{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import praw\n",
    "import pandas as pd\n",
    "\n",
    "reddit= praw.Reddit(client_id=\"Q1w42RHhLq2fgwljAk_k-Q\",\t\t # your client id\n",
    "\t\t\t\t\tclient_secret=\"enUJfFthiZRynGfPQtoK1nCxRer2Dw\",\t # your client secret\n",
    "                    usernme = \"xl395\", #profile username\n",
    "                    password = \"12xiao34quanAria!\", #profile password\n",
    "\t\t\t\t\tuser_agent=\"706_post\")\t # your user agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "expected ':' (3580341109.py, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[35], line 8\u001b[0;36m\u001b[0m\n\u001b[0;31m    def extract_comments(input_url)\u001b[0m\n\u001b[0m                                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected ':'\n"
     ]
    }
   ],
   "source": [
    "from praw.models import MoreComments\n",
    "from transformers import pipeline\n",
    "from transformers import DistilBertTokenizerFast\n",
    "classifier = pipeline(\"sentiment-analysis\", model=\"michellejieli/NSFW_text_classifier\")\n",
    "\n",
    "input_url = \"https://www.reddit.com/r/europe/comments/r0hthg/sweden_is_taking_the_lead_to_persuade_the_rest_of/\"\n",
    "\n",
    "def extract_comments(input_url):\n",
    "    submission = reddit.submission(url=input_url)\n",
    "    posts_dict = {\"Post text\":[],}\n",
    "    # posts_dict = {\"Post text\":[], \"class\": []}\n",
    "    for top_level_comment in submission.comments:\n",
    "        if isinstance(top_level_comment, MoreComments):\n",
    "            continue\n",
    "        \n",
    "        posts_dict[\"Post text\"].append(top_level_comment.body)\n",
    "        #post_dict[\"class\"].append(classifier(top_level_comment.body))\n",
    "    df = pd.DataFrame(posts_dict)\n",
    "    return df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_process = extract_comments(input_url)\n",
    "\n",
    "# find how many are NSFW - output message"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.4 (main, Nov 29 2022, 20:00:25) [GCC 9.4.0]"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "3ad933181bd8a04b432d3370b9dc3b0662ad032c4dfaa4e4f1596c548f763858"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}