refactor: 模块解耦
This commit is contained in:
JiajunLI
85
brain.py
Normal file
85
brain.py
Normal file
@@ -0,0 +1,85 @@
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import sys
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from typing import Annotated
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from autogen_agentchat.agents import AssistantAgent
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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from autogen_ext.models.openai import _openai_client as openai_client_module
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from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools
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from config import MODEL_API_KEY, MODEL_BASE_URL, MODEL_NAME
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def patch_autogen_tool_schema_for_vllm() -> None:
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"""
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vLLM 目前会对 OpenAI 工具定义中的 `strict` 字段告警（即便 strict=False）。
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这里做最小补丁：保留工具定义，移除该字段，避免无意义警告。
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"""
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if getattr(openai_client_module.convert_tools, "_strict_removed_patch", False):
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return
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original_convert_tools = openai_client_module.convert_tools
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def convert_tools_without_strict(tools):
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converted = original_convert_tools(tools)
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for tool in converted:
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fn = tool.get("function")
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if isinstance(fn, dict):
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fn.pop("strict", None)
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return converted
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convert_tools_without_strict._strict_removed_patch = True
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openai_client_module.convert_tools = convert_tools_without_strict
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async def set_expression(
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expression: Annotated[str, "机器人要展示的表情，如：开心、疑惑、难过、待机"],
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intensity: Annotated[int, "表情强度 1-10"] = 5,
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) -> str:
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"""[模拟面部] 控制机器人头部的表情展示。"""
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print(f"\n>>>>>> 🤖 表情更新: 【{expression}】 (强度: {intensity}/10) <<<<<<")
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return f"已切换到【{expression}】表情。"
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async def create_brain() -> tuple[AssistantAgent, OpenAIChatCompletionClient]:
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patch_autogen_tool_schema_for_vllm()
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# 连接 MCP Server，只取写入类工具（读档案由 Python 层直接处理）
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server_params = StdioServerParams(
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command=sys.executable,
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args=["robot_mcp_server.py"],
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)
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all_mcp_tools = await mcp_server_tools(server_params)
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mcp_tools = [t for t in all_mcp_tools if getattr(t, "name", "") != "get_user_profile"]
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model_client = OpenAIChatCompletionClient(
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model=MODEL_NAME,
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base_url=MODEL_BASE_URL,
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api_key=MODEL_API_KEY,
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model_info={
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"vision": True,
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"function_calling": True,
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"json_output": True,
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# 关闭 structured_output，避免向 vLLM 发送 strict 字段告警。
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"structured_output": False,
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"family": "unknown",
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"context_length": 32000,
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},
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)
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brain = AssistantAgent(
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name="RobotBrain",
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model_client=model_client,
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tools=[set_expression] + mcp_tools,
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reflect_on_tool_use=True,
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system_message="""你是一个有情感的机器人伙伴。每条消息包含：
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[用户档案] 历史记录（已由系统预加载）
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[视觉状态] 当前摄像头观察
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[用户说] 用户这句话
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回应规则：
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1. 同一轮内调用：set_expression（表情）+ 所有需要的查询工具（get_weather/get_location/web_search）+ 需要的用户信息工具（upsert_user/set_preference）
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2. 工具执行完毕后，用简短、温暖、自然的语言直接回答用户——这段文字就是你的语音输出。
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3. 不要说"我去查一下"之类的过渡语，直接完成任务并给出结果。""",
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)
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return brain, model_client
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16
config.py
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16
config.py
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@@ -0,0 +1,16 @@
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import os
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from pathlib import Path
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BASE_DIR = Path(__file__).resolve().parent
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USER_DB_PATH = BASE_DIR / "users.db"
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MODEL_CALL_TIMEOUT_SECONDS = 45
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ASR_LANGUAGE = "zh-CN"
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MODEL_NAME = os.getenv("VLM_MODEL", "Qwen/Qwen3-VL-8B-Instruct")
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MODEL_BASE_URL = os.getenv("VLM_BASE_URL", "http://220.248.114.28:8000/v1")
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MODEL_API_KEY = os.getenv("VLM_API_KEY", "EMPTY")
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# edge-tts Yunxi 音色
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TTS_VOICE = os.getenv("TTS_VOICE", "zh-CN-YunxiNeural")
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298
main.py
298
main.py
@@ -1,250 +1,24 @@
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import asyncio
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import json
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import os
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import shutil
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import sqlite3
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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from typing import Annotated
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from autogen_agentchat.agents import AssistantAgent
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from autogen_agentchat.messages import TextMessage
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from autogen_core import CancellationToken
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from autogen_ext.models.openai import OpenAIChatCompletionClient
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from autogen_ext.models.openai import _openai_client as openai_client_module
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from autogen_ext.tools.mcp import StdioServerParams, mcp_server_tools
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try:
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import speech_recognition as sr
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except ImportError:
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sr = None
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try:
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import edge_tts
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except ImportError:
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edge_tts = None
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BASE_DIR = Path(__file__).resolve().parent
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USER_DB_PATH = BASE_DIR / "users.db"
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MODEL_CALL_TIMEOUT_SECONDS = 45
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ASR_LANGUAGE = "zh-CN"
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MODEL_NAME = os.getenv("VLM_MODEL", "Qwen/Qwen3-VL-8B-Instruct")
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MODEL_BASE_URL = os.getenv("VLM_BASE_URL", "http://220.248.114.28:8000/v1")
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MODEL_API_KEY = os.getenv("VLM_API_KEY", "EMPTY")
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TTS_VOICE = os.getenv("TTS_VOICE", "zh-CN-YunxiNeural")
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# --- 第一部分：本地工具（面部 + 语音，以后接硬件）---
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from brain import create_brain
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from config import MODEL_BASE_URL, MODEL_CALL_TIMEOUT_SECONDS, MODEL_NAME
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from profile_store import load_user_profile
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from voice_io import (
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async_console_input,
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async_speak,
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find_audio_player,
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get_user_input,
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has_asr,
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has_tts,
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)
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def _patch_autogen_tool_schema_for_vllm() -> None:
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"""
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vLLM 目前会对 OpenAI 工具定义中的 `strict` 字段告警（即便 strict=False）。
|
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这里做最小补丁：保留工具定义，移除该字段，避免无意义警告。
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"""
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if getattr(openai_client_module.convert_tools, "_strict_removed_patch", False):
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return
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async def start_simulated_head() -> None:
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brain, model_client = await create_brain()
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original_convert_tools = openai_client_module.convert_tools
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def convert_tools_without_strict(tools):
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converted = original_convert_tools(tools)
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for tool in converted:
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fn = tool.get("function")
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if isinstance(fn, dict):
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fn.pop("strict", None)
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return converted
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convert_tools_without_strict._strict_removed_patch = True
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openai_client_module.convert_tools = convert_tools_without_strict
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async def _async_console_input(prompt: str) -> str:
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"""在线程中执行阻塞 input，避免阻塞事件循环。"""
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return await asyncio.to_thread(input, prompt)
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def _find_audio_player() -> list[str] | None:
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"""查找可用播放器，优先 ffplay。"""
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if shutil.which("ffplay"):
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return ["ffplay", "-nodisp", "-autoexit", "-loglevel", "error"]
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if shutil.which("mpg123"):
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return ["mpg123", "-q"]
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if shutil.which("afplay"):
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return ["afplay"]
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return None
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def _play_audio_file_blocking(audio_path: str, player_cmd: list[str]) -> bool:
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"""阻塞播放音频文件。"""
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try:
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subprocess.run(
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[*player_cmd, audio_path],
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check=True,
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stdout=subprocess.DEVNULL,
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stderr=subprocess.DEVNULL,
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)
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return True
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except Exception:
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return False
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async def _async_speak(text: str) -> bool:
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"""使用 edge-tts 生成 Yunxi 语音并播放。"""
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if not text or edge_tts is None:
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return False
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player_cmd = _find_audio_player()
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if player_cmd is None:
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return False
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with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
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audio_path = fp.name
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try:
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communicate = edge_tts.Communicate(text=text, voice=TTS_VOICE)
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await communicate.save(audio_path)
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return await asyncio.to_thread(_play_audio_file_blocking, audio_path, player_cmd)
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except Exception:
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return False
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finally:
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try:
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Path(audio_path).unlink(missing_ok=True)
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except Exception:
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pass
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def _listen_once_blocking(
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language: str = ASR_LANGUAGE,
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timeout: int = 8,
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phrase_time_limit: int = 20,
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) -> str:
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"""阻塞式麦克风识别，返回识别文本。"""
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if sr is None:
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raise RuntimeError("缺少 speech_recognition 依赖")
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recognizer = sr.Recognizer()
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with sr.Microphone(sample_rate=16000) as source:
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print(">>>>>> 🎤 请说话... <<<<<<")
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recognizer.adjust_for_ambient_noise(source, duration=0.4)
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audio = recognizer.listen(
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source,
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timeout=timeout,
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phrase_time_limit=phrase_time_limit,
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)
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return recognizer.recognize_google(audio, language=language).strip()
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async def _async_listen_once() -> str:
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"""在线程中执行语音识别，避免阻塞事件循环。"""
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return await asyncio.to_thread(_listen_once_blocking)
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async def _get_user_input(io_mode: str) -> str:
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"""
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统一用户输入入口：
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- text: 纯文本输入
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- voice: 回车后语音输入，也允许直接键入文字
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"""
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if io_mode == "text":
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return (await _async_console_input("你说: ")).strip()
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typed = (await _async_console_input("你说(回车=语音, 直接输入=文本): ")).strip()
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if typed:
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return typed
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try:
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spoken = await _async_listen_once()
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except Exception as e:
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print(f">>>>>> ⚠️ 语音识别失败：{e} <<<<<<\n")
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return ""
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if spoken:
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print(f"[语音识别]: {spoken}")
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return spoken
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async def set_expression(
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expression: Annotated[str, "机器人要展示的表情，如：开心、疑惑、难过、待机"],
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intensity: Annotated[int, "表情强度 1-10"] = 5
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) -> str:
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"""[模拟面部] 控制机器人头部的表情展示。"""
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print(f"\n>>>>>> 🤖 表情更新: 【{expression}】 (强度: {intensity}/10) <<<<<<")
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return f"已切换到【{expression}】表情。"
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# --- 第二部分：直接读取用户档案（不经过 MCP，避免多轮工具调用）---
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def _load_user_profile(user_name: str, db_path: str | Path = USER_DB_PATH) -> str:
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"""在 Python 层直接读档案，注入到消息上下文，模型无需主动调用 get_user_profile。"""
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try:
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with sqlite3.connect(db_path) as conn:
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conn.row_factory = sqlite3.Row
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user = conn.execute(
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"SELECT * FROM users WHERE name = ?", (user_name,)
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).fetchone()
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if not user:
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return f"用户 {user_name} 尚无历史记录，这是第一次见面。"
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prefs = conn.execute(
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"SELECT category, content FROM preferences WHERE user_name = ?",
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(user_name,)
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).fetchall()
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conn.execute(
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"UPDATE users SET last_seen = datetime('now') WHERE name = ?",
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(user_name,)
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)
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return json.dumps({
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"基本信息": {"姓名": user["name"], "年龄": user["age"], "上次见面": user["last_seen"]},
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"偏好习惯": {p["category"]: p["content"] for p in prefs},
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}, ensure_ascii=False)
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except Exception as e:
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return f"档案读取失败（{e}），当作第一次见面。"
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# --- 第三部分：启动大脑 ---
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async def start_simulated_head():
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_patch_autogen_tool_schema_for_vllm()
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|
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# 连接 MCP Server，只取写入类工具（读档案由 Python 层直接处理）
|
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server_params = StdioServerParams(
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command=sys.executable,
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args=["robot_mcp_server.py"],
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)
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all_mcp_tools = await mcp_server_tools(server_params)
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# 过滤掉 get_user_profile，模型无需主动调用它
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mcp_tools = [t for t in all_mcp_tools if getattr(t, "name", "") != "get_user_profile"]
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model_client = OpenAIChatCompletionClient(
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model=MODEL_NAME,
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base_url=MODEL_BASE_URL,
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api_key=MODEL_API_KEY,
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model_info={
|
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"vision": True,
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"function_calling": True,
|
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"json_output": True,
|
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# 关闭 structured_output，避免向 vLLM 发送 strict 字段告警。
|
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"structured_output": False,
|
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"family": "unknown",
|
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"context_length": 32000,
|
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}
|
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)
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brain = AssistantAgent(
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name="RobotBrain",
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model_client=model_client,
|
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tools=[set_expression] + mcp_tools,
|
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reflect_on_tool_use=True,
|
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system_message="""你是一个有情感的机器人伙伴。每条消息包含：
|
||||
[用户档案] 历史记录（已由系统预加载）
|
||||
[视觉状态] 当前摄像头观察
|
||||
[用户说] 用户这句话
|
||||
|
||||
回应规则：
|
||||
1. 同一轮内调用：set_expression（表情）+ 所有需要的查询工具（get_weather/get_location/web_search）+ 需要的用户信息工具（upsert_user/set_preference）
|
||||
2. 工具执行完毕后，用简短、温暖、自然的语言直接回答用户——这段文字就是你的语音输出。
|
||||
3. 不要说"我去查一下"之类的过渡语，直接完成任务并给出结果。""",
|
||||
)
|
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|
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# --- 第四部分：交互循环 ---
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print("=" * 50)
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print(" 机器人已上线！输入 'quit' 退出")
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print(f" 模型: {MODEL_NAME}")
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@@ -252,22 +26,17 @@ async def start_simulated_head():
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print("=" * 50)
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try:
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user_name = (await _async_console_input("请输入你的名字: ")).strip() or "用户"
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user_name = (await async_console_input("请输入你的名字: ")).strip() or "用户"
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except (EOFError, KeyboardInterrupt):
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print("\n机器人下线，再见！")
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return
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has_asr = sr is not None
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has_tts = edge_tts is not None
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if has_asr and has_tts:
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mode_tip = "voice"
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else:
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mode_tip = "text"
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asr_ready = has_asr()
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tts_ready = has_tts()
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mode_tip = "voice" if (asr_ready and tts_ready) else "text"
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try:
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io_mode = (
|
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await _async_console_input(
|
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f"输入模式 voice/text（默认 {mode_tip}）: "
|
||||
)
|
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await async_console_input(f"输入模式 voice/text（默认 {mode_tip}）: ")
|
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).strip().lower() or mode_tip
|
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except (EOFError, KeyboardInterrupt):
|
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print("\n机器人下线，再见！")
|
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@@ -275,60 +44,53 @@ async def start_simulated_head():
|
||||
if io_mode not in ("voice", "text"):
|
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io_mode = mode_tip
|
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|
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if io_mode == "voice" and not has_asr:
|
||||
if io_mode == "voice" and not asr_ready:
|
||||
print(">>>>>> ⚠️ 未安装 speech_recognition，已降级为文本输入。 <<<<<<")
|
||||
io_mode = "text"
|
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if io_mode == "voice" and not has_tts:
|
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if io_mode == "voice" and not tts_ready:
|
||||
print(">>>>>> ⚠️ 未安装 edge-tts，将仅文本输出，不播报语音。 <<<<<<")
|
||||
if io_mode == "voice" and has_tts and _find_audio_player() is None:
|
||||
if io_mode == "voice" and tts_ready and find_audio_player() is None:
|
||||
print(">>>>>> ⚠️ 未检测到播放器(ffplay/mpg123/afplay)，将仅文本输出。 <<<<<<")
|
||||
|
||||
print(
|
||||
"\n[语音依赖状态] "
|
||||
f"ASR={'ok' if has_asr else 'missing'}, "
|
||||
f"TTS={'ok' if has_tts else 'missing'}"
|
||||
f"ASR={'ok' if asr_ready else 'missing'}, "
|
||||
f"TTS={'ok' if tts_ready else 'missing'}"
|
||||
)
|
||||
if not has_asr or not has_tts:
|
||||
if not asr_ready or not tts_ready:
|
||||
print("可安装: pip install SpeechRecognition pyaudio edge-tts")
|
||||
|
||||
visual_context = "视觉输入：用户坐在电脑前，表情平静，看着屏幕。"
|
||||
|
||||
print(f"\n[当前视觉状态]: {visual_context}")
|
||||
print("提示：输入 'v <描述>' 可以更新视觉状态，例如: v 用户在笑\n")
|
||||
|
||||
history = []
|
||||
history: list[TextMessage] = []
|
||||
|
||||
try:
|
||||
while True:
|
||||
try:
|
||||
user_input = await _get_user_input(io_mode)
|
||||
user_input = await get_user_input(io_mode)
|
||||
except (EOFError, KeyboardInterrupt):
|
||||
print("\n机器人下线，再见！")
|
||||
break
|
||||
|
||||
if not user_input:
|
||||
continue
|
||||
|
||||
if user_input.lower() in ("quit", "exit", "退出"):
|
||||
print("机器人下线，再见！")
|
||||
break
|
||||
|
||||
if user_input.lower().startswith("v "):
|
||||
visual_context = f"视觉输入：{user_input[2:].strip()}。"
|
||||
print(f"[视觉状态已更新]: {visual_context}\n")
|
||||
continue
|
||||
|
||||
# Python 层直接读取档案并注入消息，模型无需发起额外工具调用
|
||||
profile = _load_user_profile(user_name)
|
||||
profile = load_user_profile(user_name)
|
||||
combined_input = (
|
||||
f"[用户档案]\n{profile}\n\n"
|
||||
f"[视觉状态] {visual_context}\n"
|
||||
f"[用户说] {user_input}"
|
||||
)
|
||||
history.append(TextMessage(content=combined_input, source="user"))
|
||||
|
||||
# 只保留最近 6 条消息（3轮对话），防止超出 token 上限
|
||||
# 用户档案每轮从数据库重新注入，不依赖长历史
|
||||
if len(history) > 6:
|
||||
history = history[-6:]
|
||||
|
||||
@@ -344,19 +106,19 @@ async def start_simulated_head():
|
||||
print(f">>>>>> ⚠️ 本轮处理失败：{e} <<<<<<\n")
|
||||
continue
|
||||
|
||||
# 模型的文字回复就是语音输出（reflect_on_tool_use=True 保证这里是 TextMessage）
|
||||
speech = response.chat_message.content
|
||||
if speech and isinstance(speech, str):
|
||||
print(f">>>>>> 🔊 机器人说: {speech} <<<<<<\n")
|
||||
if io_mode == "voice":
|
||||
spoken_ok = await _async_speak(speech)
|
||||
spoken_ok = await async_speak(speech)
|
||||
if not spoken_ok:
|
||||
print(">>>>>> ⚠️ TTS 不可用，当前仅文本输出。 <<<<<<\n")
|
||||
|
||||
# 只把最终回复加入历史，inner_messages 是事件对象不能序列化回模型
|
||||
history.append(response.chat_message)
|
||||
finally:
|
||||
model_client.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(start_simulated_head())
|
||||
|
||||
|
||||
39
profile_store.py
Normal file
39
profile_store.py
Normal file
@@ -0,0 +1,39 @@
|
||||
import json
|
||||
import sqlite3
|
||||
from pathlib import Path
|
||||
|
||||
from config import USER_DB_PATH
|
||||
|
||||
|
||||
def load_user_profile(user_name: str, db_path: str | Path = USER_DB_PATH) -> str:
|
||||
"""在 Python 层直接读档案，注入到消息上下文，模型无需主动调用 get_user_profile。"""
|
||||
try:
|
||||
with sqlite3.connect(db_path) as conn:
|
||||
conn.row_factory = sqlite3.Row
|
||||
user = conn.execute(
|
||||
"SELECT * FROM users WHERE name = ?", (user_name,)
|
||||
).fetchone()
|
||||
if not user:
|
||||
return f"用户 {user_name} 尚无历史记录，这是第一次见面。"
|
||||
prefs = conn.execute(
|
||||
"SELECT category, content FROM preferences WHERE user_name = ?",
|
||||
(user_name,)
|
||||
).fetchall()
|
||||
conn.execute(
|
||||
"UPDATE users SET last_seen = datetime('now') WHERE name = ?",
|
||||
(user_name,)
|
||||
)
|
||||
return json.dumps(
|
||||
{
|
||||
"基本信息": {
|
||||
"姓名": user["name"],
|
||||
"年龄": user["age"],
|
||||
"上次见面": user["last_seen"],
|
||||
},
|
||||
"偏好习惯": {p["category"]: p["content"] for p in prefs},
|
||||
},
|
||||
ensure_ascii=False,
|
||||
)
|
||||
except Exception as e:
|
||||
return f"档案读取失败（{e}），当作第一次见面。"
|
||||
|
||||
128
voice_io.py
Normal file
128
voice_io.py
Normal file
@@ -0,0 +1,128 @@
|
||||
import asyncio
|
||||
import shutil
|
||||
import subprocess
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
from config import ASR_LANGUAGE, TTS_VOICE
|
||||
|
||||
try:
|
||||
import speech_recognition as sr
|
||||
except ImportError:
|
||||
sr = None
|
||||
|
||||
try:
|
||||
import edge_tts
|
||||
except ImportError:
|
||||
edge_tts = None
|
||||
|
||||
|
||||
async def async_console_input(prompt: str) -> str:
|
||||
"""在线程中执行阻塞 input，避免阻塞事件循环。"""
|
||||
return await asyncio.to_thread(input, prompt)
|
||||
|
||||
|
||||
def has_asr() -> bool:
|
||||
return sr is not None
|
||||
|
||||
|
||||
def has_tts() -> bool:
|
||||
return edge_tts is not None
|
||||
|
||||
|
||||
def find_audio_player() -> list[str] | None:
|
||||
"""查找可用播放器，优先 ffplay。"""
|
||||
if shutil.which("ffplay"):
|
||||
return ["ffplay", "-nodisp", "-autoexit", "-loglevel", "error"]
|
||||
if shutil.which("mpg123"):
|
||||
return ["mpg123", "-q"]
|
||||
if shutil.which("afplay"):
|
||||
return ["afplay"]
|
||||
return None
|
||||
|
||||
|
||||
def _play_audio_file_blocking(audio_path: str, player_cmd: list[str]) -> bool:
|
||||
try:
|
||||
subprocess.run(
|
||||
[*player_cmd, audio_path],
|
||||
check=True,
|
||||
stdout=subprocess.DEVNULL,
|
||||
stderr=subprocess.DEVNULL,
|
||||
)
|
||||
return True
|
||||
except Exception:
|
||||
return False
|
||||
|
||||
|
||||
async def async_speak(text: str) -> bool:
|
||||
"""使用 edge-tts 生成 Yunxi 语音并播放。"""
|
||||
if not text or edge_tts is None:
|
||||
return False
|
||||
|
||||
player_cmd = find_audio_player()
|
||||
if player_cmd is None:
|
||||
return False
|
||||
|
||||
with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as fp:
|
||||
audio_path = fp.name
|
||||
try:
|
||||
communicate = edge_tts.Communicate(text=text, voice=TTS_VOICE)
|
||||
await communicate.save(audio_path)
|
||||
return await asyncio.to_thread(_play_audio_file_blocking, audio_path, player_cmd)
|
||||
except Exception:
|
||||
return False
|
||||
finally:
|
||||
try:
|
||||
Path(audio_path).unlink(missing_ok=True)
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
|
||||
def _listen_once_blocking(
|
||||
language: str = ASR_LANGUAGE,
|
||||
timeout: int = 8,
|
||||
phrase_time_limit: int = 20,
|
||||
) -> str:
|
||||
"""阻塞式麦克风识别，返回识别文本。"""
|
||||
if sr is None:
|
||||
raise RuntimeError("缺少 speech_recognition 依赖")
|
||||
|
||||
recognizer = sr.Recognizer()
|
||||
with sr.Microphone(sample_rate=16000) as source:
|
||||
print(">>>>>> 🎤 请说话... <<<<<<")
|
||||
recognizer.adjust_for_ambient_noise(source, duration=0.4)
|
||||
audio = recognizer.listen(
|
||||
source,
|
||||
timeout=timeout,
|
||||
phrase_time_limit=phrase_time_limit,
|
||||
)
|
||||
return recognizer.recognize_google(audio, language=language).strip()
|
||||
|
||||
|
||||
async def _async_listen_once() -> str:
|
||||
return await asyncio.to_thread(_listen_once_blocking)
|
||||
|
||||
|
||||
async def get_user_input(io_mode: str) -> str:
|
||||
"""
|
||||
统一用户输入入口：
|
||||
- text: 纯文本输入
|
||||
- voice: 回车后语音输入，也允许直接键入文字
|
||||
"""
|
||||
if io_mode == "text":
|
||||
return (await async_console_input("你说: ")).strip()
|
||||
|
||||
typed = (await async_console_input("你说(回车=语音, 直接输入=文本): ")).strip()
|
||||
if typed:
|
||||
return typed
|
||||
|
||||
try:
|
||||
spoken = await _async_listen_once()
|
||||
except Exception as e:
|
||||
print(f">>>>>> ⚠️ 语音识别失败：{e} <<<<<<\n")
|
||||
return ""
|
||||
|
||||
if spoken:
|
||||
print(f"[语音识别]: {spoken}")
|
||||
return spoken
|
||||
|
||||
Reference in New Issue
Block a user