web/pages/model-compare-result.html

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    <title>模型对比结果 - 远光软件微调平台</title>
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                <span class="text-green-800 text-base leading-relaxed" id="questionText"></span>
            </div>
        </div>
    </div>

    <!-- 模型输出网格（2x2） -->
    <div id="outputGrid" class="grid grid-cols-2 gap-4 flex-1 min-h-0">
        <!-- 输出卡片将通过JS动态生成 -->
    </div>

    <script>
        // 使用 IIFE 避免全局变量污染
        (function() {
            console.log('>>> model-compare-result.html 脚本开始加载');
            // API 基础地址 - 优先使用 main.html 中定义的全局变量
            const getApiBase = () => {
                const protocol = window.location.protocol;
                const hostname = window.location.hostname;
                return `${protocol}//${hostname}:7861/api`;
            };
            const API_BASE = typeof window.API_BASE !== 'undefined' ? window.API_BASE : getApiBase();

        // 模拟模型数据（用于演示）
        const mockModels = [
            { id: 1, name: 'GPT-4', type: 'LLM', description: 'OpenAI GPT-4 大语言模型' },
            { id: 2, name: 'Claude-3', type: 'LLM', description: 'Anthropic Claude-3 模型' },
            { id: 3, name: '文心一言', type: 'LLM', description: '百度文心一言大模型' },
            { id: 4, name: '通义千问', type: 'LLM', description: '阿里通义千问大模型' },
            { id: 5, name: 'ChatGLM', type: 'LLM', description: '智谱ChatGLM对话模型' },
            { id: 6, name: '星火认知', type: 'LLM', description: '讯飞星火认知大模型' }
        ];

        let allModels = [];
        let selectedModelIds = [];
        let currentStreamingIntervals = [];
        let compareTaskId = null;
        let taskName = '';
        let userQuestion = '';
        let systemPrompt = '';
        let temperature = 0.7;
        let topP = 0.9;
        let topK = 50;
        let maxTokens = 2048;
        let chatResults = [];
        let useLocalStorageResults = false; // 是否使用localStorage中的结果

        // 模拟回复内容
        const mockResponses = [
            "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。",
            "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。",
            "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。",
            "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。"
        ];

        // 模型类型常量
        const MODEL_TYPE = {
            API: 'api',           // API调用模型（如OpenAI、百度等）
            LOCAL: 'local',       // 本地模型（vLLM）
            TRAINED: 'trained'    // 训练后的模型（llamafactory）
        };

        // 页面初始化
        async function initPage() {
            try {
                const urlParams = new URLSearchParams(window.location.search);
                compareTaskId = urlParams.get('taskId');
                taskName = urlParams.get('taskName') || '对比任务';
                userQuestion = decodeURIComponent(urlParams.get('question') || '');
                systemPrompt = decodeURIComponent(urlParams.get('systemPrompt') || '');
                temperature = parseFloat(urlParams.get('temperature') || 0.7);
                topP = parseFloat(urlParams.get('topP') || 0.9);
                topK = parseInt(urlParams.get('topK') || 50);
                maxTokens = parseInt(urlParams.get('maxTokens') || 2048);
                const needRealData = urlParams.get('real') === '1';

                console.log('[INIT] 推理参数:', { temperature, topP, topK, maxTokens, systemPrompt });

                // 设置用户提问
                const questionTextEl = document.getElementById('questionText');
                if (questionTextEl) {
                    questionTextEl.textContent = userQuestion;
                }

                // 加载模型列表和任务数据
                await Promise.all([loadModels(), loadCompareTask()]);

                // 初始化输出卡片（显示加载中状态）
                initializeOutputCards(true);

                // 读取 localStorage 结果
                let shouldUseStoredResults = false;
                const storedResults = localStorage.getItem('chatResults');

                if (storedResults && !needRealData) {
                    try {
                        const parsed = JSON.parse(storedResults);
                        const isRecent = (Date.now() - parsed.timestamp) < 5 * 60 * 1000;
                        if (parsed.results && isRecent) {
                            chatResults = parsed.results;
                            shouldUseStoredResults = true;
                        }
                    } catch (e) {
                        console.error('解析localStorage失败:', e);
                    }
                }

                // 如果需要真实数据或没有缓存结果，则调用API
                if (needRealData || !shouldUseStoredResults) {
                    if (selectedModelIds.length > 0 && userQuestion) {
                        await fetchChatResults();
                        // 存储到 localStorage
                        localStorage.setItem('chatResults', JSON.stringify({
                            results: chatResults,
                            timestamp: Date.now()
                        }));
                    }
                }

                // 重新初始化卡片（移除加载状态）
                initializeOutputCards(false);

                // 开始流式输出
                simulateStreaming();
            } catch (err) {
                console.error('初始化失败:', err);
                const contentGrid = document.getElementById('outputGrid');
                if (contentGrid) {
                    contentGrid.innerHTML = `<div class="text-red-500 p-8">初始化失败: ${err.message}</div>`;
                }
            }
        }

        // 使用 transformers 本地模型进行对话
        async function chatWithLocalModel(modelPath, modelName) {
            try {
                const response = await fetch(`${API_BASE}/model-chat/local/chat`, {
                    method: 'POST',
                    headers: { 'Content-Type': 'application/json' },
                    body: JSON.stringify({
                        model_path: modelPath,
                        system_prompt: systemPrompt,
                        user_question: userQuestion,
                        temperature: temperature,
                        max_tokens: maxTokens
                    })
                });
                const result = await response.json();
                if (result.code === 0 && result.data) {
                    return {
                        model_id: modelName,
                        model_name: modelName,
                        success: true,
                        response: result.data.response || ''
                    };
                } else {
                    return {
                        model_id: modelName,
                        model_name: modelName,
                        success: false,
                        error: result.message || '推理失败'
                    };
                }
            } catch (error) {
                return {
                    model_id: modelName,
                    model_name: modelName,
                    success: false,
                    error: error.message
                };
            }
        }

        // 调用批量对话 API 获取结果
        async function fetchChatResults() {
            try {
                // 1. 预加载本地和训练后的模型
                console.log('[FETCH] 开始预加载本地/训练模型...');
                const { localModelsNeedingPreload, trainedModelsNeedingPreload } = await preloadAllModels(selectedModelIds);

                // 2. 获取所有模型详情，按类型分组
                const apiModels = [];  // API 调用模型 (使用 batch API)
                const localModels = []; // 本地 transformers 模型
                const trainedModels = []; // 训练后的 llamafactory 模型

                for (const modelId of selectedModelIds) {
                    const model = await getModelDetails(modelId);
                    if (model) {
                        const modelType = getModelType(model);
                        if (modelType === MODEL_TYPE.API) {
                            apiModels.push(modelId);
                        } else if (modelType === MODEL_TYPE.LOCAL) {
                            localModels.push({ id: modelId, name: model.name || modelId, path: model.path });
                        } else if (modelType === MODEL_TYPE.TRAINED) {
                            trainedModels.push({ id: modelId, name: model.name || modelId, basePath: model.path || '' });
                        }
                    }
                }

                // 3. 收集结果
                let results = [];

                // API 模型使用批量接口
                if (apiModels.length > 0) {
                    console.log(`[FETCH] 调用 API 模型批量接口: ${apiModels.length} 个模型`);
                    const response = await fetch(`${API_BASE}/model-chat/batch`, {
                        method: 'POST',
                        headers: { 'Content-Type': 'application/json' },
                        body: JSON.stringify({
                            model_ids: apiModels,
                            system_prompt: systemPrompt,
                            user_question: userQuestion,
                            temperature: temperature,
                            max_tokens: maxTokens
                        })
                    });
                    const result = await response.json();
                    if (result.code === 0 && result.data) {
                        results = results.concat(result.data);
                    }
                }

                // 本地 transformers 模型使用单独接口
                for (const lm of localModels) {
                    console.log(`[FETCH] 调用本地模型: ${lm.name}, 路径: ${lm.path}`);
                    const result = await chatWithLocalModel(lm.path, lm.name);
                    results.push(result);
                }

                // 训练后的 llamafactory 模型使用单独接口（如果有的话）
                // 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

                chatResults = results;
                console.log(`[FETCH] 共获取 ${results.length} 个模型的结果`);
            } catch (error) {
                console.error('调用API失败:', error);
            }
        }

        // 加载对比任务数据
        async function loadCompareTask() {
            if (!compareTaskId) return;

            try {
                const response = await fetch(`${API_BASE}/model-compare/${compareTaskId}`);
                const result = await response.json();

                if (result.code === 0) {
                    const taskData = Array.isArray(result.data)
                        ? result.data.find(item => item && item.id == compareTaskId)
                        : result.data;

                    if (taskData) {
                        const modelsField = taskData.models;
                        if (modelsField) {
                            try {
                                if (typeof modelsField === 'string') {
                                    selectedModelIds = JSON.parse(modelsField);
                                } else if (Array.isArray(modelsField)) {
                                    selectedModelIds = modelsField;
                                }
                            } catch (e) {
                                console.error('解析模型列表失败:', e);
                            }
                        }
                    }
                }
            } catch (error) {
                console.error('加载对比任务失败:', error);
            }
        }

        // 加载模型列表
        async function loadModels() {
            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>