app.py

import gradio as gr
import pandas as pd
import os
from huggingface_hub import snapshot_download

from src.display.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

# clone / pull the lmeh eval data
TOKEN = os.environ.get("TOKEN", None)
RESULTS_REPO = f"lukecq/SeaExam-results"
CACHE_PATH=os.getenv("HF_HOME", ".")
EVAL_RESULTS_PATH = os.path.join(CACHE_PATH, "eval-results")
print(EVAL_RESULTS_PATH)
snapshot_download(
    repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", 
    token=TOKEN
)

# Load the CSV file
def load_csv(file_path):
    data = pd.read_csv(file_path)
    return data

# Example path to your CSV file
csv_path = f'{EVAL_RESULTS_PATH}/SeaExam_results_0419.csv'
data = load_csv(csv_path)

def show_data():
    return data

# iface = gr.Interface(fn=show_data, inputs = None, outputs="dataframe", title="SeaExam Leaderboard", 
#                      description="Leaderboard for the SeaExam competition.")
# iface.launch()

demo = gr.Blocks(css=custom_css)
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_table = gr.components.Dataframe(
                value=data,
                # value=leaderboard_df[
                #     [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
                #     + shown_columns.value
                #     + [AutoEvalColumn.dummy.name]
                # ],
                # headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                # datatype=TYPES,
                # elem_id="leaderboard-table",
                interactive=False,
                visible=True,
                # column_widths=["2%", "33%"] 
            )

demo.launch()