import gradio as gr
import pandas as pd
import numpy as np
from gradio_leaderboard import Leaderboard, SelectColumns, ColumnFilter

# Define constants and enums
TITLE = "<h1>M-RewardBench Leaderboard</h1>"
INTRODUCTION_TEXT = "https://m-rewardbench.github.io/"
GOOGLE_SHEET_URL = "https://docs.google.com/spreadsheets/d/1qrD7plUdrBwAw7G6UeDVZAaV9ihxaNAcoiKwSaqotR4/export?gid=0&format=csv"
ABOUT_TEXT = """Welcome to M-RewardBench Leaderboard!"""


class AutoEvalColumn:
  model = {
    "name": "Model",
    "type": "markdown",
    "displayed_by_default": True,
    "never_hidden": True,
  }
  
  model_type = {
    "name": "MT",
    "type": "markdown",
    "displayed_by_default": True,
    "never_hidden": True,
  }	
  
  @classmethod
  def add_columns_from_df(cls, df, columns):
    for col in columns:
      if col.lower() != 'model':  # Skip if it's the model column since it's predefined
        setattr(cls, col, {
              "name": col,
              "type": "markdown",
              "displayed_by_default": True,
              "never_hidden": False,
        })				


def get_result_data():
  return pd.read_csv(GOOGLE_SHEET_URL)


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

  return Leaderboard(
    value=dataframe,
    datatype=[
      col["type"]
      for col in AutoEvalColumn.__dict__.values()
      if isinstance(col, dict)
    ],
    select_columns=SelectColumns(
      default_selection=[
        col["name"]
        for col in AutoEvalColumn.__dict__.values()
        if isinstance(col, dict) and col["displayed_by_default"]
      ],
      cant_deselect=[
        col["name"]
        for col in AutoEvalColumn.__dict__.values()
        if isinstance(col, dict) and col.get("never_hidden", False)
      ],
      label="Select Columns to Display:",
    ),
    search_columns=["Model"],
    interactive=False,
  )


def format_model_link(row):
  """Format model name as HTML link if URL is available"""
  model_name = row["Model"]
  # url = row.get("URL", "")
  # if pd.notna(url) and url.strip():
  #   return f'<a href="{url}" target="_blank">{model_name}</a>'
  return model_name

lang_ids = "eng_Latn	arb_Arab	tur_Latn	rus_Cyrl	ces_Latn	pol_Latn	kor_Hang	zho_Hans	zho_Hant	fra_Latn	ell_Grek	deu_Latn	ron_Latn	ita_Latn	nld_Latn	pes_Arab	hin_Deva	ukr_Cyrl	por_Latn	ind_Latn	jpn_Jpan	spa_Latn	heb_Hebr	vie_Latn"

emojis = "🔢 💬 🎯"

model_types = {"Generative RM": "💬", "DPO": "🎯", "Sequence Classifier": "🔢"}

from functools import partial
def format_with_color(val, min_val=50, max_val=100):
    """
    Formats a value with inline green color gradient CSS.
    Returns an HTML string with bold, black text and muted green background.
    """
    try:
        val = float(val)
        if pd.isna(val):
            return str(val)
            
        # Normalize value between 50 and 100 to 0-1 range
        normalized = (val - min_val) / (max_val - min_val)
        # Clamp value between 0 and 1
        normalized = max(0, min(1, normalized))
        
        # Create color gradient with reduced brightness (max 200 instead of 255)
        # and increased minimum intensity (50 instead of 0)
        intensity = int(50 + (150 * (1 - normalized)))        
        
        # Return HTML with inline CSS - bold black text
        return f'<div val={val} style="background-color: rgb({intensity}, 200, {intensity}); color: black; font-weight: bold; text-align: center; vertical-align: middle;">{val*100:.1f}</div>'
      
    except (ValueError, TypeError):
        return str(val)

demo = gr.Blocks(theme=gr.themes.Soft())
with demo:
  gr.HTML(TITLE)
  gr.Markdown(INTRODUCTION_TEXT)

  with gr.Tabs() as tabs:
    with gr.TabItem("🏅 Leaderboard"):
      df = get_result_data()
      df["Model_Type"] = df["Model_Type"].map(model_types)
    
      df["Model"] = df.apply(format_model_link, axis=1)
      
      df["zho"] = df[["zho_Hans", "zho_Hant"]].mean(axis=1)
      
      columns = lang_ids.split("\t")
      # print(df.head())
      df.pop("zho_Hans")
      df.pop("zho_Hant")
      
      df.rename(columns={
        "Model_Type": "MT",
        "Avg_Multilingual": "AVG",
      }, inplace=True)
      df.rename(columns={col: col[:3] for col in columns}, inplace=True)
  
      # df = df.style.applymap(apply_color_gradient, subset=['eng'])
      numeric_cols = df.select_dtypes(include=[np.number]).columns
      
      
      for col in numeric_cols:
        lang_format_with_color = partial(format_with_color, 
                                        min_val=df[col].min(),
                                        max_val=df[col].max())
    
        df[col] = df[col].apply(lang_format_with_color)
        
      
      # for col in numeric_cols:
      #   df[col] = (df[col] * 100).round(1).astype(str)
      
      AutoEvalColumn.add_columns_from_df(df, numeric_cols)
        
      leaderboard = init_leaderboard(df)
      

demo.launch(ssr_mode=False)