import json

import gradio as gr
import pandas as pd
from huggingface_hub import HfFileSystem


RESULTS_DATASET_ID = "datasets/open-llm-leaderboard/results"


fs = HfFileSystem()


def fetch_result_paths():
    paths = fs.glob(f"{RESULTS_DATASET_ID}/**/**/*.json")
    return paths


def filter_latest_result_path_per_model(paths):
    from collections import defaultdict

    d = defaultdict(list)
    for path in paths:
        model_id, _ = path[len(RESULTS_DATASET_ID) +1:].rsplit("/", 1)
        d[model_id].append(path)
    return {model_id: max(paths) for model_id, paths in d.items()}


def get_result_path_from_model(model_id, result_path_per_model):
    return result_path_per_model[model_id]


def load_result(result_path) -> pd.DataFrame:
    with fs.open(result_path, "r") as f:
        data = json.load(f)
    model_name = data.get("model_name", "Model")
    df = pd.json_normalize([data])
    return df.iloc[0].rename_axis("Parameters").rename(model_name).to_frame()  # .reset_index()


def render_result_1(model_id, results):
    result_path = get_result_path_from_model(model_id, latest_result_path_per_model)
    result = load_result(result_path)
    return pd.concat([result, results.iloc[:, [0, 2]].set_index("Parameters")], axis=1).reset_index()


def render_result_2(model_id, results):
    result_path = get_result_path_from_model(model_id, latest_result_path_per_model)
    result = load_result(result_path)
    return pd.concat([results.iloc[:, [0, 1]].set_index("Parameters"), result], axis=1).reset_index()


# if __name__ == "__main__":
latest_result_path_per_model = filter_latest_result_path_per_model(fetch_result_paths())

with gr.Blocks(fill_height=True) as demo:
    gr.HTML("<h1 style='text-align: center;'>Compare Results of the 🤗 Open LLM Leaderboard</h1>")
    gr.HTML("<h3 style='text-align: center;'>Select 2 results to load and compare</h3>")

    with gr.Row():
        with gr.Column():
            model_id_1 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Results")
            load_btn_1 = gr.Button("Load")
        with gr.Column():
            model_id_2 = gr.Dropdown(choices=list(latest_result_path_per_model.keys()), label="Results")
            load_btn_2 = gr.Button("Load")

    with gr.Row():
        compared_results = gr.Dataframe(
            label="Results",
            headers=["Parameters", "Result-1", "Result-2"],
            interactive=False,
            column_widths=["30%", "30%", "30%"],
            wrap=True
        )

    load_btn_1.click(
        fn=render_result_1,
        inputs=[model_id_1, compared_results],
        outputs=compared_results,
    )
    load_btn_2.click(
        fn=render_result_2,
        inputs=[model_id_2, compared_results],
        outputs=compared_results,
    )

demo.launch()