548 lines
23 KiB
Python
548 lines
23 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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翻譯服務
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Author: PANJIT IT Team
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Created: 2024-01-28
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Modified: 2024-01-28
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"""
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import hashlib
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import time
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from pathlib import Path
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from typing import List, Dict, Any, Optional, Tuple
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from app.utils.logger import get_logger
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from app.utils.exceptions import TranslationError, FileProcessingError
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from app.services.dify_client import DifyClient
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from app.services.document_processor import DocumentProcessor, Segment
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from app.models.cache import TranslationCache
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from app.models.job import TranslationJob
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from app.utils.helpers import generate_filename, create_job_directory
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logger = get_logger(__name__)
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class DocumentParser:
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"""文件解析器基類"""
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def __init__(self, file_path: str):
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self.file_path = Path(file_path)
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if not self.file_path.exists():
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raise FileProcessingError(f"檔案不存在: {file_path}")
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def extract_text_segments(self) -> List[str]:
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"""提取文字片段"""
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raise NotImplementedError
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def generate_translated_document(self, translations: Dict[str, List[str]],
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target_language: str, output_dir: Path) -> str:
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"""生成翻譯後的文件"""
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raise NotImplementedError
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class DocxParser(DocumentParser):
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"""DOCX 文件解析器 - 使用增強的 DocumentProcessor"""
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def __init__(self, file_path: str):
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super().__init__(file_path)
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self.processor = DocumentProcessor()
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def extract_text_segments(self) -> List[str]:
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"""提取 DOCX 文件的文字片段 - 使用增強邏輯"""
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try:
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# 使用新的文檔處理器提取段落
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segments = self.processor.extract_docx_segments(str(self.file_path))
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# 轉換為文字列表
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text_segments = []
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for seg in segments:
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if seg.text.strip() and len(seg.text.strip()) > 3:
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text_segments.append(seg.text)
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logger.info(f"Enhanced extraction: {len(text_segments)} text segments from DOCX")
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return text_segments
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except Exception as e:
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logger.error(f"Failed to extract text from DOCX: {str(e)}")
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raise FileProcessingError(f"DOCX 文件解析失敗: {str(e)}")
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def extract_segments_with_context(self) -> List[Segment]:
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"""提取帶上下文的段落資訊"""
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return self.processor.extract_docx_segments(str(self.file_path))
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def generate_translated_document(self, translations: Dict[str, List[str]],
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target_language: str, output_dir: Path) -> str:
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"""生成翻譯後的 DOCX 文件 - 使用增強的翻譯插入邏輯"""
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try:
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# 生成輸出檔名
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output_filename = generate_filename(
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self.file_path.name,
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'translated',
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'translated',
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target_language
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)
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output_path = output_dir / output_filename
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# 提取段落資訊
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segments = self.extract_segments_with_context()
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# 建立翻譯映射
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translation_map = {}
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translated_texts = translations.get(target_language, [])
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# 對應文字段落與翻譯
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text_index = 0
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for seg in segments:
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if text_index < len(translated_texts):
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translation_map[(target_language, seg.text)] = translated_texts[text_index]
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text_index += 1
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# 使用增強的翻譯插入邏輯
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ok_count, skip_count = self.processor.insert_docx_translations(
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str(self.file_path),
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segments,
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translation_map,
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[target_language],
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str(output_path)
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)
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logger.info(f"Enhanced translation: Generated {output_path} with {ok_count} insertions, {skip_count} skips")
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return str(output_path)
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except Exception as e:
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logger.error(f"Failed to generate translated DOCX: {str(e)}")
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raise FileProcessingError(f"生成翻譯 DOCX 失敗: {str(e)}")
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class PdfParser(DocumentParser):
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"""PDF 文件解析器（只讀）"""
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def extract_text_segments(self) -> List[str]:
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"""提取 PDF 文件的文字片段"""
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try:
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from PyPDF2 import PdfReader
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reader = PdfReader(str(self.file_path))
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text_segments = []
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for page in reader.pages:
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text = page.extract_text()
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# 簡單的句子分割
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sentences = text.split('.')
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for sentence in sentences:
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sentence = sentence.strip()
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if sentence and len(sentence) > 10:
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text_segments.append(sentence)
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logger.info(f"Extracted {len(text_segments)} text segments from PDF")
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return text_segments
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except Exception as e:
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logger.error(f"Failed to extract text from PDF: {str(e)}")
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raise FileProcessingError(f"PDF 文件解析失敗: {str(e)}")
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def generate_translated_document(self, translations: Dict[str, List[str]],
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target_language: str, output_dir: Path) -> str:
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"""生成翻譯文字檔（PDF 不支援直接編輯）"""
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try:
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translated_texts = translations.get(target_language, [])
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# 生成純文字檔案
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output_filename = f"{self.file_path.stem}_{target_language}_translated.txt"
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output_path = output_dir / output_filename
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with open(output_path, 'w', encoding='utf-8') as f:
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f.write(f"翻譯結果 - {target_language}\n")
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f.write("=" * 50 + "\n\n")
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for i, text in enumerate(translated_texts):
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f.write(f"{i+1}. {text}\n\n")
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logger.info(f"Generated translated text file: {output_path}")
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return str(output_path)
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except Exception as e:
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logger.error(f"Failed to generate translated text file: {str(e)}")
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raise FileProcessingError(f"生成翻譯文字檔失敗: {str(e)}")
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class TranslationService:
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"""翻譯服務"""
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def __init__(self):
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self.dify_client = DifyClient()
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self.document_processor = DocumentProcessor()
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# 文件解析器映射
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self.parsers = {
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'.docx': DocxParser,
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'.doc': DocxParser, # 假設可以用 docx 處理
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'.pdf': PdfParser,
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# 其他格式可以稍後添加
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}
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def get_document_parser(self, file_path: str) -> DocumentParser:
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"""取得文件解析器"""
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file_ext = Path(file_path).suffix.lower()
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parser_class = self.parsers.get(file_ext)
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if not parser_class:
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raise FileProcessingError(f"不支援的檔案格式: {file_ext}")
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return parser_class(file_path)
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def split_text_into_sentences(self, text: str, language: str = 'auto') -> List[str]:
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"""將文字分割成句子 - 使用增強的分句邏輯"""
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return self.document_processor.split_text_into_sentences(text, language)
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def translate_segment_with_sentences(self, text: str, source_language: str,
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target_language: str, user_id: int = None,
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job_id: int = None) -> str:
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"""
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按段落翻譯，模仿成功版本的 translate_block_sentencewise 邏輯
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對多行文字進行逐行、逐句翻譯，並重新組合成完整段落
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"""
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if not text or not text.strip():
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return ""
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# 檢查快取 - 先檢查整個段落的快取
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cached_whole = TranslationCache.get_translation(text, source_language, target_language)
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if cached_whole:
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logger.debug(f"Whole paragraph cache hit: {text[:30]}...")
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return cached_whole
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# 按行處理
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out_lines = []
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all_successful = True
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for raw_line in text.split('\n'):
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if not raw_line.strip():
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out_lines.append("")
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continue
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# 分句處理
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sentences = self.document_processor.split_text_into_sentences(raw_line, source_language)
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if not sentences:
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sentences = [raw_line]
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translated_parts = []
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for sentence in sentences:
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sentence = sentence.strip()
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if not sentence:
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continue
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# 檢查句子級快取
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cached_sentence = TranslationCache.get_translation(sentence, source_language, target_language)
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if cached_sentence:
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translated_parts.append(cached_sentence)
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continue
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# 呼叫 Dify API 翻譯句子
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try:
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result = self.dify_client.translate_text(
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text=sentence,
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source_language=source_language,
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target_language=target_language,
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user_id=user_id,
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job_id=job_id
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)
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translated_sentence = result['translated_text']
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# 儲存句子級快取
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TranslationCache.save_translation(
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sentence, source_language, target_language, translated_sentence
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)
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translated_parts.append(translated_sentence)
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except Exception as e:
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logger.error(f"Failed to translate sentence: {sentence[:30]}... Error: {str(e)}")
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translated_parts.append(f"【翻譯失敗｜{target_language}】{sentence}")
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all_successful = False
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# 重新組合句子為一行
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out_lines.append(" ".join(translated_parts))
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# 重新組合所有行
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final_result = "\n".join(out_lines)
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# 如果全部成功，儲存整個段落的快取
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if all_successful:
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TranslationCache.save_translation(text, source_language, target_language, final_result)
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return final_result
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def translate_text_with_cache(self, text: str, source_language: str,
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target_language: str, user_id: int = None,
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job_id: int = None) -> str:
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"""帶快取的文字翻譯"""
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# 檢查快取
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cached_translation = TranslationCache.get_translation(
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text, source_language, target_language
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)
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if cached_translation:
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logger.debug(f"Cache hit for translation: {text[:50]}...")
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return cached_translation
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# 呼叫 Dify API
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try:
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result = self.dify_client.translate_text(
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text=text,
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source_language=source_language,
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target_language=target_language,
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user_id=user_id,
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job_id=job_id
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)
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translated_text = result['translated_text']
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# 儲存到快取
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TranslationCache.save_translation(
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text, source_language, target_language, translated_text
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)
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return translated_text
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except Exception as e:
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logger.error(f"Translation failed for text: {text[:50]}... Error: {str(e)}")
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raise TranslationError(f"翻譯失敗: {str(e)}")
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def translate_document(self, job_uuid: str) -> Dict[str, Any]:
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"""翻譯文件（主要入口點）- 使用增強的文檔處理邏輯"""
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try:
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# 取得任務資訊
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job = TranslationJob.query.filter_by(job_uuid=job_uuid).first()
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if not job:
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raise TranslationError(f"找不到任務: {job_uuid}")
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logger.info(f"Starting enhanced document translation: {job_uuid}")
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# 更新任務狀態
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job.update_status('PROCESSING', progress=0)
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# 使用增強的文檔處理器直接提取段落
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file_ext = Path(job.file_path).suffix.lower()
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if file_ext in ['.docx', '.doc']:
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# 使用增強的 DOCX 處理邏輯
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segments = self.document_processor.extract_docx_segments(job.file_path)
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logger.info(f"Enhanced extraction: Found {len(segments)} segments to translate")
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if not segments:
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raise TranslationError("文件中未找到可翻譯的文字段落")
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# 使用成功版本的翻譯邏輯 - 直接按段落翻譯，不做複雜分割
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translatable_segments = []
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for seg in segments:
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if self.document_processor.should_translate_text(seg.text, job.source_language):
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translatable_segments.append(seg)
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logger.info(f"Found {len(translatable_segments)} segments to translate")
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# 批次翻譯 - 直接按原始段落翻譯
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translation_map = {} # 格式: (target_language, source_text) -> translated_text
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total_segments = len(translatable_segments)
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for target_language in job.target_languages:
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logger.info(f"Translating to {target_language}")
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for i, seg in enumerate(translatable_segments):
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try:
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# 使用整段文字進行翻譯
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translated = self.translate_segment_with_sentences(
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text=seg.text,
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source_language=job.source_language,
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target_language=target_language,
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user_id=job.user_id,
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job_id=job.id
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)
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# 直接以原始段落文字為鍵儲存翻譯結果
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translation_map[(target_language, seg.text)] = translated
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# 更新進度
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progress = (i + 1) / total_segments * 100 / len(job.target_languages)
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current_lang_index = job.target_languages.index(target_language)
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total_progress = (current_lang_index * 100 + progress) / len(job.target_languages)
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job.update_status('PROCESSING', progress=total_progress)
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# 短暫延遲避免過快請求
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time.sleep(0.1)
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except Exception as e:
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logger.error(f"Failed to translate segment: {seg.text[:50]}... Error: {str(e)}")
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# 翻譯失敗時保留原文
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translation_map[(target_language, seg.text)] = f"[翻譯失敗] {seg.text}"
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# 生成翻譯文件
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logger.info("Generating translated documents with enhanced insertion")
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output_dir = Path(job.file_path).parent
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output_files = {}
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for target_language in job.target_languages:
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try:
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# 生成輸出檔名
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output_filename = generate_filename(
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Path(job.file_path).name,
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'translated',
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'translated',
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target_language
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)
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output_path = output_dir / output_filename
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# 使用增強的翻譯插入邏輯
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ok_count, skip_count = self.document_processor.insert_docx_translations(
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job.file_path,
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segments,
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translation_map,
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[target_language],
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str(output_path)
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)
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output_files[target_language] = str(output_path)
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# 記錄翻譯檔案到資料庫
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file_size = Path(output_path).stat().st_size
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job.add_translated_file(
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language_code=target_language,
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filename=Path(output_path).name,
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file_path=str(output_path),
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file_size=file_size
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)
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logger.info(f"Generated {target_language}: {ok_count} insertions, {skip_count} skips")
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except Exception as e:
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logger.error(f"Failed to generate translated document for {target_language}: {str(e)}")
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raise TranslationError(f"生成 {target_language} 翻譯文件失敗: {str(e)}")
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else:
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# 對於非 DOCX 文件，使用原有邏輯
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logger.info(f"Using legacy processing for {file_ext} files")
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parser = self.get_document_parser(job.file_path)
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# 提取文字片段
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text_segments = parser.extract_text_segments()
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if not text_segments:
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raise TranslationError("文件中未找到可翻譯的文字")
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# 分割成句子
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all_sentences = []
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for segment in text_segments:
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sentences = self.split_text_into_sentences(segment, job.source_language)
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all_sentences.extend(sentences)
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# 去重複
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unique_sentences = list(dict.fromkeys(all_sentences))
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logger.info(f"Found {len(unique_sentences)} unique sentences to translate")
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# 批次翻譯
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translation_results = {}
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total_sentences = len(unique_sentences)
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for target_language in job.target_languages:
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logger.info(f"Translating to {target_language}")
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translated_sentences = []
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for i, sentence in enumerate(unique_sentences):
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try:
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translated = self.translate_text_with_cache(
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text=sentence,
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source_language=job.source_language,
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target_language=target_language,
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user_id=job.user_id,
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job_id=job.id
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)
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translated_sentences.append(translated)
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# 更新進度
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progress = (i + 1) / total_sentences * 100 / len(job.target_languages)
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current_lang_index = job.target_languages.index(target_language)
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total_progress = (current_lang_index * 100 + progress) / len(job.target_languages)
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job.update_status('PROCESSING', progress=total_progress)
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time.sleep(0.1)
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except Exception as e:
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logger.error(f"Failed to translate sentence: {sentence[:50]}... Error: {str(e)}")
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translated_sentences.append(f"[翻譯失敗] {sentence}")
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translation_results[target_language] = translated_sentences
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# 生成翻譯文件
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output_dir = Path(job.file_path).parent
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output_files = {}
|
||
|
||
for target_language, translations in translation_results.items():
|
||
translation_mapping = {target_language: translations}
|
||
|
||
output_file = parser.generate_translated_document(
|
||
translations=translation_mapping,
|
||
target_language=target_language,
|
||
output_dir=output_dir
|
||
)
|
||
|
||
output_files[target_language] = output_file
|
||
|
||
file_size = Path(output_file).stat().st_size
|
||
job.add_translated_file(
|
||
language_code=target_language,
|
||
filename=Path(output_file).name,
|
||
file_path=output_file,
|
||
file_size=file_size
|
||
)
|
||
|
||
# 計算總成本
|
||
total_cost = self._calculate_job_cost(job.id)
|
||
|
||
# 更新任務狀態為完成
|
||
job.update_status('COMPLETED', progress=100)
|
||
job.total_cost = total_cost
|
||
# 計算實際使用的 token 數（從 API 使用統計中獲取）
|
||
from sqlalchemy import func
|
||
from app.models.stats import APIUsageStats
|
||
from app import db
|
||
|
||
actual_tokens = db.session.query(
|
||
func.sum(APIUsageStats.total_tokens)
|
||
).filter_by(job_id=job.id).scalar()
|
||
|
||
job.total_tokens = int(actual_tokens) if actual_tokens else 0
|
||
|
||
db.session.commit()
|
||
|
||
logger.info(f"Enhanced document translation completed: {job_uuid}")
|
||
|
||
return {
|
||
'success': True,
|
||
'job_uuid': job_uuid,
|
||
'output_files': output_files,
|
||
'total_sentences': len(texts_to_translate) if 'texts_to_translate' in locals() else len(unique_sentences) if 'unique_sentences' in locals() else 0,
|
||
'total_cost': float(total_cost),
|
||
'target_languages': job.target_languages
|
||
}
|
||
|
||
except TranslationError:
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"Enhanced document translation failed: {job_uuid}. Error: {str(e)}")
|
||
raise TranslationError(f"文件翻譯失敗: {str(e)}")
|
||
|
||
def _calculate_job_cost(self, job_id: int) -> float:
|
||
"""計算任務總成本"""
|
||
from app import db
|
||
from sqlalchemy import func
|
||
from app.models.stats import APIUsageStats
|
||
|
||
total_cost = db.session.query(
|
||
func.sum(APIUsageStats.cost)
|
||
).filter_by(job_id=job_id).scalar()
|
||
|
||
return float(total_cost) if total_cost else 0.0 |