Upload folder using huggingface_hub

.gitattributes

@@ -8,8 +8,6 @@
 *.h5 filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
 *.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.lz4 filter=lfs diff=lfs merge=lfs -text
-*.mds filter=lfs diff=lfs merge=lfs -text
 *.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
 *.msgpack filter=lfs diff=lfs merge=lfs -text
@@ -27,7 +25,6 @@
 *.safetensors filter=lfs diff=lfs merge=lfs -text
 saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
 *.tflite filter=lfs diff=lfs merge=lfs -text
 *.tgz filter=lfs diff=lfs merge=lfs -text
 *.wasm filter=lfs diff=lfs merge=lfs -text
@@ -35,29 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-# Audio files - uncompressed
-*.pcm filter=lfs diff=lfs merge=lfs -text
-*.sam filter=lfs diff=lfs merge=lfs -text
-*.raw filter=lfs diff=lfs merge=lfs -text
-# Audio files - compressed
-*.aac filter=lfs diff=lfs merge=lfs -text
-*.flac filter=lfs diff=lfs merge=lfs -text
-*.mp3 filter=lfs diff=lfs merge=lfs -text
-*.ogg filter=lfs diff=lfs merge=lfs -text
-*.wav filter=lfs diff=lfs merge=lfs -text
-# Image files - uncompressed
-*.bmp filter=lfs diff=lfs merge=lfs -text
-*.gif filter=lfs diff=lfs merge=lfs -text
-*.png filter=lfs diff=lfs merge=lfs -text
-*.tiff filter=lfs diff=lfs merge=lfs -text
-# Image files - compressed
-*.jpg filter=lfs diff=lfs merge=lfs -text
-*.jpeg filter=lfs diff=lfs merge=lfs -text
-*.webp filter=lfs diff=lfs merge=lfs -text
-# Video files - compressed
-*.mp4 filter=lfs diff=lfs merge=lfs -text
-*.webm filter=lfs diff=lfs merge=lfs -text
-DOUBAO_ENDPOINT="ep-20250117120525-pp8fp"
-DOUBAO_1_5_ENDPOINT="ep-20250122173512-4tqwl"
-DOUBAO_API_KEY="43e9209b-5c60-478e-8f2f-1b6077f5dc57"
-DOUBAO_1_5_256K_ENDPOINT="ep-20250123113810-mxjq2"
+scale-hf-logo.png filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,13 @@
+auto_evals/
+venv/
+__pycache__/
+.env
+.ipynb_checkpoints
+*ipynb
+.vscode/
+
+eval-queue/
+eval-results/
+eval-queue-bk/
+eval-results-bk/
+logs/

.pre-commit-config.yaml

@@ -0,0 +1,53 @@
+# Copyright (c) 2022, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+default_language_version:
+  python: python3
+
+ci:
+  autofix_prs: true
+  autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+  autoupdate_schedule: quarterly
+
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: check-yaml
+      - id: check-case-conflict
+      - id: detect-private-key
+      - id: check-added-large-files
+        args: ['--maxkb=1000']
+      - id: requirements-txt-fixer
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.12.0
+    hooks:
+      - id: isort
+        name: Format imports
+
+  - repo: https://github.com/psf/black
+    rev: 22.12.0
+    hooks:
+      - id: black
+        name: Format code
+        additional_dependencies: ['click==8.0.2']
+
+  - repo: https://github.com/charliermarsh/ruff-pre-commit
+    # Ruff version.
+    rev: 'v0.0.267'
+    hooks:
+      - id: ruff

Makefile

@@ -0,0 +1,13 @@
+.PHONY: style format
+
+
+style:
+	python -m black --line-length 119 .
+	python -m isort .
+	ruff check --fix .
+
+
+quality:
+	python -m black --check --line-length 119 .
+	python -m isort --check-only .
+	ruff check .

README.md

@@ -1,42 +1,46 @@
 ---
-license: cc-by-4.0
+title: Web Bench Leaderboard
+emoji: 🥇
+colorFrom: green
+colorTo: indigo
+sdk: gradio
+app_file: app.py
+pinned: true
+license: apache-2.0
+short_description: Duplicate this leaderboard to initialize your own!
+sdk_version: 5.19.0
 ---
 
-# Web-Bench
-
-English | [中文 README](README.zh_CN.md)
-
-## 📖 Overview
-
-**Web-Bench** is a benchmark designed to evaluate the performance of LLMs in actual Web development. Web-Bench contains 50 projects, each consisting of 20 tasks with sequential dependencies. The tasks implement project features in sequence, simulating real-world human development workflows. When designing Web-Bench, we aim to cover the foundational elements of Web development: Web Standards and Web Frameworks. Given the scale and complexity of these projects, which were designed by engineers with 5-10 years of experience, each presents a significant challenge. On average, a single project takes 4–8 hours for a senior engineer to complete. On our given benchmark agent (Web-Agent), SOTA (Claude 3.7 Sonnet) achieves only 25.1\% Pass@1.
-
-The distribution of the experimental data aligns well with the current code generation capabilities of mainstream LLMs.
-<img width="500" alt="pass@1" src="./docs/assets/pass-1.png" />
-
-HumanEval and MBPP have approached saturation. APPS and EvalPlus are approaching saturation. The SOTA for Web-Bench is 25.1\%, which is lower (better) than that of the SWE-bench Full and Verified sets.
-<img width="500" alt="SOTAs" src="./docs/assets/sotas.png" />
-
-## Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks
-The datasets was presented in the paper [Web-Bench: A LLM Code Benchmark Based on Web Standards and Frameworks](https://huggingface.co/papers/2505.07473).
-
-## 🏅 Leaderboard
-
-[Leaderboard](https://huggingface.co/spaces/bytedance-research/Web-Bench-Leaderboard)
-
-
-## Dataset Structure
-
-An example of a Web-Bench datum is as follows:
-
-```
-id: (str) Task id, init | task-n
-project: (str) Task project name
-description: (str) Task details description
-date: (str) Task publish date, filter contaminated model
-level: (str) Task level: easy | moderate | challenging
+# Start the configuration
+
+Most of the variables to change for a default leaderboard are in `src/env.py` (replace the path for your leaderboard) and `src/about.py` (for tasks).
+
+Results files should have the following format and be stored as json files:
+```json
+{
+    "config": {
+        "model_dtype": "torch.float16", # or torch.bfloat16 or 8bit or 4bit
+        "model_name": "path of the model on the hub: org/model",
+        "model_sha": "revision on the hub",
+    },
+    "results": {
+        "task_name": {
+            "metric_name": score,
+        },
+        "task_name2": {
+            "metric_name": score,
+        }
+    }
+}
 ```
 
+Request files are created automatically by this tool.
+
+If you encounter problem on the space, don't hesitate to restart it to remove the create eval-queue, eval-queue-bk, eval-results and eval-results-bk created folder.
 
-## 📘 Usage
+# Code logic for more complex edits
 
-[GitHub](https://github.com/bytedance/web-bench)
+You'll find 
+- the main table' columns names and properties in `src/display/utils.py`
+- the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
+- the logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`

app.py

@@ -0,0 +1,177 @@
+import gradio as gr
+from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
+import pandas as pd
+from apscheduler.schedulers.background import BackgroundScheduler
+from huggingface_hub import snapshot_download
+
+from src.about import (
+    CITATION_BUTTON_LABEL,
+    CITATION_BUTTON_TEXT,
+    EVALUATION_QUEUE_TEXT,
+    INTRODUCTION_TEXT,
+    LLM_BENCHMARKS_TEXT,
+    TITLE,
+)
+from src.display.css_html_js import custom_css, block_css, js
+from src.display.utils import (
+    BENCHMARK_COLS,
+    COLS,
+    EVAL_COLS,
+    EVAL_TYPES,
+    AutoEvalColumn,
+    ModelType,
+    fields,
+    WeightType,
+)
+from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
+from src.populate import get_evaluation_queue_df, get_leaderboard_df
+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()
+
+
+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)
+
+def init_leaderboard(dataframe):
+    if dataframe is None or dataframe.empty:
+        raise ValueError("Leaderboard DataFrame is empty or None.")
+    print(f"dataframe {dataframe}")
+    return Leaderboard(
+        value=dataframe,
+        datatype=[c.type for c in fields(AutoEvalColumn)],
+        select_columns=SelectColumns(
+            default_selection=[c.name for c in fields(AutoEvalColumn) if c.displayed_by_default],
+            cant_deselect=[c.name for c in fields(AutoEvalColumn) if c.never_hidden],
+            label="Select Columns to Display:",
+        ),
+        search_columns=[AutoEvalColumn.model.name],
+        hide_columns=[c.name for c in fields(AutoEvalColumn) if c.hidden],
+        filter_columns=[
+            ColumnFilter(AutoEvalColumn.provider.name, type="dropdown", label="Provider"),
+            # ColumnFilter(
+            #     AutoEvalColumn.pass2.name,
+            #     type="slider",
+            #     min=0.01,
+            #     max=100,
+            #     label="Select the number of Pass@2",
+            # ),
+            ColumnFilter(AutoEvalColumn.openness.name, type="checkboxgroup", label="Openness"),
+        ],
+        bool_checkboxgroup_label="Hide models",
+        interactive=False,
+    )
+
+
+demo = gr.Blocks(css=custom_css, js=js, elem_classes="custom-block")
+with demo:
+    block = gr.Blocks(css=block_css, elem_classes="custom-block")
+    with block:
+        gr.HTML(TITLE)
+        gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")
+
+        with gr.Tabs(elem_classes="tab-buttons") as tabs:
+            with gr.TabItem("🏅 Benchmark", elem_id="llm-benchmark-tab-table1", id=1):
+                leaderboard = init_leaderboard(LEADERBOARD_DF)
+            with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table2", id=2):
+                gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")
+
+            with gr.TabItem("🚀 Submit", elem_id="llm-benchmark-tab-table3", id=3):
+                gr.Markdown(EVALUATION_QUEUE_TEXT, elem_classes="markdown-text")
+                # with gr.Column():
+                #     with gr.Row():
+                        
+
+                #     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")
+                #     with gr.Column():
+                #         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,
+                #     ],
+                #     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()

pyproject.toml

@@ -0,0 +1,13 @@
+[tool.ruff]
+# Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select = ["E", "F"]
+ignore = ["E501"] # line too long (black is taking care of this)
+line-length = 119
+fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
+
+[tool.isort]
+profile = "black"
+line_length = 119
+
+[tool.black]
+line-length = 119

requirements.txt

@@ -0,0 +1,16 @@
+APScheduler
+black
+datasets
+gradio
+gradio[oauth]
+gradio_leaderboard==0.0.13
+gradio_client
+huggingface-hub>=0.18.0
+matplotlib
+numpy
+pandas
+python-dateutil
+tqdm
+transformers
+tokenizers>=0.15.0
+sentencepiece

src/about.py

@@ -0,0 +1,75 @@
+from dataclasses import dataclass
+from enum import Enum
+
+@dataclass
+class Task:
+    benchmark: str
+    metric: str
+    col_name: str
+
+
+# Select your tasks here
+# ---------------------------------------------------
+class Tasks(Enum):
+    # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
+    task0 = Task("anli_r1", "acc", "ANLI")
+    task1 = Task("logiqa", "acc_norm", "LogiQA")
+
+NUM_FEWSHOT = 0 # Change with your few shot
+# ---------------------------------------------------
+
+
+
+# Your leaderboard name
+TITLE = """<h1 align="center" id="space-title">Web-Bench Leaderboard</h1>"""
+
+CONTAINER = """
+<div id="main-container" style="display: flex; justify-content: center; align-items: center;">
+</div>
+"""
+
+# What does your leaderboard evaluate?
+INTRODUCTION_TEXT = """
+
+**Web-Bench** is a benchmark designed to evaluate the performance of LLMs in actual Web development. Web-Bench contains 50 projects, each consisting of 20 tasks with sequential dependencies. The tasks implement project features in sequence, simulating real-world human development workflows. When designing Web-Bench, we aim to cover the foundational elements of Web development: Web Standards and Web Frameworks. Given the scale and complexity of these projects, which were designed by engineers with 5-10 years of experience, each presents a significant challenge. On average, a single project takes 4–8 hours for a senior engineer to complete. On our given benchmark agent (Web-Agent), SOTA (Claude 3.7 Sonnet) achieves only 25.1\% Pass@1.
+"""
+
+# Which evaluations are you running? how can people reproduce what you have?
+LLM_BENCHMARKS_TEXT = f"""
+## More Information
+
+More information could be found in [Paper](https://arxiv.org/abs/2505.07473) or [Github](https://github.com/bytedance/web-bench)
+
+"""
+
+EVALUATION_QUEUE_TEXT = """
+We welcome community submissions of new model evaluation results. Those submissions will be listed as 'External', and authors must upload their generated outputs for peer review.
+
+## Evaluation
+
+Evaluation [Setup](https://github.com/bytedance/web-bench?tab=readme-ov-file#-set-up) and [Usage](https://github.com/bytedance/web-bench?tab=readme-ov-file#-usage). This will generate a markdown report summarizing the results.
+
+
+## Submission
+
+To submit your results, create a **Pull Request** in the [Community Tab](https://huggingface.co/spaces/bytedance-research/Web-Bench-Leaderboard/discussions) to add them to the [src/custom-eval-results](https://huggingface.co/spaces/bytedance-research/Web-Bench-Leaderboard/tree/main/src/custom-eval-results) folder in this repository:
+- Create a new folder named with your provider and model names (e.g., [provider_modelname_template](https://huggingface.co/spaces/bytedance-research/Web-Bench-Leaderboard/tree/main/src/custom-eval-results/provider_modelname_temple), using underscores to separate parts).
+- Each folder stores the evaluation results of only one model.
+- Add a `base_meta.json` file with the following fields:
+    - `Model`: the name of your model
+    - `ModelLink`: the link to the model page
+    - `Provider`: the name of the provider
+    - `Openness`: the openness of the model
+    - `Agent`: the agent used for evaluation, `Web-Agent` or your custom agent name
+- Put your generated reports (e.g. `eval-20250513-102235`) in your folder.
+- The title of the PR should be: [Community Submission] Model: org/model, Username: your_username.
+
+We will review your submission and merge it upon acceptance.
+
+**Tips**: `gen_meta.json` will be created after our review.
+
+"""
+
+CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"
+CITATION_BUTTON_TEXT = r"""
+"""

src/custom-eval-results/provider_modelname_temple/base_meta.json

@@ -0,0 +1,7 @@
+{
+  "Provider": "Temple",
+  "Openness": "Closed",
+  "Thinking": false,
+  "Agent": "Web-Agent",
+  "ModelLink": "Your ModelLink"
+}

src/custom-eval-results/provider_modelname_temple/gen_meta.json

@@ -0,0 +1,9 @@
+{
+  "Pass@2": 23.799999999999997,
+  "Pass@1": 17.169999999999998,
+  "Error@1": 8.15,
+  "Version": "v1.0.0",
+  "Source": "🔶 External",
+  "Model": "test",
+  "PRLink": ""
+}

src/data.py

@@ -0,0 +1,84 @@
+import json
+from pathlib import Path
+import os
+
+def get_first_level_dirs(dir_a):
+    # 检查目标目录是否存在
+    if not os.path.isdir(dir_a):
+        raise ValueError(f"目录不存在: {dir_a}")
+    
+    # 遍历 dir_a 下的所有条目，筛选出一级文件夹
+    first_level_dirs = []
+    for entry in os.listdir(dir_a):
+        entry_path = os.path.join(dir_a, entry)
+        # 仅保留是目录且非符号链接的条目（避免误判软链接）
+        if os.path.isdir(entry_path) and not os.path.islink(entry_path):
+            first_level_dirs.append(entry)  # 保存文件夹名称（或改为 entry_path 保存完整路径）
+    
+    return first_level_dirs
+
+def read_json_files(filename=None):
+    """
+    读取当前目录下的 JSON 文件
+    
+    参数:
+        filename (str, optional): 指定要读取的文件名（例如 "data.json"），默认读取所有 JSON 文件
+    
+    返回:
+        如果指定 filename: 返回对应文件的解析内容（字典/list）
+        如果未指定 filename: 返回字典 {文件名: 内容}（例如 {"data": {...}}）
+    """
+    current_dir = Path.cwd()
+    result = {}
+
+    try:
+        # 读取单个文件
+        if filename:
+            file_path = current_dir / 'src' / filename
+            print("JSON数据加载成功:", current_dir, file_path)
+
+            with open(file_path, "r", encoding="utf-8") as f:
+                return json.load(f)
+        
+        # 批量读取所有 JSON 文件
+        for json_file in current_dir.glob("*.json"):
+            with open(json_file, "r", encoding="utf-8") as f:
+                result[json_file.stem] = json.load(f)
+        return result
+
+    except FileNotFoundError:
+        print(f"错误：文件 {filename} 不存在")
+    except json.JSONDecodeError:
+        print(f"错误：文件 {filename} 不是有效的 JSON 格式")
+    except Exception as e:
+        print(f"未知错误：{str(e)}")
+    return None
+
+
+
+def get_custom_data():
+    dir_list =  Path.cwd() / 'src' / 'custom-eval-results'
+    res = []
+    for dir in dir_list.iterdir():
+        print('dirname', dir.name != 'provider_modelname_temple', dir.name) 
+        if dir.is_dir() and dir.name != 'provider_modelname_temple':
+            # base_meta
+            print("dirname", dir.name)
+            with open(dir / 'base_meta.json', "r", encoding="utf-8") as f:
+                base_meta = json.load(f)
+            with open(dir / 'gen_meta.json', "r", encoding="utf-8") as f:
+                gen_meta = json.load(f)
+
+            base_meta.update(gen_meta)
+            print(base_meta)
+            base_meta['Model'] = f"<a target=\"_blank\" href=\"{base_meta['ModelLink']}\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">{base_meta['Model']}</a>",
+            base_meta['Type'] = '🔶 External'
+            base_meta['Source'] = f"<a target=\"_blank\" href=\"{base_meta['PRLink']}\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">Pull Request</a>" if base_meta['PRLink'] else '',
+            res.append(base_meta)
+    return res
+
+
+# # 示例用法
+# if __name__ == "__main__":
+
+#     print(get_custom_data())

src/display/css_html_js.py

@@ -0,0 +1,131 @@
+custom_css = """
+
+.markdown-text {
+    font-size: 16px !important;
+}
+
+.hide-container:not([id^="component-"]) {
+    display: none;
+}
+
+#component-0 {
+    max-width: 1546px;
+    margin: 0 auto;
+}
+
+#models-to-add-text {
+    font-size: 18px !important;
+}
+
+#citation-button span {
+    font-size: 16px !important;
+}
+
+#citation-button textarea {
+    font-size: 16px !important;
+}
+
+#citation-button > label > button {
+    margin: 6px;
+    transform: scale(1.3);
+}
+
+#leaderboard-table {
+    margin-top: 15px
+}
+
+#leaderboard-table-lite {
+    margin-top: 15px
+}
+
+#search-bar-table-box > div:first-child {
+    background: none;
+    border: none;
+}
+ 
+#search-bar {
+    padding: 0px;
+}
+
+/* Limit the width of the first AutoEvalColumn so that names don't expand too much */
+#leaderboard-table td:nth-child(2),
+#leaderboard-table th:nth-child(2) {
+    max-width: 400px;
+    overflow: auto;
+    white-space: nowrap;
+}
+
+.tab-buttons button {
+    font-size: 20px;
+}
+
+#scale-logo {
+    border-style: none !important;
+    box-shadow: none;
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+    max-width: 600px;
+}
+
+#scale-logo .download {
+    display: none;
+}
+#filter_type{
+    border: 0;
+    padding-left: 0;
+    padding-top: 0;
+}
+#filter_type label {
+    display: flex;
+}
+#filter_type label > span{
+    margin-top: var(--spacing-lg);
+    margin-right: 0.5em;
+}
+#filter_type label > .wrap{
+    width: 103px;
+}
+#filter_type label > .wrap .wrap-inner{  
+    padding: 2px;
+}
+#filter_type label > .wrap .wrap-inner input{
+    width: 1px
+}
+#filter-columns-type{
+    border:0;
+    padding:0.5;
+}
+#filter-columns-size{
+    border:0;
+    padding:0.5;
+}
+#box-filter > .form{
+    border: 0
+}
+"""
+
+block_css = """
+.custom_block {
+    max-width: 1536px;
+	
+}
+
+
+"""
+
+get_window_url_params = """
+    function(url_params) {
+        const params = new URLSearchParams(window.location.search);
+        url_params = Object.fromEntries(params);
+        return url_params;
+    }
+    """
+
+js="""
+function() {
+    const comp = document.querySelector("body > div:nth-child(1) > div.gradio-container.gradio-container-5-19-0.svelte-1reuit1")
+    console.log("comp", comp)
+    comp.style.maxHeight = '1215px'
+}
+"""

src/display/formatting.py

@@ -0,0 +1,27 @@
+def model_hyperlink(link, model_name):
+    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
+
+
+def make_clickable_model(model_name):
+    link = f"https://huggingface.co/{model_name}"
+    return model_hyperlink(link, model_name)
+
+
+def styled_error(error):
+    return f"<p style='color: red; font-size: 20px; text-align: center;'>{error}</p>"
+
+
+def styled_warning(warn):
+    return f"<p style='color: orange; font-size: 20px; text-align: center;'>{warn}</p>"
+
+
+def styled_message(message):
+    return f"<p style='color: green; font-size: 20px; text-align: center;'>{message}</p>"
+
+
+def has_no_nan_values(df, columns):
+    return df[columns].notna().all(axis=1)
+
+
+def has_nan_values(df, columns):
+    return df[columns].isna().any(axis=1)

src/display/utils.py

@@ -0,0 +1,90 @@
+from dataclasses import dataclass, make_dataclass
+from enum import Enum
+
+import pandas as pd
+
+def fields(raw_class):
+    return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
+
+
+# These classes are for user facing column names,
+# to avoid having to change them all around the code
+# when a modif is needed
+@dataclass
+class ColumnContent:
+    name: str
+    type: str
+    displayed_by_default: bool
+    hidden: bool = False
+    never_hidden: bool = False
+
+## Leaderboard columns
+auto_eval_column_dict = []
+# Init
+auto_eval_column_dict.append(["source_value", ColumnContent, ColumnContent("Type", "str", True, never_hidden=True)])
+auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
+
+# Model information
+auto_eval_column_dict.append(["pass2", ColumnContent, ColumnContent("Pass@2", "number", True)])
+auto_eval_column_dict.append(["pass1", ColumnContent, ColumnContent("Pass@1", "number", True)])
+auto_eval_column_dict.append(["error1", ColumnContent, ColumnContent("Error@1", "number", True)])
+auto_eval_column_dict.append(["source", ColumnContent, ColumnContent("Source", "markdown", True, False)])
+auto_eval_column_dict.append(["provider", ColumnContent, ColumnContent("Provider", "number", False)])
+auto_eval_column_dict.append(["version", ColumnContent, ColumnContent("Version", "str", False, True)])
+auto_eval_column_dict.append(["agent", ColumnContent, ColumnContent("Agent", "str", False)])
+auto_eval_column_dict.append(["openness", ColumnContent, ColumnContent("Openness", "str", False)])
+auto_eval_column_dict.append(["thinking", ColumnContent, ColumnContent("Thinking", "boolean", True)])
+
+# We use make dataclass to dynamically fill the scores from Tasks
+AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
+
+## For the queue columns in the submission tab
+@dataclass(frozen=True)
+class EvalQueueColumn:  # Queue column
+    model = ColumnContent("model", "markdown", True)
+    private = ColumnContent("private", "bool", True)
+    status = ColumnContent("status", "str", True)
+
+## All the model information that we might need
+@dataclass
+class ModelDetails:
+    name: str
+    display_name: str = ""
+    symbol: str = "" # emoji
+
+
+class ModelType(Enum):
+    PT = ModelDetails(name="pretrained", symbol="🟢")
+    FT = ModelDetails(name="fine-tuned", symbol="🔶")
+    IFT = ModelDetails(name="instruction-tuned", symbol="⭕")
+    RL = ModelDetails(name="RL-tuned", symbol="🟦")
+    Unknown = ModelDetails(name="", symbol="?")
+
+    def to_str(self, separator=" "):
+        return f"{self.value.symbol}{separator}{self.value.name}"
+
+    @staticmethod
+    def from_str(type):
+        if "fine-tuned" in type or "🔶" in type:
+            return ModelType.FT
+        if "pretrained" in type or "🟢" in type:
+            return ModelType.PT
+        if "RL-tuned" in type or "🟦" in type:
+            return ModelType.RL
+        if "instruction-tuned" in type or "⭕" in type:
+            return ModelType.IFT
+        return ModelType.Unknown
+
+class WeightType(Enum):
+    Adapter = ModelDetails("Adapter")
+    Original = ModelDetails("Original")
+    Delta = ModelDetails("Delta")
+
+# Column selection
+COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
+
+EVAL_COLS = [c.name for c in fields(EvalQueueColumn)]
+EVAL_TYPES = [c.type for c in fields(EvalQueueColumn)]
+
+BENCHMARK_COLS = []
+

src/envs.py

@@ -0,0 +1,25 @@
+import os
+
+from huggingface_hub import HfApi
+
+# Info to change for your repository
+# ----------------------------------
+TOKEN = os.environ.get("HF_TOKEN") # A read/write token for your org
+
+OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
+# ----------------------------------
+
+REPO_ID = f"{OWNER}/leaderboard"
+QUEUE_REPO = f"{OWNER}/requests"
+RESULTS_REPO = f"{OWNER}/results"
+
+# If you setup a cache later, just change HF_HOME
+CACHE_PATH=os.getenv("HF_HOME", ".")
+
+# Local caches
+EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")
+EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
+EVAL_REQUESTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-queue-bk")
+EVAL_RESULTS_PATH_BACKEND = os.path.join(CACHE_PATH, "eval-results-bk")
+
+API = HfApi(token=TOKEN)

src/leaderboard/read_evals.py

@@ -0,0 +1,160 @@
+import glob
+import json
+import math
+import os
+from dataclasses import dataclass
+
+import dateutil
+import numpy as np
+
+from src.display.formatting import make_clickable_model
+from src.display.utils import AutoEvalColumn, ModelType, WeightType
+from src.submission.check_validity import is_model_on_hub
+
+
+@dataclass
+class EvalResult:
+    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
+    """
+    eval_name: str # org_model_precision (uid)
+    full_model: str # org/model (path on hub)
+    org: str 
+    model: str
+    results: dict
+    num_params: int = 0
+    date: str = "" # submission date of request file
+
+    @classmethod
+    def init_from_json_file(self, json_filepath):
+        """Inits the result from the specific model result file"""
+        with open(json_filepath) as fp:
+            data = json.load(fp)
+
+        config = data.get("config")
+
+        # Get model and org
+        org_and_model = config.get("model_name", config.get("model_args", None))
+        org_and_model = org_and_model.split("/", 1)
+
+        if len(org_and_model) == 1:
+            org = None
+            model = org_and_model[0]
+        else:
+            org = org_and_model[0]
+            model = org_and_model[1]
+            result_key = f"{org}_{model}"
+        full_model = "/".join(org_and_model)
+
+        # Extract results available in this file (some results are split in several files)
+        results = {}
+
+        return self(
+            eval_name=result_key,
+            full_model=full_model,
+            org=org,
+            model=model,
+            results=results,
+        )
+
+    def update_with_request_file(self, requests_path):
+        """Finds the relevant request file for the current model and updates info with it"""
+        request_file = get_request_file_for_model(requests_path, self.full_model)
+
+        try:
+            with open(request_file, "r") as f:
+                request = json.load(f)
+            self.pass2 = request.get("pass2", 0)
+            self.pass1 = request.get("pass1", 0)
+            self.error1 = request.get("error1", 0)
+            self.openness = request.get("openness", 'Closed')
+            self.provider = request.get("provider", 'Unknown')
+            self.source = request.get("source", 'Unknown')
+            self.source_value = request.get("source_value", 'Unknown')
+            self.agent = request.get("agent", 'Web-Agent')
+            self.version = request.get("version", 'v1.0.0')
+            self.thinking = request.get("thinking", True)
+            self.date = request.get("submitted_time", "")
+        except Exception:
+            print(f"Could not find request file for {self.org}/{self.model}")
+
+    def to_dict(self):
+        """Converts the Eval Result to a dict compatible with our dataframe display"""
+        data_dict = {
+            "eval_name": self.eval_name,  # not a column, just a save name,
+            AutoEvalColumn.model.name: make_clickable_model(self.full_model),
+            AutoEvalColumn.pass2.name: self.pass2,
+            AutoEvalColumn.pass1.name: self.pass1,
+            AutoEvalColumn.openness.name: self.openness,
+            AutoEvalColumn.error1.name: self.error1,
+            AutoEvalColumn.provider.name: self.provider,
+            AutoEvalColumn.source.name: self.source,
+            AutoEvalColumn.source_value.name: self.source_value,
+            AutoEvalColumn.version.name: self.version,
+            AutoEvalColumn.agent.name: self.agent,
+            AutoEvalColumn.thinking.name: self.thinking,
+        }
+
+      
+        return data_dict
+
+
+def get_request_file_for_model(requests_path, model_name):
+    """Selects the correct request file for a given model. Only keeps runs tagged as FINISHED"""
+    request_files = os.path.join(
+        requests_path,
+        f"{model_name}_eval_request_*.json",
+    )
+    request_files = glob.glob(request_files)
+    print("")
+    request_file = ""
+    request_files = sorted(request_files, reverse=True)
+    for tmp_request_file in request_files:
+        with open(tmp_request_file, "r") as f:
+            req_content = json.load(f)
+            if (
+                req_content["status"] in ["FINISHED"]
+            ):
+                request_file = tmp_request_file
+    return request_file
+
+
+def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResult]:
+    """From the path of the results folder root, extract all needed info for results"""
+    model_result_filepaths = []
+
+    for root, _, files in os.walk(results_path):
+        # We should only have json files in model results
+        if len(files) == 0 or any([not f.endswith(".json") for f in files]):
+            continue
+
+        # Sort the files by date
+        try:
+            files.sort(key=lambda x: x.removesuffix(".json").removeprefix("results_")[:-7])
+        except dateutil.parser._parser.ParserError:
+            files = [files[-1]]
+
+        for file in files:
+            model_result_filepaths.append(os.path.join(root, file))
+
+    eval_results = {}
+    for model_result_filepath in model_result_filepaths:
+        # Creation of result
+        eval_result = EvalResult.init_from_json_file(model_result_filepath)
+        eval_result.update_with_request_file(requests_path)
+
+        # Store results of same eval together
+        eval_name = eval_result.eval_name
+        if eval_name in eval_results.keys():
+            eval_results[eval_name].results.update({k: v for k, v in eval_result.results.items() if v is not None})
+        else:
+            eval_results[eval_name] = eval_result
+
+    results = []
+    for v in eval_results.values():
+        try:
+            v.to_dict() # we test if the dict version is complete
+            results.append(v)
+        except KeyError:  # not all eval values present
+            continue
+
+    return results

src/populate.py

@@ -0,0 +1,73 @@
+import json
+import os
+
+import pandas as pd
+from pathlib import Path
+
+from src.display.formatting import has_no_nan_values, make_clickable_model
+from src.display.utils import AutoEvalColumn, EvalQueueColumn
+from src.leaderboard.read_evals import get_raw_eval_results
+from src.data import read_json_files, get_custom_data
+from src.envs import CACHE_PATH
+
+
+def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:
+    """Creates a dataframe from all the individual experiment results"""
+
+    json_data = read_json_files('results.json')
+    print('read_json_files', json_data)
+    current_dir = Path.cwd()
+
+    for item in json_data:
+        val = { 'Type': '🟢 Base' }
+        item.update(val)
+
+    if len(get_custom_data()) > 0:
+        custom_data = get_custom_data()
+        json_data.extend(custom_data)
+
+    all_data_json = json_data
+
+    print('CACHE_PATH', CACHE_PATH, results_path, current_dir)
+
+    df = pd.DataFrame.from_records(all_data_json)
+    df = df.sort_values(by=[AutoEvalColumn.pass2.name], ascending=False)
+    df = df[cols].round(decimals=2)
+
+    # filter out if any of the benchmarks have not been produced
+    df = df[has_no_nan_values(df, benchmark_cols)]
+    return df
+
+
+def get_evaluation_queue_df(save_path: str, cols: list) -> list[pd.DataFrame]:
+    """Creates the different dataframes for the evaluation queues requestes"""
+    entries = [entry for entry in os.listdir(save_path) if not entry.startswith(".")]
+    all_evals = []
+
+    for entry in entries:
+        if ".json" in entry:
+            file_path = os.path.join(save_path, entry)
+            with open(file_path) as fp:
+                data = json.load(fp)
+
+            data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+
+            all_evals.append(data)
+        elif ".md" not in entry:
+            # this is a folder
+            sub_entries = [e for e in os.listdir(f"{save_path}/{entry}") if os.path.isfile(e) and not e.startswith(".")]
+            for sub_entry in sub_entries:
+                file_path = os.path.join(save_path, entry, sub_entry)
+                with open(file_path) as fp:
+                    data = json.load(fp)
+
+                data[EvalQueueColumn.model.name] = make_clickable_model(data["model"])
+                all_evals.append(data)
+
+    pending_list = [e for e in all_evals if e["status"] in ["PENDING", "RERUN"]]
+    running_list = [e for e in all_evals if e["status"] == "RUNNING"]
+    finished_list = [e for e in all_evals if e["status"].startswith("FINISHED") or e["status"] == "PENDING_NEW_EVAL"]
+    df_pending = pd.DataFrame.from_records(pending_list, columns=cols)
+    df_running = pd.DataFrame.from_records(running_list, columns=cols)
+    df_finished = pd.DataFrame.from_records(finished_list, columns=cols)
+    return df_finished[cols], df_running[cols], df_pending[cols]

src/results.json

@@ -0,0 +1,535 @@
+[
+  {
+    "eval_name": "gpt-4o",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/gpt-4o\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gpt-4o</a>",
+    "Pass@2": 23.799999999999997,
+    "Pass@1": 17.169999999999998,
+    "Error@1": 8.15,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gpt-4o-mini",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/gpt-4o-mini\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gpt-4o-mini</a>",
+    "Pass@2": 13.04,
+    "Pass@1": 8.48,
+    "Error@1": 7.28,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gpt-4.1",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/gpt-4.1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gpt-4.1</a>",
+    "Pass@2": 25.11,
+    "Pass@1": 21.09,
+    "Error@1": 6.52,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "o3-mini",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/o3-mini\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">o3-mini</a>",
+    "Pass@2": 14.24,
+    "Pass@1": 9.13,
+    "Error@1": 7.93,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "o1",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/o1\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">o1</a>",
+    "Pass@2": 12.389999999999999,
+    "Pass@1": 10.43,
+    "Error@1": 9.89,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gpt-4.1-mini",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/gpt-4.1-mini\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gpt-4.1-mini</a>",
+    "Pass@2": 23.7,
+    "Pass@1": 20.76,
+    "Error@1": 6.8500000000000005,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gpt-4.1-nano",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/gpt-4.1-nano\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gpt-4.1-nano</a>",
+    "Pass@2": 12.280000000000001,
+    "Pass@1": 7.07,
+    "Error@1": 6.959999999999999,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "o4-mini",
+    "Model": "<a target=\"_blank\" href=\"https://platform.openai.com/docs/models/o4-mini\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">o4-mini</a>",
+    "Pass@2": 22.93,
+    "Pass@1": 13.26,
+    "Error@1": 9.89,
+    "Provider": "OpenAI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "claude-3-7-sonnet-20250219",
+    "Model": "<a target=\"_blank\" href=\"https://www.anthropic.com/news/claude-3-7-sonnet\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">claude-3-7-sonnet-20250219</a>",
+    "Pass@2": 30.98,
+    "Pass@1": 22.5,
+    "Error@1": 8.260000000000002,
+    "Provider": "Anthropic",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "claude-3-5-sonnet-20241022",
+    "Model": "<a target=\"_blank\" href=\"https://www.anthropic.com/news/3-5-models-and-computer-use\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">claude-3-5-sonnet-20241022</a>",
+    "Pass@2": 32.39,
+    "Pass@1": 23.04,
+    "Error@1": 9.46,
+    "Provider": "Anthropic",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "claude-3-5-haiku-20241022",
+    "Model": "<a target=\"_blank\" href=\"https://www.anthropic.com/news/3-5-models-and-computer-use\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">claude-3-5-haiku-20241022</a>",
+    "Pass@2": 21.740000000000002,
+    "Pass@1": 15.43,
+    "Error@1": 7.61,
+    "Provider": "Anthropic",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "claude-3-5-sonnet-20240620",
+    "Model": "<a target=\"_blank\" href=\"https://www.anthropic.com/news/claude-3-5-sonnet\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">claude-3-5-sonnet-20240620</a>",
+    "Pass@2": 30.330000000000002,
+    "Pass@1": 21.959999999999997,
+    "Error@1": 8.799999999999999,
+    "Provider": "Anthropic",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "claude-3-7-sonnet-20250219-thinking",
+    "Model": "<a target=\"_blank\" href=\"https://www.anthropic.com/news/claude-3-7-sonnet\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">claude-3-7-sonnet-20250219-thinking</a>",
+    "Pass@2": 35.33,
+    "Pass@1": 25.11,
+    "Error@1": 9.02,
+    "Provider": "Anthropic",
+    "Openness": "Closed",
+    "Thinking": true,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "deepseek-r1",
+    "Model": "<a target=\"_blank\" href=\"https://api-docs.deepseek.com/news/news250120\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">deepseek-r1</a>",
+    "Pass@2": 26.200000000000003,
+    "Pass@1": 14.46,
+    "Error@1": 9.89,
+    "Provider": "DeepSeek",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "deepseek-coder-v2",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">deepseek-coder-v2</a>",
+    "Pass@2": 23.150000000000002,
+    "Pass@1": 16.74,
+    "Error@1": 7.829999999999999,
+    "Provider": "DeepSeek",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "deepseek-v3-0324",
+    "Model": "<a target=\"_blank\" href=\"https://api-docs.deepseek.com/news/news250325\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">deepseek-v3-0324</a>",
+    "Pass@2": 23.59,
+    "Pass@1": 17.07,
+    "Error@1": 7.93,
+    "Provider": "DeepSeek",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-2.0-flash",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models#gemini-2.0-flash\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-2.0-flash</a>",
+    "Pass@2": 20.87,
+    "Pass@1": 15.329999999999998,
+    "Error@1": 7.07,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-pro-1.5",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models#gemini-1.5-pro\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-pro-1.5</a>",
+    "Pass@2": 20.87,
+    "Pass@1": 14.78,
+    "Error@1": 7.28,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-flash-1.5",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models#gemini-1.5-flash\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-flash-1.5</a>",
+    "Pass@2": 17.07,
+    "Pass@1": 12.83,
+    "Error@1": 7.07,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-2.5-pro-03-25",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models?hl=zh-cn#gemini-2.5-pro-preview-05-06\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-2.5-pro-03-25</a>",
+    "Pass@2": 24.02,
+    "Pass@1": 15.67,
+    "Error@1": 8.04,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-2.0-flash-thinking",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models?hl=zh-cn#gemini-2.5-pro-preview-05-06\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-2.0-flash-thinking</a>",
+    "Pass@2": 19.24,
+    "Pass@1": 14.89,
+    "Error@1": 6.959999999999999,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": true,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemma-3-27b",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/google/gemma-3-27b-it\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemma-3-27b</a>",
+    "Pass@2": 11.85,
+    "Pass@1": 9.89,
+    "Error@1": 6.2,
+    "Provider": "Google",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "gemini-2.5-pro-0506",
+    "Model": "<a target=\"_blank\" href=\"https://ai.google.dev/gemini-api/docs/models?hl=zh-cn#gemini-2.5-pro-preview-05-06\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">gemini-2.5-pro-0506</a>",
+    "Pass@2": 31.96,
+    "Pass@1": 20.76,
+    "Error@1": 8.7,
+    "Provider": "Google",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "llama-3.3",
+    "Model": "<a target=\"_blank\" href=\"https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_3/\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">llama-3.3</a>",
+    "Pass@2": 9.569999999999999,
+    "Pass@1": 6.63,
+    "Error@1": 6.2,
+    "Provider": "Meta",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "llama-4 Scout",
+    "Model": "<a target=\"_blank\" href=\"https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">llama-4 Scout</a>",
+    "Pass@2": 7.720000000000001,
+    "Pass@1": 5,
+    "Error@1": 6.63,
+    "Provider": "Meta",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "llama-4 Maverick",
+    "Model": "<a target=\"_blank\" href=\"https://www.llama.com/docs/model-cards-and-prompt-formats/llama4_omni/\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">llama-4 Maverick</a>",
+    "Pass@2": 20.87,
+    "Pass@1": 15.98,
+    "Error@1": 7.07,
+    "Provider": "Meta",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "qwen-max-2025-01-25",
+    "Model": "<a target=\"_blank\" href=\"https://www.alibabacloud.com/help/en/model-studio/what-is-qwen-llm#c2d5833ae4jmo\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">qwen-max-2025-01-25</a>",
+    "Pass@2": 19.02,
+    "Pass@1": 15.870000000000001,
+    "Error@1": 6.3,
+    "Provider": "QWen",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "qwen-plus-2025-01-25",
+    "Model": "<a target=\"_blank\" href=\"https://www.alibabacloud.com/help/en/model-studio/what-is-qwen-llm#6ad3cd90f0c5r\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">qwen-plus-2025-01-25</a>",
+    "Pass@2": 15.110000000000001,
+    "Pass@1": 11.85,
+    "Error@1": 6.2,
+    "Provider": "QWen",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "qwen-2.5-72b-instruct",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/Qwen/Qwen2.5-72B-Instruct\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">qwen-2.5-72b-instruct</a>",
+    "Pass@2": 13.700000000000001,
+    "Pass@1": 10.54,
+    "Error@1": 6.63,
+    "Provider": "QWen",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "qwen-turbo-2024-11-01",
+    "Model": "<a target=\"_blank\" href=\"https://www.alibabacloud.com/help/en/model-studio/what-is-qwen-llm#ede6678dedqbz\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">qwen-turbo-2024-11-01</a>",
+    "Pass@2": 5.11,
+    "Pass@1": 2.6100000000000003,
+    "Error@1": 6.8500000000000005,
+    "Provider": "QWen",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "mistral-large-2411",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">mistral-large-2411</a>",
+    "Pass@2": 18.7,
+    "Pass@1": 13.04,
+    "Error@1": 7.28,
+    "Provider": "MistralAI",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "grok-2-1212",
+    "Model": "<a target=\"_blank\" href=\"https://openrouter.ai/x-ai/grok-2-vision-1212\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">grok-2-1212</a>",
+    "Pass@2": 17.169999999999998,
+    "Pass@1": 11.3,
+    "Error@1": 7.93,
+    "Provider": "X-AI",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "doubao-pro-1.5-32k",
+    "Model": "<a target=\"_blank\" href=\"https://www.volcengine.com/docs/82379/1554678\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">doubao-pro-1.5-32k</a>",
+    "Pass@2": 22.93,
+    "Pass@1": 16.63,
+    "Error@1": 7.28,
+    "Provider": "Doubao",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "doubao-pro-1.5-thinking",
+    "Model": "<a target=\"_blank\" href=\"http://volcengine.com/docs/82379/1536428\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">doubao-pro-1.5-thinking</a>",
+    "Pass@2": 30.220000000000002,
+    "Pass@1": 20.11,
+    "Error@1": 10.56,
+    "Provider": "Doubao",
+    "Openness": "Closed",
+    "Thinking": true,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "doubao-pro-1.5-32k-lite",
+    "Model": "<a target=\"_blank\" href=\"https://www.volcengine.com/docs/82379/1554516\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">doubao-pro-1.5-32k-lite</a>",
+    "Pass@2": 5.9799999999999995,
+    "Pass@1": 3.4799999999999995,
+    "Error@1": 6.41,
+    "Provider": "Doubao",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "GLM-4-0414",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/THUDM/GLM-4-32B-0414\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">GLM-4-0414</a>",
+    "Pass@2": 9.02,
+    "Pass@1": 7.5,
+    "Error@1": 6.3,
+    "Provider": "Thudm",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "step-fun-2-16k",
+    "Model": "<a target=\"_blank\" href=\"https://www.stepfun.com/company#step2\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">step-fun-2-16k</a>",
+    "Pass@2": 15.870000000000001,
+    "Pass@1": 13.700000000000001,
+    "Error@1": 6.09,
+    "Provider": "StepFun",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "sense-chat-5",
+    "Model": "<a target=\"_blank\" href=\"https://chat.sensetime.com/\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">sense-chat-5</a>",
+    "Pass@2": 12.72,
+    "Pass@1": 8.48,
+    "Error@1": 6.8500000000000005,
+    "Provider": "SenseTime",
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "360-gpt2-o1",
+    "Model": "<a target=\"_blank\" href=\"https://aiplus.360.cn/tags/360gpt2-o1.html\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">360-gpt2-o1</a>",
+    "Pass@2": 14.46,
+    "Pass@1": 8.260000000000002,
+    "Error@1": 7.39,
+    "Provider": 360,
+    "Openness": "Closed",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "minimax-text",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/MiniMaxAI/MiniMax-Text-01\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">minimax-text</a>",
+    "Pass@2": 10.76,
+    "Pass@1": 8.48,
+    "Error@1": 6.2,
+    "Provider": "MiniMax",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  },
+  {
+    "eval_name": "moonshot-kimi-latest",
+    "Model": "<a target=\"_blank\" href=\"https://huggingface.co/moonshotai\" style=\"color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;\">moonshot-kimi-latest</a>",
+    "Pass@2": 11.85,
+    "Pass@1": 5.220000000000001,
+    "Error@1": 10.22,
+    "Provider": "MoonshotAI",
+    "Openness": "Open",
+    "Thinking": false,
+    "Version": "v1.0.0",
+    "Agent": "Web-Agent",
+    "Source": ""
+  }
+]

src/submission/check_validity.py

@@ -0,0 +1,85 @@
+import json
+import os
+import re
+from collections import defaultdict
+from datetime import datetime, timedelta, timezone
+
+import huggingface_hub
+from huggingface_hub import ModelCard
+from huggingface_hub.hf_api import ModelInfo
+from transformers import AutoConfig
+from transformers.models.auto.tokenization_auto import AutoTokenizer
+
+def check_model_card(repo_id: str) -> tuple[bool, str]:
+    """Checks if the model card and license exist and have been filled"""
+    try:
+        card = ModelCard.load(repo_id)
+    except huggingface_hub.utils.EntryNotFoundError:
+        return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
+
+    # Enforce card content
+    if len(card.text) < 200:
+        return False, "Please add a description to your model card, it is too short."
+
+    return True, ""
+
+def is_model_on_hub(model_name: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
+    """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
+    try:
+        config = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote_code, token=token)
+        if test_tokenizer:
+            try:
+                tk = AutoTokenizer.from_pretrained(model_name, trust_remote_code=trust_remote_code, token=token)
+            except ValueError as e:
+                return (
+                    False,
+                    f"uses a tokenizer which is not in a transformers release: {e}",
+                    None
+                )
+            except Exception as e:
+                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
+        return True, None, config
+
+    except ValueError:
+        return (
+            False,
+            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
+            None
+        )
+
+    except Exception as e:
+        return False, "was not found on hub!", None
+
+
+def get_model_size(model_info: ModelInfo):
+    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
+    try:
+        model_size = round(model_info.safetensors["total"] / 1e9, 3)
+    except (AttributeError, TypeError):
+        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
+
+    size_factor = 8
+    model_size = size_factor * model_size
+    return model_size
+
+def already_submitted_models(requested_models_dir: str) -> set[str]:
+    """Gather a list of already submitted models to avoid duplicates"""
+    depth = 1
+    file_names = []
+    users_to_submission_dates = defaultdict(list)
+
+    for root, _, files in os.walk(requested_models_dir):
+        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
+        if current_depth == depth:
+            for file in files:
+                if not file.endswith(".json"):
+                    continue
+                with open(os.path.join(root, file), "r") as f:
+                    info = json.load(f)
+                    # Select organisation
+                    if info["model"].count("/") == 0 or "submitted_time" not in info:
+                        continue
+                    organisation, _ = info["model"].split("/")
+                    users_to_submission_dates[organisation].append(info["submitted_time"])
+
+    return set(file_names), users_to_submission_dates

src/submission/submit.py

@@ -0,0 +1,83 @@
+import json
+import os
+from datetime import datetime, timezone
+
+from src.display.formatting import styled_error, styled_message, styled_warning
+from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
+from src.submission.check_validity import (
+    already_submitted_models,
+    check_model_card,
+    get_model_size,
+    is_model_on_hub,
+)
+
+REQUESTED_MODELS = None
+USERS_TO_SUBMISSION_DATES = None
+
+def add_new_eval(
+    model: str,
+    base_model: str,
+):
+    global REQUESTED_MODELS
+    global USERS_TO_SUBMISSION_DATES
+    if not REQUESTED_MODELS:
+        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
+
+    user_name = ""
+    model_path = model
+    if "/" in model:
+        user_name = model.split("/")[0]
+        model_path = model.split("/")[1]
+
+    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+
+    # Is the model info correctly filled?
+    try:
+        model_info = API.model_info(repo_id=model)
+    except Exception:
+        return styled_error("Could not get your model information. Please fill it up properly.")
+
+    model_size = get_model_size(model_info=model_info)
+
+    modelcard_OK, error_msg = check_model_card(model)
+    if not modelcard_OK:
+        return styled_error(error_msg)
+
+    # Seems good, creating the eval
+    print("Adding new eval")
+
+    eval_entry = {
+        "model": model,
+        "base_model": base_model,
+        "status": "PENDING",
+        "submitted_time": current_time,
+        "private": False,
+    }
+
+    # Check for duplicate submission
+    if f"{model}" in REQUESTED_MODELS:
+        return styled_warning("This model has been already submitted.")
+
+    print("Creating eval file")
+    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
+    os.makedirs(OUT_DIR, exist_ok=True)
+    out_path = f"{OUT_DIR}/{model_path}_eval_request_False.json"
+
+    with open(out_path, "w") as f:
+        f.write(json.dumps(eval_entry))
+
+    print("Uploading eval file")
+    API.upload_file(
+        path_or_fileobj=out_path,
+        path_in_repo=out_path.split("eval-queue/")[1],
+        repo_id=QUEUE_REPO,
+        repo_type="dataset",
+        commit_message=f"Add {model} to eval queue",
+    )
+
+    # Remove the local file
+    os.remove(out_path)
+
+    return styled_message(
+        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
+    )