import gradio as gr
from utils import get_leaderboard_data, SUPER_GROUPS, MODEL_GROUPS
import os
from constants import *

# Get the directory of the current script
current_dir = os.path.dirname(os.path.abspath(__file__))

# Construct the path to the CSS file
css_file = os.path.join(current_dir, "static", "css", "style.css")

# Read the CSS file
with open(css_file, "r") as f:
    css = f.read()

def update_leaderboard(selected_super_group, selected_model_group):
    headers, data = get_leaderboard_data(selected_super_group, selected_model_group)
    return gr.Dataframe(
        value=data,
        headers=headers,
        datatype=["str"] + ["number"] * (len(headers) - 1),
    )

with gr.Blocks(css=css) as block:
    gr.Markdown(
        LEADERBOARD_INTRODUCTION
    )
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("📊 MEGA-Bench", elem_id="qa-tab-table1", id=1):
            with gr.Row():
                with gr.Accordion("Citation", open=False):
                    citation_button = gr.Textbox(
                        value=CITATION_BUTTON_TEXT,
                        label=CITATION_BUTTON_LABEL,
                        elem_id="citation-button",
                        lines=10,
                    )
            gr.Markdown(
                TABLE_INTRODUCTION
            )

            with gr.Row():
                super_group_selector = gr.Radio(
                    choices=list(SUPER_GROUPS.keys()),
                    label="Select a dimension to display breakdown results. We use different column colors to distinguish the overall benchmark scores and breakdown results.",
                    value=list(SUPER_GROUPS.keys())[0]
                )
                model_group_selector = gr.Radio(
                    choices=list(MODEL_GROUPS.keys()),
                    label="Select a model group",
                    value="All"
                )

            initial_headers, initial_data = get_leaderboard_data(list(SUPER_GROUPS.keys())[0], "All")
            gr.Markdown(
                "**Table 1: MEGA-Bench full results.** <br> The Core set contains $N_{\\text{core}} = 440$ tasks evaluated by rule-based metrics, and the Open-ended set contains $N_{\\text{open}} = 65$ tasks evaluated by a VLM judge (we use GPT-4o-0806). <br> $\\text{Overall} \\ = \\ \\frac{\\max(\\text{Core w/o CoT}, \\ \\text{Core w/ CoT}) \\ \\cdot \\ N_{\\text{core}} \\ + \\ \\text{Open-ended} \\ \\cdot \\ N_{\\text{open}}}{N_{\\text{core}} \\ + \\ N_{\\text{open}}}$",
                elem_classes="table-caption", 
                latex_delimiters=[ {"left": "$", "right": "$", "display": False }],
            )
            data_component = gr.Dataframe(
                value=initial_data,
                headers=initial_headers,
                datatype=["str"] + ["number"] * (len(initial_headers) - 1),
                interactive=False,
                elem_classes="custom-dataframe",
                max_height=1200,
            )
            refresh_button = gr.Button("Refresh")
            refresh_button.click(fn=update_leaderboard, inputs=[super_group_selector, model_group_selector], outputs=[data_component])
            super_group_selector.change(fn=update_leaderboard, inputs=[super_group_selector, model_group_selector], outputs=[data_component])
            model_group_selector.change(fn=update_leaderboard, inputs=[super_group_selector, model_group_selector], outputs=[data_component])

        with gr.TabItem("📝 Data Information", elem_id="qa-tab-table2", id=2):
            gr.Markdown(DATA_INFO, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit", elem_id="submit-tab", id=3):
            with gr.Row():
                gr.Markdown(SUBMIT_INTRODUCTION, elem_classes="markdown-text")



if __name__ == "__main__":
    block.launch(share=True)