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zsss0316 commited on 9 days ago

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 <!DOCTYPE html>
 <html lang="en">
 <head>
-<meta charset="utf-8">
-<title>Infernet Econometrics Benchmark Leaderboard</title>
-<script type="module" crossorigin src="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js"></script>
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css"/>
-<style>
-body { margin:0; background-color:#f7f8fa; }
-</style>
 </head>
 <body>
-<gradio-lite>
-import gradio as gr
-import pandas as pd
-CSS = """
-.gradio-container {
-    max-width: 1280px !important;
-    margin: 24px auto !important;
-    padding: 0 20px !important;
-    background-color: #ffffff;
-    border-radius: 12px;
-    box-shadow: 0 2px 12px rgba(0,0,0,0.06);
-}
-.stat-card {
-    background: linear-gradient(135deg,#f0f4f9,#ffffff);
-    padding:20px 12px;
-    border-radius:12px;
-    text-align:center;
-    border:1px solid #e2e8f0;
-    box-shadow:0 1px 4px rgba(0,0,0,0.04);
-}
-.stat-val {
-    font-size:28px;
-    font-weight:700;
-    color:#2b549c;
-    margin-bottom:4px;
 }
-.stat-label {
-    font-size:14px;
-    color:#475569;
-}
-h1 {
-    color:#1e293b;
-}
-"""
-def load_data():
-    df = pd.read_csv("results.csv")
-    df["Overall Score"] = df[["Compilation %","Replication %","Direction %"]].mean(axis=1).round(2)
-    return df.sort_values("Overall Score", ascending=False).reset_index(drop=True)
-with gr.Blocks(css=CSS, title="Infernet Leaderboard") as demo:
-    gr.HTML("""
-    <div style="text-align:center;padding-top:28px;padding-bottom:12px;">
-        <h1 style="margin:0 0 8px 0;color:#0f172a;">🏆 Infernet Econometrics Benchmark Leaderboard</h1>
-        <p style="color:#475569;font-size:15px;">
-            CamoAiLab, HKU&nbsp;&nbsp;|&nbsp;&nbsp;
-            Dataset: <a target="_blank" href="https://huggingface.co/datasets/CamoAiLab/Infernet">CamoAiLab/Infernet</a>
-            &nbsp;&nbsp;Paper: <a target="_blank" href="https://arxiv.org/abs/2506.00856">arXiv:2506.00856</a>
-        </p>
-    </div>
-    """)
-    df_init = load_data()
-    with gr.Row():
-        with gr.Column():
-            gr.HTML(f'<div class="stat-card"><div class="stat-val">{len(df_init)}</div><div class="stat-label">Total Evaluated Models</div></div>')
-        with gr.Column():
-            gr.HTML(f'<div class="stat-card"><div class="stat-val">{df_init["Overall Score"].max()}%</div><div class="stat-label">Best Overall Score</div></div>')
-        with gr.Column():
-            gr.HTML(f'<div class="stat-card"><div class="stat-val">1000</div><div class="stat-label">Test Instances</div></div>')
-    gr.Markdown("<hr style='margin:24px 0'>")
-    with gr.Tabs():
-        with gr.TabItem("📊 Main Leaderboard"):
-            search_box = gr.Textbox(
-                placeholder="🔍 Search model name ...",
-                label=None,
-                show_label=False
-            )
-            table = gr.Dataframe(
-                value=df_init,
-                interactive=False,
-                wrap=True
-            )
-            def filter_data(query):
-                full = load_data()
-                if not query:
-                    return full
-                return full[full["Model"].str.contains(query, case=False)]
-            search_box.change(filter_data, inputs=search_box, outputs=table)
-        with gr.TabItem("📖 Metric Definitions"):
-            gr.Markdown("""
-### Evaluation Metrics Explanation
-1. **Compilation %**
-Code compilation success rate: proportion of generated econometric code that runs without runtime errors.
-2. **Replication % (Partial Replication)**
-Partial replication rate: proportion of tasks where model partially reproduces target estimation outputs.
-3. **Direction % (Correct Coefficient Direction)**
-Core metric: accuracy for predicting correct positive/negative sign of target regression coefficients.
-4. **Overall Score**
-Simple average of above three metrics for comprehensive comparison.
-""")
-        with gr.TabItem("✉️ Submit Results"):
-            gr.Markdown("""
-To submit your model results:
-1. Open a **Discussion** on dataset page: [CamoAiLab/Infernet](https://huggingface.co/datasets/CamoAiLab/Infernet)
-2. Provide model name, scores and reproduction evidence/logs.
-3. After verification, we will update the leaderboard.
-""")
-demo
-</gradio-lite>
-<gradio-file name="results.csv" url="./results.csv"/>
 </body>
 </html>

 <!DOCTYPE html>
 <html lang="en">
 <head>
+    <meta charset="UTF-8">
+    <title>Infernet Econometrics Benchmark Leaderboard</title>
+    <style>
+        body {font-family: Arial; max-width:1100px; margin:30px auto; padding:0 20px;}
+        table {width:100%; border-collapse: collapse; margin:15px 0 40px;}
+        th,td {border:1px solid #ccc; padding:10px; text-align:left;}
+        th {background:#1f77b4; color:white;}
+        .tab{margin:8px 4px;padding:8px 16px;border:1px solid #999;background:#eee;cursor:pointer;}
+        .tab.active{background:#1f77b4;color:white;border-color:#1f77b4;}
+        .panel{display:none;}
+        .panel.show{display:block;}
+    </style>
 </head>
 <body>
+<h1>🏆 Infernet Econometrics Benchmark Leaderboard</h1>
+<p><strong>CamoAiLab | HKU CAMO</strong><br>
+Dataset: <a href="https://huggingface.co/datasets/CamoAiLab/Infernet">CamoAiLab/Infernet</a>
+</p>
+<div>
+    <button class="tab active" onclick="switchTab(0)">1. Compilation Success Rate</button>
+    <button class="tab" onclick="switchTab(1)">2. Partial Replication Rate</button>
+    <button class="tab" onclick="switchTab(2)">3. Correct Coefficient Direction</button>
+</div>
+<div class="panel show" id="panel-compile"></div>
+<div class="panel" id="panel-rep"></div>
+<div class="panel" id="panel-dir"></div>
+<h3>📌 Metric Definition</h3>
+<ul>
+    <li><strong>Compilation Success:</strong> Generated code runs without runtime errors.</li>
+    <li><strong>Partial Replication:</strong> Partially reproduce target estimation results.</li>
+    <li><strong>Correct Coefficient Direction:</strong> Correct sign of estimated coefficients.</li>
+</ul>
+<script>
+const panels = document.querySelectorAll(".panel");
+const tabs = document.querySelectorAll(".tab");
+function switchTab(idx){
+    panels.forEach(p=>p.classList.remove("show"));
+    tabs.forEach(t=>t.classList.remove("active"));
+    panels[idx].classList.add("show");
+    tabs[idx].classList.add("active");
 }
+function parseCSV(text) {
+    text = text.replace(/\r\n/g,"\n").replace(/\r/g,"\n");
+    const lines = text.trim().split("\n").filter(line=>line.trim()!=='');
+    const headers = lines[0].split(",").map(h=>h.trim());
+    const rows = [];
+    for(let i=1;i<lines.length;i++){
+        const vals = lines[i].split(",").map(v=>v.trim());
+        const obj = {};
+        headers.forEach((h,idx)=>{
+            obj[h] = vals[idx];
+        });
+        rows.push(obj);
+    }
+    return rows;
+}
+function buildTable(title, data, sortKey){
+    const sorted = [...data].sort((a,b)=> Number(b[sortKey]) - Number(a[sortKey]));
+    let html = `<h3>${title}</h3><table><tr><th>Rank</th><th>Model</th><th>${sortKey}</th></tr>`;
+    sorted.forEach((row,i)=>{
+        html += `<tr>
+            <td>${i+1}</td>
+            <td>${row["Model ID"]}</td>
+            <td>${row[sortKey]}%</td>
+        </tr>`;
+    })
+    html += "</table>";
+    return html;
+}
+// 读取space本地csv，不再跨域访问dataset
+const csvUrl = "./results.csv";
+async function loadData(){
+    try {
+        const res = await fetch(csvUrl);
+        if(!res.ok) throw new Error(`HTTP ${res.status}`);
+        const csvText = await res.text();
+        const data = parseCSV(csvText);
+        if(data.length ===0){
+            document.body.insertAdjacentHTML("beforeend",`<p style="color:red">Warning: CSV parsed zero rows</p>`);
+            return;
+        }
+        document.getElementById("panel-compile").innerHTML = buildTable(
+            "Ranked by Code Compilation Success",
+            data,
+            "Compilation Success (%)"
+        );
+        document.getElementById("panel-rep").innerHTML = buildTable(
+            "Ranked by Partial Replication Rate",
+            data,
+            "Partial Replication (%)"
+        );
+        document.getElementById("panel-dir").innerHTML = buildTable(
+            "Ranked by Correct Coefficient Direction",
+            data,
+            "Correct Coefficient Direction (%)"
+        );
+    } catch(err){
+        console.error("load csv error",err);
+        document.body.insertAdjacentHTML("beforeend",`<p style='color:red'>Failed to load ranking data: ${err.message}</p>`);
+    }
+}
+loadData();
+</script>
 </body>
 </html>