{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://ipip.ori.org/New_IPIP-50-item-scale.htm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.system('pip install openpyxl') #works for huggingface-spaces\n",
    "#score of each latent variable ranges in [10, 50]\n",
    "def correct_score(score, big_5_indicator):\n",
    "    big_5_sign = big_5_indicator[0]\n",
    "    big_5_num = int(big_5_indicator[1])\n",
    "    dict1 = {\n",
    "        1 : 'Extraversion',\n",
    "        2 : 'Agreeableness',\n",
    "        3 : 'Conscientiousness',\n",
    "        4 : 'Neuroticism',\n",
    "        5 : 'Openness'\n",
    "    }\n",
    "    if big_5_sign=='+':\n",
    "        return [dict1[big_5_num], score]\n",
    "    elif big_5_sign =='-':\n",
    "        return [dict1[big_5_num], 6-score]\n",
    "# correct_score(3, big_5_indicator='-2')\n",
    "\n",
    "def make_question(q):\n",
    "    q = gr.Radio(choices=[1, 2, 3, 4, 5], label=q, value=1) #, value=5\n",
    "    return q\n",
    "\n",
    "def calculate_personality_score(questions, list_questions):\n",
    "    personality = {\n",
    "        'Extraversion': 0,\n",
    "        'Agreeableness': 0,\n",
    "        'Conscientiousness': 0,\n",
    "        'Neuroticism': 0,\n",
    "        'Openness': 0\n",
    "    }\n",
    "    for question in list_questions:\n",
    "        big_5_indicator = questions[question[0]]\n",
    "        score = correct_score(question[1], big_5_indicator=big_5_indicator)\n",
    "        personality[score[0]] += score[1]\n",
    "    return personality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "\n",
      "To create a public link, set `share=True` in `launch()`.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"http://127.0.0.1:7862/\" width=\"900\" height=\"500\" allow=\"autoplay; camera; microphone;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<gradio.routes.App at 0x27427d923a0>, 'http://127.0.0.1:7862/', None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gradio as gr\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "#\n",
    "df_big5_test = pd.read_csv('Big5.csv', sep=';', header=None)\n",
    "df_big5_test = df_big5_test[[0, 6]]\n",
    "df_big5_test.columns = ['question', 'score']\n",
    "df_big5_test['score'] = df_big5_test['score'].apply(lambda x : x.replace('(', '').replace(')', '')[::-1])\n",
    "# df.index = df.pop('question')\n",
    "questions = dict()\n",
    "for el in df_big5_test.values.tolist():\n",
    "    questions[el[0]] = el[1]\n",
    "questions\n",
    "\n",
    "#the first module becomes text1, the second module file1\n",
    "def greet(*args): \n",
    "    args_list = [item for item in args]\n",
    "\n",
    "    #extract big5\n",
    "    q_list = list(questions)\n",
    "    a_list = args_list[:]\n",
    "    list_total = [[q_list[x], a_list[x]] for x in range(len(a_list))]\n",
    "    personality = calculate_personality_score(questions, list_total)\n",
    "    personality = { x : personality[x]/50 for x in personality}\n",
    "    # personality = [personality['Openness'], personality['Conscientiousness'], personality['Extraversion'], personality['Agreeableness'], personality['Neuroticism']]\n",
    "    # personality = [x/50 for x in personality]\n",
    "\n",
    "    #to return a file:\n",
    "    #return 'new.xlsx'\n",
    "    return personality\n",
    "\n",
    "iface = gr.Interface(\n",
    "    fn=greet, \n",
    "    inputs=[make_question(q) for q in list(questions)], \n",
    "        outputs=[\"text\"]\n",
    "    )\n",
    "iface.launch(share=False)"
   ]
  }
 ],
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