app.py

import gradio as gr
import numpy as np
import pandas as pd

from constants import *


def get_data(verified, dataset, ipc, label_type, metric_weights=None):
    if metric_weights is None:
        metric_weights = [1.0 / len(METRICS) for _ in METRICS]
    if not isinstance(label_type, list):
        label_type = [label_type]

    data = pd.read_csv("data.csv")
    # filter data with no hlr or ior (no nan)
    data = data.dropna(subset=["hlr", "ior"])
    data["verified"] = data["verified"].apply(lambda x: bool(x))
    data["dataset"] = data["dataset"].apply(lambda x: DATASET_LIST[x])
    data["ipc"] = data["ipc"].apply(lambda x: IPC_LIST[x])
    data["label_type"] = data["label_type"].apply(lambda x: LABEL_TYPE_LIST[x])
    if verified:
        data = data[data["verified"] == verified]
    data = data[data["dataset"] == dataset]
    data = data[data["ipc"] == ipc]
    data = data[data["label_type"].apply(lambda x: x in label_type)]

    if len(data) == 0:
        return pd.DataFrame(columns=COLUMN_NAMES)

    # create a new column for the score
    data["score"] = data[METRICS[0].lower()] * 0.0
    for i, metric in enumerate(METRICS):
        data["score"] += data[metric.lower()] * metric_weights[i] * METRICS_SIGN[i]
    data["score"] = (np.exp(-0.01 * data["score"]) - np.exp(-1.0)) / (np.exp(1.0) - np.exp(-1.0))
    data = data.sort_values(by="score", ascending=False)
    data["ranking"] = range(1, len(data) + 1)

    for metric in METRICS:
        data[metric.lower()] = data[metric.lower()].apply(lambda x: round(x, 3))
    data["score"] = data["score"].apply(lambda x: round(x, 3))

    # formatting
    data["method"] = "[" + data["method"] + "](" + data["method_reference"] + ")"
    data["verified"] = data["verified"].apply(lambda x: "✅" if x else "")
    data = data.drop(columns=["method_reference", "dataset", "ipc"])
    data = data[['ranking', 'method', 'verified', 'date', 'label_type', 'hlr', 'ior', 'score']]
    if label_type == "Hard Label":
        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR%↓", "ior": "IOR%↑", "score": "DDRS↑", "verified": "Verified"})
    else:
        data = data.rename(columns={"ranking": "Ranking", "method": "Method", "date": "Date", "label_type": "Label Type", "hlr": "HLR%↓", "ior": "IOR%↑", "score": "DDRS↑", "verified": "Verified"})
    return data


with gr.Blocks() as leaderboard:
    gr.HTML(LEADERBOARD_HEADER)
    gr.Markdown(LEADERBOARD_INTRODUCTION)

    verified = gr.Checkbox(
        label="Verified by DD-Ranking Team (Uncheck to view all submissions)",
        value=True,
        interactive=True
    )

    dataset = gr.Radio(
        label="Dataset",
        choices=DATASET_LIST,
        value=DATASET_LIST[0],
        interactive=True,
    )
    ipc = gr.Radio(
        label="IPC",
        choices=DATASET_IPC_LIST[dataset.value],
        value=DATASET_IPC_LIST[dataset.value][0],
        interactive=True,
        info=IPC_INFO
    )
    label = gr.CheckboxGroup(
        label="Label Type",
        choices=LABEL_TYPE_LIST,
        value=LABEL_TYPE_LIST,
        info=LABEL_TYPE_INFO,
        interactive=True,
    )

    with gr.Accordion("Adjust Score Weights", open=False):
        gr.Markdown(WEIGHT_ADJUSTMENT_INTRODUCTION, latex_delimiters=[
              {'left': '$$', 'right': '$$', 'display': True},
              {'left': '$', 'right': '$', 'display': False},
              {'left': '\\(', 'right': '\\)', 'display': False},
              {'left': '\\[', 'right': '\\]', 'display': True}
          ])
        metric_sliders = []
        # for metric in METRICS:
        #     metric_sliders.append(gr.Slider(label=f"Weight for {metric}", minimum=0.0, maximum=1.0, value=0.5, interactive=True))
        metric_sliders.append(
            gr.Slider(label=f"Weight for HLR", minimum=0.0, maximum=1.0, value=0.5, interactive=True))
        adjust_btn = gr.Button("Adjust Weights")

    with gr.Accordion("Metric Definitions", open=False):
        gr.Markdown(METRIC_DEFINITION_INTRODUCTION, latex_delimiters=[
              {'left': '$$', 'right': '$$', 'display': True},
              {'left': '$', 'right': '$', 'display': False},
              {'left': '\\(', 'right': '\\)', 'display': False},
              {'left': '\\[', 'right': '\\]', 'display': True}
          ])

    # metric_weights = [s.value for s in metric_sliders]
    metric_weights = [metric_sliders[0].value, 1.0 - metric_sliders[0].value]
    board = gr.components.Dataframe(
        value=get_data(verified.value, dataset.value, ipc.value, label.value, metric_weights),
        headers=COLUMN_NAMES,
        type="pandas",
        datatype=DATA_TITLE_TYPE,
        interactive=False,
        visible=True,
        max_height=500,
    )

    for component in [verified, dataset, ipc, label]:
        component.change(lambda v, d, i, l, *m: gr.components.Dataframe(
            value=get_data(v, d, i, l, [m[0], 1.0 - m[0]]),
            headers=COLUMN_NAMES,
            type="pandas",
            datatype=DATA_TITLE_TYPE,
            interactive=False,
            visible=True,
            max_height=500,
        ), inputs=[verified, dataset, ipc, label] + metric_sliders, outputs=board)

    dataset.change(lambda d, i: gr.Radio(
        label="IPC",
        choices=DATASET_IPC_LIST[d],
        value=i if i in DATASET_IPC_LIST[d] else DATASET_IPC_LIST[d][0],
        interactive=True,
        info=IPC_INFO
    ), inputs=[dataset, ipc], outputs=ipc)

    adjust_btn.click(fn=lambda v, d, i, l, *m: gr.components.Dataframe(
            value=get_data(v, d, i, l, [m[0], 1.0 - m[0]]),
            headers=COLUMN_NAMES,
            type="pandas",
            datatype=DATA_TITLE_TYPE,
            interactive=False,
            visible=True,
            max_height=500,
        ), inputs=[verified, dataset, ipc, label] + metric_sliders, outputs=board)

    citation_button = gr.Textbox(
        value=CITATION_BUTTON_TEXT,
        label=CITATION_BUTTON_LABEL,
        elem_id="citation-button",
        lines=6,
        show_copy_button=True,
    )

leaderboard.launch()