更新介绍内容；主页介绍HTML增加渲染效果，构建时准备内容

README.md

@@ -1,10 +1,16 @@
 ---
-title: InfoRadar – Visualize Text Information Density
+title: InfoRadar –  Analyze & Visualize Text Information Density
 emoji: 📡
-colorFrom: blue
+colorFrom: gray
 colorTo: red
 sdk: docker
-short_description: analyzes text to visualize token-level information density
+short_description: Visualize token-level information density to help find key content.
+tags:
+  - nlp
+  - text-analysis
+  - information
+  - visualization
+  - reading-tools
 app_port: 7860
 pinned: false
 license: apache-2.0

client/src/content/home.en.html

@@ -1,15 +1,6 @@
 <!-- 简介 / Hero（始终可见） -->
-<div class="intro-brief">
-    <p>
-        Tired of low-quality articles? Struggling to find key points in long texts?
-        Want to skip redundancy and fluff at a glance?
-        Or just curious about the information-theoretic nature of language?
-    </p>
-
-    <p>Try <strong>InfoRadar</strong>. InfoRadar uses large language models to analyze text information density and
-        visualizes where the important parts
-        are.</p>
-    <p>The color intensity of each token indicates how much information it carries. Try it yourself!</p>
+<div class="intro-brief" style="--intro-rgb: 255, 71, 64">
+    <span class="intro-token" style="--a:0.38">T</span><span class="intro-token" style="--a:0.38">ired</span><span class="intro-token" style="--a:0.02"> of</span><span class="intro-token" style="--a:0.58"> redundancy</span><span class="intro-token" style="--a:0.20"> and</span><span class="intro-token" style="--a:0.69"> fl</span><span class="intro-token" style="--a:0.03">uff</span><span class="intro-token" style="--a:0.11">?</span><span class="intro-token" style="--a:0.14"> Want</span><span class="intro-token" style="--a:0.69"> key</span><span class="intro-token" style="--a:0.40"> points</span><span class="intro-token" style="--a:0.40"> at</span><span class="intro-token" style="--a:0.04"> a</span><span class="intro-token" style="--a:0.00"> glance</span><span class="intro-token" style="--a:0.05">?</span><span class="intro-token" style="--a:0.31"> Or</span><span class="intro-token" style="--a:0.18"> simply</span><span class="intro-token" style="--a:0.31"> curious</span><span class="intro-token" style="--a:0.04"> about</span><span class="intro-token" style="--a:0.08"> the</span><span class="intro-token" style="--a:0.33"> information</span><span class="intro-token" style="--a:0.57">-the</span><span class="intro-token" style="--a:0.01">oret</span><span class="intro-token" style="--a:0.00">ic</span><span class="intro-token" style="--a:0.26"> nature</span><span class="intro-token" style="--a:0.00"> of</span><span class="intro-token" style="--a:0.30"> language</span><span class="intro-token" style="--a:0.23">?</span><br><br><span class="intro-token" style="--a:0.32">Try</span><span class="intro-token" style="--a:0.47"> Info</span><span class="intro-token" style="--a:0.29">R</span><span class="intro-token" style="--a:0.21">adar</span><span class="intro-token" style="--a:0.15">.</span><span class="intro-token" style="--a:0.20"> Info</span><span class="intro-token" style="--a:0.00">R</span><span class="intro-token" style="--a:0.00">adar</span><span class="intro-token" style="--a:0.26"> uses</span><span class="intro-token" style="--a:0.37"> large</span><span class="intro-token" style="--a:0.08"> language</span><span class="intro-token" style="--a:0.00"> models</span><span class="intro-token" style="--a:0.04"> to</span><span class="intro-token" style="--a:0.21"> analyze</span><span class="intro-token" style="--a:0.17"> text</span><span class="intro-token" style="--a:0.44"> information</span><span class="intro-token" style="--a:0.33"> density</span><span class="intro-token" style="--a:0.06"> and</span><span class="intro-token" style="--a:0.40"> visual</span><span class="intro-token" style="--a:0.33">izes</span><span class="intro-token" style="--a:0.43"> where</span><span class="intro-token" style="--a:0.10"> the</span><span class="intro-token" style="--a:0.29"> important</span><span class="intro-token" style="--a:0.13"> parts</span><span class="intro-token" style="--a:0.04"> are</span><span class="intro-token" style="--a:0.11">.</span><br><br><span class="intro-token" style="--a:0.18">The</span><span class="intro-token" style="--a:0.44"> color</span><span class="intro-token" style="--a:0.33"> intensity</span><span class="intro-token" style="--a:0.04"> of</span><span class="intro-token" style="--a:0.12"> each</span><span class="intro-token" style="--a:0.27"> token</span><span class="intro-token" style="--a:0.22"> indicates</span><span class="intro-token" style="--a:0.09"> how</span><span class="intro-token" style="--a:0.07"> much</span><span class="intro-token" style="--a:0.05"> information</span><span class="intro-token" style="--a:0.05"> it</span><span class="intro-token" style="--a:0.08"> carries</span><span class="intro-token" style="--a:0.05">.</span><span class="intro-token" style="--a:0.47"> Try</span><span class="intro-token" style="--a:0.08"> it</span><span class="intro-token" style="--a:0.13"> yourself</span><span class="intro-token" style="--a:0.17">!</span>
 </div>
 
 <!-- 了解更多（默认折叠） -->
@@ -29,7 +20,7 @@
 
     <!-- 技术定义 -->
     <div class="intro-block intro-technical">
-        <h4>Information Theory Perspective</h4>
+        <h4>Information-Theoretic Perspective</h4>
         <p>In our implementation, the information content of each token comes from how difficult it is for the LLM to
             predict that token from left to right.</p>
         <p>
@@ -52,9 +43,9 @@
         </p>
         <p>Therefore, the gap between current results and reader perception mainly comes from two aspects:</p>
         <ul>
-            <li><strong>Model capability vs reader:</strong> The model's understanding and knowledge may be less than,
-                or exceed, the reader's. Imagine comparing a state-of-the-art LLM with a ten-year-old reader.</li>
-            <li><strong>Model context vs reader:</strong> The model only has the text read so far as context, much less
+            <li><strong>Model capability vs human reader:</strong> The model's understanding and knowledge may be generally less than,
+                or possibly exceed, the reader's. Imagine comparing a state-of-the-art LLM with a ten-year-old reader.</li>
+            <li><strong>Model context vs human reader:</strong> The model only has the text read so far as context, much less
                 than the reader's. This project uses base models without instruction tuning or prompts (which actually
                 gives the best results).</li>
         </ul>

client/src/content/home.zh.html

@@ -1,8 +1,6 @@
 <!-- 简介 / Hero（始终可见） -->
-<div class="intro-brief">
-    <p>受够了低质量文章？文章太长找不到重点？想一眼跳过冗余、废话？或者只是好奇文字的信息论奥秘？</p>
-    <p>试试 <strong>InfoRadar（信息雷达）</strong>吧。它使用大语言模型分析文本的信息密度，可视化展示哪里更重要。</p>
-    <p>每个字的颜色深浅，表示它承载的信息量大小。自己试试吧！</p>
+<div class="intro-brief" style="--intro-rgb: 255, 71, 64">
+    <span class="intro-token" style="--a:0.70">想</span><span class="intro-token" style="--a:0.57">一眼</span><span class="intro-token" style="--a:0.53">跳</span><span class="intro-token" style="--a:0.19">过</span><span class="intro-token" style="--a:0.53">冗</span><span class="intro-token" style="--a:0.03">余</span><span class="intro-token" style="--a:0.32">、</span><span class="intro-token" style="--a:0.40">废话</span><span class="intro-token" style="--a:0.12">，</span><span class="intro-token" style="--a:0.33">找到</span><span class="intro-token" style="--a:0.30">文章</span><span class="intro-token" style="--a:0.20">的关键</span><span class="intro-token" style="--a:0.06">点</span><span class="intro-token" style="--a:0.40">？</span><span class="intro-token" style="--a:0.32">或者</span><span class="intro-token" style="--a:0.34">只是</span><span class="intro-token" style="--a:0.38">好奇</span><span class="intro-token" style="--a:0.38">文字</span><span class="intro-token" style="--a:0.44">的信息</span><span class="intro-token" style="--a:0.51">论</span><span class="intro-token" style="--a:0.49">奥</span><span class="intro-token" style="--a:0.02">秘</span><span class="intro-token" style="--a:0.16">？</span><br><br><span class="intro-token" style="--a:0.56">试试</span><span class="intro-token" style="--a:0.70"> Info</span><span class="intro-token" style="--a:0.27">R</span><span class="intro-token" style="--a:0.29">adar</span><span class="intro-token" style="--a:0.25">（</span><span class="intro-token" style="--a:0.10">信息</span><span class="intro-token" style="--a:0.00">雷达</span><span class="intro-token" style="--a:0.04">）</span><span class="intro-token" style="--a:0.16">吧</span><span class="intro-token" style="--a:0.10">。</span><span class="intro-token" style="--a:0.09">它</span><span class="intro-token" style="--a:0.26">使用</span><span class="intro-token" style="--a:0.36">大</span><span class="intro-token" style="--a:0.03">语言</span><span class="intro-token" style="--a:0.00">模型</span><span class="intro-token" style="--a:0.17">分析</span><span class="intro-token" style="--a:0.10">文本</span><span class="intro-token" style="--a:0.39">的信息</span><span class="intro-token" style="--a:0.12">密度</span><span class="intro-token" style="--a:0.04">，</span><span class="intro-token" style="--a:0.57">可视化</span><span class="intro-token" style="--a:0.30">展示</span><span class="intro-token" style="--a:0.54">哪里</span><span class="intro-token" style="--a:0.33">更重要</span><span class="intro-token" style="--a:0.17">。</span><br><br><span class="intro-token" style="--a:0.44">每个</span><span class="intro-token" style="--a:0.26">字</span><span class="intro-token" style="--a:0.37">的颜色</span><span class="intro-token" style="--a:0.16">深</span><span class="intro-token" style="--a:0.00">浅</span><span class="intro-token" style="--a:0.17">，</span><span class="intro-token" style="--a:0.14">表示</span><span class="intro-token" style="--a:0.16">它</span><span class="intro-token" style="--a:0.37">承载</span><span class="intro-token" style="--a:0.02">的信息</span><span class="intro-token" style="--a:0.01">量</span><span class="intro-token" style="--a:0.15">大小</span><span class="intro-token" style="--a:0.04">。</span><span class="intro-token" style="--a:0.52">自己</span><span class="intro-token" style="--a:0.20">试试</span><span class="intro-token" style="--a:0.11">吧</span><span class="intro-token" style="--a:0.11">！</span>
 </div>
 
 <!-- 了解更多（默认折叠） -->
@@ -36,7 +34,7 @@
         <p>对于一个想象中的理想模型（它的包含了上下文的知识量和阅读者一致），那么它评估出的结果应该和阅读者的主观感受是完全一致的。</p>
         <p>所以，目前的实际结果和阅读者主观感受之间的差距，主要来自两个方面：</p>
         <ul>
-            <li><strong>模型能力和阅读者的差异：</strong>模型的理解能力和知识量可能不如阅读者，也有可能过剩，想象一下目前的SOTA大模型和一个十岁孩子阅读者相比。</li>
+            <li><strong>模型能力和阅读者的差异：</strong>模型的理解能力和知识量很可能不如阅读者，也有小可能性过剩，想象一下目前的SOTA大模型和一个十岁孩子阅读者相比。</li>
             <li><strong>模型上下文和阅读者的差异：</strong>模型只有文章已读部分作为上下文，远小于��读者。项目使用没有instruct微调的base模型，也没有任何提示词（其实这样效果已经是最好了）。
             </li>
         </ul>

client/src/css/start.scss

@@ -380,6 +380,11 @@ select {
   }
 }
 
+.intro-brief .intro-token {
+  background-color: rgba(var(--intro-rgb), var(--a, 0));
+  border-radius: 6px;
+}
+
 // 首页“了解更多”折叠区：将 <summary> 做成明显的 CTA（避免像普通链接/原文样式）
 .intro-more {
   margin-top: 10px;

client/src/package.json

@@ -5,6 +5,8 @@
   "main": "webpack.config.js",
   "scripts": {
     "test": "echo \"Error: no test specified\" && exit 1",
+    "prebuild": "node scripts/updateIntroHTML.js",
+    "prebuild:dev": "node scripts/updateIntroHTML.js",
     "wp": "npm run build:dev",
     "ww": "npm run watch",
     "stats": "webpack --mode production --json --profile > stats.json",

client/src/scripts/updateIntroHTML.js

@@ -0,0 +1,165 @@
+/**
+ * 构建时脚本：从JSON生成带颜色的HTML
+ */
+
+const fs = require('fs');
+const path = require('path');
+
+// 文件路径配置
+const paths = {
+    en: {
+        json: path.resolve(__dirname, '../../../data/demo/public/InfoRadar-intro.json'),
+        html: path.resolve(__dirname, '../content/home.en.html')
+    },
+    zh: {
+        json: path.resolve(__dirname, '../../../data/demo/public/CN/InfoRadar-介绍.json'),
+        html: path.resolve(__dirname, '../content/home.zh.html')
+    }
+};
+
+// ==========================================
+// 颜色计算逻辑（从 SurprisalColorConfig.ts 复制）
+// ==========================================
+
+const TOKEN_SURPRISAL_MAX = 18;
+
+/**
+ * 计算 surprisal（信息量）
+ */
+function calculateSurprisal(probability) {
+    return -Math.log2(Math.max(probability, Number.EPSILON));
+}
+
+/** RGB 部分，通过 CSS 变量复用 */
+const INTRO_RGB = '255, 71, 64';
+
+/** alpha 小数位数 */
+const ALPHA_PRECISION = 2;
+
+/**
+ * 根据 surprisal 计算 alpha（0–0.7），保留指定位数
+ */
+function getTokenAlpha(surprisal) {
+    const normalizedValue = surprisal < 0 ? 0 :
+                           surprisal >= TOKEN_SURPRISAL_MAX ? 1 :
+                           surprisal / TOKEN_SURPRISAL_MAX;
+    const alpha = Math.max(0, Math.min(1, normalizedValue)) * 0.7;
+    return alpha.toFixed(ALPHA_PRECISION);
+}
+
+// ==========================================
+// HTML 生成逻辑
+// ==========================================
+
+/**
+ * 转义HTML特殊字符
+ */
+function escapeHtml(text) {
+    return text
+        .replace(/&/g, '&amp;')
+        .replace(/</g, '&lt;')
+        .replace(/>/g, '&gt;')
+        .replace(/"/g, '&quot;')
+        .replace(/'/g, '&#039;');
+}
+
+/**
+ * 从JSON生成带颜色的HTML（使用 CSS 变量 --intro-rgb，span 仅写 alpha）
+ */
+function generateColoredHTML(jsonPath) {
+    try {
+        const content = fs.readFileSync(jsonPath, 'utf-8');
+        const data = JSON.parse(content);
+
+        let html = '';
+        for (const token of data.result.bpe_strings) {
+            const text = token.raw;
+            const prob = token.real_topk[1];
+            const surprisal = calculateSurprisal(prob);
+            const alpha = getTokenAlpha(surprisal);
+
+            const escapedText = escapeHtml(text);
+
+            if (text.includes('\n')) {
+                const parts = text.split(/(\n)/);
+                for (const part of parts) {
+                    if (part === '\n') {
+                        html += '<br>';
+                    } else if (part) {
+                        html += `<span class="intro-token" style="--a:${alpha}">${escapeHtml(part)}</span>`;
+                    }
+                }
+            } else {
+                html += `<span class="intro-token" style="--a:${alpha}">${escapedText}</span>`;
+            }
+        }
+
+        return html;
+    } catch (error) {
+        console.error(`Failed to generate HTML from JSON: ${jsonPath}`, error);
+        return null;
+    }
+}
+
+/**
+ * 更新HTML文件中的intro-brief内容
+ */
+function updateHTMLIntro(htmlPath, coloredHTML) {
+    try {
+        let html = fs.readFileSync(htmlPath, 'utf-8');
+        
+        // 匹配 <div class="intro-brief" ...> 到 </div> 之间的内容
+        const regex = /(<div class="intro-brief"[^>]*>)([\s\S]*?)(<\/div>)/;
+        
+        if (!regex.test(html)) {
+            console.error(`intro-brief not found in ${htmlPath}`);
+            return false;
+        }
+        
+        // 替换为带颜色的HTML，容器上定义 CSS 变量供 span 复用
+        const replacement = `<div class="intro-brief" style="--intro-rgb: ${INTRO_RGB}">\n    ${coloredHTML}\n</div>`;
+        html = html.replace(regex, replacement);
+        
+        fs.writeFileSync(htmlPath, html, 'utf-8');
+        console.log(`✓ Updated ${path.basename(htmlPath)}`);
+        return true;
+    } catch (error) {
+        console.error(`Failed to update HTML file: ${htmlPath}`, error);
+        return false;
+    }
+}
+
+/**
+ * 主函数
+ */
+function main() {
+    console.log('Generating colored intro HTML from JSON...\n');
+    
+    let success = true;
+    
+    // 生成并更新英文
+    const enHTML = generateColoredHTML(paths.en.json);
+    if (enHTML) {
+        success = updateHTMLIntro(paths.en.html, enHTML) && success;
+    } else {
+        success = false;
+    }
+    
+    // 生成并更新中文
+    const zhHTML = generateColoredHTML(paths.zh.json);
+    if (zhHTML) {
+        success = updateHTMLIntro(paths.zh.html, zhHTML) && success;
+    } else {
+        success = false;
+    }
+    
+    if (success) {
+        console.log('\n✓ All intro HTML files generated successfully');
+    } else {
+        console.error('\n✗ Some generation failed');
+        process.exit(1);
+    }
+}
+
+// 执行
+main();

data/demo/public/CN/InfoRadar-介绍.json

@@ -0,0 +1,3455 @@
+{
+  "request": {
+    "text": "想一眼跳过冗余、废话，找到文章的关键点？或者只是好奇文字的信息论奥秘？\n\n试试 InfoRadar（信息雷达）吧。它使用大语言模型分析文本的信息密度，可视化展示哪里更重要。\n\n每个字的颜色深浅，表示它承载的信息量大小。自己试试吧！"
+  },
+  "result": {
+    "model": "qwen3.0-0.6b",
+    "bpe_strings": [
+      {
+        "offset": [
+          0,
+          1
+        ],
+        "raw": "想",
+        "real_topk": [
+          0,
+          1.3709068298339844e-06
+        ],
+        "pred_topk": [
+          [
+            "Human",
+            0.054840087890625
+          ],
+          [
+            "The",
+            0.031982421875
+          ],
+          [
+            "What",
+            0.023223876953125
+          ],
+          [
+            "#",
+            0.022857666015625
+          ],
+          [
+            "以下",
+            0.020172119140625
+          ],
+          [
+            "Given",
+            0.016204833984375
+          ],
+          [
+            "@",
+            0.01558685302734375
+          ],
+          [
+            "###",
+            0.01419830322265625
+          ],
+          [
+            "import",
+            0.00945281982421875
+          ],
+          [
+            "Find",
+            0.00894927978515625
+          ]
+        ]
+      },
+      {
+        "offset": [
+          1,
+          3
+        ],
+        "raw": "一眼",
+        "real_topk": [
+          0,
+          3.5762786865234375e-05
+        ],
+        "pred_topk": [
+          [
+            "----------------------------------------------------------------------",
+            0.020416259765625
+          ],
+          [
+            "\u0014",
+            0.0199432373046875
+          ],
+          [
+            "　",
+            0.01678466796875
+          ],
+          [
+            "''",
+            0.016143798828125
+          ],
+          [
+            ":)",
+            0.015655517578125
+          ],
+          [
+            "����",
+            0.01318359375
+          ],
+          [
+            "................................................................",
+            0.0098724365234375
+          ],
+          [
+            "||",
+            0.00878143310546875
+          ],
+          [
+            "--",
+            0.00844573974609375
+          ],
+          [
+            "\u0001",
+            0.008056640625
+          ]
+        ]
+      },
+      {
+        "offset": [
+          3,
+          4
+        ],
+        "raw": "跳",
+        "real_topk": [
+          0,
+          8.606910705566406e-05
+        ],
+        "pred_topk": [
+          [
+            "就",
+            0.25244140625
+          ],
+          [
+            "就能",
+            0.0806884765625
+          ],
+          [
+            "，",
+            0.050506591796875
+          ],
+          [
+            "就知道",
+            0.03582763671875
+          ],
+          [
+            "就觉得",
+            0.0294647216796875
+          ],
+          [
+            "就想",
+            0.0290069580078125
+          ],
+          [
+            "就会",
+            0.02789306640625
+          ],
+          [
+            "都",
+            0.0261993408203125
+          ],
+          [
+            "就是",
+            0.0261993408203125
+          ],
+          [
+            "就可以",
+            0.01084136962890625
+          ]
+        ]
+      },
+      {
+        "offset": [
+          4,
+          5
+        ],
+        "raw": "过",
+        "real_topk": [
+          0,
+          0.036773681640625
+        ],
+        "pred_topk": [
+          [
+            "起来",
+            0.11871337890625
+          ],
+          [
+            "到",
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+}

data/demo/public/InfoRadar-intro.json

@@ -0,0 +1,3773 @@
+{
+  "request": {
+    "text": "Tired of redundancy and fluff? Want key points at a glance? Or simply curious about the information-theoretic nature of language?\n\nTry InfoRadar. InfoRadar uses large language models to analyze text information density and visualizes where the important parts are.\n\nThe color intensity of each token indicates how much information it carries. Try it yourself!"
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