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zsss0316 commited on 9 days ago

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Update index.html

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index.html +41 -35

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@@ -2,66 +2,72 @@
 <head>
     <meta charset="UTF-8">
     <title>Infernet Leaderboard</title>
-    <!-- 加载 Gradio-lite 引擎 -->
     <script type="module" 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 { background-color: #f9fafb; }
     </style>
 </head>
 <body>
     <gradio-lite>
-        <!-- 这里编写您的 Python 代码 -->
         <gradio-file name="app.py" entrypoint>
 import gradio as gr
-import pandas as pd
-import io
-# 1. 模拟数据（您也可以在此处直接修改数据 ）
-data_csv = """Model,Compilation %,Replication %,Direction %
-Econometrics-Agent (Ours),92.5,88.0,85.0
-GPT-4o,98.0,82.5,75.1
-Claude 3.5 Sonnet,96.2,80.4,72.8
-Llama-3-70B,85.0,65.0,60.5
-"""
-def load_data():
-    df = pd.read_csv(io.StringIO(data_csv))
-    df["Overall Score"] = df[["Compilation %", "Replication %", "Direction %"]].mean(axis=1).round(2)
-    return df.sort_values("Overall Score", ascending=False)
 # 2. 界面构建
 with gr.Blocks(title="Infernet Leaderboard") as demo:
-    gr.HTML("<h1 style='text-align: center; color: #1f2937;'>🏆 Infernet Econometrics Leaderboard</h1>")
-    gr.HTML("<p style='text-align: center; color: #6b7280;'>CamoAiLab | Free Browser-based Edition</p>")
     with gr.Row():
-        df_init = load_data()
-        gr.Number(label="Total Models", value=len(df_init), interactive=False)
-        gr.Number(label="Best Overall (%)", value=df_init["Overall Score"].max(), interactive=False)
     gr.Markdown("---")
     with gr.Tabs():
-        with gr.TabItem("📊 Leaderboard"):
-            search = gr.Textbox(placeholder="🔍 Search model...", label=None, show_label=False)
-            table = gr.Dataframe(value=df_init, interactive=False)
-            def filter_table(query):
-                full_df = load_data()
-                if not query: return full_df
-                return full_df[full_df["Model"].str.contains(query, case=False)]
-            search.change(fn=filter_table, inputs=search, outputs=table)
-        with gr.TabItem("📖 Definitions"):
             gr.Markdown("""
-            ### 计量经济学评测指标
-            - **Compilation %**: 代码运行成功率。
-            - **Replication %**: 统计结果复现率。
-            - **Direction %**: **核心指标**。系数正负号正确率。
             """)
 demo.launch()
         </gradio-file>
     </gradio-lite>

 <head>
     <meta charset="UTF-8">
     <title>Infernet Leaderboard</title>
     <script type="module" 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 { background-color: #f3f4f6; padding-top: 20px; }
+        .gradio-container { border-radius: 15px !important; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1 ) !important; }
     </style>
 </head>
 <body>
     <gradio-lite>
         <gradio-file name="app.py" entrypoint>
 import gradio as gr
+# 1. 核心数据（原生列表格式，无需 pandas）
+# 格式：[模型名, 编译%, 复现%, 方向%, 综合分]
+RAW_DATA = [
+    ["Econometrics-Agent (Ours)", 92.5, 88.0, 85.0, 88.5],
+    ["GPT-4o", 98.0, 82.5, 75.1, 85.2],
+    ["Claude 3.5 Sonnet", 96.2, 80.4, 72.8, 83.1],
+    ["Llama-3-70B", 85.0, 65.0, 60.5, 70.2]
+]
+HEADERS = ["Model", "Compilation %", "Replication %", "Direction %", "Overall Score"]
+def get_leaderboard(query=""):
+    # 过滤与排序逻辑
+    filtered = [row for row in RAW_DATA if query.lower() in row[0].lower()]
+    # 按最后一列（综合分）降序排列
+    sorted_data = sorted(filtered, key=lambda x: x[4], reverse=True)
+    return sorted_data
 # 2. 界面构建
 with gr.Blocks(title="Infernet Leaderboard") as demo:
+    gr.HTML("<h1 style='text-align: center; color: #111827;'>🏆 Infernet Econometrics Leaderboard</h1>")
+    gr.HTML("<p style='text-align: center; color: #4b5563; margin-bottom: 20px;'>CamoAiLab | Browser-based Empirical AI Benchmark</p>")
     with gr.Row():
+        gr.Number(label="Total Models", value=len(RAW_DATA), interactive=False)
+        gr.Number(label="Best Score (%)", value=max(row[4] for row in RAW_DATA), interactive=False)
     gr.Markdown("---")
     with gr.Tabs():
+        with gr.TabItem("📊 Main Leaderboard"):
+            search = gr.Textbox(placeholder="🔍 Search for a model...", label=None, show_label=False)
+            # 使用原生列表渲染表格
+            table = gr.Dataframe(
+                headers=HEADERS,
+                value=get_leaderboard(),
+                datatype=["str", "number", "number", "number", "number"],
+                interactive=False
+            )
+            search.change(fn=get_leaderboard, inputs=search, outputs=table)
+        with gr.TabItem("📖 Metric Definitions"):
             gr.Markdown("""
+            ### 🔍 计量经济学评测指标说明
+            1. **Compilation %**: Agent 生成的代码是否能成功运行通过。
+            2. **Replication %**: 回归系数等结果与原始论文的重合度。
+            3. **Direction %**: **核心指标**。回归系数的正负号预测是否正确。
             """)
+        with gr.TabItem("✉️ Submission"):
+            gr.Markdown("请将您的预测文件上传至 [CamoAiLab/Infernet](https://huggingface.co/datasets/CamoAiLab/Infernet ) 社区。")
 demo.launch()
         </gradio-file>
     </gradio-lite>