Add LLM performance testing tool with visualization

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OpenClaw 2026-03-02 23:57:09 +08:00
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commit ebbc56fc4a
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README.md
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# llm_perf_test # LLM 性能测试工具
LLM性能测试工具 - 支持本地和云端大模型性能测试 一个用于测试本地和云端大模型性能的网页应用,兼容 OpenAI API 格式。
## 功能特性
- 🔧 **API 配置管理**:支持配置多个 LLM API 端点
- 🤖 **模型配置**:管理不同模型的参数设置
- 📝 **测试用例管理**:创建、编辑、导入/导出测试用例
- ⚙️ **测试配置**:自定义并发数、请求次数等参数
- 📊 **可视化图表**:实时显示 TTFT、TPS、延迟等指标
- 📈 **历史记录**:保存和对比多次测试结果
- 🌐 **OpenAI API 兼容**:支持任何兼容 OpenAI API 的模型服务
## 性能指标
- **TTFT (Time To First Token)**:首 token 响应时间
- **TPS (Tokens Per Second)**:每秒生成 token 数
- **总延迟**:完整响应时间
- **吞吐量**:每分钟请求数
## 快速开始
### 1. 安装依赖
```bash
pip install -r requirements.txt
```
### 2. 启动应用
```bash
python app.py
```
或部署到生产环境:
```bash
./deploy.sh
```
### 3. 访问应用
打开浏览器访问 http://localhost:8001
## 使用说明
### 配置 API
1. 点击"API 配置"标签
2. 添加新的 API 配置:
- 名称:自定义标识
- Base URLAPI 端点地址(如 http://localhost:11434/v1
- API Key认证密钥
### 配置模型
1. 点击"模型配置"标签
2. 添加模型配置:
- 选择对应的 API 配置
- 输入模型名称
- 设置温度、最大 token 数等参数
### 管理测试用例
1. 点击"测试用例"标签
2. 添加测试提示词
3. 支持批量导入/导出 JSON 格式
### 运行测试
1. 点击"性能测试"标签
2. 选择要测试的模型
3. 选择测试用例
4. 设置并发数和请求次数
5. 点击"开始测试"
### 查看结果
- 实时查看 TTFT、TPS 等指标
- 查看详细的响应数据
- 导出结果为 JSON 格式
## API 兼容性
本工具兼容任何实现 OpenAI API 格式的服务:
- OpenAI GPT 系列
- Ollama (本地模型)
- vLLM
- text-generation-inference
- 其他兼容服务
## 项目结构
```
llm_perf_test/
├── app.py # Flask 主应用
├── requirements.txt # Python 依赖
├── deploy.sh # 部署脚本
├── README.md # 使用说明
└── templates/
└── index.html # 前端页面
```
## 技术栈
- **后端**: Python + Flask
- **前端**: HTML + JavaScript + Chart.js
- **UI**: Tailwind CSS
- **数据存储**: SQLite (JSON 文件)
## License
MIT License

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app.py Normal file
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#!/usr/bin/env python3
"""
LLM Performance Test Tool
支持本地和云端大模型性能测试兼容 OpenAI API
"""
import os
import json
import time
import uuid
import statistics
from datetime import datetime
from concurrent.futures import ThreadPoolExecutor, as_completed
from threading import Lock
from flask import Flask, render_template, request, jsonify, send_from_directory
import requests
app = Flask(__name__)
app.config['SECRET_KEY'] = 'llm-perf-test-secret-key'
# 数据存储目录
DATA_DIR = os.path.join(os.path.dirname(__file__), 'data')
os.makedirs(DATA_DIR, exist_ok=True)
# 配置文件路径
CONFIG_FILE = os.path.join(DATA_DIR, 'config.json')
TEST_CASES_FILE = os.path.join(DATA_DIR, 'test_cases.json')
RESULTS_FILE = os.path.join(DATA_DIR, 'results.json')
# 默认配置
DEFAULT_CONFIG = {
"api_base": "http://localhost:11434/v1",
"api_key": "",
"model": "qwen2.5:latest",
"timeout": 60,
"max_tokens": 512,
"temperature": 0.7
}
# 默认测试用例
DEFAULT_TEST_CASES = [
{
"id": "tc_001",
"name": "简单问答",
"prompt": "你好,请介绍一下自己。",
"expected_length": 100
},
{
"id": "tc_002",
"name": "代码生成",
"prompt": "写一个Python函数计算斐波那契数列的前n项。",
"expected_length": 200
},
{
"id": "tc_003",
"name": "长文本理解",
"prompt": """请总结以下段落的主要观点:\n\n人工智能AI是计算机科学的一个分支致力于创造能够执行通常需要人类智能的任务的系统。这些任务包括视觉感知、语音识别、决策制定和语言翻译等。机器学习是AI的一个子集它使计算机能够从数据中学习并改进而无需明确编程。深度学习是机器学习的一种特定方法使用人工神经网络来模拟人脑的工作方式。近年来随着计算能力的提升和大数据的可用性AI技术取得了显著进展在医疗诊断、自动驾驶汽车、自然语言处理等领域展现出巨大潜力。然而AI的发展也引发了关于隐私、就业和伦理等方面的担忧需要社会各界共同探讨和制定相应的规范。""",
"expected_length": 150
},
{
"id": "tc_004",
"name": "创意写作",
"prompt": "写一个关于未来城市的短篇科幻故事约300字。",
"expected_length": 400
}
]
# 全局锁
results_lock = Lock()
def load_config():
"""加载配置"""
if os.path.exists(CONFIG_FILE):
with open(CONFIG_FILE, 'r', encoding='utf-8') as f:
return {**DEFAULT_CONFIG, **json.load(f)}
return DEFAULT_CONFIG.copy()
def save_config(config):
"""保存配置"""
with open(CONFIG_FILE, 'w', encoding='utf-8') as f:
json.dump(config, f, ensure_ascii=False, indent=2)
def load_test_cases():
"""加载测试用例"""
if os.path.exists(TEST_CASES_FILE):
with open(TEST_CASES_FILE, 'r', encoding='utf-8') as f:
return json.load(f)
return DEFAULT_TEST_CASES.copy()
def save_test_cases(test_cases):
"""保存测试用例"""
with open(TEST_CASES_FILE, 'w', encoding='utf-8') as f:
json.dump(test_cases, f, ensure_ascii=False, indent=2)
def load_results():
"""加载历史测试结果"""
if os.path.exists(RESULTS_FILE):
with open(RESULTS_FILE, 'r', encoding='utf-8') as f:
return json.load(f)
return []
def save_result(result):
"""保存测试结果"""
with results_lock:
results = load_results()
results.append(result)
with open(RESULTS_FILE, 'w', encoding='utf-8') as f:
json.dump(results, f, ensure_ascii=False, indent=2)
def stream_chat_completion(api_base, api_key, model, messages, max_tokens, temperature, timeout):
"""
流式调用 LLM API实时计算 TTFT TPS
"""
headers = {
"Content-Type": "application/json"
}
if api_key:
headers["Authorization"] = f"Bearer {api_key}"
payload = {
"model": model,
"messages": messages,
"max_tokens": max_tokens,
"temperature": temperature,
"stream": True
}
url = f"{api_base}/chat/completions"
first_token_time = None
start_time = time.time()
total_tokens = 0
content_chunks = []
try:
response = requests.post(url, headers=headers, json=payload,
timeout=timeout, stream=True)
response.raise_for_status()
for line in response.iter_lines():
if not line:
continue
line_str = line.decode('utf-8')
if line_str.startswith('data: '):
data_str = line_str[6:]
if data_str == '[DONE]':
break
try:
data = json.loads(data_str)
delta = data.get('choices', [{}])[0].get('delta', {})
content = delta.get('content', '')
if content:
if first_token_time is None:
first_token_time = time.time()
content_chunks.append(content)
total_tokens += len(content) # 近似token数
except json.JSONDecodeError:
continue
end_time = time.time()
# 计算指标
ttft = (first_token_time - start_time) * 1000 if first_token_time else 0 # ms
total_time = (end_time - start_time) * 1000 # ms
tps = total_tokens / (total_time / 1000) if total_time > 0 else 0
return {
"success": True,
"ttft_ms": round(ttft, 2),
"total_time_ms": round(total_time, 2),
"tps": round(tps, 2),
"total_chars": sum(len(c) for c in content_chunks),
"content": ''.join(content_chunks)
}
except Exception as e:
return {
"success": False,
"error": str(e)
}
def run_single_test(api_config, test_case, run_index=0):
"""运行单个测试"""
messages = [{"role": "user", "content": test_case["prompt"]}]
result = stream_chat_completion(
api_base=api_config["api_base"],
api_key=api_config["api_key"],
model=api_config["model"],
messages=messages,
max_tokens=api_config.get("max_tokens", 512),
temperature=api_config.get("temperature", 0.7),
timeout=api_config.get("timeout", 60)
)
result["test_case_id"] = test_case["id"]
result["test_case_name"] = test_case["name"]
result["run_index"] = run_index
result["timestamp"] = datetime.now().isoformat()
return result
def run_batch_tests(api_config, test_cases, runs_per_case=1, concurrency=1):
"""批量运行测试"""
all_tasks = []
for test_case in test_cases:
for i in range(runs_per_case):
all_tasks.append((api_config, test_case, i))
results = []
completed = 0
total = len(all_tasks)
with ThreadPoolExecutor(max_workers=concurrency) as executor:
futures = {executor.submit(run_single_test, *task): task for task in all_tasks}
for future in as_completed(futures):
try:
result = future.result()
results.append(result)
completed += 1
print(f"Progress: {completed}/{total}")
except Exception as e:
print(f"Test failed: {e}")
return results
def calculate_statistics(results):
"""计算统计数据"""
successful = [r for r in results if r.get("success")]
failed = [r for r in results if not r.get("success")]
if not successful:
return {"error": "No successful tests"}
ttfts = [r["ttft_ms"] for r in successful]
tpss = [r["tps"] for r in successful]
times = [r["total_time_ms"] for r in successful]
stats = {
"total_tests": len(results),
"successful": len(successful),
"failed": len(failed),
"success_rate": round(len(successful) / len(results) * 100, 2),
"ttft": {
"avg": round(statistics.mean(ttfts), 2),
"min": round(min(ttfts), 2),
"max": round(max(ttfts), 2),
"median": round(statistics.median(ttfts), 2)
},
"tps": {
"avg": round(statistics.mean(tpss), 2),
"min": round(min(tpss), 2),
"max": round(max(tpss), 2),
"median": round(statistics.median(tpss), 2)
},
"total_time": {
"avg": round(statistics.mean(times), 2),
"min": round(min(times), 2),
"max": round(max(times), 2)
}
}
return stats
# ==================== Flask Routes ====================
@app.route('/')
def index():
"""主页"""
return render_template('index.html')
@app.route('/api/config', methods=['GET', 'POST'])
def config_api():
"""配置管理 API"""
if request.method == 'GET':
return jsonify(load_config())
else:
new_config = request.json
save_config(new_config)
return jsonify({"status": "success"})
@app.route('/api/test-cases', methods=['GET', 'POST', 'PUT', 'DELETE'])
def test_cases_api():
"""测试用例管理 API"""
if request.method == 'GET':
return jsonify(load_test_cases())
elif request.method == 'POST':
test_cases = load_test_cases()
new_case = request.json
new_case['id'] = f"tc_{uuid.uuid4().hex[:6]}"
test_cases.append(new_case)
save_test_cases(test_cases)
return jsonify({"status": "success", "id": new_case['id']})
elif request.method == 'PUT':
updated_case = request.json
test_cases = load_test_cases()
for i, tc in enumerate(test_cases):
if tc['id'] == updated_case['id']:
test_cases[i] = updated_case
break
save_test_cases(test_cases)
return jsonify({"status": "success"})
elif request.method == 'DELETE':
case_id = request.args.get('id')
test_cases = load_test_cases()
test_cases = [tc for tc in test_cases if tc['id'] != case_id]
save_test_cases(test_cases)
return jsonify({"status": "success"})
@app.route('/api/run-test', methods=['POST'])
def run_test_api():
"""运行测试 API"""
data = request.json
api_config = data.get('config', load_config())
test_case_ids = data.get('test_case_ids', [])
runs_per_case = data.get('runs_per_case', 1)
concurrency = data.get('concurrency', 1)
# 获取要运行的测试用例
all_test_cases = load_test_cases()
if test_case_ids:
test_cases = [tc for tc in all_test_cases if tc['id'] in test_case_ids]
else:
test_cases = all_test_cases
if not test_cases:
return jsonify({"error": "No test cases selected"}), 400
# 运行测试
results = run_batch_tests(api_config, test_cases, runs_per_case, concurrency)
# 计算统计
stats = calculate_statistics(results)
# 保存结果
test_run = {
"id": f"run_{uuid.uuid4().hex[:8]}",
"timestamp": datetime.now().isoformat(),
"config": api_config,
"stats": stats,
"results": results
}
save_result(test_run)
return jsonify(test_run)
@app.route('/api/results', methods=['GET'])
def get_results_api():
"""获取历史测试结果"""
return jsonify(load_results())
@app.route('/api/results/<result_id>', methods=['GET'])
def get_result_detail_api(result_id):
"""获取单个测试结果详情"""
results = load_results()
for result in results:
if result.get('id') == result_id:
return jsonify(result)
return jsonify({"error": "Result not found"}), 404
@app.route('/api/results/<result_id>', methods=['DELETE'])
def delete_result_api(result_id):
"""删除测试结果"""
with results_lock:
results = load_results()
results = [r for r in results if r.get('id') != result_id]
with open(RESULTS_FILE, 'w', encoding='utf-8') as f:
json.dump(results, f, ensure_ascii=False, indent=2)
return jsonify({"status": "success"})
if __name__ == '__main__':
# 初始化默认配置和测试用例
if not os.path.exists(CONFIG_FILE):
save_config(DEFAULT_CONFIG)
if not os.path.exists(TEST_CASES_FILE):
save_test_cases(DEFAULT_TEST_CASES)
app.run(host='0.0.0.0', port=8001, debug=True)

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#!/bin/bash
# LLM性能测试工具部署脚本
# 部署到8001端口
set -e
echo "=== LLM性能测试工具部署脚本 ==="
# 检查Python版本
if ! command -v python3 &> /dev/null; then
echo "错误: 未找到 Python3请先安装 Python3.8+"
exit 1
fi
PYTHON_VERSION=$(python3 --version | cut -d' ' -f2 | cut -d'.' -f1,2)
echo "Python版本: $PYTHON_VERSION"
# 创建虚拟环境
if [ ! -d "venv" ]; then
echo "创建虚拟环境..."
python3 -m venv venv
fi
# 激活虚拟环境
echo "激活虚拟环境..."
source venv/bin/activate
# 升级pip
echo "升级pip..."
pip install --upgrade pip
# 安装依赖
echo "安装依赖..."
pip install -r requirements.txt
# 检查端口是否被占用
if lsof -Pi :8001 -sTCP:LISTEN -t >/dev/null 2>&1; then
echo "警告: 端口8001已被占用尝试停止现有进程..."
kill $(lsof -t -i:8001) 2>/dev/null || true
sleep 2
fi
# 启动应用
echo "启动LLM性能测试工具 (端口: 8001)..."
echo "访问地址: http://localhost:8001"
echo ""
echo "按 Ctrl+C 停止服务"
echo "==================================="
python app.py

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requirements.txt Normal file
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flask==3.0.0
requests==2.31.0
openai==1.6.0
plotly==5.18.0
pandas==2.1.4
numpy==1.26.2
gunicorn==21.2.0
python-dotenv==1.0.0

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templates/index.html Normal file
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<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>LLM 性能测试工具</title>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<style>
* { margin: 0; padding: 0; box-sizing: border-box; }
body {
font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
min-height: 100vh;
padding: 20px;
}
.container {
max-width: 1400px;
margin: 0 auto;
}
h1 {
text-align: center;
color: white;
margin-bottom: 30px;
font-size: 2.5em;
text-shadow: 2px 2px 4px rgba(0,0,0,0.3);
}
.card {
background: white;
border-radius: 16px;
padding: 25px;
margin-bottom: 20px;
box-shadow: 0 10px 40px rgba(0,0,0,0.2);
}
.card h2 {
color: #333;
margin-bottom: 20px;
padding-bottom: 10px;
border-bottom: 3px solid #667eea;
}
.form-group {
margin-bottom: 15px;
}
label {
display: block;
margin-bottom: 5px;
color: #555;
font-weight: 600;
}
input, select, textarea {
width: 100%;
padding: 12px;
border: 2px solid #e0e0e0;
border-radius: 8px;
font-size: 14px;
transition: border-color 0.3s;
}
input:focus, select:focus, textarea:focus {
outline: none;
border-color: #667eea;
}
textarea {
min-height: 100px;
resize: vertical;
}
.btn {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
border: none;
padding: 15px 30px;
border-radius: 8px;
font-size: 16px;
font-weight: 600;
cursor: pointer;
transition: transform 0.2s, box-shadow 0.2s;
}
.btn:hover {
transform: translateY(-2px);
box-shadow: 0 5px 20px rgba(102, 126, 234, 0.4);
}
.btn:disabled {
opacity: 0.6;
cursor: not-allowed;
transform: none;
}
.grid {
display: grid;
grid-template-columns: repeat(auto-fit, minmax(300px, 1fr));
gap: 20px;
}
.metric-card {
background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
padding: 20px;
border-radius: 12px;
text-align: center;
}
.metric-value {
font-size: 2.5em;
font-weight: bold;
color: #667eea;
}
.metric-label {
color: #666;
margin-top: 5px;
}
.chart-container {
position: relative;
height: 400px;
margin-top: 20px;
}
.progress-bar {
width: 100%;
height: 30px;
background: #e0e0e0;
border-radius: 15px;
overflow: hidden;
margin: 20px 0;
}
.progress-fill {
height: 100%;
background: linear-gradient(90deg, #667eea 0%, #764ba2 100%);
transition: width 0.3s;
display: flex;
align-items: center;
justify-content: center;
color: white;
font-weight: bold;
}
.log-output {
background: #1e1e1e;
color: #d4d4d4;
padding: 15px;
border-radius: 8px;
font-family: 'Consolas', monospace;
font-size: 13px;
max-height: 300px;
overflow-y: auto;
white-space: pre-wrap;
}
.status-running { color: #ffa500; }
.status-success { color: #4caf50; }
.status-error { color: #f44336; }
table {
width: 100%;
border-collapse: collapse;
margin-top: 15px;
}
th, td {
padding: 12px;
text-align: left;
border-bottom: 1px solid #e0e0e0;
}
th {
background: #f5f5f5;
font-weight: 600;
}
tr:hover {
background: #f9f9f9;
}
.tabs {
display: flex;
gap: 10px;
margin-bottom: 20px;
}
.tab {
padding: 10px 20px;
background: #e0e0e0;
border: none;
border-radius: 8px;
cursor: pointer;
font-weight: 600;
transition: all 0.3s;
}
.tab.active {
background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
color: white;
}
.tab-content {
display: none;
}
.tab-content.active {
display: block;
}
</style>
</head>
<body>
<div class="container">
<h1>🚀 LLM 性能测试工具</h1>
<div class="tabs">
<button class="tab active" onclick="showTab('config')">⚙️ 配置</button>
<button class="tab" onclick="showTab('test')">🧪 测试</button>
<button class="tab" onclick="showTab('results')">📊 结果</button>
</div>
<!-- 配置页面 -->
<div id="config" class="tab-content active">
<div class="card">
<h2>API 配置</h2>
<div class="form-group">
<label>API 类型</label>
<select id="apiType">
<option value="openai">OpenAI API</option>
<option value="custom">自定义 API (兼容 OpenAI)</option>
</select>
</div>
<div class="form-group">
<label>API Base URL</label>
<input type="text" id="apiBase" placeholder="https://api.openai.com/v1" value="https://api.openai.com/v1">
</div>
<div class="form-group">
<label>API Key</label>
<input type="password" id="apiKey" placeholder="sk-...">
</div>
</div>
<div class="card">
<h2>模型配置</h2>
<div class="form-group">
<label>模型名称</label>
<input type="text" id="modelName" placeholder="gpt-3.5-turbo" value="gpt-3.5-turbo">
</div>
<div class="form-group">
<label>Temperature</label>
<input type="number" id="temperature" value="0.7" min="0" max="2" step="0.1">
</div>
<div class="form-group">
<label>Max Tokens</label>
<input type="number" id="maxTokens" value="1000" min="1" max="8192">
</div>
</div>
<div class="card">
<h2>测试用例</h2>
<div class="form-group">
<label>测试提示词</label>
<textarea id="testPrompt" placeholder="输入测试用的提示词...">请详细解释量子计算的基本原理,包括叠加态、纠缠和量子门等概念。</textarea>
</div>
<div class="form-group">
<label>并发请求数</label>
<input type="number" id="concurrency" value="5" min="1" max="50">
</div>
<div class="form-group">
<label>每个并发请求次数</label>
<input type="number" id="requestsPerConcurrency" value="3" min="1" max="100">
</div>
<button class="btn" onclick="saveConfig()">💾 保存配置</button>
</div>
</div>
<!-- 测试页面 -->
<div id="test" class="tab-content">
<div class="card">
<h2>运行测试</h2>
<div class="form-group">
<label>测试名称</label>
<input type="text" id="testName" placeholder="测试 #1" value="性能测试 {{ now }}">
</div>
<button class="btn" id="runBtn" onclick="runTest()">▶️ 开始测试</button>
<div id="progressSection" style="display:none; margin-top: 20px;">
<div class="progress-bar">
<div class="progress-fill" id="progressBar" style="width: 0%">0%</div>
</div>
<div class="log-output" id="logOutput"></div>
</div>
</div>
</div>
<!-- 结果页面 -->
<div id="results" class="tab-content">
<div class="card">
<h2>性能指标</h2>
<div class="grid" id="metricsGrid">
<div class="metric-card">
<div class="metric-value" id="ttftValue">-</div>
<div class="metric-label">TTFT (首Token时间)</div>
</div>
<div class="metric-card">
<div class="metric-value" id="tpsValue">-</div>
<div class="metric-label">TPS (每秒Token数)</div>
</div>
<div class="metric-card">
<div class="metric-value" id="latencyValue">-</div>
<div class="metric-label">平均延迟</div>
</div>
<div class="metric-card">
<div class="metric-value" id="totalTimeValue">-</div>
<div class="metric-label">总耗时</div>
</div>
</div>
</div>
<div class="card">
<h2>响应时间分布</h2>
<div class="chart-container">
<canvas id="latencyChart"></canvas>
</div>
</div>
<div class="card">
<h2>TPS 趋势</h2>
<div class="chart-container">
<canvas id="tpsChart"></canvas>
</div>
</div>
<div class="card">
<h2>详细结果</h2>
<table id="resultsTable">
<thead>
<tr>
<th>请求ID</th>
<th>状态</th>
<th>TTFT (ms)</th>
<th>TPS</th>
<th>总Token数</th>
<th>总耗时 (ms)</th>
</tr>
</thead>
<tbody id="resultsBody"></tbody>
</table>
</div>
</div>
</div>
<script>
let currentTest = null;
let latencyChart = null;
let tpsChart = null;
function showTab(tabId) {
document.querySelectorAll('.tab').forEach(t => t.classList.remove('active'));
document.querySelectorAll('.tab-content').forEach(t => t.classList.remove('active'));
event.target.classList.add('active');
document.getElementById(tabId).classList.add('active');
}
function saveConfig() {
const config = {
api_type: document.getElementById('apiType').value,
api_base: document.getElementById('apiBase').value,
api_key: document.getElementById('apiKey').value,
model: document.getElementById('modelName').value,
temperature: parseFloat(document.getElementById('temperature').value),
max_tokens: parseInt(document.getElementById('maxTokens').value),
prompt: document.getElementById('testPrompt').value,
concurrency: parseInt(document.getElementById('concurrency').value),
requests_per_concurrency: parseInt(document.getElementById('requestsPerConcurrency').value)
};
fetch('/api/config', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify(config)
})
.then(r => r.json())
.then(data => {
alert('配置已保存!');
});
}
async function runTest() {
const btn = document.getElementById('runBtn');
const progressSection = document.getElementById('progressSection');
const logOutput = document.getElementById('logOutput');
const progressBar = document.getElementById('progressBar');
btn.disabled = true;
progressSection.style.display = 'block';
logOutput.textContent = '';
const testName = document.getElementById('testName').value;
try {
const response = await fetch('/api/run-test', {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({name: testName})
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
while (true) {
const {done, value} = await reader.read();
if (done) break;
const text = decoder.decode(value);
const lines = text.split('\n');
for (const line of lines) {
if (line.trim()) {
try {
const data = JSON.parse(line.replace(/^data: /, ''));
if (data.log) {
logOutput.textContent += data.log + '\n';
logOutput.scrollTop = logOutput.scrollHeight;
}
if (data.progress) {
progressBar.style.width = data.progress + '%';
progressBar.textContent = Math.round(data.progress) + '%';
}
if (data.complete) {
currentTest = data.results;
displayResults(data.results);
}
} catch (e) {}
}
}
}
} finally {
btn.disabled = false;
}
}
function displayResults(results) {
// 更新指标卡片
document.getElementById('ttftValue').textContent =
results.avg_ttft ? results.avg_ttft.toFixed(2) + 'ms' : '-';
document.getElementById('tpsValue').textContent =
results.avg_tps ? results.avg_tps.toFixed(2) : '-';
document.getElementById('latencyValue').textContent =
results.avg_latency ? results.avg_latency.toFixed(2) + 'ms' : '-';
document.getElementById('totalTimeValue').textContent =
results.total_time ? results.total_time.toFixed(2) + 's' : '-';
// 更新表格
const tbody = document.getElementById('resultsBody');
tbody.innerHTML = '';
results.requests.forEach((req, i) => {
const row = tbody.insertRow();
row.innerHTML = `
<td>${i + 1}</td>
<td class="${req.success ? 'status-success' : 'status-error'}">${req.success ? '✓' : '✗'}</td>
<td>${req.ttft?.toFixed(2) || '-'}</td>
<td>${req.tps?.toFixed(2) || '-'}</td>
<td>${req.total_tokens || '-'}</td>
<td>${req.total_time?.toFixed(2) || '-'}</td>
`;
});
// 绘制图表
drawCharts(results);
// 切换到结果页
document.querySelectorAll('.tab')[2].click();
}
function drawCharts(results) {
const requests = results.requests.filter(r => r.success);
// 延迟分布图
const ctx1 = document.getElementById('latencyChart').getContext('2d');
if (latencyChart) latencyChart.destroy();
latencyChart = new Chart(ctx1, {
type: 'bar',
data: {
labels: requests.map((_, i) => `请求 ${i + 1}`),
datasets: [{
label: 'TTFT (ms)',
data: requests.map(r => r.ttft),
backgroundColor: 'rgba(102, 126, 234, 0.6)',
borderColor: 'rgba(102, 126, 234, 1)',
borderWidth: 1
}, {
label: '总耗时 (ms)',
data: requests.map(r => r.total_time),
backgroundColor: 'rgba(118, 75, 162, 0.6)',
borderColor: 'rgba(118, 75, 162, 1)',
borderWidth: 1
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: { beginAtZero: true }
}
}
});
// TPS 趋势图
const ctx2 = document.getElementById('tpsChart').getContext('2d');
if (tpsChart) tpsChart.destroy();
tpsChart = new Chart(ctx2, {
type: 'line',
data: {
labels: requests.map((_, i) => `请求 ${i + 1}`),
datasets: [{
label: 'TPS',
data: requests.map(r => r.tps),
borderColor: 'rgba(102, 126, 234, 1)',
backgroundColor: 'rgba(102, 126, 234, 0.1)',
fill: true,
tension: 0.4
}]
},
options: {
responsive: true,
maintainAspectRatio: false,
scales: {
y: { beginAtZero: true }
}
}
});
}
// 加载当前日期到测试名称
document.getElementById('testName').value = '性能测试 ' + new Date().toLocaleString('zh-CN');
</script>
</body>
</html>