{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "import gradio as gr\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_leaderboard_df():\n",
    "    filepaths = list(Path(\"eval_results\").rglob(\"*.json\"))\n",
    "\n",
    "    # Parse filepaths to get unique models\n",
    "    models = set()\n",
    "    for filepath in filepaths:\n",
    "        path_parts = Path(filepath).parts\n",
    "        model_revision = \"_\".join(path_parts[1:4])\n",
    "        models.add(model_revision)\n",
    "\n",
    "    # Initialize DataFrame\n",
    "    df = pd.DataFrame(index=list(models))\n",
    "\n",
    "    # Extract data from each file and populate the DataFrame\n",
    "    for filepath in filepaths:\n",
    "        path_parts = Path(filepath).parts\n",
    "        model_revision = \"_\".join(path_parts[1:4])\n",
    "        task = path_parts[4].capitalize()\n",
    "        # Extract timestamp from filepath\n",
    "        timestamp = filepath.stem.split(\"_\")[-1][:-3]\n",
    "        df.loc[model_revision, \"Timestamp\"] = timestamp\n",
    "\n",
    "        with open(filepath, \"r\") as file:\n",
    "            data = json.load(file)\n",
    "            first_result_key = next(iter(data[\"results\"]))  # gets the first key in 'results'\n",
    "            # TruthfulQA has two metrics, so we need to pick the `mc2` one that's reported on the leaderboard\n",
    "            if task == \"truthfulqa\":\n",
    "                value = data[\"results\"][first_result_key][\"truthfulqa_mc2\"]\n",
    "            else:\n",
    "                first_metric_key = next(iter(data[\"results\"][first_result_key]))  # gets the first key in the first result\n",
    "                value = data[\"results\"][first_result_key][first_metric_key]  # gets the value of the first metric\n",
    "            df.loc[model_revision, task] = value\n",
    " \n",
    "    df.insert(loc=0, column=\"Average\", value=df.mean(axis=1, numeric_only=True))\n",
    "    df = df.sort_values(by=[\"Average\"], ascending=False)\n",
    "    df = df.reset_index().rename(columns={\"index\": \"Model\"}).round(3)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = get_leaderboard_df()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Average</th>\n",
       "      <th>Truthfulqa</th>\n",
       "      <th>Winogrande</th>\n",
       "      <th>Gsm8k</th>\n",
       "      <th>Hellaswag</th>\n",
       "      <th>Arc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Qwen_Qwen1.5-0.5B-Chat_main</td>\n",
       "      <td>2024-02-28T07-35-58.803</td>\n",
       "      <td>0.296</td>\n",
       "      <td>0.271</td>\n",
       "      <td>0.519</td>\n",
       "      <td>0.039</td>\n",
       "      <td>0.363</td>\n",
       "      <td>0.287</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                         Model                Timestamp  Average  Truthfulqa  \\\n",
       "0  Qwen_Qwen1.5-0.5B-Chat_main  2024-02-28T07-35-58.803    0.296       0.271   \n",
       "\n",
       "   Winogrande  Gsm8k  Hellaswag    Arc  \n",
       "0       0.519  0.039      0.363  0.287  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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