update data

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
 import pandas as pd
 from apscheduler.schedulers.background import BackgroundScheduler
+import html
+from pathlib import Path
 from huggingface_hub import snapshot_download
 
 from src.about import (
@@ -32,30 +34,41 @@ from src.submission.submit import add_new_eval
 def restart_space():
     API.restart_space(repo_id=REPO_ID)
 
-### Space initialisation
-try:
-    print(EVAL_REQUESTS_PATH)
-    snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
-try:
-    print(EVAL_RESULTS_PATH)
-    snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
-    )
-except Exception:
-    restart_space()
+ENABLE_REMOTE_DATA_SYNC = False
 
+if ENABLE_REMOTE_DATA_SYNC:
+    try:
+        print(EVAL_REQUESTS_PATH)
+        snapshot_download(
+            repo_id=QUEUE_REPO,
+            local_dir=EVAL_REQUESTS_PATH,
+            repo_type="dataset",
+            tqdm_class=None,
+            etag_timeout=30,
+            token=TOKEN,
+        )
+    except Exception:
+        restart_space()
+    try:
+        print(EVAL_RESULTS_PATH)
+        snapshot_download(
+            repo_id=RESULTS_REPO,
+            local_dir=EVAL_RESULTS_PATH,
+            repo_type="dataset",
+            tqdm_class=None,
+            etag_timeout=30,
+            token=TOKEN,
+        )
+    except Exception:
+        restart_space()
 
-LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
+    LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, BENCHMARK_COLS)
 
-(
-    finished_eval_queue_df,
-    running_eval_queue_df,
-    pending_eval_queue_df,
-) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
+    (
+        finished_eval_queue_df,
+        running_eval_queue_df,
+        pending_eval_queue_df,
+    ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)
 
 def init_leaderboard(dataframe):
     if dataframe is None or dataframe.empty:
@@ -90,115 +103,201 @@ def init_leaderboard(dataframe):
 
 
 demo = gr.Blocks(css=custom_css)
+import json
+
+def create_score_df(json_path: Path, decimals: int):
+    data = json.loads(json_path.read_text(encoding="utf-8"))
+
+    if not data:
+        return pd.DataFrame()
+
+    records = []
+    for item in data:
+        flat_record = {"ID": item.get("ID"), "Model": item.get("Model")}
+        for category, sub_items in item.items():
+            if category in ("ID", "Model"):
+                continue
+            if isinstance(sub_items, dict):
+                cleaned_category = str(category).replace("\n", " ")
+                for sub_category, value in sub_items.items():
+                    header = f"{cleaned_category}\n{sub_category}"
+                    flat_record[header] = value
+        records.append(flat_record)
+
+    df = pd.DataFrame(records)
+    score_cols = [c for c in df.columns if c not in ("ID", "Model")]
+    for col in score_cols:
+        df[col] = df[col].apply(
+            lambda v: (
+                ""
+                if pd.isna(v)
+                else (f"{float(v):.{decimals}f}" if isinstance(v, (int, float)) else v)
+            )
+        )
+    return df
+
+
+def create_raw_score_df():
+    raw_path = Path(__file__).resolve().parent / "src" / "raw_score.json"
+    return create_score_df(raw_path, decimals=2)
+
+
+def create_unweighted_z_score_df():
+    z_path = Path(__file__).resolve().parent / "src" / "unweighted_z_score.json"
+    return create_score_df(z_path, decimals=4)
+
+
+def create_weighted_z_score_df():
+    z_path = Path(__file__).resolve().parent / "src" / "weighted_z_score.json"
+    data = json.loads(z_path.read_text(encoding="utf-8"))
+
+    if not data:
+        return pd.DataFrame()
+
+    records = []
+    for item in data:
+        flat_record = {"ID": item.get("ID"), "Model": item.get("Model")}
+        for category, sub_items in item.items():
+            if category in ("ID", "Model"):
+                continue
+            if not isinstance(sub_items, dict):
+                continue
+
+            cleaned_category = str(category).replace("\n", " ")
+            for sub_category, value in sub_items.items():
+                if cleaned_category == "Overall Score":
+                    header = "Overall Score"
+                else:
+                    header = f"{cleaned_category}\n{sub_category}"
+                flat_record[header] = value
+        records.append(flat_record)
+
+    df = pd.DataFrame(records)
+
+    overall_col = "Overall Score" if "Overall Score" in df.columns else None
+    if overall_col is None:
+        for c in df.columns:
+            if isinstance(c, str) and c.endswith("\nOverall Score"):
+                overall_col = c
+                break
+
+    if overall_col is not None:
+        df[overall_col] = pd.to_numeric(df[overall_col], errors="coerce")
+        df = df.sort_values(by=overall_col, ascending=False, kind="mergesort")
+
+        cols = list(df.columns)
+        fixed = [c for c in ("ID", "Model") if c in cols]
+        rest = [c for c in cols if c not in set(fixed + [overall_col])]
+        df = df[fixed + [overall_col] + rest]
+
+    score_cols = [c for c in df.columns if c not in ("ID", "Model")]
+    for col in score_cols:
+        df[col] = pd.to_numeric(df[col], errors="coerce").apply(
+            lambda v: "" if pd.isna(v) else f"{float(v) * 100:.2f}%"
+        )
+
+    if "ID" in df.columns:
+        df["ID"] = pd.to_numeric(df["ID"], errors="coerce").astype("Int64")
+
+    return df
+
+
+def create_weights_table_html():
+    weights_path = Path(__file__).resolve().parent / "src" / "weights.json"
+    payload = json.loads(weights_path.read_text(encoding="utf-8"))
+    bounds = payload["bounds"]
+    min_row = bounds["min_row"]
+    min_col = bounds["min_col"]
+    rows = payload["rows"]
+
+    covered = set()
+    spans = {}
+    for m in payload["merges"]:
+        r1, c1, r2, c2 = m["r1"], m["c1"], m["r2"], m["c2"]
+        spans[(r1, c1)] = {"rowspan": r2 - r1 + 1, "colspan": c2 - c1 + 1}
+        for r in range(r1, r2 + 1):
+            for c in range(c1, c2 + 1):
+                if (r, c) != (r1, c1):
+                    covered.add((r, c))
+
+    parts = ["<div class=\"weights-scroll\"><table class=\"weights-table\">"
+    ]
+    for r_index, row in enumerate(rows, start=min_row):
+        parts.append("<tr>")
+        for c_index, value in enumerate(row, start=min_col):
+            if (r_index, c_index) in covered:
+                continue
+            span = spans.get((r_index, c_index))
+            attrs = ""
+            if span is not None:
+                attrs = f" rowspan=\"{span['rowspan']}\" colspan=\"{span['colspan']}\""
+            tag = "th" if r_index == min_row else "td"
+            if r_index != min_row:
+                if isinstance(value, (int, float)):
+                    value = f"{float(value):.4f}"
+                elif isinstance(value, str):
+                    stripped = value.strip()
+                    try:
+                        value = f"{float(stripped):.4f}"
+                    except Exception:
+                        pass
+            text = "" if value is None else html.escape(str(value))
+            parts.append(f"<{tag}{attrs}>{text}</{tag}>")
+        parts.append("</tr>")
+    parts.append("</table></div>")
+    return "".join(parts)
+
 with demo:
     gr.HTML(TITLE)
     gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
 
     with gr.Tabs(elem_classes="tab-buttons") as tabs:
-        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
-            leaderboard = init_leaderboard(LEADERBOARD_DF)
+        with gr.TabItem("Result", elem_id="result-tab", id=0):
+            with gr.Tabs(elem_classes="tab-buttons") as nested_tabs:
+                with gr.TabItem("Raw Score"):
+                    raw_score_df = create_raw_score_df()
+                    column_widths = [40, 220] + [180] * (len(raw_score_df.columns) - 2)
+                    gr.DataFrame(
+                        raw_score_df,
+                        wrap=True,
+                        column_widths=column_widths,
+                        row_count=(len(raw_score_df), "fixed"),
+                        height=700,
+                        elem_id="raw-score-table",
+                    )
+                with gr.TabItem("Weights"):
+                    gr.HTML(create_weights_table_html(), elem_id="weights-table")
+                with gr.TabItem("Unweighted Z-score"):
+                    unweighted_z_df = create_unweighted_z_score_df()
+                    column_widths = [40, 220] + [180] * (len(unweighted_z_df.columns) - 2)
+                    gr.DataFrame(
+                        unweighted_z_df,
+                        wrap=True,
+                        column_widths=column_widths,
+                        row_count=(len(unweighted_z_df), "fixed"),
+                        height=700,
+                        elem_id="unweighted-z-table",
+                    )
+                with gr.TabItem("Weighted Z-score"):
+                    weighted_z_df = create_weighted_z_score_df()
+                    column_widths = [40, 220] + [180] * (len(weighted_z_df.columns) - 2)
+                    gr.DataFrame(
+                        weighted_z_df,
+                        wrap=True,
+                        column_widths=column_widths,
+                        row_count=(len(weighted_z_df), "fixed"),
+                        height=700,
+                        elem_id="weighted-z-table",
+                    )
 
-        with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
+        with gr.TabItem("About", elem_id="llm-benchmark-tab-table", id=2):
             gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
 
-        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
-            with gr.Column():
-                with gr.Row():
-                    gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
-
-                with gr.Column():
-                    with gr.Accordion(
-                        f"✅ Finished Evaluations ({len(finished_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            finished_eval_table = gr.components.Dataframe(
-                                value=finished_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-                    with gr.Accordion(
-                        f"🔄 Running Evaluation Queue ({len(running_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            running_eval_table = gr.components.Dataframe(
-                                value=running_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-
-                    with gr.Accordion(
-                        f"⏳ Pending Evaluation Queue ({len(pending_eval_queue_df)})",
-                        open=False,
-                    ):
-                        with gr.Row():
-                            pending_eval_table = gr.components.Dataframe(
-                                value=pending_eval_queue_df,
-                                headers=EVAL_COLS,
-                                datatype=EVAL_TYPES,
-                                row_count=5,
-                            )
-            with gr.Row():
-                gr.Markdown("# ✉️✨ Submit your model here!", elem_classes="markdown-text")
-
-            with gr.Row():
-                with gr.Column():
-                    model_name_textbox = gr.Textbox(label="Model name")
-                    revision_name_textbox = gr.Textbox(label="Revision commit", placeholder="main")
-                    model_type = gr.Dropdown(
-                        choices=[t.to_str(" : ") for t in ModelType if t != ModelType.Unknown],
-                        label="Model type",
-                        multiselect=False,
-                        value=None,
-                        interactive=True,
-                    )
 
-                with gr.Column():
-                    precision = gr.Dropdown(
-                        choices=[i.value.name for i in Precision if i != Precision.Unknown],
-                        label="Precision",
-                        multiselect=False,
-                        value="float16",
-                        interactive=True,
-                    )
-                    weight_type = gr.Dropdown(
-                        choices=[i.value.name for i in WeightType],
-                        label="Weights type",
-                        multiselect=False,
-                        value="Original",
-                        interactive=True,
-                    )
-                    base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-
-            submit_button = gr.Button("Submit Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
 
-    with gr.Row():
-        with gr.Accordion("📙 Citation", open=False):
-            citation_button = gr.Textbox(
-                value=CITATION_BUTTON_TEXT,
-                label=CITATION_BUTTON_LABEL,
-                lines=20,
-                elem_id="citation-button",
-                show_copy_button=True,
-            )
+
 
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()
+demo.queue(default_concurrency_limit=40).launch()

src/about.py

@@ -21,20 +21,47 @@ NUM_FEWSHOT = 0 # Change with your few shot
 
 
 # Your leaderboard name
-TITLE = """<h1 align="center" id="space-title">Demo leaderboard</h1>"""
+TITLE = """<h1 align="center" id="space-title">WebCoderBench</h1>"""
 
 # What does your leaderboard evaluate?
 INTRODUCTION_TEXT = """
-Intro text
 """
 
 # Which evaluations are you running? how can people reproduce what you have?
 LLM_BENCHMARKS_TEXT = f"""
-## How it works
+## About WebCoderBench
 
-## Reproducibility
-To reproduce our results, here is the commands you can run:
+Web applications (web apps) have become a key arena for large language models (LLMs) to demonstrate code generation capabilities and commercial potential. However, building a benchmark for LLM-generated web apps remains challenging due to:
 
+- The need for real-world user requirements
+- Generalizable evaluation metrics without relying on ground-truth implementations or test cases
+- Interpretable evaluation results
+
+---
+
+## What Is Included
+
+WebCoderBench is a real-world-collected, generalizable, and interpretable benchmark for web app generation.
+
+**At a glance**
+
+- **1,572** real-world user requirements
+- Diverse modalities and expression styles reflecting realistic user intentions
+- **24** fine-grained evaluation metrics across **9** perspectives
+
+---
+
+## How It Is Evaluated
+
+WebCoderBench combines **rule-based evaluation** and the **LLM-as-a-judge** paradigm to enable fully automated, objective, and general evaluation.
+
+To make the overall ranking more interpretable, we adopt **human-preference-aligned weights** over metrics to yield overall scores.
+
+---
+
+## Key Findings
+
+Experiments across **12 representative LLMs** and **2 LLM-based agents** show that there is **no dominant model across all evaluation metrics**, offering opportunities for targeted optimization.
 """
 
 EVALUATION_QUEUE_TEXT = """

src/display/css_html_js.py

@@ -94,6 +94,63 @@ custom_css = """
 #box-filter > .form{
     border: 0
 }
+
+#raw-score-table {
+    overflow-x: auto !important;
+    max-width: 100%;
+    min-width: 0;
+    min-height: 78vh;
+}
+
+#raw-score-table > div {
+    overflow-x: auto !important;
+    max-width: 100%;
+    min-width: 0;
+}
+
+#result-tab {
+    min-height: 78vh;
+}
+
+#raw-score-table th,
+#unweighted-z-table th,
+#weighted-z-table th {
+    white-space: pre-line;
+}
+
+#raw-score-table td,
+#unweighted-z-table td,
+#weighted-z-table td {
+    white-space: nowrap;
+}
+
+#weights-table {
+    overflow-x: auto !important;
+    max-width: 100%;
+}
+
+#weights-table .weights-scroll {
+    overflow-x: auto;
+    max-width: 100%;
+}
+
+#weights-table table {
+    border-collapse: collapse;
+    width: max-content;
+}
+
+#weights-table th,
+#weights-table td {
+    border: 1px solid rgba(0,0,0,0.08);
+    padding: 10px 12px;
+    vertical-align: middle;
+    white-space: nowrap;
+}
+
+#weights-table th {
+    font-weight: 600;
+    background: rgba(0,0,0,0.02);
+}
 """
 
 get_window_url_params = """

src/raw_score.json

@@ -0,0 +1,590 @@
+[
+    {
+        "ID": 1,
+        "Model": "DeepSeek-R1-0528",
+        "Code Quality": {
+            "General Functionality Correctness": 54.67,
+            "Best Practices": 95.71,
+            "Error Handling": 78.87,
+            "Runtime Console Errors": 95.23,
+            "Static Syntax Checking (Linting)": 97.53
+        },
+        "Visual Quality": {
+            "General Visual Experience": 54.36,
+            "Component Style Consistency": 96.53,
+            "Icon Style Consistency": 57.58,
+            "Layout Consistency": 86.69,
+            "Layout Sparsity": 87.82,
+            "Visual Harmony Degree": 70.38
+        },
+        "Content Quality": {
+            "Copywriting Quality": 98.6,
+            "Media Quality": 56.66,
+            "Placeholder Quality": 95.99,
+            "Resource Validity": 88.15
+        },
+        "Performance Quality": {
+            "General Performance": 97.02
+        },
+        "Accessbility": {
+            "Accessibility Core Metrics": 92.94,
+            "Cross-Browser Compatibility": 96.47,
+            "Mobile Device Compatibility": 89.47
+        },
+        "Maintainability": {
+            "Code Redundancy Rate": 95.89,
+            "Comment Rate": 78.76
+        },
+        "Alignment": {
+            "Functional Alignment": 87.61,
+            "Visual Alignment": 92.7,
+            "Content Alignment": 88.77
+        }
+    },
+    {
+        "ID": 2,
+        "Model": "DeepSeek-V3.1",
+        "Code Quality": {
+            "General Functionality Correctness": 53.68,
+            "Best Practices": 95.68,
+            "Error Handling": 78.53,
+            "Runtime Console Errors": 92.44,
+            "Static Syntax Checking (Linting)": 96.72
+        },
+        "Visual Quality": {
+            "General Visual Experience": 40.65,
+            "Component Style Consistency": 98.03,
+            "Icon Style Consistency": 77.69,
+            "Layout Consistency": 89.55,
+            "Layout Sparsity": 82.99,
+            "Visual Harmony Degree": 68.86
+        },
+        "Content Quality": {
+            "Copywriting Quality": 98.67,
+            "Media Quality": 51.94,
+            "Placeholder Quality": 92.51,
+            "Resource Validity": 69.33
+        },
+        "Performance Quality": {
+            "General Performance": 97.6
+        },
+        "Accessbility": {
+            "Accessibility Core Metrics": 91.27,
+            "Cross-Browser Compatibility": 95.6,
+            "Mobile Device Compatibility": 82.64
+        },
+        "Maintainability": {
+            "Code Redundancy Rate": 96.18,
+            "Comment Rate": 76.3
+        },
+        "Alignment": {
+            "Functional Alignment": 89.02,
+            "Visual Alignment": 93.02,
+            "Content Alignment": 87.66
+        }
+    },
+    {
+        "ID": 3,
+        "Model": "DeepSeek-V3.1-Terminus",
+        "Code Quality": {
+            "General Functionality Correctness": 56.25,
+            "Best Practices": 95.94,
+            "Error Handling": 76.12,
+            "Runtime Console Errors": 96.5,
+            "Static Syntax Checking (Linting)": 97.56
+        },
+        "Visual Quality": {
+            "General Visual Experience": 86.4,
+            "Component Style Consistency": 97.86,
+            "Icon Style Consistency": 66,
+            "Layout Consistency": 89.13,
+            "Layout Sparsity": 87.51,
+            "Visual Harmony Degree": 70.15
+        },
+        "Content Quality": {
+            "Copywriting Quality": 98.64,
+            "Media Quality": 38.12,
+            "Placeholder Quality": 93.2,
+            "Resource Validity": 76.44
+        },
+        "Performance Quality": {
+            "General Performance": 98.19
+        },
+        "Accessbility": {
+            "Accessibility Core Metrics": 92.63,
+            "Cross-Browser Compatibility": 96.43,
+            "Mobile Device Compatibility": 88.99
+        },
+        "Maintainability": {
+            "Code Redundancy Rate": 97.33,
+            "Comment Rate": 81.07
+        },
+        "Alignment": {
+            "Functional Alignment": 89.77,
+            "Visual Alignment": 92.18,
+            "Content Alignment": 88.35
+        }
+    },
+    {
+        "ID": 4,
+        "Model": "DeepSeek-V3.1-Thinking",
+        "Code Quality": {
+            "General Functionality Correctness": 54.03,
+            "Best Practices": 95.65,
+            "Error Handling": 78.74,
+            "Runtime Console Errors": 90.93,
+            "Static Syntax Checking (Linting)": 96.33
+        },
+        "Visual Quality": {
+            "General Visual Experience": 41.12,
+            "Component Style Consistency": 97.66,
+            "Icon Style Consistency": 78.38,
+            "Layout Consistency": 90.32,
+            "Layout Sparsity": 83.17,
+            "Visual Harmony Degree": 68.92
+        },
+        "Content Quality": {
+            "Copywriting Quality": 98.67,
+            "Media Quality": 57.21,
+            "Placeholder Quality": 92.37,
+            "Resource Validity": 70.44
+        },
+        "Performance Quality": {
+            "General Performance": 97.97
+        },
+        "Accessbility": {
+            "Accessibility Core Metrics": 91.25,
+            "Cross-Browser Compatibility": 95.83,
+            "Mobile Device Compatibility": 81.76
+        },
+        "Maintainability": {
+            "Code Redundancy Rate": 96.46,
+            "Comment Rate": 76.17
+        },
+        "Alignment": {
+            "Functional Alignment": 88.82,
+            "Visual Alignment": 91.7,
+            "Content Alignment": 87.31
+        }
+    },
+    {
+        "ID": 5,
+        "Model": "GLM-4.5",
+        "Code Quality": {
+            "General Functionality Correctness": 57.73,
+            "Best Practices": 95.84,
+            "Error Handling": 72.89,
+            "Runtime Console Errors": 97.69,
+            "Static Syntax Checking (Linting)": 97.6
+        },
+        "Visual Quality": {
+            "General Visual Experience": 87.42,
+            "Component Style Consistency": 98.1,
+            "Icon Style Consistency": 72.42,
+            "Layout Consistency": 89.06,
+            "Layout Sparsity": 86.22,
+            "Visual Harmony Degree": 69.21
+        },
+        "Content Quality": {
+            "Copywriting Quality": 98.64,
+            "Media Quality": 77.73,
+            "Placeholder Quality": 100,
+            "Resource Validity": 93.33
+        },
+        "Performance Quality": {
+            "General Performance": 97.21
+        },
+        "Accessbility": {
+            "Accessibility Core Metrics": 91.64,
+            "Cross-Browser Compatibility": 96.56,
+            "Mobile Device Compatibility": 84.01
+        },
+        "Maintainability": {
+            "Code Redundancy Rate": 96.4,
+            "Comment Rate": 77.52
+        },
+        "Alignment": {
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src/unweighted_z_score.json

@@ -0,0 +1,590 @@
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src/weighted_z_score.json

@@ -0,0 +1,632 @@
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src/weights.json

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