812 lines
37 KiB
HTML
812 lines
37 KiB
HTML
<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>模型对比结果 - 远光软件微调平台</title>
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<script src="../lib/tailwindcss/tailwind.js"></script>
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<script>
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if (typeof console !== 'undefined' && console.warn) {
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const originalWarn = console.warn;
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console.warn = function(...args) {
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if (args[0] && args[0].includes && args[0].includes('cdn.tailwindcss.com')) {
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return;
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}
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originalWarn.apply(console, args);
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};
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}
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</script>
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<script>
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// 设置当前页面，供侧边栏高亮使用
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window.sidebarCurrentPage = 'model-compare';
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</script>
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<link href="../lib/font-awesome/css/font-awesome.min.css" rel="stylesheet">
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<!-- 侧边栏加载器 -->
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<script src="../js/components/sidebar-loader.js"></script>
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<style>
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.bg-primary { background-color: #1890ff; }
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.text-primary { color: #1890ff; }
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:root { --primary: #1890ff; }
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/* 卡片内容区域滚动条样式 */
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.markdown-content {
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overflow-y: auto;
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max-height: calc(100vh - 280px);
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scrollbar-width: thin;
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scrollbar-color: #d1d5db transparent;
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}
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.markdown-content::-webkit-scrollbar {
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width: 6px;
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}
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.markdown-content::-webkit-scrollbar-track {
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background: transparent;
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}
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.markdown-content::-webkit-scrollbar-thumb {
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background-color: #d1d5db;
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border-radius: 3px;
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}
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/* Markdown 基础样式 */
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.markdown-content h1 { font-size: 1.5em; font-weight: bold; margin: 0.5em 0; border-bottom: 1px solid #e5e7eb; padding-bottom: 0.3em; }
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.markdown-content h2 { font-size: 1.25em; font-weight: bold; margin: 0.5em 0; border-bottom: 1px solid #e5e7eb; padding-bottom: 0.2em; }
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.markdown-content h3 { font-size: 1.1em; font-weight: bold; margin: 0.4em 0; }
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.markdown-content p { margin: 0.5em 0; line-height: 1.6; }
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.markdown-content ul, .markdown-content ol { margin: 0.5em 0; padding-left: 2em; }
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.markdown-content li { margin: 0.25em 0; }
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.markdown-content code { background-color: #f3f4f6; padding: 0.2em 0.4em; border-radius: 4px; font-family: monospace; font-size: 0.9em; }
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.markdown-content pre { background-color: #1f2937; color: #f9fafb; padding: 1em; border-radius: 8px; overflow-x: auto; margin: 0.5em 0; }
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.markdown-content pre code { background: none; padding: 0; color: inherit; }
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.markdown-content blockquote { border-left: 4px solid #1890ff; padding-left: 1em; margin: 0.5em 0; color: #6b7280; }
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.markdown-content table { border-collapse: collapse; width: 100%; margin: 0.5em 0; }
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.markdown-content th, .markdown-content td { border: 1px solid #d1d5db; padding: 0.5em 1em; text-align: left; }
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.markdown-content th { background-color: #f9fafb; font-weight: bold; }
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.markdown-content tr:nth-child(even) { background-color: #f9fafb; }
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.markdown-content a { color: #1890ff; text-decoration: none; }
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.markdown-content a:hover { text-decoration: underline; }
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.markdown-content hr { border: none; border-top: 1px solid #e5e7eb; margin: 1em 0; }
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.markdown-content strong { font-weight: bold; }
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.markdown-content em { font-style: italic; }
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.markdown-content del { text-decoration: line-through; color: #9ca3af; }
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/* 流式输出光标 */
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.typing-cursor::after {
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content: '|';
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animation: blink 1s infinite;
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color: var(--primary);
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}
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@keyframes blink {
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0%, 50% { opacity: 1; }
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51%, 100% { opacity: 0; }
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}
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</style>
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</head>
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<body class="antialiased bg-gray-50 flex h-screen overflow-hidden">
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<!-- 侧边栏容器 -->
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<div id="sidebar-container"></div>
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<div class="flex-1 flex flex-col overflow-hidden">
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<!-- 顶部导航 -->
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<header class="bg-white border-b border-gray-200 shadow-sm">
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<div class="flex items-center justify-between px-6 h-14">
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<div class="flex items-center space-x-6">
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<button class="md:hidden text-gray-500 hover:text-gray-700">
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<i class="fa fa-bars"></i>
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</button>
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</div>
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<div class="flex items-center space-x-4">
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<!-- 系统性能监控 -->
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<a href="?page=config" class="flex items-center space-x-4 text-xs text-gray-500 hover:text-primary transition-colors">
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<div class="flex items-center" title="CPU使用率">
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<i class="fa fa-microchip mr-1 text-blue-500"></i>
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<span id="cpuUsage">--</span>%
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</div>
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<div class="flex items-center" title="内存使用率">
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<i class="fa fa-database mr-1 text-green-500"></i>
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<span id="memUsage">--</span>%
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</div>
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<div class="flex items-center" title="磁盘使用率">
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<i class="fa fa-hdd-o mr-1 text-orange-500"></i>
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<span id="diskUsage">--</span>%
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</div>
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</a>
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<div class="h-6 w-px bg-gray-200"></div>
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<div class="relative group">
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<img src="https://picsum.photos/id/1005/32/32" class="w-8 h-8 rounded-full cursor-pointer" alt="用户头像">
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<div class="absolute right-0 top-full pt-2 hidden group-hover:block z-50">
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<div class="bg-white rounded shadow-lg py-1 border border-gray-100 min-w-[140px]">
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<a href="login.html" class="block px-4 py-2 text-sm text-gray-700 hover:bg-gray-50 whitespace-nowrap">
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<i class="fa fa-sign-out mr-1"></i>退出登录
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</a>
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</div>
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</div>
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</div>
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</div>
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</div>
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</header>
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<!-- 页面内容 -->
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<main class="flex-1 overflow-hidden flex flex-col">
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<!-- 顶部操作栏 -->
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<div class="bg-white border-b border-gray-200 px-6 py-2 flex items-center justify-end flex-shrink-0">
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<button id="restartBtn" class="px-3 py-1 bg-primary text-white rounded text-sm hover:bg-primary/90 transition-colors">
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<i class="fa fa-refresh mr-1"></i>重新提问
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</button>
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</div>
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<!-- 用户提问显示区 -->
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<div class="px-6 py-4 flex-shrink-0">
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<div class="flex items-start">
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<div class="flex-shrink-0 bg-green-500 rounded-full px-3 py-1 mr-3">
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<span class="text-white text-xs font-medium">用户问题</span>
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</div>
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<div class="bg-green-100 rounded-2xl rounded-tl-sm px-5 py-3 max-w-3xl shadow-sm">
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<span class="text-green-800 text-base leading-relaxed" id="questionText"></span>
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</div>
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</div>
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</div>
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<!-- 模型输出网格（2x2） -->
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<div id="outputGrid" class="grid grid-cols-2 gap-4 flex-1 min-h-0">
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<!-- 输出卡片将通过JS动态生成 -->
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</div>
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<script>
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// 使用 IIFE 避免全局变量污染
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(function() {
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console.log('>>> model-compare-result.html 脚本开始加载');
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// API 基础地址 - 优先使用 main.html 中定义的全局变量
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const getApiBase = () => {
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const protocol = window.location.protocol;
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const hostname = window.location.hostname;
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return `${protocol}//${hostname}:7861/api`;
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};
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const API_BASE = typeof window.API_BASE !== 'undefined' ? window.API_BASE : getApiBase();
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// 模拟模型数据（用于演示）
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const mockModels = [
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{ id: 1, name: 'GPT-4', type: 'LLM', description: 'OpenAI GPT-4 大语言模型' },
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{ id: 2, name: 'Claude-3', type: 'LLM', description: 'Anthropic Claude-3 模型' },
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{ id: 3, name: '文心一言', type: 'LLM', description: '百度文心一言大模型' },
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{ id: 4, name: '通义千问', type: 'LLM', description: '阿里通义千问大模型' },
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{ id: 5, name: 'ChatGLM', type: 'LLM', description: '智谱ChatGLM对话模型' },
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{ id: 6, name: '星火认知', type: 'LLM', description: '讯飞星火认知大模型' }
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];
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let allModels = [];
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let selectedModelIds = [];
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let currentStreamingIntervals = [];
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let compareTaskId = null;
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let taskName = '';
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let userQuestion = '';
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let systemPrompt = '';
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let temperature = 0.7;
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let topP = 0.9;
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let topK = 50;
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let maxTokens = 2048;
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let chatResults = [];
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let useLocalStorageResults = false; // 是否使用localStorage中的结果
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// 模拟回复内容
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const mockResponses = [
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"这是一个基于深度学习的自然语言处理模型回答。我可以处理各种复杂的问题，包括代码编写、文本分析、知识问答等多种任务。我的训练数据涵盖了广泛的领域知识，能够提供准确和有用的信息。",
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"您好！我是一个人工智能语言模型，很高兴为您服务。我可以帮助您解答问题、提供建议、进行创意写作等。如果您有任何需要，请随时告诉我，我会尽力提供帮助。",
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"这是一个很有趣的问题！从技术角度来看，我们需要考虑多个因素：数据质量、模型架构、训练策略等。深度学习在近年来取得了巨大进展，但仍然存在一些挑战需要解决。",
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"根据我的理解，这个问题涉及到以下几个方面：首先，需要明确问题的具体背景；其次，要分析相关的技术方案；最后，需要评估实施的成本和收益。建议您先收集更多信息再做决定。"
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];
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// 模型类型常量
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const MODEL_TYPE = {
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API: 'api', // API调用模型（如OpenAI、百度等）
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LOCAL: 'local', // 本地模型（vLLM）
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TRAINED: 'trained' // 训练后的模型（llamafactory）
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};
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// 页面初始化
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async function initPage() {
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try {
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const urlParams = new URLSearchParams(window.location.search);
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compareTaskId = urlParams.get('taskId');
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taskName = urlParams.get('taskName') || '对比任务';
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userQuestion = decodeURIComponent(urlParams.get('question') || '');
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systemPrompt = decodeURIComponent(urlParams.get('systemPrompt') || '');
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temperature = parseFloat(urlParams.get('temperature') || 0.7);
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topP = parseFloat(urlParams.get('topP') || 0.9);
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topK = parseInt(urlParams.get('topK') || 50);
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maxTokens = parseInt(urlParams.get('maxTokens') || 2048);
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const needRealData = urlParams.get('real') === '1';
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console.log('[INIT] 推理参数:', { temperature, topP, topK, maxTokens, systemPrompt });
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// 设置用户提问
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const questionTextEl = document.getElementById('questionText');
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if (questionTextEl) {
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questionTextEl.textContent = userQuestion;
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}
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// 加载模型列表和任务数据
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await Promise.all([loadModels(), loadCompareTask()]);
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// 初始化输出卡片（显示加载中状态）
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initializeOutputCards(true);
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// 读取 localStorage 结果
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let shouldUseStoredResults = false;
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const storedResults = localStorage.getItem('chatResults');
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if (storedResults && !needRealData) {
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try {
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const parsed = JSON.parse(storedResults);
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const isRecent = (Date.now() - parsed.timestamp) < 5 * 60 * 1000;
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if (parsed.results && isRecent) {
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chatResults = parsed.results;
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shouldUseStoredResults = true;
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}
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} catch (e) {
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console.error('解析localStorage失败:', e);
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}
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}
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// 如果需要真实数据或没有缓存结果，则调用API
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if (needRealData || !shouldUseStoredResults) {
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if (selectedModelIds.length > 0 && userQuestion) {
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await fetchChatResults();
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// 存储到 localStorage
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localStorage.setItem('chatResults', JSON.stringify({
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results: chatResults,
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timestamp: Date.now()
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}));
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}
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}
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// 重新初始化卡片（移除加载状态）
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initializeOutputCards(false);
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// 开始流式输出
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simulateStreaming();
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} catch (err) {
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console.error('初始化失败:', err);
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const contentGrid = document.getElementById('outputGrid');
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if (contentGrid) {
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contentGrid.innerHTML = `<div class="text-red-500 p-8">初始化失败: ${err.message}</div>`;
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}
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}
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}
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// 使用 transformers 本地模型进行对话
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async function chatWithLocalModel(modelPath, modelName) {
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try {
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const response = await fetch(`${API_BASE}/model-chat/local/chat`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model_path: modelPath,
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system_prompt: systemPrompt,
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user_question: userQuestion,
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temperature: temperature,
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max_tokens: maxTokens
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})
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});
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const result = await response.json();
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if (result.code === 0 && result.data) {
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return {
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model_id: modelName,
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model_name: modelName,
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success: true,
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response: result.data.response || ''
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};
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} else {
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return {
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model_id: modelName,
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model_name: modelName,
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success: false,
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error: result.message || '推理失败'
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};
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}
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} catch (error) {
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return {
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model_id: modelName,
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model_name: modelName,
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success: false,
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error: error.message
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};
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}
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}
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// 调用批量对话 API 获取结果
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async function fetchChatResults() {
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try {
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// 1. 预加载本地和训练后的模型
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console.log('[FETCH] 开始预加载本地/训练模型...');
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const { localModelsNeedingPreload, trainedModelsNeedingPreload } = await preloadAllModels(selectedModelIds);
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// 2. 获取所有模型详情，按类型分组
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const apiModels = []; // API 调用模型 (使用 batch API)
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const localModels = []; // 本地 transformers 模型
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const trainedModels = []; // 训练后的 llamafactory 模型
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for (const modelId of selectedModelIds) {
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const model = await getModelDetails(modelId);
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if (model) {
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const modelType = getModelType(model);
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if (modelType === MODEL_TYPE.API) {
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apiModels.push(modelId);
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} else if (modelType === MODEL_TYPE.LOCAL) {
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localModels.push({ id: modelId, name: model.name || modelId, path: model.path });
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} else if (modelType === MODEL_TYPE.TRAINED) {
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trainedModels.push({ id: modelId, name: model.name || modelId, basePath: model.path || '' });
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}
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}
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}
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// 3. 收集结果
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let results = [];
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// API 模型使用批量接口
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if (apiModels.length > 0) {
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console.log(`[FETCH] 调用 API 模型批量接口: ${apiModels.length} 个模型`);
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const response = await fetch(`${API_BASE}/model-chat/batch`, {
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method: 'POST',
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headers: { 'Content-Type': 'application/json' },
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body: JSON.stringify({
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model_ids: apiModels,
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system_prompt: systemPrompt,
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user_question: userQuestion,
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temperature: temperature,
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max_tokens: maxTokens
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})
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});
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const result = await response.json();
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if (result.code === 0 && result.data) {
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results = results.concat(result.data);
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}
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}
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// 本地 transformers 模型使用单独接口
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for (const lm of localModels) {
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console.log(`[FETCH] 调用本地模型: ${lm.name}, 路径: ${lm.path}`);
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const result = await chatWithLocalModel(lm.path, lm.name);
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results.push(result);
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}
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// 训练后的 llamafactory 模型使用单独接口（如果有的话）
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// 这里假设 batch API 也能处理训练后的模型，如果没有则需要单独实现
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chatResults = results;
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console.log(`[FETCH] 共获取 ${results.length} 个模型的结果`);
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} catch (error) {
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console.error('调用API失败:', error);
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}
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}
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// 加载对比任务数据
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async function loadCompareTask() {
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if (!compareTaskId) return;
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try {
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const response = await fetch(`${API_BASE}/model-compare/${compareTaskId}`);
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const result = await response.json();
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if (result.code === 0) {
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const taskData = Array.isArray(result.data)
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? result.data.find(item => item && item.id == compareTaskId)
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: result.data;
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if (taskData) {
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const modelsField = taskData.models;
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if (modelsField) {
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try {
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if (typeof modelsField === 'string') {
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selectedModelIds = JSON.parse(modelsField);
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} else if (Array.isArray(modelsField)) {
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selectedModelIds = modelsField;
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}
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} catch (e) {
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console.error('解析模型列表失败:', e);
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}
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}
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}
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}
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} catch (error) {
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console.error('加载对比任务失败:', error);
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}
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}
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// 加载模型列表
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async function loadModels() {
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try {
|
||
const response = await fetch(`${API_BASE}/model-manage`);
|
||
const result = await response.json();
|
||
if (result.code === 0) {
|
||
allModels = result.data || [];
|
||
}
|
||
} catch (error) {
|
||
console.error('加载模型失败:', error);
|
||
}
|
||
}
|
||
|
||
// 获取模型详情
|
||
async function getModelDetails(modelId) {
|
||
// 支持 ID 数字或模型名称字符串
|
||
const model = allModels.find(m =>
|
||
m.id == modelId || m.id === modelId || m.name === modelId
|
||
);
|
||
if (model) return model;
|
||
|
||
// 如果没找到，尝试从 API 获取
|
||
try {
|
||
// 判断是数字ID还是名称
|
||
const isNumericId = /^\d+$/.test(modelId);
|
||
let apiUrl;
|
||
if (isNumericId) {
|
||
apiUrl = `${API_BASE}/model-manage/${modelId}`;
|
||
} else {
|
||
// 名称使用 name/<model_name> 端点
|
||
apiUrl = `${API_BASE}/model-manage/name/${encodeURIComponent(modelId)}`;
|
||
}
|
||
const response = await fetch(apiUrl);
|
||
const result = await response.json();
|
||
if (result.code === 0) {
|
||
return result.data;
|
||
}
|
||
} catch (e) {
|
||
console.error('获取模型详情失败:', e);
|
||
}
|
||
return null;
|
||
}
|
||
|
||
// 判断模型类型
|
||
function getModelType(model) {
|
||
if (!model) return null;
|
||
const source = model.model_source || '';
|
||
// 训练后的模型
|
||
if (source === 'trained' || model.is_trained === true) {
|
||
return MODEL_TYPE.TRAINED;
|
||
}
|
||
// 本地模型 (path 以 :// 开头表示 vLLM API 地址，或者是 transformers 本地路径)
|
||
if (source === 'local' || (model.path && model.path.includes('://'))) {
|
||
return MODEL_TYPE.LOCAL;
|
||
}
|
||
// API 调用模型
|
||
if (source === 'api') {
|
||
return MODEL_TYPE.API;
|
||
}
|
||
return null;
|
||
}
|
||
|
||
// 判断是否为本地模型或训练后的模型
|
||
function isLocalOrTrainedModel(model) {
|
||
const type = getModelType(model);
|
||
return type === MODEL_TYPE.LOCAL || type === MODEL_TYPE.TRAINED;
|
||
}
|
||
|
||
// 预加载训练后的模型 (使用 llamafactory)
|
||
async function preloadTrainedModel(modelName, baseModelPath) {
|
||
try {
|
||
const response = await fetch(`${API_BASE}/model-chat/trained/preload`, {
|
||
method: 'POST',
|
||
headers: { 'Content-Type': 'application/json' },
|
||
body: JSON.stringify({
|
||
model_name: modelName,
|
||
train_method: 'lora',
|
||
base_model_path: baseModelPath
|
||
})
|
||
});
|
||
const result = await response.json();
|
||
if (result.code === 0) {
|
||
console.log(`[PRELOAD] 训练模型 ${modelName} 预加载成功`);
|
||
return true;
|
||
} else {
|
||
console.warn(`[PRELOAD] 训练模型 ${modelName} 预加载失败: ${result.message}`);
|
||
return false;
|
||
}
|
||
} catch (error) {
|
||
console.error(`[PRELOAD] 训练模型 ${modelName} 预加载异常:`, error);
|
||
return false;
|
||
}
|
||
}
|
||
|
||
// 预加载本地模型 (使用 transformers)
|
||
async function preloadLocalModel(modelPath, modelName) {
|
||
try {
|
||
const response = await fetch(`${API_BASE}/model-chat/local/preload`, {
|
||
method: 'POST',
|
||
headers: { 'Content-Type': 'application/json' },
|
||
body: JSON.stringify({
|
||
model_path: modelPath,
|
||
model_name: modelName || '本地模型'
|
||
})
|
||
});
|
||
const result = await response.json();
|
||
if (result.code === 0) {
|
||
console.log(`[PRELOAD] 本地模型 ${modelName} 预加载成功`);
|
||
return true;
|
||
} else {
|
||
console.warn(`[PRELOAD] 本地模型 ${modelName} 预加载失败: ${result.message}`);
|
||
return false;
|
||
}
|
||
} catch (error) {
|
||
console.error(`[PRELOAD] 本地模型 ${modelName} 预加载异常:`, error);
|
||
return false;
|
||
}
|
||
}
|
||
|
||
// 更新卡片状态显示
|
||
function updateCardStatus(modelId, status, message) {
|
||
const statusEl = document.getElementById(`status-${modelId}`);
|
||
if (statusEl) {
|
||
statusEl.innerHTML = `<i class="fa ${status === 'loading' ? 'fa-spinner fa-spin' : status === 'success' ? 'fa-check-circle text-green-500' : 'fa-times-circle text-red-500'} mr-1"></i> ${message}`;
|
||
if (status === 'loading') {
|
||
statusEl.classList.remove('text-gray-400', 'text-green-500', 'text-red-500');
|
||
statusEl.classList.add('text-primary');
|
||
} else if (status === 'success') {
|
||
statusEl.classList.remove('text-primary', 'text-gray-400', 'text-red-500');
|
||
statusEl.classList.add('text-green-500');
|
||
} else if (status === 'error') {
|
||
statusEl.classList.remove('text-primary', 'text-gray-400', 'text-green-500');
|
||
statusEl.classList.add('text-red-500');
|
||
}
|
||
}
|
||
}
|
||
|
||
// 预加载所有本地和训练后的模型
|
||
async function preloadAllModels(modelIds) {
|
||
const localModelsNeedingPreload = [];
|
||
const trainedModelsNeedingPreload = [];
|
||
|
||
for (const modelId of modelIds) {
|
||
const model = await getModelDetails(modelId);
|
||
if (model) {
|
||
const modelType = getModelType(model);
|
||
|
||
// 本地模型 (transformers)
|
||
if (modelType === MODEL_TYPE.LOCAL) {
|
||
localModelsNeedingPreload.push({
|
||
id: modelId,
|
||
name: model.name || modelId,
|
||
path: model.path || ''
|
||
});
|
||
}
|
||
// 训练后的模型 (llamafactory)
|
||
if (modelType === MODEL_TYPE.TRAINED) {
|
||
trainedModelsNeedingPreload.push({
|
||
id: modelId,
|
||
name: model.name || modelId,
|
||
basePath: model.path || model.base_model_path || ''
|
||
});
|
||
}
|
||
}
|
||
}
|
||
|
||
// 预加载本地模型 (transformers)
|
||
if (localModelsNeedingPreload.length > 0) {
|
||
// 更新所有本地模型的状态为"加载中"
|
||
for (const lm of localModelsNeedingPreload) {
|
||
updateCardStatus(lm.id, 'loading', '正在加载模型...');
|
||
}
|
||
|
||
// 预加载每个本地模型
|
||
for (const lm of localModelsNeedingPreload) {
|
||
console.log(`[PRELOAD] 开始预加载本地模型: ${lm.name}, 路径: ${lm.path}`);
|
||
await preloadLocalModel(lm.path, lm.name);
|
||
}
|
||
|
||
// 更新状态为"加载完成"
|
||
for (const lm of localModelsNeedingPreload) {
|
||
updateCardStatus(lm.id, 'success', '模型已加载');
|
||
}
|
||
}
|
||
|
||
// 预加载训练后的模型 (llamafactory)
|
||
if (trainedModelsNeedingPreload.length > 0) {
|
||
// 更新所有训练模型的状态为"加载中"
|
||
for (const tm of trainedModelsNeedingPreload) {
|
||
updateCardStatus(tm.id, 'loading', '正在加载模型...');
|
||
}
|
||
|
||
// 预加载每个训练模型
|
||
for (const tm of trainedModelsNeedingPreload) {
|
||
console.log(`[PRELOAD] 开始预加载训练模型: ${tm.name}`);
|
||
await preloadTrainedModel(tm.name, tm.basePath);
|
||
}
|
||
|
||
// 更新状态为"加载完成"
|
||
for (const tm of trainedModelsNeedingPreload) {
|
||
updateCardStatus(tm.id, 'success', '模型已加载');
|
||
}
|
||
}
|
||
|
||
return { localModelsNeedingPreload, trainedModelsNeedingPreload };
|
||
}
|
||
|
||
// 初始化输出卡片
|
||
function initializeOutputCards(showLoading = false) {
|
||
const grid = document.getElementById('outputGrid');
|
||
const allAvailableModels = [...allModels, ...mockModels];
|
||
|
||
// 如果没有选择模型，使用前4个演示模型
|
||
const displayModelIds = selectedModelIds.length > 0
|
||
? selectedModelIds.slice(0, 4)
|
||
: [1, 2, 3, 4];
|
||
|
||
const statusText = showLoading ? '准备中...' : '等待中';
|
||
const statusIcon = showLoading ? 'fa-spinner fa-spin' : 'fa-clock-o';
|
||
const statusClass = showLoading ? 'text-primary' : 'text-gray-400';
|
||
const contentText = showLoading ? '<span class="text-gray-400">正在加载模型并准备推理...</span>' : '<span class="text-gray-300">模型即将开始生成回答...</span>';
|
||
|
||
grid.innerHTML = displayModelIds.map((modelId, index) => {
|
||
// 支持 ID 数字或模型名称字符串匹配
|
||
const model = allAvailableModels.find(m =>
|
||
m.id == modelId || m.id === modelId || m.name === modelId
|
||
) || { name: modelId };
|
||
const colors = ['bg-blue-100 text-blue-700', 'bg-green-100 text-green-700', 'bg-purple-100 text-purple-700', 'bg-orange-100 text-orange-700'];
|
||
const colorClass = colors[index % colors.length];
|
||
return `
|
||
<div class="bg-white rounded-lg shadow-sm flex flex-col h-full" id="output-${modelId}">
|
||
<div class="flex items-center justify-between px-5 py-3 border-b border-gray-100 flex-shrink-0">
|
||
<span class="px-3 py-1 rounded text-sm font-medium ${colorClass}">${model.name}</span>
|
||
<span id="status-${modelId}" class="text-xs ${statusClass} flex items-center">
|
||
<i class="fa ${statusIcon} mr-1"></i>
|
||
${statusText}
|
||
</span>
|
||
</div>
|
||
<div id="content-${modelId}" class="markdown-content flex-1 p-8 text-base text-gray-600 leading-relaxed">
|
||
${contentText}
|
||
</div>
|
||
</div>
|
||
`;
|
||
}).join('');
|
||
}
|
||
|
||
// 模拟流式输出
|
||
function simulateStreaming() {
|
||
currentStreamingIntervals.forEach(interval => clearInterval(interval));
|
||
currentStreamingIntervals = [];
|
||
|
||
// 禁用重新提问按钮
|
||
const btn = document.getElementById('restartBtn');
|
||
if (btn) {
|
||
btn.disabled = true;
|
||
btn.classList.add('opacity-50', 'cursor-not-allowed');
|
||
}
|
||
|
||
const displayModelIds = selectedModelIds.length > 0
|
||
? selectedModelIds.slice(0, 4)
|
||
: [1, 2, 3, 4];
|
||
|
||
displayModelIds.forEach((modelId, index) => {
|
||
setTimeout(() => {
|
||
streamModelResponse(modelId, index);
|
||
}, index * 500);
|
||
});
|
||
}
|
||
|
||
// 获取模型对应的真实响应
|
||
function getRealResponse(modelId) {
|
||
if (chatResults.length === 0) return null;
|
||
const result = chatResults.find(r => r.model_id == modelId || r.modelId == modelId);
|
||
if (result && result.response && result.response.trim() !== '') {
|
||
return result.response;
|
||
}
|
||
return null;
|
||
}
|
||
|
||
// 获取错误信息
|
||
function getErrorMessage(modelId) {
|
||
if (chatResults.length === 0) return null;
|
||
const result = chatResults.find(r => r.model_id == modelId || r.modelId == modelId);
|
||
// 如果有 error 字段且 response 为空，返回错误信息
|
||
if (result && result.error && (!result.response || result.response.trim() === '')) {
|
||
return result.error;
|
||
}
|
||
return null;
|
||
}
|
||
|
||
// 流式输出单个模型
|
||
function streamModelResponse(modelId, responseIndex) {
|
||
const contentEl = document.getElementById(`content-${modelId}`);
|
||
const statusEl = document.getElementById(`status-${modelId}`);
|
||
|
||
if (!contentEl || !statusEl) return;
|
||
|
||
statusEl.innerHTML = '<i class="fa fa-spinner fa-spin mr-1"></i> 生成中...';
|
||
statusEl.classList.remove('text-gray-400');
|
||
statusEl.classList.add('text-primary');
|
||
|
||
contentEl.innerHTML = '';
|
||
contentEl.classList.add('typing-cursor');
|
||
|
||
// 获取响应内容（真实数据或模拟数据）
|
||
const realResponse = getRealResponse(modelId);
|
||
const errorMessage = getErrorMessage(modelId);
|
||
|
||
let response;
|
||
let isRealData = false;
|
||
if (realResponse) {
|
||
response = realResponse;
|
||
isRealData = true;
|
||
} else if (errorMessage) {
|
||
response = `[错误] ${errorMessage}`;
|
||
} else {
|
||
response = mockResponses[responseIndex % mockResponses.length];
|
||
}
|
||
|
||
// 对于错误信息直接显示，不进行 Markdown 渲染
|
||
const isError = errorMessage && !realResponse;
|
||
// 标记是否需要 Markdown 渲染（真实数据需要）
|
||
const needMarkdown = isRealData && !isError;
|
||
|
||
// 统计信息
|
||
const startTime = Date.now();
|
||
let firstCharTime = null;
|
||
let charIndex = 0;
|
||
|
||
const interval = setInterval(() => {
|
||
if (charIndex < response.length) {
|
||
// 记录第一个字符输出的时间
|
||
if (charIndex === 0) {
|
||
firstCharTime = Date.now();
|
||
}
|
||
// 流式输出期间直接显示 Markdown 格式
|
||
const currentText = response.substring(0, charIndex + 1);
|
||
if (needMarkdown) {
|
||
contentEl.innerHTML = marked.parse(currentText);
|
||
} else {
|
||
contentEl.textContent = currentText;
|
||
}
|
||
charIndex++;
|
||
contentEl.scrollTop = contentEl.scrollHeight;
|
||
} else {
|
||
clearInterval(interval);
|
||
const idx = currentStreamingIntervals.indexOf(interval);
|
||
if (idx > -1) currentStreamingIntervals.splice(idx, 1);
|
||
contentEl.classList.remove('typing-cursor');
|
||
|
||
// 计算统计信息
|
||
const totalTime = (Date.now() - startTime) / 1000;
|
||
const firstCharLatency = firstCharTime ? ((firstCharTime - startTime) / 1000).toFixed(2) : 0;
|
||
const charCount = response.length;
|
||
const speed = totalTime > 0 ? (charCount / totalTime).toFixed(1) : 0;
|
||
|
||
// 根据状态更新UI
|
||
if (isError) {
|
||
statusEl.innerHTML = '<i class="fa fa-times-circle text-red-500 mr-1"></i> 失败';
|
||
statusEl.classList.remove('text-primary');
|
||
statusEl.classList.add('text-red-500');
|
||
} else {
|
||
statusEl.innerHTML = `<i class="fa fa-check-circle text-green-500 mr-1"></i> 完成 <span class="text-gray-400 ml-2">${speed} 字/秒 · ${totalTime.toFixed(1)}秒 · 首字 ${firstCharLatency}秒</span>`;
|
||
statusEl.classList.remove('text-primary');
|
||
statusEl.classList.add('text-green-500');
|
||
}
|
||
|
||
// 所有模型完成时启用按钮
|
||
if (currentStreamingIntervals.length === 0) {
|
||
const btn = document.getElementById('restartBtn');
|
||
if (btn) {
|
||
btn.disabled = false;
|
||
btn.classList.remove('opacity-50', 'cursor-not-allowed');
|
||
}
|
||
}
|
||
}
|
||
}, needMarkdown ? 20 : 30 + Math.random() * 40);
|
||
|
||
currentStreamingIntervals.push(interval);
|
||
}
|
||
|
||
// 重新提问
|
||
function restartQuestion() {
|
||
window.location.href = `main.html?page=model-compare-chat&id=${compareTaskId}`;
|
||
}
|
||
|
||
// 页面加载完成后初始化
|
||
setTimeout(() => {
|
||
initPage().catch(err => console.error('初始化失败:', err));
|
||
}, 50);
|
||
|
||
const restartBtn = document.getElementById('restartBtn');
|
||
if (restartBtn) {
|
||
restartBtn.onclick = restartQuestion;
|
||
}
|
||
})();
|
||
</script>
|
||
</body>
|
||
</html>
|