first push

Big5.csv

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+Am the life of the party.;;;;;;(1+)
+Feel little concern for others.;;;;;;(2-)
+Am always prepared.;;;;;;(3+)
+Get stressed out easily.;;;;;;(4-)
+Have a rich vocabulary.;;;;;;(5+)
+Don't talk a lot.;;;;;;(1-)
+Am interested in people.;;;;;;(2+)
+Leave my belongings around.;;;;;;(3-)
+Am relaxed most of the time.;;;;;;(4+)
+Have difficulty understanding abstract ideas.;;;;;;(5-)
+Feel comfortable around people.;;;;;;(1+)
+Insult people.;;;;;;(2-)
+Pay attention to details.;;;;;;(3+)
+Worry about things.;;;;;;(4-)
+Have a vivid imagination.;;;;;;(5+)
+Keep in the background.;;;;;;(1-)
+Sympathize with others' feelings.;;;;;;(2+)
+Make a mess of things.;;;;;;(3-)
+Seldom feel blue.;;;;;;(4+)
+Am not interested in abstract ideas.;;;;;;(5-)
+Start conversations.;;;;;;(1+)
+Am not interested in other people's problems.;;;;;;(2-)
+Get chores done right away.;;;;;;(3+)
+Am easily disturbed.;;;;;;(4-)
+Have excellent ideas.;;;;;;(5+)
+Have little to say.;;;;;;(1-)
+Have a soft heart.;;;;;;(2+)
+Often forget to put things back in their proper place.;;;;;;(3-)
+Get upset easily.;;;;;;(4-)
+Do not have a good imagination.;;;;;;(5-)
+Talk to a lot of different people at parties.;;;;;;(1+)
+Am not really interested in others.;;;;;;(2-)
+Like order.;;;;;;(3+)
+Change my mood a lot.;;;;;;(4-)
+Am quick to understand things.;;;;;;(5+)
+Don't like to draw attention to myself.;;;;;;(1-)
+Take time out for others.;;;;;;(2+)
+Shirk my duties.;;;;;;(3-)
+Have frequent mood swings.;;;;;;(4-)
+Use difficult words.;;;;;;(5+)
+Don't mind being the center of attention.;;;;;;(1+)
+Feel others' emotions.;;;;;;(2+)
+Follow a schedule.;;;;;;(3+)
+Get irritated easily.;;;;;;(4-)
+Spend time reflecting on things.;;;;;;(5+)
+Am quiet around strangers.;;;;;;(1-)
+Make people feel at ease.;;;;;;(2+)
+Am exacting in my work.;;;;;;(3+)
+Often feel blue.;;;;;;(4-)
+Am full of ideas.;;;;;;(5+)

app.py

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+#os.system('pip install openpyxl') #works for huggingface-spaces
+#score of each latent variable ranges in [10, 50]
+def correct_score(score, big_5_indicator):
+    big_5_sign = big_5_indicator[0]
+    big_5_num = int(big_5_indicator[1])
+    dict1 = {
+        1 : 'Extraversion',
+        2 : 'Agreeableness',
+        3 : 'Conscientiousness',
+        4 : 'Neuroticism',
+        5 : 'Openness'
+    }
+    if big_5_sign=='+':
+        return [dict1[big_5_num], score]
+    elif big_5_sign =='-':
+        return [dict1[big_5_num], 6-score]
+# correct_score(3, big_5_indicator='-2')
+
+def make_question(q):
+    q = gr.Radio(choices=[1, 2, 3, 4, 5], label=q) #, value=5
+    return q
+
+def calculate_personality_score(questions, list_questions):
+    personality = {
+        'Extraversion': 0,
+        'Agreeableness': 0,
+        'Conscientiousness': 0,
+        'Neuroticism': 0,
+        'Openness': 0
+    }
+    for question in list_questions:
+        big_5_indicator = questions[question[0]]
+        score = correct_score(question[1], big_5_indicator=big_5_indicator)
+        personality[score[0]] += score[1]
+    return personality
+
+import gradio as gr
+import pandas as pd
+import numpy as np
+import os
+
+#
+df_big5_test = pd.read_csv('Big5.csv', sep=';', header=None)
+df_big5_test = df_big5_test[[0, 6]]
+df_big5_test.columns = ['question', 'score']
+df_big5_test['score'] = df_big5_test['score'].apply(lambda x : x.replace('(', '').replace(')', '')[::-1])
+# df.index = df.pop('question')
+questions = dict()
+for el in df_big5_test.values.tolist():
+    questions[el[0]] = el[1]
+questions
+
+#the first module becomes text1, the second module file1
+def greet(*args): 
+    args_list = [item for item in args]
+
+    #extract big5
+    q_list = list(questions)
+    a_list = args_list[6:]
+    list_total = [[q_list[x], a_list[x]] for x in range(len(a_list))]
+    personality = calculate_personality_score(questions, list_total)
+    personality = { x : dict1[x]/50 for x in dict1}
+    # personality = [personality['Openness'], personality['Conscientiousness'], personality['Extraversion'], personality['Agreeableness'], personality['Neuroticism']]
+    # personality = [x/50 for x in personality]
+
+    #to return a file:
+    #return 'new.xlsx'
+    return personality
+
+iface = gr.Interface(
+    fn=greet, 
+    inputs=[make_question(q) for q in list(questions)], 
+        outputs=["text"]
+    )
+iface.launch()

big5_gradio.ipynb

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+{
+ "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": 14,
+   "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=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": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7868\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7868/\" 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 0x15ecde2ab80>, 'http://127.0.0.1:7868/', None)"
+      ]
+     },
+     "execution_count": 24,
+     "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[6:]\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 : dict1[x]/50 for x in dict1}\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)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.0 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.0"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "fdf377d643bc1cb065454f0ad2ceac75d834452ecf289e7ba92c6b3f59a7cee1"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}