fix fasthttpuser & update readme & add github link

Author: LuckyYC

README.md

@@ -18,19 +18,19 @@ LMeterX is a professional large language model performance testing platform that
 
 ## ✨ Core Features
 
-- **Universal compatibility**: Supports any language/multimodal/CoT model (GPT/Claude/Llama, etc.) service API stress testing
-- **Smart load testing**: Precise concurrency control & Real user simulation
-- **Professional metrics**: TTFT, TPS, RPS, success/error rate, etc
-- **Multi-scenario support**: Text conversations & Multimodal (image+text) & Custom dataset
-- **Visualize the results**: Performance reports & AI Summary & Model arena
-- **Real-time monitoring**: Hierarchical monitoring of tasks and services
-- **Enterprise ready**: Docker deployment & Web management console & Scalable architecture
+- **Full Model Compatibility** - Supports mainstream LLMs like GPT, Claude, and Llama with one-click stress testing
+- **High-Load Stress Testing** - Simulates high-concurrency requests to accurately detect model performance limits
+- **Multi-Scenario Coverage** - Supports streaming/non-streaming, supports text/multimodal/custom datasets<sup>![NEW](https://img.shields.io/badge/NEW-brightgreen?style=flat-square)</sup>
+- **Professional Metrics** - Core performance metrics including first token latency, throughput(RPS、TPS), and success rate
+- **AI Smart Reports** - AI-powered performance analysis<sup>![NEW](https://img.shields.io/badge/NEW-brightgreen?style=flat-square)</sup>margin-left: 8px;">NEW</sup>, multi-dimensional model comparison and visualization
+- **Web Console** - One-stop management for task creation, stopping, status tracking, and full-chain log monitoring
+- **Enterprise-level Deployment** - Docker containerization with elastic scaling and distributed deployment support
 
 ## 🏗️ System Architecture
 
 LMeterX adopts a microservices architecture design, consisting of four core components:
 
-1. **Backend API Service**: FastAPI-based REST API service responsible for task management and result storage
+1. **Backend Service**: FastAPI-based REST API service responsible for task management and result storage
 2. **Load Testing Engine**: Locust-based load testing engine that executes actual performance testing tasks
 3. **Frontend Interface**: Modern Web interface based on React + TypeScript + Ant Design
 4. **MySQL Database**: Stores test tasks, result data, and configuration information
@@ -117,7 +117,6 @@ LMeterX/
 ├── docker-compose.yml        # Docker Compose configuration
 ├── Makefile                  # Run complete code checks
 ├── README.md                 # English README
-└── README_CN.md              # Chinese README
 ```
 
 ### Development Environment Setup
@@ -142,7 +141,7 @@ LMeterX/
 - [Deployment Guide](docs/DEPLOYMENT_GUIDE.md) - Detailed deployment instructions and configuration guide
 - [Contributing Guide](docs/CONTRIBUTING.md) - How to participate in project development and contribute code
 
-## 🤝 Contributors
+## 👥 Contributors
 
 Thanks to all developers who have contributed to the LMeterX project:
 
@@ -154,5 +153,6 @@ Thanks to all developers who have contributed to the LMeterX project:
 This project is licensed under the [Apache 2.0 License](LICENSE).
 
 ---
-
+<div align="center">
 **⭐ If this project helps you, please give us a Star! Your support is our motivation for continuous improvement.**
+</div>

README_CN.md

@@ -16,24 +16,24 @@ LMeterX 是一个专业的大语言模型性能测试平台，支持对 LLM 服
   <img src="docs/images/images.gif" alt="LMeterX Demo" width="700"/>
 </div>
 
-## ✨ 核心功能
+## ✨ 核心特性
 
-- **通用兼容性**：支持任何语言/多模态/CoT模型（GPT/Claude/Llama等）服务API压测
-- **智能负载测试**：精确并发控制 & 真实用户模拟
-- **专业性能指标**：TTFT、TPS、RPS、成功/错误率等
-- **多场景支持**：文本对话 & 多模态（图像+文本）& 自定义数据集
-- **可视化结果**：性能报告一键生成 & AI智能分析 & 多维度模型对比
-- **实时监控**：任务和服务的分层监控
-- **企业就绪**: Docker部署 & Web管理控制台 &可扩展架构
+- **全模型兼容** - 支持 GPT、Claude、Llama 等主流大模型，一键发起压测
+- **高负载压测** - 模拟高并发请求，精准探测模型性能极限
+- **多场景覆盖** - 支持流式/非流式、文本/多模态/自定义数据集<sup>![NEW](https://img.shields.io/badge/NEW-brightgreen?style=flat-square)</sup>
+- **专业指标统计** - 首Token延迟、吞吐量(RPS、TPS)、成功率等核心性能指标
+- **AI智能报告** - AI智能分析报告<sup>![NEW](https://img.shields.io/badge/NEW-brightgreen?style=flat-square)</sup>，多维度多模型可视化结果对比
+- **Web控制台** - 提供任务创建、停止、状态跟踪、全链路日志监控等一站式管理
+- **企业级部署** - Docker容器化，支持弹性扩展与分布式部署
 
 ## 🏗️ 系统架构
 
-LMeterX 采用微服务架构设计，由以下四个核心组件构成：
+LMeterX 采用微服务架构，由四个核心组件构成：
 
-1. **后端 API 服务**：基于 FastAPI 的 REST API 服务，负责任务管理和结果存储
-2. **压测引擎**：基于 Locust 的负载测试引擎，执行实际的性能测试任务
-3. **前端界面**：基于 React + TypeScript + Ant Design 的现代化 Web 界面
-4. **MySQL 数据库**：存储测试任务、结果数据和配置信息
+- **后端API服务** - FastAPI REST API，负责任务管理和数据存储
+- **压测引擎** - Locust负载测试引擎，执行性能测试任务
+- **前端界面** - React + TypeScript + Ant Design 现代化Web界面
+- **MySQL数据库** - 存储测试任务、结果数据和配置信息
 
 <div align="center">
   <img src="docs/images/tech-arch.png" alt="LMeterX tech arch" width="800"/>
@@ -54,80 +54,67 @@ LMeterX 采用微服务架构设计，由以下四个核心组件构成：
 使用预构建的 Docker 镜像，一键启动所有服务：
 
 ```bash
-# 下载并运行一键部署脚本
+# 一键启动所有服务
 curl -fsSL https://raw.githubusercontent.com/MigoXLab/LMeterX/main/quick-start.sh | bash
 ```
 
 ### 使用指南
 
-1. **访问 Web 界面**：http://localhost:8080
-2. **创建测试任务**：
-   - 配置目标 API 地址和模型参数
-   - 选择测试类型（文本对话/图文对话）
-   - 设置并发用户数和测试持续时间
-   - 配置其他高级参数（可选）
-3. **监控测试过程**：实时查看测试日志和性能指标
-4. **测试结果查看和导出**：查看详细的性能结果并导出报告
-5. **AI分析服务**：在系统配置页面配置AI服务后，可在任务结果页面对性能结果进行AI评估和总结
+1. **访问界面** - 打开 http://localhost:8080
+2. **创建任务** - 配置API地址、模型参数、测试类型
+3. **实时监控** - 查看测试日志和性能指标
+4. **结果分析** - 查看详细性能结果，导出报告
+5. **AI分析** - 配置AI服务后，获得智能性能评估
 
 ## 🔧 配置说明
 
-### 环境变量配置
+### 环境变量
 
-#### 通用配置
 ```bash
-SECRET_KEY=your_secret_key_here        # 应用安全密钥
-FLASK_DEBUG=false                      # 调试模式开关
-```
-
-#### 数据库配置
-```bash
-DB_HOST=mysql                         # 数据库主机地址
-DB_PORT=3306                          # 数据库端口
-DB_USER=lmeterx                       # 数据库用户名
-DB_PASSWORD=lmeterx_password          # 数据库密码
-DB_NAME=lmeterx                       # 数据库名称
-```
-
-#### 前端配置
-```bash
-VITE_API_BASE_URL=/api                # API 基础路径
+# 通用配置
+SECRET_KEY=your_secret_key_here
+FLASK_DEBUG=false
+
+# 数据库配置
+DB_HOST=mysql
+DB_PORT=3306
+DB_USER=lmeterx
+DB_PASSWORD=lmeterx_password
+DB_NAME=lmeterx
+
+# 前端配置
+VITE_API_BASE_URL=/api
 ```
 
 ## 🤝 开发指南
 
-> 我们欢迎所有形式的贡献！请阅读我们的 [贡献指南](docs/CONTRIBUTING.md) 了解详情。
+> 💡 **欢迎贡献**！查看 [贡献指南](docs/CONTRIBUTING.md) 了解详情
 
 ### 技术栈
 
-LMeterX 采用现代化的技术栈，确保系统的可靠性和可维护性：
+- **后端** - Python + FastAPI + SQLAlchemy + MySQL
+- **压测引擎** - Python + Locust + 自定义扩展
+- **前端** - React + TypeScript + Ant Design + Vite
+- **部署** - Docker + Docker Compose + Nginx
 
-- **后端服务**：Python + FastAPI + SQLAlchemy + MySQL
-- **压测引擎**：Python + Locust + 自定义扩展
-- **前端界面**：React + TypeScript + Ant Design + Vite
-- **部署运维**：Docker + Docker Compose + Nginx
 
-### 项目结构
-
-```LMeterX/
-├── backend/                  # 后端服务
-├── st_engine/                # 压测引擎服务
-├── frontend/                 # 前端服务
-├── docs/                     # 文档目录
-├── docker-compose.yml        # Docker Compose配置
-├── Makefile                  # 运行完整代码检查
-├── README.md                 # 英文README
-└── README_CN.md              # 中文README
+```
+LMeterX/
+├── backend/          # 后端服务
+├── st_engine/        # 压测引擎
+├── frontend/         # 前端服务
+├── docs/             # 文档
+├── docker-compose.yml
+└── README_CN.md      # 中文说明
 ```
 
 ### 开发环境搭建
 
-1. **Fork 项目**到您的 GitHub 账户
-2. **克隆您的 Fork**，创建开发分支进行开发
-3. **遵循代码规范**，使用清晰的提交信息（遵循约定式提交规范）
-4. **运行代码检查**：在提交 PR 之前，确保代码检查、格式化和测试均已通过，可执行 `make all`
-5. **编写清晰文档**：为新功能或变更撰写相应的文档
-6. **积极参与 Review**：在审核过程中积极响应反馈
+1. **Fork项目** → 克隆到本地
+2. **创建分支** → 进行功能开发
+3. **代码检查** → 运行 `make all` 确保质量
+4. **提交PR** → 遵循约定式提交规范
+5. **文档更新** → 为新功能撰写文档
 
 ## 🗺️ 发展路线图
 
@@ -139,12 +126,12 @@ LMeterX 采用现代化的技术栈，确保系统的可靠性和可维护性：
 
 ## 📚 相关文档
 
-- [部署指南](docs/DEPLOYMENT_GUIDE_CN.md) - 详细的部署说明和配置指南
-- [贡献指南](docs/CONTRIBUTING.md) - 如何参与项目开发和贡献代码
+- [部署指南](docs/DEPLOYMENT_GUIDE_CN.md) - 详细部署说明
+- [贡献指南](docs/CONTRIBUTING.md) - 参与开发指南
 
-## 🤝 贡献者
+## 👥 贡献者
 
-感谢所有为 LMeterX 项目做出贡献的开发者：
+感谢所有为 LMeterX 做出贡献的开发者：
 
 - [@LuckyYC](https://github.com/LuckyYC) - 项目维护者 & 核心开发者
 - [@del-zhenwu](https://github.com/del-zhenwu) - 核心开发者
@@ -155,4 +142,8 @@ LMeterX 采用现代化的技术栈，确保系统的可靠性和可维护性：
 
 ---
 
+<div align="center">
+
 **⭐ 如果这个项目对您有帮助，请给我们一个 Star！您的支持是我们持续改进的动力。**
+
+</div>

backend/service/analysis_service.py

@@ -78,9 +78,7 @@ async def analyze_task_svc(
         try:
             ai_config = await get_ai_service_config_internal_svc(request)
         except HTTPException as e:
-            error_msg = (
-                f"Failed to get AI service configuration for task {task_id}: {str(e)}"
-            )
+            error_msg = f"Failed to get AI service configuration. {str(e)}"
             logger.error(error_msg, exc_info=True)
             return AnalysisResponse(
                 task_id=task_id,

backend/service/task_service.py

@@ -5,6 +5,7 @@
 
 import json
 import os
+import ssl
 import time
 import uuid
 from typing import Dict, List, Optional, Tuple, Union
@@ -831,6 +832,77 @@ def _prepare_request_payload(body: TaskCreateReq) -> Dict:
             raise ValueError("Request payload is required for custom API endpoints")
 
 
+def _validate_certificate_files(
+    cert_file: str, key_file: Optional[str]
+) -> Tuple[bool, str]:
+    """
+    Validate certificate files.
+    """
+    try:
+        if not cert_file:
+            return False, "No certificate file provided"
+
+        def _exists(path: str) -> bool:
+            return isinstance(path, str) and len(path) > 0 and os.path.exists(path)
+
+        def _is_pkcs12(path: str) -> bool:
+            lower = path.lower()
+            return lower.endswith(".p12") or lower.endswith(".pfx")
+
+        def _read(path: str) -> str:
+            try:
+                with open(path, "r", encoding="utf-8", errors="ignore") as f:
+                    return f.read()
+            except Exception:
+                return ""
+
+        if not _exists(cert_file):
+            return False, f"Certificate file does not exist: {cert_file}"
+        if _is_pkcs12(cert_file):
+            return (
+                False,
+                "P12/PFX certificates are not supported, please convert to a PEM file containing the private key",
+            )
+
+        cert_text = _read(cert_file)
+        if "BEGIN CERTIFICATE" not in cert_text:
+            return (
+                False,
+                "Certificate file is not a valid PEM format (missing BEGIN CERTIFICATE)",
+            )
+
+        if key_file:
+            if not _exists(key_file):
+                return False, f"Private key file does not exist: {key_file}"
+            if _is_pkcs12(key_file):
+                return (
+                    False,
+                    "Private key file cannot be P12/PFX, please provide a PEM private key",
+                )
+            key_text = _read(key_file)
+            if (
+                "BEGIN PRIVATE KEY" not in key_text
+                and "BEGIN RSA PRIVATE KEY" not in key_text
+                and "BEGIN EC PRIVATE KEY" not in key_text
+            ):
+                return False, "Private key file is not a valid PEM private key"
+        else:
+            # 仅提供了一个 cert_file，需同时包含证书+私钥
+            if (
+                "BEGIN PRIVATE KEY" not in cert_text
+                and "BEGIN RSA PRIVATE KEY" not in cert_text
+                and "BEGIN EC PRIVATE KEY" not in cert_text
+            ):
+                return (
+                    False,
+                    "Only a certificate file was provided, but it does not contain a private key, please upload the private key or provide a merged PEM file",
+                )
+
+        return True, ""
+    except Exception as e:
+        return False, f"Certificate validation error: {str(e)}"
+
+
 def _prepare_client_cert(body: TaskCreateReq):
     """Prepare SSL certificate configuration for the HTTP client."""
     client_cert: Optional[Union[str, Tuple[str, str]]] = None
@@ -841,14 +913,17 @@ def _prepare_client_cert(body: TaskCreateReq):
     # Use absolute paths directly from upload service
     if cert_file or key_file:
         try:
+            is_valid, err_msg = _validate_certificate_files(cert_file, key_file or None)
+            if not is_valid:
+                logger.error(f"Invalid client certificate configuration: {err_msg}")
+                return None
+
             if cert_file and key_file:
                 # Both cert and key files provided
-                if os.path.exists(cert_file) and os.path.exists(key_file):
-                    client_cert = (cert_file, key_file)
+                client_cert = (cert_file, key_file)
             elif cert_file:
                 # Only cert file provided (combined cert+key file)
-                if os.path.exists(cert_file):
-                    client_cert = cert_file
+                client_cert = cert_file
         except Exception as e:
             logger.error(f"Error preparing certificate configuration: {e}")
             return None
@@ -950,6 +1025,21 @@ async def test_api_endpoint_svc(request: Request, body: TaskCreateReq):
                 )
                 return await _handle_non_streaming_response(response)
 
+    except ssl.SSLError as e:
+        msg = str(e)
+        hint = ""
+        if "PEM lib" in msg or "PEM routines" in msg:
+            hint = "Client certificate/private key format error: only PEM is supported. Please upload a PEM file containing the private key, or provide both PEM certificate and PEM private key; P12/PFX is not supported."
+        elif "no certificate or crl found" in msg:
+            hint = (
+                "No valid certificate content found, please confirm the file is correct"
+            )
+        logger.error(f"SSL error when testing API endpoint: {e}")
+        return {
+            "status": "error",
+            "error": f"SSL error: {msg}. {hint}",
+            "response": None,
+        }
     except httpx.TimeoutException as e:
         logger.error(f"Request timeout when testing API endpoint: {e}")
         return {