import os
import json
import gradio as gr
import pandas as pd
import numpy as np

from pathlib import Path
from apscheduler.schedulers.background import BackgroundScheduler
from huggingface_hub import snapshot_download

from src.about import (
    CITATION_BUTTON_LABEL,
    CITATION_BUTTON_TEXT,
    EVALUATION_QUEUE_TEXT,
    INTRODUCTION_TEXT,
    LLM_BENCHMARKS_TEXT,
    TITLE,
    ABOUT_TEXT,
    SUBMISSION_TEXT
)
from src.display.css_html_js import custom_css
from src.display.formatting import has_no_nan_values, make_clickable_model, model_hyperlink

# 定义列组
COLUMN_GROUPS = {
    "ALL": ["Model", "Perception", "Reasoning", "IF", "Safety", "AMU Score", 
            "Modality Selection", "Instruction Following", "Modality Synergy", 
            "AMG Score", "Overall", "Verified"],
    "AMU": ["Model", "Perception", "Reasoning", "IF", "Safety", "AMU Score", "Verified"],
    "AMG": ["Model", "Modality Selection", "Instruction Following", "Modality Synergy", "AMG Score", "Verified"]
}

def format_table(df):
    """Format the dataframe for display"""
    # 设置列的显示格式
    float_cols = df.select_dtypes(include=['float64']).columns
    for col in float_cols:
        df[col] = df[col].apply(lambda x: f"{x:.2f}")  # 修改为保留2位小数
        
    bold_columns = ['AMU Score', 'AMG Score', 'Overall']
    for col in bold_columns:
        if col in df.columns:
            df[col] = df[col].apply(lambda x: f'**{x}**')
    
    # 添加模型链接
    model_links = dict(zip(df['Model'], df['Model Link']))
    # df['Model'] = df['Model'].apply(lambda x: f'<a href="{model_links[x]}" target="_blank">{x}</a>')
    df['Model'] = df['Model'].apply(lambda x: f'[{x}]({model_links[x]})')
    # df['Model'] = df.apply(lambda x: model_hyperlink(model_links[x['Model']], x['Model']), axis=1)
    return df

def regex_table(dataframe, regex, filter_button, column_group="ALL"):
    """Takes a model name as a regex, then returns only the rows that has that in it."""
    # 深拷贝确保不修改原始数据
    df = dataframe.copy()
    
    # 选择要显示的列
    columns_to_show = COLUMN_GROUPS.get(column_group, COLUMN_GROUPS["ALL"])
    df = df[columns_to_show]
    
    # Split regex statement by comma and trim whitespace around regexes
    if regex:
        regex_list = [x.strip() for x in regex.split(",")]
        # Join the list into a single regex pattern with '|' acting as OR
        combined_regex = '|'.join(regex_list)
        # Filter based on model name regex
        df = df[df["Model"].str.contains(combined_regex, case=False, na=False)]
    
    df = df.sort_values(by='Overall' if 'Overall' in columns_to_show else columns_to_show[-1], ascending=False)
    df.reset_index(drop=True, inplace=True)
    
    # Add index column
    df.insert(0, '', range(1, 1 + len(df)))
    
    return df


df = pd.read_csv("data/eval_board.csv").sort_values(by='Overall', ascending=False)
total_models = len(df)

# Format numbers and add links
df = format_table(df)

with gr.Blocks(css=custom_css) as app:
    gr.HTML(TITLE)
    with gr.Row():
        with gr.Column(scale=6):
            gr.Markdown(INTRODUCTION_TEXT.format(str(total_models)))
    
    with gr.Tabs(elem_classes="tab-buttons") as tabs:
        with gr.TabItem("🏆 Model Performance Leaderboard"):
            with gr.Row():
                search_overall = gr.Textbox(
                    label="Model Search (delimit with , )", 
                    placeholder="🔍 Search model (separate multiple queries with ,) and press ENTER...",
                    show_label=False
                )
                column_group = gr.Radio(
                    choices=list(COLUMN_GROUPS.keys()),
                    value="ALL",
                    label="Select columns to show"
                )
            
            with gr.Row():
                performance_table_hidden = gr.Dataframe(
                    df,
                    headers=df.columns.tolist(),
                    elem_id="performance_table_hidden",
                    wrap=True,
                    visible=False,
                    datatype='markdown',
                )
                performance_table = gr.Dataframe(
                    regex_table(df.copy(), "", []),
                    headers=df.columns.tolist(),
                    elem_id="performance_table",
                    wrap=True,
                    show_label=False,
                    datatype='markdown',
                )
        
        with gr.TabItem("About"):
            gr.Markdown(ABOUT_TEXT)
            
        with gr.TabItem("Submit results 🚀", id=3):
            gr.Markdown(SUBMISSION_TEXT)
    
    with gr.Accordion("📚 Citation", open=False):
        citation_button = gr.Textbox(
            value=CITATION_BUTTON_TEXT,
            lines=7,
            label="Copy the following to cite these results.",
            elem_id="citation-button",
            show_copy_button=True,
        )
    
    # Set up event handlers
    def update_table(search_text, selected_group):
        return regex_table(df, search_text, [], selected_group)
    
    search_overall.change(
        update_table,
        inputs=[search_overall, column_group],
        outputs=performance_table
    )
    
    column_group.change(
        update_table,
        inputs=[search_overall, column_group],
        outputs=performance_table
    )

# Set up scheduler
scheduler = BackgroundScheduler()
scheduler.add_job(lambda: None, "interval", seconds=18000)  # every 5 hours
scheduler.start()

# Launch the app
app.launch(share=True)