更新公文校对相关逻辑
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parent
d1ba8905cc
commit
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498
app.py
498
app.py
@ -6,68 +6,16 @@ from typing import Optional, List, Set,Iterable
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if sys.platform.startswith("win") and hasattr(asyncio, "WindowsSelectorEventLoopPolicy"):
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asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
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try:
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import ahocorasick
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except ImportError:
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ahocorasick = None
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import ahocorasick
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from pydantic import BaseModel, Field, model_validator
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from fastapi import FastAPI, File, Path, UploadFile, HTTPException, Form, Request, Header, Body, BackgroundTasks
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from fastapi.responses import StreamingResponse
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try:
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from langchain_core.prompts import FewShotPromptTemplate, PromptTemplate
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except ImportError:
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try:
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from langchain.prompts import FewShotPromptTemplate, PromptTemplate
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except ImportError:
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class PromptTemplate:
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def __init__(self, input_variables=None, template: str = ""):
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self.input_variables = input_variables or []
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self.template = template
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def format(self, **kwargs):
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return self.template.format(**kwargs)
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class FewShotPromptTemplate:
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def __init__(
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self,
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examples=None,
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example_prompt=None,
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prefix: str = "",
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suffix: str = "",
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input_variables=None,
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example_separator: str = "\n\n",
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):
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self.examples = examples or []
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self.example_prompt = example_prompt
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self.prefix = prefix
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self.suffix = suffix
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self.input_variables = input_variables or []
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self.example_separator = example_separator
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def format(self, **kwargs):
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rendered_examples = []
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for example in self.examples:
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rendered_examples.append(self.example_prompt.format(**example))
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parts = [self.prefix]
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if rendered_examples:
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parts.append(self.example_separator.join(rendered_examples))
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parts.append(self.suffix.format(**kwargs))
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return self.example_separator.join(part for part in parts if part)
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from langchain.prompts import FewShotPromptTemplate, PromptTemplate
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from openai.types.chat import ChatCompletionSystemMessageParam, ChatCompletionUserMessageParam, ChatCompletionMessageParam
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from chunk_text import (
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get_chunk_bbox,
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split_text_preserve_sentences,
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data_replace,
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chunk_check,
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merge_short_slices,
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find_and_read_content_list,
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process_pdf_file,
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process_other_file,
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safe_original_filename,
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image_base64_to_data_url,
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extract_image_text,
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prepare_split_image,
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)
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from chunk_text import get_chunk_bbox, split_text_preserve_sentences, data_replace, chunk_check,merge_short_slices,find_and_read_content_list,process_pdf_file,process_other_file
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from fastapi.responses import JSONResponse, StreamingResponse
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import requests
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from pathlib import PurePath, Path as PathLib
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@ -120,47 +68,14 @@ from checkpointer_config import (
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CheckpointerManager,
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checkpointer_manager
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)
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from gw_write import cotprompt,build_review_prompt,length_convert,length_display,build_length_instruction,dedupe_and_filter,filter_positive_word_false_positives,append_dictionary_results,find_keywords,normalize_words,process_rag_prompt
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app = FastAPI(max_request_size=1024 * 1024 * 10)
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_doc2pdf_converter: Optional[Doc2PDF] = None
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converter = Doc2PDF()
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DATA_DIR = "/app/files"
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load_dotenv()
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VLM_SEMAPHORE = asyncio.Semaphore(1)
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logger = logging.getLogger(__name__)
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REWRITE_TYPE_MAP = {
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"quotePolish": "金句润色",
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"formalStyle": "书面化",
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"speechStyle": "讲稿化",
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"formatting": "规整",
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"expand": "扩写",
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"continueWriting": "续写",
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"report": "汇报",
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"quote": "金句",
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"summarize": "精简",
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"condense": "总结",
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}
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# /Review 流式并发配置：块大小 5000，最多 5 块并行
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REVIEW_CHUNK_SIZE = 5000
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REVIEW_MAX_CONCURRENCY = 5
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REVIEW_SYSTEM_PROMPT = (
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"你是一个专业的文本校对专家，能够根据要求进行内容的纠正和改错"
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"请严格按照用户要求的格式输出。"
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"不要输出推理过程、解释性文字、前缀或总结。"
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)
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def get_doc2pdf_converter() -> Doc2PDF:
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global _doc2pdf_converter
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if _doc2pdf_converter is None:
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try:
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_doc2pdf_converter = Doc2PDF()
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except RuntimeError as exc:
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logger.error(f"LibreOffice 初始化失败: {exc}")
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raise HTTPException(status_code=500, detail=str(exc)) from exc
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return _doc2pdf_converter
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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@ -210,6 +125,18 @@ class RewriteRequest(BaseModel):
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sentence: Optional[str] = None
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REWRITE_TYPE_MAP = {
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"quotePolish": "金句润色",
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"formalStyle": "书面化",
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"speechStyle": "讲稿化",
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"formatting": "规整",
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"expand": "扩写",
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"continueWriting": "续写",
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"report": "汇报",
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"quote": "金句",
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"summarize": "精简",
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"condense": "总结",
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}
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def build_rewrite_prompt(request: RewriteRequest) -> str:
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@ -268,6 +195,65 @@ class OutlineRequest(BaseModel):
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prompt: Optional[str] = None
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# aidoc-web 的公文编辑器会把 Markdown 中的 LaTeX 公式交给 KaTeX 渲染。
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WRITE_FORMULA_INSTRUCTION = r"""
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【公式输出规范（必须遵守）】
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1. 仅在正文确实需要数学、统计或技术公式时使用 LaTeX；普通数字、百分比、日期、编号和金额均使用普通文本，不要为了排版而生成公式。
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2. 行内公式必须且只能写成 `$公式内容$`，例如：增长率为 $r=\frac{x_2-x_1}{x_1}\times 100\%$。开始和结束的 `$` 之间不得换行。
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3. 独占一行的公式必须且只能写成以下形式，起止符各自完整且公式内容位于中间：
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$$
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公式内容
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$$
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4. 只输出 KaTeX 支持的标准 LaTeX。禁止使用 `\(...\)`、`\[...\]`、LaTeX 文档环境、代码块或 ``` 包裹公式；禁止将公式写成图片、HTML、MathML、JSON，也不要转义公式两侧的 `$`。
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5. 每个公式的定界符必须成对闭合。公式中的说明文字使用 `\text{...}`；百分号写作 `\%`，乘号优先写作 `\times`。
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6. 人民币、预算等金额直接写成“100元”“20万元”等普通文本，不得使用 `$` 作为货币符号，以免被前端误识别为公式。
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7. 若正文不需要公式，不要输出任何 `$` 或 `$$`。
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"""
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WRITE_SYSTEM_PROMPT = (
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"你是专业公文写作助手，请直接输出正文内容。"
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"输出使用可由前端 Markdown 和 KaTeX 直接解析的内容；如需公式，必须严格遵守用户提示中的公式输出规范。"
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)
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def length_convert(length: Optional[str]):
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"""将篇幅解析为 {target, max}：target 为建议目标字数，max 为硬上限。"""
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if length == "短":
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return {"target": 700, "max": 1000}
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if length == "中":
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return {"target": 2500, "max": 3000}
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if length == "长":
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return {"target": 4500, "max": 5000}
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# 兜底：从自定义篇幅字符串里解析出最大字数
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if length:
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nums = re.findall(r"\d+", length)
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if nums:
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max_chars = int(nums[-1])
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return {"target": int(max_chars * 0.8), "max": max_chars}
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return None
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def length_display(length: Optional[str]) -> str:
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"""模板 {length} 占位符用的字符串。"""
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info = length_convert(length)
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if info:
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return f"{info['target']}字左右（不超过{info['max']}字）"
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return length or ""
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def build_length_instruction(length: Optional[str]) -> str:
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"""把 target/max 写进提示词：建议目标 + 硬上限 + 优先级。"""
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info = length_convert(length)
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if not info:
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return ""
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return (
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f"全文建议写到{info['target']}字左右，绝对不得超过{info['max']}字；"
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"字数为硬性要求，不得注水或重复凑字数。"
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)
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def build_write_prompt(request: WriteRequest) -> str:
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prompt = request.prompt or ""
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template = request.template or ""
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@ -281,7 +267,7 @@ def build_write_prompt(request: WriteRequest) -> str:
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if not title.strip():
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raise HTTPException(status_code=400, detail="title不能为空")
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return content+prompt_template.format(
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document_prompt = content + prompt_template.format(
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role=request.role or "",
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title=title,
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length=length_display(request.length),
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@ -289,13 +275,14 @@ def build_write_prompt(request: WriteRequest) -> str:
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references=request.references or "",
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outline=request.outline or "",
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)
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return document_prompt + WRITE_FORMULA_INSTRUCTION
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async def stream_write_content(prompt: str):
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async for chunk in model_api.OpenaiAPI.open_api_chat_stream(
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query=prompt,
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model=None,
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system_prompt="你是专业公文写作助手，请直接输出正文内容。",
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system_prompt=WRITE_SYSTEM_PROMPT,
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messages=[],
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):
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if chunk:
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@ -379,6 +366,203 @@ class ReviewResponse(BaseModel):
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data: List[ReviewItem]
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def normalize_words(words: List[str]) -> Set[str]:
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return {word.strip() for word in words if word and word.strip()}
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def find_keywords(text: str, keyword_set: Set[str]) -> List[str]:
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if not text or not keyword_set:
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return []
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automaton = ahocorasick.Automaton()
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for idx, keyword in enumerate(keyword_set):
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automaton.add_word(keyword, (idx, keyword))
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automaton.make_automaton()
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found = set()
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for _, (_, keyword) in automaton.iter(text):
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found.add(keyword)
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return list(found)
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def append_dictionary_results(
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items: List[dict],
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content: str,
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sensitive_words: Set[str],
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negative_words: Set[str],
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) -> List[dict]:
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for word in find_keywords(content, sensitive_words):
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items.append({
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"original": word,
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"error": "敏感词汇",
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"suggestion": "",
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})
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for word in find_keywords(content, negative_words):
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items.append({
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"original": word,
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"error": "错误词汇",
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"suggestion": "",
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})
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return items
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def filter_positive_word_false_positives(
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items: List[dict],
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positive_words: Set[str],
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) -> List[dict]:
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if not positive_words:
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return items
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result = []
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for item in items:
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original = str(item.get("original", ""))
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error = str(item.get("error", ""))
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has_positive_word = any(word in original for word in positive_words)
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is_typo_error = (
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"错别字" in error
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or "错别词" in error
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or "错别词语" in error
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)
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if has_positive_word and is_typo_error:
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continue
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result.append(item)
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return result
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def dedupe_and_filter(items: List[dict], source_text: str) -> List[dict]:
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seen = set()
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result = []
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for item in items:
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original = str(item.get("original", "")).strip()
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error = str(item.get("error", "")).strip()
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suggestion = str(item.get("suggestion", "")).strip()
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if not original:
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continue
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if original == suggestion:
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continue
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if original not in source_text:
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continue
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key = (original, error, suggestion)
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if key in seen:
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continue
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seen.add(key)
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result.append({
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"original": original,
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"error": error,
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"suggestion": suggestion,
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})
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return result
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def cotprompt(query):
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# 精简 Few-Shot：只示范「输入文本 -> 结构化修改结果」，不再包含推理过程/思考链，
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# 从源头杜绝模型输出解释、分析、括号备注等多余内容，同时大幅缩短每次请求的前缀长度。
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examples = [
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{
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# 综合示例：覆盖错别字/错别词语/标点不规范/重复内容，
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# 重点示范：多余标点(。。。)、全半角括号 -> 标点不规范（不是逻辑不通）
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"question": "现,代教育中，智能教学系统【】让每个雪生受益，自动文达系统很方便。现就安全生产大检查工作提出如下意见。。。(本报记者 小陈)",
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"answer": (
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"###\n"
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"错误：标点不规范\n原文：现,代教育\n建议：现代教育\n\n"
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"错误：标点不规范\n原文：智能教学系统【】\n建议：智能教学系统\n\n"
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"错误：错别字\n原文：每个雪生\n建议：每个学生\n\n"
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"错误：错别词语\n原文：自动文达系统\n建议：自动问答系统\n\n"
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"错误：标点不规范\n原文：如下意见。。。\n建议：如下意见。\n\n"
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"错误：标点不规范\n原文：(本报记者 小陈)\n建议：（本报记者 小陈）\n"
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"###"
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),
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},
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{
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# 删除类示例：整段重复/冗余内容应删除时，建议字段留空（表示删除），
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# 严禁写“（删除该句……）”之类说明文字，否则前端替换会把说明插入原文。
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"question": "综上，各相关部门应认真履行尽责，扎实推进工作落实见效落地。综上，各相关部门应认真履行尽责，扎实推进工作落实见效落地。",
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"answer": (
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"###\n"
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"错误：重复内容\n原文：综上，各相关部门应认真履行尽责，扎实推进工作落实见效落地。\n建议：\n"
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"###"
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),
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},
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{
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# 无明显错误示例：只返回空标记，禁止输出任何说明、分析或括号备注
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"question": "全年审核采购合同、对账单据数百份，均按规定完成登记归档。",
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"answer": "###\n###",
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},
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]
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# 单个示例模板：只保留 问题/答案，删除“推理过程”
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example_prompt = PromptTemplate(
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input_variables=["question", "answer"],
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template="问题：{question}\n答案：{answer}",
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)
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few_shot_prompt = FewShotPromptTemplate(
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examples=examples,
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example_prompt=example_prompt,
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prefix=(
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"你是专业文本校对专家，只检查错别字、错误标点、重复内容、逻辑不通与合规问题。\n"
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"输出规则（务必严格遵守）：\n"
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"1. 每个错误必须连续占三行：第一行“错误：<错误类型>”，第二行“原文：<有错的原文片段>”，第三行“建议：<修改后的正确文本>”。这三行之间严禁插入空行；只允许在一条完整错误的“建议”行之后、下一条错误的“错误”行之前空一行。整体用 ### 包裹。\n"
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"2. 错误类型只能从以下选择：错别字、错别词语、标点不规范、重复内容、逻辑不通、合规问题。\n"
|
||||
"3. 凡是标点问题（多余或重复标点如“。。。”、全角/半角标点混用如英文括号()应为中文（）、标点缺失或误用），一律归为“标点不规范”，禁止归为“逻辑不通”；“逻辑不通”只用于前后文语义矛盾、指代不清等真正的逻辑问题。\n"
|
||||
"4. 建议字段只写修改后的正确内容，禁止输出任何解释、分析、推理、评论或括号备注（例如禁止出现“（删除该句……）”“（此处无明显错误……）”“（通常……可接受）”这类内容）。\n"
|
||||
"5. 删除类错误（重复插入、冗余整句等需要删掉的内容）：建议字段一律留空，即“建议：”后不写任何字符，用留空表示该原文片段应被删除；绝不能写“（删除）”或删除原因。\n"
|
||||
"6. 若整段文本没有任何错误，只返回：###\n###，不要输出其它任何字符。\n"
|
||||
"请参照以下示例完成校对："
|
||||
),
|
||||
suffix="问题：{question}\n答案：",
|
||||
input_variables=["question"],
|
||||
example_separator="\n\n",
|
||||
)
|
||||
return few_shot_prompt.format(question=query)
|
||||
|
||||
|
||||
def build_review_prompt(types: List[str], content: str, require: Optional[str]) -> Iterable[ChatCompletionMessageParam]:
|
||||
if require is None:
|
||||
require = ""
|
||||
require = require.strip()
|
||||
review_t = "现在需要你帮我完成以下文本校对任务："
|
||||
for i , t in enumerate(types):
|
||||
if t =="逻辑校对":
|
||||
review_t += f"{i} 对这段文本进行逻辑校对\n"
|
||||
if t =="基础校对":
|
||||
review_t += f"{i}.1 进行错别字校对，错别字错误主要以文本用字不当为主，例如：星光店电，正确的应该为：星光点点。\n"
|
||||
review_t += f"{i}.2 进行标点校对，主要以标点符号使用不当为主，例如：星光点点；万里无云，正确的应该为：星光点点，万里无云\n"
|
||||
review_t += f"{i}.3 进行格式规范校对\n"
|
||||
review_t += f"{i}.4 进行重复内容校对，主要以原文中出现重复词语、句子或文本为主，例如：“今天今天心情相当不错，我很开心。我很开心。“，正确的内容应该为：今天心情相当不错，我很开心。\n"
|
||||
if t=="合规性检查":
|
||||
review_t += f"{i} 对这段文本进行合规性检查\n"
|
||||
require_text = f"\n额外要求：{require}\n" if require else ""
|
||||
query = f"""
|
||||
我会给你对应的文本，进行相应的检查，{review_t}
|
||||
{require_text}
|
||||
注意：只输出结构化校对结果（错误/原文/建议），每条结果的错误、原文、建议必须连续三行输出，三行之间不得有空行；建议只写修改后的正确文本，不要输出任何解释、分析、推理或括号备注；没有错误时只返回 ###\n###。
|
||||
其中：标点问题（多余/重复标点、全半角括号混用等）一律归为“标点不规范”，不要归为“逻辑不通”；需要删除的重复或冗余内容，建议字段留空表示删除，禁止写“（删除…）”之类文字。
|
||||
"""
|
||||
query1 = cotprompt(content)
|
||||
return [
|
||||
ChatCompletionUserMessageParam(role="user", content="请不要进行思考，直接输出内容"),
|
||||
ChatCompletionUserMessageParam(role="user", content=query),
|
||||
ChatCompletionUserMessageParam(role="user", content=query1),
|
||||
|
||||
]
|
||||
|
||||
|
||||
|
||||
|
||||
@ -389,6 +573,7 @@ _REVIEW_BLOCK_RE = re.compile(
|
||||
re.S,
|
||||
)
|
||||
|
||||
|
||||
def parse_review_output(text: str) -> List[dict]:
|
||||
text = text.strip()
|
||||
|
||||
@ -410,6 +595,12 @@ def parse_review_output(text: str) -> List[dict]:
|
||||
return result
|
||||
|
||||
|
||||
REVIEW_SYSTEM_PROMPT = (
|
||||
"你是一个专业的文本校对专家，能够根据要求进行内容的纠正和改错"
|
||||
"请严格按照用户要求的格式输出。"
|
||||
"不要输出推理过程、解释性文字、前缀或总结。"
|
||||
)
|
||||
|
||||
|
||||
async def stream_review_model(prompt: Iterable[ChatCompletionMessageParam]):
|
||||
async for chunk in model_api.OpenaiAPI.open_api_chat_stream(
|
||||
@ -462,6 +653,10 @@ def build_review_stream_require(
|
||||
return "\n".join(requirements)
|
||||
|
||||
|
||||
# /Review 流式并发配置：块大小 5000，最多 5 块并行
|
||||
REVIEW_CHUNK_SIZE = 5000
|
||||
REVIEW_MAX_CONCURRENCY = 5
|
||||
|
||||
|
||||
async def stream_review_content(
|
||||
content: str,
|
||||
@ -626,7 +821,39 @@ class FeedbackClassifyRequest(BaseModel):
|
||||
|
||||
|
||||
|
||||
# =============== 辅助函数 ===============
|
||||
def process_rag_prompt(rag_prompt: Any) -> str:
|
||||
"""
|
||||
处理 rag_prompt 参数，将各种类型转换为字符串
|
||||
|
||||
Args:
|
||||
rag_prompt: 可以是字符串、列表、字典等任意类型
|
||||
|
||||
Returns:
|
||||
处理后的字符串
|
||||
"""
|
||||
if rag_prompt is None:
|
||||
return ""
|
||||
|
||||
# 如果是字符串，直接返回
|
||||
if isinstance(rag_prompt, str):
|
||||
return rag_prompt.strip()
|
||||
|
||||
# 如果是列表，转换为多行字符串
|
||||
if isinstance(rag_prompt, list):
|
||||
# 将列表中的每个元素转换为字符串，并用换行符连接
|
||||
return "\n".join(str(item) for item in rag_prompt if item)
|
||||
|
||||
# 如果是字典，转换为 JSON 字符串
|
||||
if isinstance(rag_prompt, dict):
|
||||
try:
|
||||
return json.dumps(rag_prompt, ensure_ascii=False, indent=2)
|
||||
except Exception as e:
|
||||
logger.warning(f"rag_prompt 字典转换失败: {e}")
|
||||
return str(rag_prompt)
|
||||
|
||||
# 其他类型，直接转换为字符串
|
||||
return str(rag_prompt).strip()
|
||||
|
||||
|
||||
def format_stream_event(event_type: str,
|
||||
@ -1141,6 +1368,14 @@ async def download_file(file_name: str):
|
||||
)
|
||||
|
||||
|
||||
@app.get("/api/health")
|
||||
async def health_check():
|
||||
"""健康检查接口"""
|
||||
return {
|
||||
"status": "healthy",
|
||||
"message": "服务正常运行，每次请求时创建新的Agent实例"
|
||||
}
|
||||
|
||||
|
||||
|
||||
@app.post("/api/v1/feedback/classify")
|
||||
@ -1197,78 +1432,6 @@ class RequestWrapper(BaseModel):
|
||||
pdf_contents: List[PdfContentItem]
|
||||
|
||||
|
||||
class Base64ImageRequest(BaseModel):
|
||||
content: Optional[str] = Field(
|
||||
None,
|
||||
description="Optional text around the image, for example: 图3.1 船舶主发动机组成图（images/test.jpg）",
|
||||
)
|
||||
filename: str = Field(..., description="Original image filename, for example test.png")
|
||||
image_base64: str = Field(..., description="Base64 image content, with or without data:image/... prefix")
|
||||
|
||||
|
||||
async def analyze_image_with_vlm(image_bytes: bytes, suffix: str) -> str:
|
||||
image_url = image_base64_to_data_url(image_bytes, suffix)
|
||||
async with VLM_SEMAPHORE:
|
||||
return await model_api.OpenaiAPI.open_api_vl_without_thinking(image_url)
|
||||
|
||||
|
||||
@app.post("/split_image")
|
||||
async def split_image(request_data: Base64ImageRequest):
|
||||
filename = safe_original_filename(request_data.filename)
|
||||
suffix = PathLib(filename).suffix.lower()
|
||||
if suffix not in {".jpg", ".jpeg", ".png", ".bmp", ".webp", ".gif", ".tif", ".tiff"}:
|
||||
raise HTTPException(status_code=400, detail="Unsupported image type")
|
||||
|
||||
image_bytes, processed_suffix = prepare_split_image(request_data.image_base64, suffix)
|
||||
if not image_bytes:
|
||||
raise HTTPException(status_code=400, detail="decoded image is empty")
|
||||
|
||||
temp_path = None
|
||||
try:
|
||||
with tempfile.NamedTemporaryFile(delete=False, suffix=processed_suffix) as tmp:
|
||||
tmp.write(image_bytes)
|
||||
temp_path = PathLib(tmp.name)
|
||||
|
||||
ocr_data = await extract_image_text(temp_path)
|
||||
try:
|
||||
temp_path.unlink()
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to delete temp image {temp_path}: {e}")
|
||||
finally:
|
||||
temp_path = None
|
||||
|
||||
content = (request_data.content or "").strip()
|
||||
ocr_full_text = (ocr_data.get("full_text") or "").strip()
|
||||
|
||||
if not content and not ocr_full_text:
|
||||
vlm_text = await analyze_image_with_vlm(image_bytes, processed_suffix)
|
||||
image_content = "图片文本描述为：" + (vlm_text or "").strip()
|
||||
elif content and ocr_full_text:
|
||||
image_content = f"图片上下文内容为：{content}, {ocr_full_text}"
|
||||
elif content:
|
||||
image_content = "图片上下文内容为：" + content
|
||||
else:
|
||||
image_content = ocr_full_text
|
||||
|
||||
image_content = "图片文件名为：" + filename + "，图片相关内容为：" + image_content
|
||||
return JSONResponse(
|
||||
{
|
||||
"code": 200,
|
||||
"message": "ok",
|
||||
"data": {
|
||||
"image_content": image_content,
|
||||
"filename": filename,
|
||||
},
|
||||
}
|
||||
)
|
||||
finally:
|
||||
if temp_path and temp_path.exists():
|
||||
try:
|
||||
temp_path.unlink()
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to delete temp image {temp_path}: {e}")
|
||||
|
||||
|
||||
|
||||
|
||||
@app.post("/split_content_list")
|
||||
@ -1350,7 +1513,7 @@ async def split_result(
|
||||
logger.info(f"转换 DOCX -> PDF: {filename}")
|
||||
|
||||
# 执行转换
|
||||
get_doc2pdf_converter().convert(str(temp_input_path), output_dir=str(DATA_DIR))
|
||||
converter.convert(str(temp_input_path), output_dir=str(DATA_DIR))
|
||||
|
||||
# ? 使用临时文件的基础名查找 PDF
|
||||
temp_base_name = temp_input_path.stem # 如 tmp2bb0l4cn
|
||||
@ -1448,13 +1611,6 @@ async def split_result(
|
||||
except Exception as e:
|
||||
logger.warning(f"Failed to delete temp file {path}: {e}")
|
||||
|
||||
@app.get("/api/health")
|
||||
async def health_check():
|
||||
"""健康检查接口"""
|
||||
return {
|
||||
"status": "healthy",
|
||||
"message": "服务正常运行，每次请求时创建新的Agent实例"
|
||||
}
|
||||
|
||||
@app.get("/")
|
||||
async def root():
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user