import json
import gradio as gr
from gradio_leaderboard import Leaderboard, ColumnFilter, SelectColumns
import pandas as pd
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download
from datasets import load_dataset

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    TASK_TEXT,
    SUBMIT_TEMPLATE,
    LLM_BENCHMARKS_TEXT,
    TITLE,
)
from src.display.css_html_js import custom_css
from src.display.utils import (
    BENCHMARK_COLS,
    COLS,
    EVAL_COLS,
    EVAL_TYPES,
    AutoEvalColumn,
    ModelType,
    fields,
    WeightType,
    Precision
)
from src.envs import API, EVAL_RESULTS_PATH, GOLDEN_REPO, REPO_ID, TOKEN
from src.populate import get_evaluation_queue_df, get_leaderboard_df
from src.submission.submit import add_new_eval
from src.evaluation import evaluate

import pdb

def restart_space():
    API.restart_space(repo_id=REPO_ID)

### Space initialisation
# try:
#     print(EVAL_REQUESTS_PATH)
#     snapshot_download(
#         repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
#     )
# except Exception:
#     restart_space()
# try:
#     print(EVAL_RESULTS_PATH)
#     snapshot_download(
#         repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
#     )
# except Exception:
#     restart_space()

try:
    golden = load_dataset(GOLDEN_REPO, token=TOKEN)
    print(golden)
except Exception:
    restart_space()

task = ['Overall', 'Crossword', 'Acrostic', 'Logic_Puzzle', 'Cryptogram', 'Sudoku', 'Drop_Quote']
leaderboard_dict = {}
for t in task:
    leaderboard_dict[t] = get_leaderboard_df(EVAL_RESULTS_PATH, COLS, task=t)


def init_leaderboard(dataframe):
    if dataframe is None or dataframe.empty:
        raise ValueError("Leaderboard DataFrame is empty or None.")

    # pdb.set_trace()
    def highlight_max_bold(s):
        return ['font-weight: bold' if v == s.max() and v != s.min() else '' for v in s]
    num_cols = dataframe.select_dtypes(include=['float']).columns
    styler = dataframe.style.format({col: "{:.1f}" for col in num_cols})
    styler = styler.apply(highlight_max_bold, subset=num_cols)
    return gr.components.Dataframe(
        value=styler,
        headers=[c.name for c in fields(AutoEvalColumn)],
        datatype=[c.type for c in fields(AutoEvalColumn)],
        row_count=10,
        interactive=False,
        column_widths=[180, 60, 80, 80, 80, 80, 60],
    )


    
def eval_json(file):
    try:
        with open(file.name, 'r', encoding='utf-8') as f:
            data = json.load(f)
            
        tasks = ["crossword", "acrostic", "logic", "cryptogram", "sudoku", "drop"]
        
        eval_dict = {}
        
        for task in tasks:
            data_list = data["results"][task]
            golden_list = golden[task]
            result = evaluate(data_list, golden_list, task)
            eval_dict[task] = result
        
        return json.dumps(eval_dict, indent=4)

        
    except Exception as e:
        return str(e)



demo = gr.Blocks(css=custom_css)
with demo:
    gr.HTML(TITLE)
    gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text")

    with gr.Tabs(elem_id="main-tabs", elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏅 LLM Benchmark", elem_id="llm-benchmark-tab-table", id=0):
            # leaderboard = init_leaderboard(LEADERBOARD_DF)
            with gr.Tabs():
                for i, t in enumerate(task):
                    with gr.TabItem(t.replace("_", " "), elem_id=f"llm-benchmark-tab-table-{t}", id=i):
                        if TASK_TEXT.get(t, None):
                            gr.Markdown(TASK_TEXT[t], elem_classes="markdown-text")
                        leaderboard = init_leaderboard(leaderboard_dict[t])


        # with gr.TabItem("📝 About", elem_id="llm-benchmark-tab-table", id=2):
        #     gr.Markdown(LLM_BENCHMARKS_TEXT, elem_classes="markdown-text")

        with gr.TabItem("🚀 Submit here! ", elem_id="llm-benchmark-tab-table", id=3):
            with gr.Row():
                gr.Markdown("# ✉️✨ Submit your results here!", elem_classes="markdown-text")

            gr.Markdown("## Submission Template", elem_classes="markdown-text")
            gr.Markdown("See [submission_template.json](https://github.com/Ultramarine-spec/LR2Bench/blob/main/submission_template.json) for detail. The following is an example for the JSON structure.", elem_classes="markdown-text")
            gr.Markdown(SUBMIT_TEMPLATE, elem_classes="markdown-text", height=250)
            
            file_input = gr.File(label="Upload JSON File", file_types=[".json"], height=150)
            json_output = gr.JSON(label="Your Model Performance")  # 输出 JSON 数据
            submit_button = gr.Button("Submit")
            submit_button.click(fn=eval_json, inputs=file_input, outputs=json_output)


    with gr.Row():
        # gr.Markdown()
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            label=CITATION_BUTTON_LABEL,
            elem_id="citation-button",
            show_copy_button=True,
        )

scheduler = BackgroundScheduler()
scheduler.add_job(restart_space, "interval", seconds=1800)
scheduler.start()
demo.queue(default_concurrency_limit=40).launch()