app.py

import gradio as gr
import pandas as pd
import json

from src.about import (
    REPRODUCIBILITY_TEXT,
    INTRODUCTION_TEXT,
    ABOUT_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css, custom_js
# from src.display.utils import (
#     COLS,
#     ST_BENCHMARK_COLS,
#     AGENTIC_BENCHMARK_COLS,
#     EVAL_COLS,
#     AutoEvalColumn,
#     fields,
# )
# 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, TASK_NAME_INVERSE_MAP
# from src.submission.submit import add_new_eval
from src.display.formatting import make_clickable_field


# 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()


# ST_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, ST_BENCHMARK_COLS)
# AGENTIC_LEADERBOARD_DF = get_leaderboard_df(EVAL_RESULTS_PATH, EVAL_REQUESTS_PATH, COLS, AGENTIC_BENCHMARK_COLS)

# (
#     finished_eval_queue_df,
#     running_eval_queue_df,
#     pending_eval_queue_df,
# ) = get_evaluation_queue_df(EVAL_REQUESTS_PATH, EVAL_COLS)

# def bold_max(s):
#     is_max = s == s.max()  # Boolean Series: True for the max value(s)
#     return ['font-weight: bold' if v else '' for v in is_max]

# def init_leaderboard(df, benchmark_type):
#     if df is None or df.empty:
#         raise ValueError("Leaderboard DataFrame is empty or None.")
    
#     non_task_cols = ["Model"]
#     if benchmark_type == "agentic":
#         # Include agent column
#         non_task_cols.append("Agent")
#     elif benchmark_type == "base":
#         # Drop agent column
#         dataframe = dataframe.drop(columns=["Agent"])
#     AutoEvalColumnSubset = [c for c in fields(AutoEvalColumn) if ((c.name in non_task_cols) or (TASK_NAME_INVERSE_MAP.get(c.name, dict()).get("type", "")==benchmark_type))]

    # styler = dataframe.style.apply(bold_max, subset=pd.IndexSlice[:, dataframe.columns[1:]])
    # df.style.set_table_styles([
    #     {'selector': 'th', 'props': [('text-align', 'center')]},
    #     {'selector': 'td', 'props': [('text-align', 'center')]}
    # ])
    # Define a common tooltip text
    # tooltip_text = "This is the common tooltip"

    # # Create a tooltip DataFrame with the same shape as df,
    # # filled with the same tooltip text for each cell.
    # tooltips = pd.DataFrame(tooltip_text, index=df.index, columns=df.columns)

    # # Apply the tooltips to the DataFrame
    # styled_df = df.style.set_tooltips(tooltips)


    # return gr.components.Dataframe(
    #     value=df,
    #     datatype=[c.type for c in AutoEvalColumnSubset],
    #     column_widths=["150px" if c.name != "Model" else "250px" for c in AutoEvalColumnSubset],
    #     wrap=False,
    # )



def build_leaderboard(type):
    with open('data/results.json', 'r') as f:
        results = json.load(f)

    with open('data/tasks.json', 'r') as f:
        tasks = json.load(f)

    # Filter tasks based on type
    filtered_tasks = {k: v for k, v in tasks.items() if v['type'] == type}

    data = []
    for model_name, model_data in results.items():
        # For agentic type, skip models that have all null values for agentic tasks
        if type == "agentic":
            has_agentic_results = any(
                model_data['results'].get(task, {}).get(tasks[task]['metric']) is not None 
                for task in filtered_tasks
            )
            if not has_agentic_results:
                continue

        model_sha = model_data["config"]["model_sha"]
        model_name = model_data["config"]["model_name"]
        row = {
            'Model': make_clickable_field(model_name, model_sha)
        }
        
        for dataset, metrics in model_data['results'].items():
            # Only include metrics for tasks of the specified type
            if dataset in filtered_tasks:
                value = next(iter(metrics.values()))
                log_url = metrics.get('log_url')
                # Use display name from tasks.json instead of raw dataset name
                display_name = filtered_tasks[dataset]['display_name']
                # Round non-null values to 2 decimal places and make clickable if log_url exists
                if value is not None:
                    value = round(value*100, 2)
                    if log_url:
                        value = make_clickable_field(value, log_url)
                row[display_name] = value
        data.append(row)

    results_df = pd.DataFrame(data)
    
    # Round all numeric columns to 2 decimal places
    numeric_cols = results_df.select_dtypes(include=['float64', 'float32']).columns
    results_df[numeric_cols] = results_df[numeric_cols].round(2)

    # Fill null values with "-"
    results_df = results_df.fillna("--")

    if type == "agentic":
        # Include agent column as second column after Model
        results_df.insert(1, 'Agent', '[Basic Agent](https://inspect.ai-safety-institute.org.uk/agents.html#sec-basic-agent)')
    
    return gr.components.Dataframe(
        value=results_df,
        datatype=["html" for _ in results_df.columns],
        column_widths=["250px" if c == "Model" else "150px" for c in results_df.columns],
        wrap=False,
    )


black_logo_path = "src/assets/logo-icon-black.png"
white_logo_path = "src/assets/logo-icon-white.png"

demo = gr.Blocks(
    css=custom_css,
    js=custom_js,
    theme=gr.themes.Default(primary_hue=gr.themes.colors.pink),
    fill_height=True,
    fill_width=True,
)
with demo:
    gr.HTML(f"""
    <div id="page-header">
        <div id="header-container">
            <div id="left-container">
                <img id="black-logo" src="/gradio_api/file={black_logo_path}">
                <img id="white-logo" src="/gradio_api/file={white_logo_path}">
            </div>
            <div id="centre-container">
                <h1 style="margin-bottom: 0.25rem;">{TITLE}</h1>
                <p style="color:#eb088a; margin:0; font-size:1.2rem;">Explore Interactive Results &amp; Traces</p>
            </div>
            <div id="right-container">
            </div>
        </div>
    </div>
    """)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="intro-text", sanitize_html=False)

    with gr.Tabs(elem_classes=["leaderboard-table", "tab-buttons"]) as tabs:
        with gr.TabItem("Base Benchmarks", elem_classes="llm-benchmark-tab-table", id=0):
            build_leaderboard("base")

        with gr.TabItem("Agentic Benchmarks", elem_classes="llm-benchmark-tab-table", id=1):
            build_leaderboard("agentic")

        with gr.TabItem("About", elem_classes="llm-benchmark-tab-table", id=2):
            gr.Markdown(ABOUT_TEXT, elem_classes="markdown-text", sanitize_html=False)

        with gr.TabItem("Reproducibility", elem_classes="llm-benchmark-tab-table", id=3):
            gr.Markdown(REPRODUCIBILITY_TEXT, elem_classes="markdown-text", sanitize_html=False)

assets = [black_logo_path, white_logo_path]
demo.launch()