cleaner update

app.py

@@ -1,66 +1,66 @@
 import gradio as gr
 import pandas as pd
 
-from src.about import (
-    CITATION_BUTTON_LABEL,
-    CITATION_BUTTON_TEXT,
-    EVALUATION_QUEUE_TEXT,
-    INTRODUCTION_TEXT,
-    LLM_BENCHMARKS_TEXT,
-    TITLE,
-)
-
-# Simplified DataFrame for the leaderboard
+# Simplified leaderboard data
 data = {
     "Model": [
         "Handwritten TAG",
         "Zero-shot Text2SQL",
         "Zero-shot Text2SQL + LM Generation",
         "RAG (E5)",
-        "RAG (E5) + LM Rerank"
-    ],
-    "Code": [
-        "",  # Handwritten TAG doesn't have a code link
-        "",  # Zero-shot Text2SQL doesn't have a code link
-        "",  # Zero-shot Text2SQL + LM Generation doesn't have a code link
-        "",  # RAG (E5) doesn't have a code link
-        ""   # RAG (E5) + LM Rerank doesn't have a code link
+        "RAG (E5) + LM Rerank",
     ],
-    "Execution Accuracy": [
-        "55%",  # Handwritten TAG
-        "17%",  # Zero-shot Text2SQL
-        "13%",  # Zero-shot Text2SQL + LM Generation
-        "0%",   # RAG (E5)
-        "2%"    # RAG (E5) + LM Rerank
-    ]
+    "Execution Accuracy": ["55%", "17%", "13%", "0%", "2%"],
 }
 
+# Create a DataFrame
 leaderboard_df = pd.DataFrame(data)
 
-# Simplified Gradio app
-with gr.Blocks() as demo:
-    gr.HTML(TITLE)
-    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+# Convert Execution Accuracy to numeric for sorting
+leaderboard_df["Execution Accuracy (numeric)"] = (
+    leaderboard_df["Execution Accuracy"].str.rstrip("%").astype(float)
+)
+leaderboard_df = leaderboard_df.sort_values(
+    "Execution Accuracy (numeric)", ascending=False
+).reset_index(drop=True)
 
-    with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            # Highlight the top row in green for "Handwritten TAG"
-            with gr.Row():
-                gr.Dataframe(
-                    value=leaderboard_df,
-                    headers=["Model", "Code", "Execution Accuracy"],
-                    datatype=["str", "str", "str"],
-                    row_count=(5, "dynamic"),
-                    wrap=True,
-                    elem_id="leaderboard",
-                    type="pandas"
-                )
+# Add the Rank column
+leaderboard_df.insert(0, "Rank", leaderboard_df.index + 1)
 
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
-            gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+# Drop the numeric column for display
+leaderboard_df = leaderboard_df.drop(columns=["Execution Accuracy (numeric)"])
+
+# Add hyperlinks to the Model column
+def hyperlink_model(model):
+    base_url = "https://github.com/TAG-Research/TAG-Bench/tree/main"
+    return f'<a href="{base_url}" target="_blank">{model}</a>'
+
+leaderboard_df["Model"] = leaderboard_df["Model"].apply(hyperlink_model)
+
+# Gradio app
+with gr.Blocks() as demo:
+    # Title and subtitle
+    gr.HTML(
+        """
+        <div style="text-align: center;">
+            <h1 style="font-size: 2.5rem; margin-bottom: 0.5rem;">Execution Accuracy Leaderboard</h1>
+            <p style="font-size: 1.25rem; color: gray;">Comparing baseline approaches for structured data queries</p>
+        </div>
+        """
+    )
 
-        with gr.TabItem("🚀 Submission Instructions ", elem_id="llm-benchmark-tab-table", id=3):
-            gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+    # Leaderboard table
+    gr.HTML(
+        leaderboard_df.to_html(
+            index=False,
+            escape=False,
+            classes="leaderboard-table",
+        )
+    )
 
+    # Footer or additional info (optional)
+    gr.Markdown(
+        "Note: Execution accuracy is based on the percentage of correctly answered queries."
+    )
 
 demo.launch()