import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter
import pandas as pd

# dataset = load_dataset("your_dataset_name")


from datetime import datetime



def gradio_interface():
    with gr.Blocks(title="OpenADMET ADMET Challenge") as demo:



        # --- Welcome markdown message ---
        welcome_md = """
        # 🧪 OpenADMET + XXX  
        ## Computational Blind Challenge in ADMET

        Welcome to the **XXX**, hosted by **OpenADMET** in collaboration with **XXX**.

        Your task is to develop and submit predictive models for key ADMET properties on a blinded test set of real world drug discovery data.


        📅 **Timeline**:  
        - TBD

        ---

        """

        # --- Gradio Interface ---
        with gr.Tabs(elem_classes="tab-buttons"):

            with gr.TabItem("Welcome"):
                gr.Markdown(welcome_md)

            with gr.TabItem("Submit Predictions"):
                gr.Markdown("Upload your prediction files here.")
                filename = gr.State(value=None) 
                eval_state = gr.State(value=None) 
                user_state = gr.State(value=None)

            with gr.TabItem("Leaderboard"):
                gr.Markdown("View the leaderboard here.")
                df = pd.DataFrame({
                    "user": ["User1", "User2", "User3"],
                    "Model": ["A", "B", "C"],
                    "R2": [0.94, 0.92, 0.89],
                    "Spearman R": [0.93, 0.91, 0.88],
                })
                Leaderboard(
                    value=df,
                    # Optionally configure columns:
                    select_columns=["Model", "R2", "Spearman R"],
                    # Additional options: search_columns, filter_columns, hide_columns, etc.
                    search_columns=["Model", "user"],
                )


            with gr.TabItem("About"):
                gr.Markdown("Learn more about the challenge and the organizers.")

    return demo

if __name__ == "__main__":
    gradio_interface().launch()