From 1e507626c15770a2de6f4c2edb6adf25608086e1 Mon Sep 17 00:00:00 2001 From: xunbu Date: Tue, 29 Jul 2025 20:08:23 +0800 Subject: [PATCH] =?UTF-8?q?=E5=89=8D=E5=90=8E=E7=AB=AF=E4=BB=A3=E7=A0=81v2?= =?UTF-8?q?-=E5=9F=BA=E4=BA=8E=E5=B7=A5=E4=BD=9C=E6=B5=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docutranslate/app.py | 445 +++++++++++++++++--------------- docutranslate/static/index.html | 2 +- 2 files changed, 240 insertions(+), 207 deletions(-) diff --git a/docutranslate/app.py b/docutranslate/app.py index 3d4fef7..6289217 100644 --- a/docutranslate/app.py +++ b/docutranslate/app.py @@ -9,16 +9,13 @@ import time import uuid from contextlib import asynccontextmanager, closing from pathlib import Path -from typing import List, Dict, Any, Optional, Literal, Union +from typing import List, Dict, Any, Optional, Literal, Union, Annotated from urllib.parse import quote import httpx import uvicorn from fastapi import FastAPI, HTTPException, APIRouter, Body, Path as FastApiPath -from fastapi.openapi.docs import ( - get_swagger_ui_html, - get_swagger_ui_oauth2_redirect_html, get_redoc_html, -) +from fastapi.openapi.docs import get_swagger_ui_html, get_swagger_ui_oauth2_redirect_html, get_redoc_html from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse, FileResponse from fastapi.staticfiles import StaticFiles from pydantic import BaseModel, Field @@ -50,6 +47,12 @@ tasks_log_histories: Dict[str, List[str]] = {} MAX_LOG_HISTORY = 200 httpx_client: httpx.AsyncClient +# --- [NEW] 工作流到 Manager 的映射 --- +WORKFLOW_TO_MANAGER: Dict[str, type[BaseManager]] = { + "markdown": MarkdownBasedManager, + "text": TXTManager, +} + # --- 辅助函数 (MODIFIED) --- def _create_default_task_state() -> Dict[str, Any]: @@ -64,7 +67,7 @@ def _create_default_task_state() -> Dict[str, Any]: } -# --- Manager 工厂函数 (NEW) --- +# --- [KEPT FOR TEMP ENDPOINT] Manager 工厂函数 (旧逻辑，仅为临时接口保留) --- def _get_manager_for_file(filename: str, logger: logging.Logger) -> BaseManager: """根据文件名后缀选择并返回合适的 Manager 实例。这是扩展点。""" suffix = Path(filename).suffix.lower() @@ -122,8 +125,133 @@ async def lifespan(app: FastAPI): print("应用关闭，资源已清理。") -# --- Background Task Logic (核心业务逻辑, 已重构) --- -async def _perform_translation(task_id: str, params: Dict[str, Any], file_contents: bytes, original_filename: str): +# --- FastAPI 应用和路由设置 (保持不变) --- +tags_metadata = [ + { + "name": "Service API", + "description": "核心的服务API，用于提交、管理和下载翻译任务。", + }, + { + "name": "Application", + "description": "应用本身的相关端点，如元信息和默认参数。", + }, + { + "name": "Temp", + "description": "测试用接口。", + }, + +] + +app = FastAPI( + docs_url=None, + redoc_url=None, + lifespan=lifespan, + title="DocuTranslate API", + description=f""" +DocuTranslate 后端服务 API，提供文档翻译、状态查询、结果下载等功能。 + +**注意**: 所有任务状态都保存在服务进程的内存中，服务重启将导致所有任务信息丢失。 + +### 主要工作流程: +1. **`POST /service/translate`**: 提交文件和包含`workflow_type`的翻译参数，启动一个后台任务。服务会自动生成并返回一个唯一的 `task_id`。 +2. **`GET /service/status/{{task_id}}`**: 使用获取到的 `task_id` 轮询此端点，获取任务的实时状态。 +3. **`GET /service/logs/{{task_id}}`**: (可选) 获取实时的翻译日志。 +4. **`GET /service/download/{{task_id}}/{{file_type}}`**: 任务完成后 (当 `download_ready` 为 `true` 时)，通过此端点下载结果文件。 +5. **`GET /service/content/{{task_id}}/{{file_type}}`**: 任务完成后(当 `download_ready` 为 `true` 时)，以JSON格式获取文件内容。 +6. **`POST /service/cancel/{{task_id}}`**: (可选) 取消一个正在进行的任务。 +7. **`POST /service/release/{{task_id}}`**: (可选) 当任务不再需要时，释放其在服务器上占用的所有资源。 + +**版本**: {__version__} +""", + version=__version__, + openapi_tags=tags_metadata, +) + +service_router = APIRouter(prefix="/service", tags=["Service API"]) +STATIC_DIR = resource_path("static") +app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static") + + +# =================================================================== +# --- [NEW/MODIFIED] Pydantic Models for Service API --- +# =================================================================== + +# 1. 定义所有工作流共享的基础参数 +class BaseWorkflowParams(BaseModel): + base_url: str = Field(..., description="LLM API的基础URL。", examples=["https://api.openai.com/v1"]) + apikey: str = Field(..., description="LLM API的密钥。", examples=["sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx"]) + model_id: str = Field(..., description="要使用的LLM模型ID。", examples=["gpt-4o"]) + to_lang: str = Field(default="中文", description="目标翻译语言。", examples=["简体中文", "English"]) + chunk_size: int = Field(default_params["chunk_size"], description="文本分割的块大小（字符）。") + concurrent: int = Field(default_params["concurrent"], description="并发请求数。") + temperature: float = Field(default_params["temperature"], description="LLM温度参数。") + custom_prompt_translate: Optional[str] = Field(None, description="用户自定义的翻译Prompt。") + + +# 2. 为每个工作流创建独立的参数模型 +class MarkdownWorkflowParams(BaseWorkflowParams): + workflow_type: Literal['markdown'] = Field(..., description="指定使用基于Markdown的翻译工作流。") + + # --- Markdown-specific Converter Params --- + convert_engin: Optional[Literal["mineru", "docling"]] = Field( + None, + description="文档解析引擎。`mineru`在线服务, `docling`本地引擎。如果输入文件是.md，此项可为`null`或不传。", + examples=["mineru", "docling"] + ) + mineru_token: Optional[str] = Field(None, description="当 `convert_engin` 为 'mineru' 时必填的API令牌。") + formula_ocr: bool = Field(True, description="是否对公式进行OCR识别。对 `mineru` 和 `docling` 均有效。") + code_ocr: bool = Field(True, description="是否对代码块进行OCR识别。仅 `docling` 引擎有效。") + + +class TextWorkflowParams(BaseWorkflowParams): + workflow_type: Literal['text'] = Field(..., description="指定使用纯文本的翻译工作流。") + # TXT 工作流没有额外的参数 + + +# 3. 使用可辨识联合类型（Discriminated Union）将它们组合起来 +TranslatePayload = Annotated[ + Union[MarkdownWorkflowParams, TextWorkflowParams], + Field(discriminator='workflow_type') +] + + +# 4. 创建最终的请求体模型 +class TranslateServiceRequest(BaseModel): + file_name: str = Field(..., description="上传的原始文件名，含扩展名。", examples=["my_paper.pdf", "chapter1.txt"]) + file_content: str = Field(..., description="Base64编码的文件内容。", examples=["JVBERi0xLjQK..."]) + payload: TranslatePayload = Field(..., description="包含工作流类型和相应参数的载荷。") + + class Config: + json_schema_extra = { + "example": { + "file_name": "annual_report_2023.pdf", + "file_content": "JVBERi0xLjcKJeLjz9MKMSAwIG9iago8PC9...", + "payload": { + "workflow_type": "markdown", + "base_url": "https://api.openai.com/v1", + "apikey": "sk-your-api-key-here", + "model_id": "gpt-4o", + "to_lang": "简体中文", + "convert_engin": "mineru", + "mineru_token": "your-mineru-token-if-any", + "formula_ocr": True, + "code_ocr": True, + "chunk_size": 3000, + "concurrent": 10, + "temperature": 0.1, + "custom_prompt_translate": "将所有技术术语翻译为业界公认的中文对应词汇。" + } + } + } + + +# --- [MODIFIED] Background Task Logic --- +async def _perform_translation( + task_id: str, + payload: TranslatePayload, + file_contents: bytes, + original_filename: str +): task_state = tasks_state[task_id] log_queue = tasks_log_queues[task_id] log_history = tasks_log_histories[task_id] @@ -137,24 +265,27 @@ async def _perform_translation(task_id: str, params: Dict[str, Any], file_conten task_handler.setFormatter(logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')) task_logger.addHandler(task_handler) - task_logger.info(f"后台翻译任务开始: 文件 '{original_filename}'") + task_logger.info(f"后台翻译任务开始: 文件 '{original_filename}', 工作流: '{payload.workflow_type}'") task_state["status_message"] = f"正在处理 '{original_filename}'..." try: - # 1. 选择合适的 Manager - manager = _get_manager_for_file(original_filename, task_logger) + # 1. 根据工作流类型选择合适的 Manager + manager_class = WORKFLOW_TO_MANAGER.get(payload.workflow_type) + if not manager_class: + raise ValueError(f"不支持的工作流类型: '{payload.workflow_type}'") + manager = manager_class(logger=task_logger) - # 2. 从扁平化的 params 构建结构化的 Config 对象 + # 2. 从 payload 构建通用的 AiTranslateConfig ai_config = AiTranslateConfig( - base_url=params['base_url'], - api_key=params['apikey'], - model_id=params['model_id'], - to_lang=params['to_lang'], - custom_prompt=params['custom_prompt_translate'], - temperature=params['temperature'], - timeout=2000, # 保持默认或从params获取 - chunk_size=params['chunk_size'], - concurrent=params['concurrent'], + base_url=payload.base_url, + api_key=payload.apikey, + model_id=payload.model_id, + to_lang=payload.to_lang, + custom_prompt=payload.custom_prompt_translate, + temperature=payload.temperature, + timeout=2000, + chunk_size=payload.chunk_size, + concurrent=payload.concurrent, logger=task_logger ) @@ -163,44 +294,45 @@ async def _perform_translation(task_id: str, params: Dict[str, Any], file_conten file_suffix = Path(original_filename).suffix manager.read_bytes(content=file_contents, stem=file_stem, suffix=file_suffix) - # 4. 根据 Manager 类型执行不同的翻译流程 - if isinstance(manager, MarkdownBasedManager): - task_logger.info("使用 Markdown 翻译流程。") + # 4. 根据 payload 的具体类型执行不同的翻译流程 (类型安全!) + if isinstance(payload, MarkdownWorkflowParams) and isinstance(manager, MarkdownBasedManager): + task_logger.info("执行 Markdown 翻译流程。") translate_config = MDTranslateConfig(**ai_config.__dict__) - convert_engin = params['convert_engin'] + convert_config = None - if convert_engin == 'mineru': - if not params.get('mineru_token'): + if payload.convert_engin == 'mineru': + if not payload.mineru_token: raise ValueError("使用 'mineru' 引擎需要提供 'mineru_token'。") convert_config = ConverterMineruConfig( - mineru_token=params['mineru_token'], - formula=params['formula_ocr'] + mineru_token=payload.mineru_token, + formula=payload.formula_ocr ) - elif convert_engin == 'docling': + elif payload.convert_engin == 'docling': convert_config = ConverterDoclingConfig( - code=params['code_ocr'], - formula=params['formula_ocr'] + code=payload.code_ocr, + formula=payload.formula_ocr ) await manager.translate_async( - convert_engin=convert_engin, + convert_engin=payload.convert_engin, convert_config=convert_config, translate_config=translate_config ) - elif isinstance(manager, TXTManager): - task_logger.info("使用 TXT 翻译流程。") + elif isinstance(payload, TextWorkflowParams) and isinstance(manager, TXTManager): + task_logger.info("执行 TXT 翻译流程。") translate_config = TXTTranslateConfig(**ai_config.__dict__) await manager.translate_async(translate_config=translate_config) else: - raise TypeError(f"不支持的 Manager 类型: {type(manager).__name__}") + raise TypeError( + f"工作流类型 '{payload.workflow_type}'与Manager类型 '{type(manager).__name__}' 不匹配或未实现。") # 5. 任务成功，存储 manager 实例并更新状态 end_time = time.time() duration = end_time - task_state["task_start_time"] task_state.update({ - "manager_instance": manager, # <--- 存储实例 + "manager_instance": manager, "status_message": f"翻译成功！用时 {duration:.2f} 秒。", "download_ready": True, "error_flag": False, @@ -240,10 +372,10 @@ async def _perform_translation(task_id: str, params: Dict[str, Any], file_conten task_logger.removeHandler(task_handler) -# --- 核心任务启动与取消逻辑 (保持不变) --- +# --- [MODIFIED] 核心任务启动逻辑 --- async def _start_translation_task( task_id: str, - params: Dict[str, Any], + payload: TranslatePayload, file_contents: bytes, original_filename: str ): @@ -259,7 +391,7 @@ async def _start_translation_task( task_state["is_processing"] = True task_state.update({ "status_message": "任务初始化中...", "error_flag": False, "download_ready": False, - "manager_instance": None, # 重置 + "manager_instance": None, "original_filename_stem": Path(original_filename).stem, "original_filename": original_filename, "task_start_time": time.time(), "task_end_time": 0, "current_task_ref": None, @@ -281,7 +413,7 @@ async def _start_translation_task( try: loop = asyncio.get_running_loop() - task = loop.create_task(_perform_translation(task_id, params, file_contents, original_filename)) + task = loop.create_task(_perform_translation(task_id, payload, file_contents, original_filename)) task_state["current_task_ref"] = task return {"task_started": True, "task_id": task_id, "message": "翻译任务已成功启动，请稍候..."} except Exception as e: @@ -309,118 +441,19 @@ def _cancel_translation_logic(task_id: str): return {"cancelled": True, "message": "取消请求已发送。请等待状态更新。"} -# --- FastAPI 应用和路由设置 (保持不变) --- -tags_metadata = [ - { - "name": "Service API", - "description": "核心的服务API，用于提交、管理和下载翻译任务。", - }, - { - "name": "Application", - "description": "应用本身的相关端点，如元信息和默认参数。", - }, - { - "name": "Temp", - "description": "测试用接口。", - }, - -] - -app = FastAPI( - docs_url=None, - redoc_url=None, - lifespan=lifespan, - title="DocuTranslate API", - description=f""" -DocuTranslate 后端服务 API，提供文档翻译、状态查询、结果下载等功能。 - -**注意**: 所有任务状态都保存在服务进程的内存中，服务重启将导致所有任务信息丢失。 - -### 主要工作流程: -1. **`POST /service/translate`**: 提交文件和翻译参数，启动一个后台任务。服务会自动生成并返回一个唯一的 `task_id`。 -2. **`GET /service/status/{{task_id}}`**: 使用获取到的 `task_id` 轮询此端点，获取任务的实时状态。 -3. **`GET /service/logs/{{task_id}}`**: (可选) 获取实时的翻译日志。 -4. **`GET /service/download/{{task_id}}/{{file_type}}`**: 任务完成后 (当 `download_ready` 为 `true` 时)，通过此端点下载结果文件。 -5. **`GET /service/content/{{task_id}}/{{file_type}}`**: 任务完成后(当 `download_ready` 为 `true` 时)，以JSON格式获取文件内容。 -6. **`POST /service/cancel/{{task_id}}`**: (可选) 取消一个正在进行的任务。 -7. **`POST /service/release/{{task_id}}`**: (可选) 当任务不再需要时，释放其在服务器上占用的所有资源。 - -**版本**: {__version__} -""", - version=__version__, - openapi_tags=tags_metadata, -) - -service_router = APIRouter(prefix="/service", tags=["Service API"]) -STATIC_DIR = resource_path("static") -app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static") - - -# =================================================================== -# --- Pydantic Models for Service API (MODIFIED) --- -# =================================================================== -class TranslateServiceRequest(BaseModel): - base_url: str = Field(..., description="LLM API的基础URL。", examples=["https://api.openai.com/v1"]) - apikey: str = Field(..., description="LLM API的密钥。", examples=["sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxx"]) - model_id: str = Field(..., description="要使用的LLM模型ID。", examples=["gpt-4o"]) - to_lang: str = Field(default="中文", description="目标翻译语言。", examples=["简体中文", "English"]) - - # --- Converter Params --- - convert_engin: Literal["mineru", "docling", "auto"] = Field( - "auto", - description="文档解析引擎。`mineru`在线服务, `docling`本地引擎, `auto`自动选择(优先mineru)。", - examples=["mineru", "docling", "auto"] - ) - mineru_token: Optional[str] = Field(None, description="当 `convert_engin` 为 'mineru' 时必填的API令牌。") - formula_ocr: bool = Field(True, description="是否对公式进行OCR识别。对 `mineru` 和 `docling` 均有效。") - code_ocr: bool = Field(True, description="是否对代码块进行OCR识别。仅 `docling` 引擎有效。") - - # --- Translator Params --- - chunk_size: int = Field(default_params["chunk_size"], description="文本分割的块大小（字符）。") - concurrent: int = Field(default_params["concurrent"], description="并发请求数。") - temperature: float = Field(default_params["temperature"], description="LLM温度参数。") - custom_prompt_translate: Optional[str] = Field(None, description="用户自定义的翻译Prompt。") - - # --- File Info --- - file_name: str = Field(..., description="上传的原始文件名，含扩展名。", examples=["my_paper.pdf"]) - file_content: str = Field(..., description="Base64编码的文件内容。", examples=["JVBERi0xLjQK..."]) - - # refine_markdown: bool = Field(False, description="[已废弃] 此功能在新版中已移除。") - - class Config: - json_schema_extra = { - "example": { - "base_url": "https://api.openai.com/v1", - "apikey": "sk-your-api-key-here", - "model_id": "gpt-4o", - "to_lang": "简体中文", - "convert_engin": "mineru", - "mineru_token": "your-mineru-token-if-any", - "formula_ocr": True, - "code_ocr": True, - "chunk_size": 3000, - "concurrent": 10, - "temperature": 0.1, - "custom_prompt_translate": "将所有技术术语翻译为业界公认的中文对应词汇。", - "file_name": "annual_report_2023.pdf", - "file_content": "JVBERi0xLjcKJeLjz9MKMSAwIG9iago8PC9..." - } - } - - # =================================================================== # --- Service Endpoints (/service) (部分已重构) --- # =================================================================== @service_router.post( "/translate", - summary="提交翻译任务 (Base64)", + summary="提交翻译任务 (统一入口)", description=""" -接收一个包含文件内容（Base64编码）和翻译参数的JSON请求，启动一个后台翻译任务。 +接收一个包含文件内容（Base64编码）和工作流参数的JSON请求，启动一个后台翻译任务。 -- **异步处理**: 此端点会立即返回，不会等待翻译完成。 -- **任务ID**: 成功启动后，服务会自动生成并返回任务ID (`task_id`)。 -- **后续步骤**: 客户端应使用返回的 `task_id` 轮询 `/service/status/{task_id}` 接口来获取任务进度和结果。 +- **工作流选择**: 请求体中的 `payload.workflow_type` 字段决定了本次任务的类型（如 `markdown` 或 `text`）。 +- **动态参数**: 根据所选工作流，API需要不同的参数集。请参考下面的Schema或示例。 +- **异步处理**: 此端点会立即返回任务ID，客户端需轮询状态接口获取进度。 """, responses={ 200: { @@ -430,6 +463,8 @@ class TranslateServiceRequest(BaseModel): }, 400: {"description": "请求体中的Base64文件内容无效。", "content": {"application/json": {"example": {"detail": "无效的Base64文件内容: Incorrect padding"}}}}, + 422: {"description": "请求体验证失败，例如为错误的工作流提供了无效的参数。", + "content": {"application/json": {"example": {"detail": "[Validation Error Details]"}}}}, 429: {"description": "服务器内部任务冲突，请重试。", "content": { "application/json": { "example": {"task_started": False, "message": "任务ID 'b2865b93' 正在进行中，请稍后再试。"}}}}, @@ -440,11 +475,6 @@ class TranslateServiceRequest(BaseModel): } ) async def service_translate(request: TranslateServiceRequest = Body(..., description="翻译任务的详细参数和文件内容。")): - """ - 提交一个文件进行翻译，并启动一个后台任务。 - 文件内容需以Base64编码，任务ID将由后端自动生成并返回。 - 后续可凭此ID查询状态和下载结果。 - """ task_id = uuid.uuid4().hex[:8] try: @@ -452,17 +482,10 @@ async def service_translate(request: TranslateServiceRequest = Body(..., descrip except (binascii.Error, TypeError) as e: raise HTTPException(status_code=400, detail=f"无效的Base64文件内容: {e}") - params = request.model_dump(exclude={'file_name', 'file_content'}) - - # 自动选择引擎逻辑 - if params['convert_engin'] == 'auto': - params['convert_engin'] = 'mineru' if params.get('mineru_token') else 'docling' - print(f"[{task_id}] 自动选择解析引擎: {params['convert_engin']}") - try: response_data = await _start_translation_task( task_id=task_id, - params=params, + payload=request.payload, file_contents=file_contents, original_filename=request.file_name ) @@ -593,8 +616,8 @@ async def service_release_task( "downloads": {} } }, - "completed": { - "summary": "已完成", + "completed_md": { + "summary": "已完成 (Markdown)", "value": { "task_id": "b2865b93", "is_processing": False, @@ -612,21 +635,24 @@ async def service_release_task( } } }, - "error": { - "summary": "出错", + "completed_txt": { + "summary": "已完成 (TXT)", "value": { - "task_id": "b2865b93", + "task_id": "c3976ca4", "is_processing": False, - "status_message": "翻译过程中发生错误 (用时 45.67 秒): APIConnectionError(...)", - "error_flag": True, - "download_ready": False, - "original_filename_stem": "annual_report_2023", - "original_filename": "annual_report_2023.pdf", - "task_start_time": 1678886400.123, - "task_end_time": 1678886445.793, - "downloads": {} + "status_message": "翻译成功！用时 23.45 秒。", + "error_flag": False, + "download_ready": True, + "original_filename_stem": "my_notes", + "original_filename": "my_notes.txt", + "task_start_time": 1678887400.123, + "task_end_time": 1678887423.573, + "downloads": { + "txt": "/service/download/c3976ca4/txt", + "html": "/service/download/c3976ca4/html" + } } - } + }, } } } @@ -644,7 +670,7 @@ async def service_get_status( if not task_state: raise HTTPException(status_code=404, detail=f"找不到任务ID '{task_id}'。") - # (MODIFIED) 动态生成可用的下载链接 + # [MODIFIED] 动态生成可用的下载链接 downloads = {} if task_state.get("download_ready") and task_state.get("manager_instance"): manager = task_state["manager_instance"] @@ -680,8 +706,8 @@ async def service_get_status( "content": {"application/json": { "example": { "logs": [ - "2023-10-27 10:30:05 - INFO - 后台翻译任务开始: 文件 'annual_report_2023.pdf'", - "2023-10-27 10:30:05 - INFO - 使用 Base URL: https://api.openai.com/v1, Model: gpt-4o", + "2023-10-27 10:30:05 - INFO - 后台翻译任务开始: 文件 'annual_report_2023.pdf', 工作流: 'markdown'", + "2023-10-27 10:30:05 - INFO - 执行 Markdown 翻译流程。", "2023-10-27 10:30:15 - INFO - 正在转化为markdown", "2023-10-27 10:30:25 - INFO - markdown分为50块", "2023-10-27 10:30:30 - INFO - 正在翻译markdown" @@ -713,7 +739,7 @@ async def service_get_logs( FileType = Literal["markdown", "markdown_zip", "html", "txt"] -async def _get_content_from_manager(task_id: str, file_type: FileType) -> tuple[bytes | str, str, str]: +async def _get_content_from_manager(task_id: str, file_type: FileType) -> tuple[bytes, str, str]: """辅助函数，从 manager 获取内容、媒体类型和文件名""" task_state = tasks_state.get(task_id) if not task_state: @@ -725,36 +751,47 @@ async def _get_content_from_manager(task_id: str, file_type: FileType) -> tuple[ filename_stem = task_state['original_filename_stem'] try: + content_bytes: bytes + media_type: str + filename: str + if file_type == 'html' and isinstance(manager, HTMLExportable): - # 自动判断使用哪种 HTML Export Config - config = MD2HTMLExportConfig(cdn=True) if isinstance(manager, MarkdownBasedManager) else TXT2HTMLExportConfig(cdn=True) + config = MD2HTMLExportConfig(cdn=True) if isinstance(manager, + MarkdownBasedManager) else TXT2HTMLExportConfig( + cdn=True) try: - # 尝试连接CDN，失败则回退 - await httpx_client.head("https://s4.zstatic.net/ajax/libs/KaTeX/0.16.9/contrib/auto-render.min.js", timeout=3) + await httpx_client.head("https://s4.zstatic.net/ajax/libs/KaTeX/0.16.9/contrib/auto-render.min.js", + timeout=3) except (httpx.TimeoutException, httpx.RequestError): - manager.logger.info("CDN连接失败，使用本地JS进行渲染。") + manager.logger.warning("CDN连接失败，使用本地JS进行渲染。") if hasattr(config, 'cdn'): config.cdn = False - content = manager.export_to_html(config) - return content.encode('utf-8'), "text/html; charset=utf-8", f"{filename_stem}_translated.html" + content_str = manager.export_to_html(config) + content_bytes, media_type, filename = content_str.encode( + 'utf-8'), "text/html; charset=utf-8", f"{filename_stem}_translated.html" - if file_type == 'markdown' and isinstance(manager, MDFormatsExportable): + elif file_type == 'markdown' and isinstance(manager, MDFormatsExportable): md_content = manager.export_to_markdown() - return md_content.encode('utf-8'), "text/markdown; charset=utf-8", f"{filename_stem}_translated.md" + content_bytes, media_type, filename = md_content.encode( + 'utf-8'), "text/markdown; charset=utf-8", f"{filename_stem}_translated.md" - if file_type == 'markdown_zip' and isinstance(manager, MDFormatsExportable): - return manager.export_to_markdown_zip(), "application/zip", f"{filename_stem}_translated.zip" + elif file_type == 'markdown_zip' and isinstance(manager, MDFormatsExportable): + content_bytes, media_type, filename = manager.export_to_markdown_zip(), "application/zip", f"{filename_stem}_translated.zip" - if file_type == 'txt' and isinstance(manager, TXTExportable): + elif file_type == 'txt' and isinstance(manager, TXTExportable): txt_content = manager.export_to_txt() - return txt_content.encode('utf-8'), "text/plain; charset=utf-8", f"{filename_stem}_translated.txt" + content_bytes, media_type, filename = txt_content.encode( + 'utf-8'), "text/plain; charset=utf-8", f"{filename_stem}_translated.txt" + + else: + raise HTTPException(status_code=404, detail=f"此任务不支持导出 '{file_type}' 类型的文件。") + + return content_bytes, media_type, filename except Exception as e: manager.logger.error(f"导出 {file_type} 时出错: {e}", exc_info=True) raise HTTPException(status_code=500, detail=f"导出 {file_type} 时发生内部错误: {e}") - raise HTTPException(status_code=404, detail=f"此任务不支持导出 '{file_type}' 类型的文件。") - @service_router.get( "/download/{task_id}/{file_type}", @@ -810,7 +847,7 @@ async def service_download_file( "summary": "HTML 内容", "value": { "file_type": "html", - "original_filename": "my_doc_translated.html", + "filename": "my_doc_translated.html", "content": "

标题

这是翻译后的HTML内容...

" } }, @@ -839,16 +876,11 @@ async def service_content( """根据任务ID和文件类型，以JSON格式返回内容。zip文件会进行Base64编码。""" content, _, filename = await _get_content_from_manager(task_id, file_type) - if isinstance(content, bytes): - try: - # For text-based formats, decode to string - final_content = content.decode('utf-8') - except UnicodeDecodeError: - # For binary formats (like zip), encode to Base64 - final_content = base64.b64encode(content).decode('utf-8') - else: # Should not happen with current _get_content_from_manager, but for safety - final_content = content - + final_content: str + if file_type == 'markdown_zip': + final_content = base64.b64encode(content).decode('utf-8') + else: + final_content = content.decode('utf-8') return JSONResponse(content={ "file_type": file_type, @@ -866,13 +898,13 @@ async def service_content( responses={ 200: { "description": "成功返回可用引擎列表。", - "content": {"application/json": {"example": ["auto", "mineru", "docling"]}} + "content": {"application/json": {"example": ["mineru", "docling"]}} } } ) async def service_get_engin_list(): """返回可用的文档解析引擎列表。""" - engin_list = ["auto", "mineru"] + engin_list = ["mineru"] if available_packages.get("docling"): engin_list.append("docling") return JSONResponse(content=engin_list) @@ -1017,7 +1049,7 @@ async def temp_translate( temperature: float = Body(default_params["temperature"], description="ai翻译请求温度"), chunk_size: int = Body(default_params["chunk_size"], description="文本分块大小（bytes）"), custom_prompt_translate: Optional[str] = Body(None, description="翻译自定义提示词", - examples=["人名保持原文不翻译"]), + examples=["人名保持原文不翻译"]), ): """一个用于快速测试的同步翻译接口。""" try: @@ -1026,6 +1058,7 @@ async def temp_translate( decoded_content = file_content.encode('utf-8') try: + # [MODIFIED] 使用旧的辅助函数，仅为这个临时接口服务 manager = _get_manager_for_file(file_name, global_logger) ai_config = AiTranslateConfig( @@ -1041,7 +1074,7 @@ async def temp_translate( convert_config = ConverterMineruConfig(mineru_token=mineru_token) if mineru_token else None convert_engin = 'mineru' if mineru_token else None await manager.translate_async(convert_engin, convert_config, translate_config) - return {"success": True, "content": manager.document_translated.get_text()} + return {"success": True, "content": manager.export_to_markdown()} elif isinstance(manager, TXTManager): translate_config = TXTTranslateConfig(**ai_config.__dict__) diff --git a/docutranslate/static/index.html b/docutranslate/static/index.html index 81373e6..800788c 100644 --- a/docutranslate/static/index.html +++ b/docutranslate/static/index.html @@ -1 +1 @@ - DocuTranslate - 交互式文档翻译

DocuTranslate

GitHub主页(欢迎star❤):
https://github.com/xunbu/docutranslate

交流QQ群: 1047781902

任务列表

当前没有任务，点击“新建任务”开始吧！

预览
原文
译文
\ No newline at end of file + DocuTranslate - 交互式文档翻译

DocuTranslate

如果上传的文件本身是.md格式，此项可不选。

GitHub主页(欢迎star❤):
https://github.com/xunbu/docutranslate

交流QQ群: 1047781902

任务列表

当前没有任务，点击“新建任务”开始吧！

预览
原文
译文
\ No newline at end of file