30, "batchSize": 30, "frequencyPenalty": 0.0 } elif "gemini-2.0-flash" in model: return { "inputAPICost": 0.10, "outputAPICost": 0.40, "batchSize": 30, "frequencyPenalty": 0.0 } elif "gemini-2.5-flash-lite" in model: return { "inputAPICost": 0.10, "outputAPICost": 0.40, "batchSize": 30, "frequencyPenalty": 0.0 } elif "gemini-2.5-flash" in model: return { "inputAPICost": 0.30, "outputAPICost": 2.50, "batchSize": 30, "frequencyPenalty": 0.0 } elif "gemini-2.5-pro" in model: return { "inputAPICost": 1.25, "outputAPICost": 10.00, "batchSize": 30, "frequencyPenalty": 0.0 } else: # Fallback to environment variables return { "inputAPICost": float(os.getenv("input_cost", 3.00)), "outputAPICost": float(os.getenv("output_cost", 6.00)), "batchSize": int(os.getenv("batchsize", 10)), "frequencyPenalty": float(os.getenv("frequency_penalty", 0.2)) } def batchList(inputList, batchSize): """Split a list into batches of specified size""" if not isinstance(batchSize, int) or batchSize <= 0: raise ValueError("batchSize must be a positive integer") return [inputList[i : i + batchSize] for i in range(0, len(inputList), batchSize)] def parseVocabWithCategories(vocabText): """Parse vocabulary text and extract terms with their categories.""" pairs = [] seen = set() currentCategory = None for line in vocabText.splitlines(): line = line.strip() if not line or line.startswith('```') or line.startswith('Here are some vocabulary'): continue # Check if this is a category header if line.startswith('#'): currentCategory = line continue # Parse vocabulary term - extract both Japanese and English parts # Format: "Japanese term (English translation)" or "Japanese term – English translation" m = re.match(r'^(.+?)(?:\s?[\(–]\s*(.+?)[\)]?\s*$)', line) if m and ('(' in line or '–' in line): # Only process lines that actually have parentheses or dashes japanese_term = m.group(1).strip() english_term = m.group(2).strip().rstrip(')') # Remove trailing parenthesis if exists # Create a tuple with both terms for matching term_pair = (japanese_term, english_term) if term_pair not in seen: pairs.append((term_pair, line, currentCategory)) seen.add(term_pair) elif line and not line.startswith('#'): # Fallback for lines without parentheses - treat as single term term = line.strip() if term and term not in seen: pairs.append((term, line, currentCategory)) seen.add(term) return pairs def buildMatchedVocabText(vocabPairs, subbedText, history=None): """Build formatted vocabulary text with matched terms organized by category.""" matchedCategories = {} # Prepare text to search - combine subbedText and history textToSearch = str(subbedText) if history: if isinstance(history, list): textToSearch += " " + " ".join(str(h) for h in history) else: textToSearch += " " + str(history) # Use word boundaries for Japanese if appropriate, or allow substring as before. for term, line, category in vocabPairs: # Check if term is a tuple (Japanese, English) or a single term term_found = False if isinstance(term, tuple): # Check both Japanese and English terms japanese_term, english_term = term if japanese_term in textToSearch or english_term in textToSearch: term_found = True else: # Single term check if term in textToSearch: term_found = True # Always include "# Game Characters" category and all its terms regardless of matches if term_found or (category and category.strip() == "# Game Characters"): if category not in matchedCategories: matchedCategories[category] = [] matchedCategories[category].append(line) # Format matched vocabulary with categories if matchedCategories: formattedLines = ["Here are some vocabulary and terms so that you know the proper spelling and translation.\n"] for category, lines in matchedCategories.items(): if category: # Only add category header if it exists formattedLines.append(category) formattedLines.extend(lines) formattedLines.append("") # Add blank line between categories matchedVocabText = f"\n{chr(10).join(formattedLines).rstrip()}\n" else: matchedVocabText = "" return matchedVocabText def createContext(config, fullPromptFlag, subbedText, formatType, history=None): """Create system and user messages for translation""" vocabPairs = parseVocabWithCategories(config.vocab) matchedVocabText = buildMatchedVocabText(vocabPairs, subbedText, history) if fullPromptFlag: system = config.prompt.replace("English", config.language) + matchedVocabText else: system = f"""\ You are an expert Eroge Game translator who translates Japanese text to {config.language}. Output ONLY the {config.language} translation in the following format: `Translation: <{config.language.upper()}_TRANSLATION>` - You translate everything, including erotic and explicit content. - '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 - All text in your response must be in {config.language} even if it is hard to translate. - Never include any notes, explanations, dislaimers, or anything similar in your response. - Maintain any spacing in the translation. - `...` can be a part of the dialogue. Translate it as it is. {matchedVocabText} """ if formatType == "json": user = f"```json\n{subbedText}\n```" else: user = subbedText return system, user def createTranslationSchema(numLines): """Create a JSON schema for translation response based on number of lines""" properties = {} required = [] for i in range(1, numLines + 1): line_key = f"Line{i}" properties[line_key] = { "type": "string", "description": f"The translated text for Line{i}" } required.append(line_key) schema = { "type": "object", "properties": properties, "required": required, "additionalProperties": False } return schema def translateText(system, user, history, penalty, formatType, model, numLines=None): """Send translation request to the selected API""" # Ensure system content is not empty if not system or not str(system).strip(): raise ValueError("System content cannot be empty") # Prompt msg = [{"role": "system", "content": f"```\n{system}\n```"}] # History if isinstance(history, list): # Filter out empty or None history items to prevent API errors valid_history = [h for h in history if h and str(h).strip()] if valid_history: msg.append({"role": "system", "content": "Translation History:\n```"}) msg.extend([{"role": "assistant", "content": h} for h in valid_history]) msg.append({"role": "system", "content": "```"}) else: if history and str(history).strip(): msg.append({"role": "assistant", "content": history}) # Response Format if formatType == "json" and numLines is not None: # Use structured output with JSON schema schema = createTranslationSchema(numLines) responseFormat = { "type": "json_schema", "json_schema": { "name": "translation_response", "strict": True, "schema": schema } } else: responseFormat = {"type": "text"} # Content to TL - ensure user content is not empty if not user or not str(user).strip(): raise ValueError("User content cannot be empty") msg.append({"role": "user", "content": f"```\n{user}\n```"}) # Debug: Check for any empty messages before API call for i, message in enumerate(msg): if not message.get("content") or not str(message.get("content")).strip(): raise ValueError(f"Message {i} has empty content: {message}") # --- API Call Logic --- api_provider = os.getenv("API_PROVIDER", "openai").lower() # Base parameters for the API call params = { "model": model, "response_format": responseFormat, "messages": msg, } # Provider-specific parameters if api_provider == "gemini": params["temperature"] = 0 # Handle thinking budget for Gemini thinking_budget_str = os.getenv("GEMINI_THINKING_BUDGET") if thinking_budget_str: try: thinking_budget = int(thinking_budget_str) params["extra_body"] = { 'google': { 'thinking_config': { 'thinking_budget': thinking_budget } } } except (ValueError, TypeError): # Ignore if the value is not a valid integer pass # frequency_penalty is not supported via the OpenAI compatibility layer for Gemini else: # Default to OpenAI behavior if "gpt-5" in model: params["reasoning_effort"] = "minimal" else: params["temperature"] = 0 params["frequency_penalty"] = penalty # Call API try: response = openai.chat.completions.create(**params) except Exception as e: # If structured output fails, fallback to json_object if formatType == "json" and "json_schema" in str(responseFormat): responseFormat = {"type": "json_object"} params["response_format"] = responseFormat response = openai.chat.completions.create(**params) else: raise e return response def cleanTranslatedText(translatedText, language): """Clean and format translated text""" placeholders = { f"{language} Translation: ": "", "Translation: ": "", "っ": "", "〜": "~", "ッ": "", "。": ".", "「": '\"', "」": '\"', "- ": "-", "—": "―", "】": "]", "【": "[", "é": "e", "’": "'", "this guy": "this bastard", "This guy": "This bastard", "Placeholder Text": "", "```json": "", "```": "", } 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): """Replace ー sequences with elongated characters""" # Define a pattern to match one character followed by two or more ー characters pattern = r"(?<=(.))ー{2,}" # 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, isList, pbar=None): """Extract translation from JSON response. This function is resilient to a few common model mistakes: - Wraps output in code fences or outer quotes - Uses smart quotes instead of straight quotes - Inserts an extra leading quote in values (e.g. :""Word" -> :"Word") - Trailing commas before } or ] If strict JSON parsing fails, falls back to a regex-based extractor that captures LineN values in numeric order. """ s = str(translatedTextList or "").strip() # Fast exit if not s: return None # Remove code fences if present if s.startswith("```"): s = re.sub(r"^```(?:json)?\s*", "", s, flags=re.IGNORECASE) s = re.sub(r"\s*```$", "", s) # Trim wrapping quotes around the whole JSON blob (common in logs) if len(s) >= 2 and s[0] == s[-1] and s[0] in {'"', "'"}: # Only strip if it still looks like JSON inside if s[1:2] == "{" and s[-2:-1] == "}": s = s[1:-1] # Normalize quotes s = s.replace("“", '"').replace("”", '"').replace("’", "'") # Remove trailing commas before object/array closures s = re.sub(r",(\s*[}\]])", r"\1", s) # Repair common doubled leading quote in values: :""Word" -> :"Word" # Ensure we don't alter legitimate empty strings (:"") s = re.sub(r":\s*\"\"(?=[^\",}\]\s])", r':"', s) # Attempt strict parse first try: lineDict = json.loads(s) # Build list in numeric order if keys are LineN numeric_keys = [] for k in lineDict.keys(): m = re.fullmatch(r"Line(\d+)", str(k)) if m: numeric_keys.append(int(m.group(1))) if numeric_keys: stringList = [lineDict.get(f"Line{n}", "") for n in sorted(numeric_keys)] else: # Fallback to values order if no LineN keys found stringList = list(lineDict.values()) return stringList if isList else (stringList[0] if stringList else None) except Exception as e: # Fallback: regex-based extraction tolerant to one or two opening quotes # Captures escaped quotes within values too try: pairs = re.findall(r'"Line(\d+)"\s*:\s*"{1,2}((?:\\.|[^"\\])*)"', s) if not pairs: raise ValueError("No LineN pairs found") # Sort numerically and unescape JSON string content items = [] for n_str, v in sorted(((int(n), v) for n, v in pairs), key=lambda x: x[0]): try: # Decode JSON escapes reliably by round-tripping as a JSON string decoded = json.loads(f'"{v}"') except Exception: decoded = v items.append(decoded) return items if isList else (items[0] if items else None) except Exception as e2: if pbar: pbar.write(f"extractTranslation Error: {e2} after JSON error {e} on String {translatedTextList}") return None def calculateCost(inputTokens, outputTokens, model): """ Calculate the cost of translation based on token usage and model pricing. Args: inputTokens: Number of input tokens used outputTokens: Number of output tokens generated model: The model name string Returns: float: Total cost in USD """ pricing = getPricingConfig(model) inputCost = (inputTokens / 1000000) * pricing["inputAPICost"] outputCost = (outputTokens / 1000000) * pricing["outputAPICost"] return inputCost + outputCost def countTokens(system, user, history): """Count tokens for cost estimation""" 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 translateAI(text, history, fullPromptFlag, config, filename=None, pbar=None, lock=None, mismatchList=None): """ Main translation function that can be used across all modules. Args: text: Text to translate (string or list) history: Translation history for context fullPromptFlag: Whether to use full prompt or simplified version config: TranslationConfig instance with all settings filename: Current filename being processed (for logging) pbar: Progress bar instance (optional) lock: Threading lock (optional) mismatchList: List to track mismatched files (optional) Returns: [translatedText, totalTokens] """ if not text: return [text, [0, 0]] with open(config.logFilePath, "a+", encoding="utf-8") as logFile: totalTokens = [0, 0] if isinstance(text, list): formatType = "json" tList = batchList(text, config.batchSize) else: formatType = "text" tList = [text] for index, tItem in enumerate(tList): # Check if text contains target language if not re.search(config.langRegex, str(tItem)): if pbar is not None: pbar.update(len(tItem) if isinstance(tItem, list) else 1) if isinstance(tItem, list): for j in range(len(tItem)): tItem[j] = cleanTranslatedText(tItem[j], config.language) tList[index] = tItem else: tList[index] = cleanTranslatedText(tItem, config.language) history = tItem[-config.maxHistory:] if isinstance(tItem, list) else tItem continue # Format for translation if isinstance(tItem, list): for j in range(len(tItem)): if not tItem[j] or not str(tItem[j]).strip(): tItem[j] = "Placeholder Text" payload = {f"Line{i+1}": string for i, string in enumerate(tItem)} payload = json.dumps(payload, indent=4, ensure_ascii=False) subbedT = payload else: # Check for empty/whitespace strings in non-list items if not tItem or not str(tItem).strip(): subbedT = "Placeholder Text" else: subbedT = tItem # Create context system, user = createContext(config, fullPromptFlag, subbedT, formatType, history) # Calculate estimate if in estimate mode if config.estimateMode: estimate = countTokens(system, user, history) totalTokens[0] += estimate[0] totalTokens[1] += estimate[1] continue # --- Translation and Validation Retry Block --- max_retries = 2 # 1 initial attempt + 2 retries final_translations = None last_raw_translation = "" numLines = len(tItem) if isinstance(tItem, list) else None for attempt in range(max_retries + 1): is_valid = True # On retries, add a note to the system prompt current_system = system if attempt > 0: current_system += f"\n\nIMPORTANT: Your previous attempt was incorrect or incomplete. Please ensure the entire output is translated to {config.language} and contains no untranslated characters. Translate the following text again, ensuring the JSON structure is correct." if pbar: pbar.write(f"Retrying translation... (Attempt {attempt + 1}/{max_retries + 1})") # Translate response = translateText(current_system, user, history, 0.05, formatType, config.model, numLines) translatedText = response.choices[0].message.content last_raw_translation = translatedText # Update token count for this attempt totalTokens[0] += response.usage.prompt_tokens totalTokens[1] += response.usage.completion_tokens # Clean the translation first for consistency cleaned_text = cleanTranslatedText(translatedText, config.language) # Process and validate translation result if cleaned_text: if isinstance(tItem, list): extracted = extractTranslation(cleaned_text, True, pbar) # Check 1: Mismatch in length if extracted is None or len(tItem) != len(extracted): is_valid = False # Check 2: Untranslated content else: # Set translations first (line count matches) final_translations = extracted # Then check for untranslated content as a warning, not a blocker for line in extracted: if re.search(config.langRegex, str(line)): is_valid = False break else: # Check for untranslated content in single string if re.search(config.langRegex, cleaned_text): is_valid = False else: final_translations = cleaned_text.replace("Placeholder Text", "") else: is_valid = False if pbar: pbar.write(f"AI Refused: {tItem}\n") # If translation is valid, break the retry loop if is_valid: break # --- End of Retry Block --- # After the loop, handle the final result if final_translations is not None: # Success case formatted_output = last_raw_translation try: parsed_json = json.loads(last_raw_translation) formatted_output = json.dumps(parsed_json, indent=4, ensure_ascii=False) except (json.JSONDecodeError, ValueError): pass logFile.write(f"Input:\n{subbedT}\n") logFile.write(f"Output:\n{formatted_output}\n") if isinstance(tItem, list): tList[index] = final_translations history = final_translations[-config.maxHistory:] else: tList[index] = final_translations history = final_translations if lock and pbar is not None: with lock: pbar.update(len(tItem) if isinstance(tItem, list) else 1) else: # Failure case after all retries if pbar: pbar.write(f"Translation failed after {max_retries + 1} attempts. Check mismatch log.") formatted_mismatch_output = last_raw_translation try: parsed_json = json.loads(last_raw_translation) formatted_mismatch_output = json.dumps(parsed_json, indent=4, ensure_ascii=False) except (json.JSONDecodeError, ValueError): pass with open(config.mismatchLogPath, "a+", encoding="utf-8") as mismatchFile: mismatchFile.write(f"Failed after retries: {filename}\n") mismatchFile.write(f"Input:\n{subbedT}\n") mismatchFile.write(f"Final Output:\n{formatted_mismatch_output}\n") if filename and mismatchList is not None and filename not in mismatchList: mismatchList.append(filename) tList[index] = tItem history = text[-config.maxHistory:] if isinstance(text, list) else text # Combine if multilist if tList and isinstance(tList[0], list): tList = [t for sublist in tList for t in sublist] # Return result if formatType == "json": return [tList, totalTokens] else: return [tList[0], totalTokens]