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前后端代码v2-基于工作流

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xunbu
2025-07-29 20:08:23 +08:00
parent 3cd229309b
commit 1e507626c1
2 changed files with 240 additions and 207 deletions
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docutranslate/app.py
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@@ -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": "<h1>标题</h1><p>这是翻译后的HTML内容...</p>"
}
},
@@ -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__)