eda_ronald.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "pd.set_option(\"display.max_rows\", 50)\n",
    "\n",
    "import json\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import datetime\n",
    "from datetime import datetime as dt\n",
    "from datetime import timedelta\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>preview</th>\n",
       "      <th>is_locked</th>\n",
       "      <th>tweet_id</th>\n",
       "      <th>sticky_comment</th>\n",
       "      <th>removal_source</th>\n",
       "      <th>link</th>\n",
       "      <th>author_flair_class</th>\n",
       "      <th>is_video_mp4</th>\n",
       "      <th>is_removed</th>\n",
       "      <th>title</th>\n",
       "      <th>...</th>\n",
       "      <th>link_metadata.twitter.parentTweet.quotedTweet.video.url</th>\n",
       "      <th>link_metadata.twitter.parentTweet.linkEmbed.image</th>\n",
       "      <th>link_metadata.twitter.parentTweet.linkEmbed.domain</th>\n",
       "      <th>link_metadata.twitter.parentTweet.linkEmbed.description</th>\n",
       "      <th>link_metadata.twitter.parentTweet.linkEmbed.title</th>\n",
       "      <th>link_metadata.twitter.parentTweet.linkEmbed.url</th>\n",
       "      <th>link_metadata.twitter.poll.options</th>\n",
       "      <th>link_metadata.twitter.poll.votes</th>\n",
       "      <th>link_metadata.twitter.poll.endTime</th>\n",
       "      <th>link_metadata.twitter.poll.winningPercentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>https://media.patriots.win/preview/199OBBRLnB.png</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>https://justthenews.com/politics-policy/elections/harris-top-three-surrogates-clinton-obama-and-biden-backfire-campaign</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Harris' top surrogates Clinton, Obama and Biden backfire on campaign trail, boosting Trump\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>https://media.patriots.win/preview/199OBBRcds.png</td>\n",
       "      <td>False</td>\n",
       "      <td>1846481206574973063</td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>https://twitter.com/LegitTargets/status/1846481206574973063</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>President Trump: \"China thinks we are a very stupid country.\"</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>https://media.patriots.win/preview/199OBBRuDq.png</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>https://rumble.com/v5htmr5-springfield-woman-accuses-haitian-neighbors-of-cooking-her-cat....html</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Springfield Woman Accuses Haitian Neighbors of Cooking Her CAT.\\r\\n\\r\\n\\r\\n</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>https://media.patriots.win/preview/199OBBQWqP.png</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>https://media.scored.co/post/HuGmi3AXLuQz.png</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Times are tough for Tim Walz's friends under inflation.</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>https://media.patriots.win/preview/199OBBRtpB.png</td>\n",
       "      <td>False</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>https://media.scored.co/post/kVoDIHDaM2Pn.jpeg</td>\n",
       "      <td></td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Greetings Patriots from Aruba! Miss all of you, just got internet, hope you are all holding down the Fort while I've been gone.   9 out of 10 people I've met are Trump Supporters, Amazing!!!</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 149 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             preview  is_locked  \\\n",
       "1  https://media.patriots.win/preview/199OBBRLnB.png      False   \n",
       "2  https://media.patriots.win/preview/199OBBRcds.png      False   \n",
       "4  https://media.patriots.win/preview/199OBBRuDq.png      False   \n",
       "5  https://media.patriots.win/preview/199OBBQWqP.png      False   \n",
       "7  https://media.patriots.win/preview/199OBBRtpB.png      False   \n",
       "\n",
       "              tweet_id  sticky_comment removal_source  \\\n",
       "1                                    0                  \n",
       "2  1846481206574973063               0                  \n",
       "4                                    0                  \n",
       "5                                    0                  \n",
       "7                                    0                  \n",
       "\n",
       "                                                                                                                      link  \\\n",
       "1  https://justthenews.com/politics-policy/elections/harris-top-three-surrogates-clinton-obama-and-biden-backfire-campaign   \n",
       "2                                                              https://twitter.com/LegitTargets/status/1846481206574973063   \n",
       "4                        https://rumble.com/v5htmr5-springfield-woman-accuses-haitian-neighbors-of-cooking-her-cat....html   \n",
       "5                                                                            https://media.scored.co/post/HuGmi3AXLuQz.png   \n",
       "7                                                                           https://media.scored.co/post/kVoDIHDaM2Pn.jpeg   \n",
       "\n",
       "  author_flair_class  is_video_mp4  is_removed  \\\n",
       "1                            False       False   \n",
       "2                            False       False   \n",
       "4                            False       False   \n",
       "5                            False       False   \n",
       "7                            False       False   \n",
       "\n",
       "                                                                                                                                                                                            title  \\\n",
       "1                                                                                                    Harris' top surrogates Clinton, Obama and Biden backfire on campaign trail, boosting Trump\\n   \n",
       "2                                                                                                                                   President Trump: \"China thinks we are a very stupid country.\"   \n",
       "4                                                                                                                     Springfield Woman Accuses Haitian Neighbors of Cooking Her CAT.\\r\\n\\r\\n\\r\\n   \n",
       "5                                                                                                                                         Times are tough for Tim Walz's friends under inflation.   \n",
       "7  Greetings Patriots from Aruba! Miss all of you, just got internet, hope you are all holding down the Fort while I've been gone.   9 out of 10 people I've met are Trump Supporters, Amazing!!!   \n",
       "\n",
       "   ... link_metadata.twitter.parentTweet.quotedTweet.video.url  \\\n",
       "1  ...                                                     NaN   \n",
       "2  ...                                                     NaN   \n",
       "4  ...                                                     NaN   \n",
       "5  ...                                                     NaN   \n",
       "7  ...                                                     NaN   \n",
       "\n",
       "   link_metadata.twitter.parentTweet.linkEmbed.image  \\\n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "7                                                NaN   \n",
       "\n",
       "  link_metadata.twitter.parentTweet.linkEmbed.domain  \\\n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "7                                                NaN   \n",
       "\n",
       "  link_metadata.twitter.parentTweet.linkEmbed.description  \\\n",
       "1                                                     NaN   \n",
       "2                                                     NaN   \n",
       "4                                                     NaN   \n",
       "5                                                     NaN   \n",
       "7                                                     NaN   \n",
       "\n",
       "   link_metadata.twitter.parentTweet.linkEmbed.title  \\\n",
       "1                                                NaN   \n",
       "2                                                NaN   \n",
       "4                                                NaN   \n",
       "5                                                NaN   \n",
       "7                                                NaN   \n",
       "\n",
       "  link_metadata.twitter.parentTweet.linkEmbed.url  \\\n",
       "1                                             NaN   \n",
       "2                                             NaN   \n",
       "4                                             NaN   \n",
       "5                                             NaN   \n",
       "7                                             NaN   \n",
       "\n",
       "   link_metadata.twitter.poll.options  link_metadata.twitter.poll.votes  \\\n",
       "1                                 NaN                               NaN   \n",
       "2                                 NaN                               NaN   \n",
       "4                                 NaN                               NaN   \n",
       "5                                 NaN                               NaN   \n",
       "7                                 NaN                               NaN   \n",
       "\n",
       "   link_metadata.twitter.poll.endTime  \\\n",
       "1                                 NaN   \n",
       "2                                 NaN   \n",
       "4                                 NaN   \n",
       "5                                 NaN   \n",
       "7                                 NaN   \n",
       "\n",
       "   link_metadata.twitter.poll.winningPercentage  \n",
       "1                                           NaN  \n",
       "2                                           NaN  \n",
       "4                                           NaN  \n",
       "5                                           NaN  \n",
       "7                                           NaN  \n",
       "\n",
       "[5 rows x 149 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import raw data\n",
    "dfs=[]\n",
    "for file in glob.glob(\"data/*.json\"):\n",
    "    dataframe = pd.read_json(file)['posts']\n",
    "    # flatten the structure\n",
    "    dataframe = pd.json_normalize(dataframe)\n",
    "    # insert file names\n",
    "    dataframe['file_name'] = os.path.basename(file)\n",
    "    dfs.append(dataframe)\n",
    "\n",
    "# Concatenate all data and drop repeated entries\n",
    "df=pd.concat(dfs).drop_duplicates(subset='uuid', keep='last')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_60927/3647978645.py:23: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  dataframe = dataframe.replace(r'^\\s*$', np.nan, regex=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "created_date\n",
       "2024-07-21     31\n",
       "2024-07-22    500\n",
       "2024-07-23    615\n",
       "2024-07-24    585\n",
       "2024-07-25    631\n",
       "             ... \n",
       "2024-11-01    634\n",
       "2024-11-02    633\n",
       "2024-11-03    610\n",
       "2024-11-04    626\n",
       "2024-11-05    501\n",
       "Name: count, Length: 108, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slice out only relevant columns \n",
    "selected_columns = [\n",
    "    'file_name',\n",
    "    'uuid', \n",
    "    'title',\n",
    "    'content',\n",
    "    'post_flair_text',\n",
    "    'author',\n",
    "    'author_flair_text',\n",
    "    'created',\n",
    "    'comments',\n",
    "    'score_up',\n",
    "    'preview',\n",
    "    'link',\n",
    "    'domain',\n",
    "    'is_image',\n",
    "    'is_video',\n",
    "    'video_link'\n",
    "    ]\t\n",
    "df = df[selected_columns]\n",
    "\n",
    "# Replace empty cell with Nan\n",
    "dataframe = dataframe.replace(r'^\\s*$', np.nan, regex=True)\n",
    "\n",
    "# Format datetime\n",
    "df['created'] = pd.to_datetime(df.created, unit='ms').dt.tz_localize('US/Eastern', ambiguous='NaT')\n",
    "\n",
    "# Crate a column storing \"date\" \n",
    "df['created_date'] = df['created'].dt.date\n",
    "df['created_date'] = pd.to_datetime(df['created_date'])\n",
    "\n",
    "# Fix empty or ill-formed domain column\n",
    "selfpost_mask = df.domain.str.match('^$')\n",
    "df.loc[selfpost_mask, 'domain'] = 'self_post'\n",
    "\n",
    "df.created_date.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpack shortened domains\n",
    "import requests\n",
    "from requests.adapters import HTTPAdapter\n",
    "from urllib3.util.retry import Retry\n",
    "import tldextract\n",
    "\n",
    "common_shorts = [\n",
    "    \"adf.ly\", \"adfoc.us\", \"amzn.to\", \"atominik.com\", \"ay.gy\", \"b.link\", \"bhpho.to\", \"bit.ly\", \"bit.do\", \n",
    "    \"bn.gy\", \"branch.io\", \"buff.ly\", \"ceesty.com\", \"chollo.to\", \"cli.re\", \"cli.fm\", \"cutt.ly\", \"cutt.us\", \n",
    "    \"db.tt\", \"f.ls\", \"fa.by\", \"fb.me\", \"flip.it\", \"fumacrom.com\", \"git.io\", \"geni.us\", \"goo.gl\", \"gns.io\", \n",
    "    \"hmm.rs\", \"ht.ly\", \"hyperurl.co\", \"ity.im\", \"intamema.com\", \"is.gd\", \"j.gs\", \"j.mp\", \"kutt.it\", \"ldn.im\", \n",
    "    \"linklyhq.com\", \"microify.com\", \"mzl.la\", \"nmc.sg\", \"nowlinks.net\", \"ow.ly\", \"prf.hn\", \"plu.sh\", \"q.gs\", \n",
    "    \"qr.ae\", \"qr.net\", \"rebrand.ly\", \"rb.gy\", \"rlu.ru\", \"rotf.lol\", \"s.coop\", \"s.id\", \"sh.st\", \"soo.gd\", \n",
    "    \"shortcm.xyz\", \"short.gy\", \"shortcm.xyz\", \"shorturl.at\", \"smu.sg\", \"smq.tc\", \"snip.ly\", \"snipr.com\", \n",
    "    \"snipurl.com\", \"snurl.com\", \"split.to\", \"surl.li\", \"t.co\", \"t.ly\", \"t2m.io\", \"tiny.cc\", \"tiny.pl\", \n",
    "    \"tinyium.com\", \"tinyurl.com\", \"tiny.one\", \"tny.im\", \"tny.sh\", \"tr.im\", \"trib.al\", \"u.to\", \"v.gd\", \n",
    "    \"virg.in\", \"vzturl.com\", \"waa.ai\", \"washex.am\", \"x.co\", \"y2u.be\", \"yourwish.es\", \"zpr.io\", \"1url.com\", \n",
    "    \"2.gp\", \"3.ly\", \"4sq.com\", \"6url.com\", \"7.ly\", \"9.bb\", \"amp.gs\", \"atz.bz\", \"binged.it\",\n",
    "    \"budurl.com\", \"cort.as\", \"decenturl.com\", \"dft.ba\", \"goo.gl\", \"inbound.org\", \"itsh.bo\", \"mcaf.ee\",\n",
    "    \"n.pr\", \"nyti.ms\", \"on.fb.me\", \"prsm.tc\", \"redd.it\", \"tnw.to\", \"twt.fi\", \"u.nu\", \"virl.io\", \"wapo.st\",\n",
    "    \"wp.me\", \"xrl.us\",  \"zurl.ws\", \"dlvr.it\", 'msft.it', \"lnkd.in \", 'spr.ly', 'rebelne.ws', 'scroll.in', 'ab.co',\n",
    "    'hubs.li', 'bbc.in', 'nature.ly', 'cbsn.ws' , 'brnw.ch', 'youtu.be'\n",
    "                ]\n",
    "\n",
    "# Set up a Requests session with retries\n",
    "session = requests.Session()\n",
    "headers = {\n",
    "    'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',\n",
    "    'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\n",
    "    'Accept-Encoding': 'gzip, deflate, br',\n",
    "    'Accept-Language': 'en-US,en;q=0.5',\n",
    "    'Connection': 'keep-alive',\n",
    "    'Upgrade-Insecure-Requests': '1',\n",
    "    'DNT': '1',\n",
    "    'referer': 'https://www.google.com/'\n",
    "}\n",
    "\n",
    "headers = {\n",
    "        'User-Agent':'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36',\n",
    "        'referer':'https://google.com',\n",
    "        'DNT':'1'\n",
    "    }\n",
    "\n",
    "# Session.headers.update(headers)\n",
    "retries = Retry(total=4, backoff_factor=1, status_forcelist=[502, 503, 504])\n",
    "session.mount('http://', HTTPAdapter(max_retries=retries))\n",
    "session.mount('https://', HTTPAdapter(max_retries=retries))\n",
    "\n",
    "def check_connection(host='http://www.google.com'):\n",
    "    try:\n",
    "        session.get(host, timeout=1)\n",
    "        return True\n",
    "    except (requests.ConnectionError, requests.Timeout):\n",
    "        return False\n",
    "\n",
    "def wait_for_connection(interval=30, max_attempts=2):\n",
    "    attempts = 0\n",
    "    while not check_connection() and attempts < max_attempts:\n",
    "        time.sleep(interval)\n",
    "        attempts += 1\n",
    "    return attempts < max_attempts\n",
    "\n",
    "def resolve_url_head(short_url):\n",
    "    if not wait_for_connection():\n",
    "        raise ConnectionError(\"Internet connection could not be re-established.\")\n",
    "    try:\n",
    "        response = requests.head(short_url, allow_redirects=True, timeout=1)\n",
    "        return response.url if response.status_code == 200 else short_url\n",
    "    except requests.RequestException:\n",
    "        return short_url\n",
    "    \n",
    "def resolve_url_get(short_url):\n",
    "    if not wait_for_connection():\n",
    "        raise ConnectionError(\"Internet connection could not be re-established.\")\n",
    "    try:\n",
    "        response = session.get(short_url, allow_redirects=True, timeout=5)\n",
    "        return response.url if response.status_code == 200 else short_url\n",
    "    except requests.RequestException:\n",
    "        return short_url\n",
    "    if not wait_for_connection():\n",
    "        raise ConnectionError(\"Internet connection could not be re-established.\")\n",
    "    try:\n",
    "        response = session.get(url=shortend_url, allow_redirects=True, timeout=1)        \n",
    "   \n",
    "    except requests.RequestException:\n",
    "        return None    \n",
    "    \n",
    "df['domain'] = df['domain'].apply(lambda x: tldextract.extract(x).domain + \".\" + tldextract.extract(x).suffix if x != 'self_post' else 'self_post')\n",
    "short_mask = df['domain'].isin(common_shorts)\n",
    "df.loc[short_mask, 'domain'] = df.loc[short_mask, 'link'].apply(lambda x: resolve_url_get(x)).apply(lambda x: tldextract.extract(x).domain + \".\" + tldextract.extract(x).suffix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "domain\n",
       "twitter.com             0.363571\n",
       "scored.co               0.277927\n",
       "patriots.win            0.143640\n",
       "self_post               0.036325\n",
       "thegatewaypundit.com    0.020612\n",
       "youtube.com             0.018185\n",
       "videy.co                0.011028\n",
       "breitbart.com           0.009016\n",
       "rumble.com              0.008233\n",
       "zerohedge.com           0.007326\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The top 10 most-shared domains on The Ronald's landing page\n",
    "df.domain.value_counts(normalize=True).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_60927/1573199433.py:5: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df = df.groupby('created_date').apply(lambda x: x.nlargest(600, 'score_up')).reset_index(drop=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "created_date\n",
       "2024-07-23    True\n",
       "2024-07-25    True\n",
       "2024-07-27    True\n",
       "2024-07-28    True\n",
       "2024-07-29    True\n",
       "              ... \n",
       "2024-10-31    True\n",
       "2024-11-01    True\n",
       "2024-11-02    True\n",
       "2024-11-03    True\n",
       "2024-11-04    True\n",
       "Length: 78, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove posts shared that were shared on 2024-07-21\n",
    "df = df[df['created_date'] != '2024-07-21']\n",
    "\n",
    "# Keep only the top 500 posts by \"score_up\" from each date so that the playing field is more leveled\n",
    "df = df.groupby('created_date').apply(lambda x: x.nlargest(600, 'score_up')).reset_index(drop=True)\n",
    "\n",
    "# Check dates with less than 500 posts\n",
    "post_date_count = df.groupby('created_date').size() == 600\n",
    "post_date_count[post_date_count]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_33493/2712004044.py:4: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
      "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
      "\n",
      "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
      "\n",
      "\n",
      "  df['twitter_handle'].fillna(np.nan, inplace=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "twitter_handle\n",
       "elonmusk        0.035531\n",
       "behizytweets    0.032566\n",
       "endwokeness     0.024023\n",
       "libsoftiktok    0.019650\n",
       "trumpwarroom    0.018846\n",
       "bennyjohnson    0.014072\n",
       "jackposobiec    0.011860\n",
       "dc_draino       0.011810\n",
       "collinrugg      0.011609\n",
       "nicksortor      0.011358\n",
       "Name: proportion, dtype: float64"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract Twitter handles from urls\n",
    "df[df.domain =='twitter.com'].link.str.split('/').str[3].str.lower()\n",
    "df.loc[df.domain == 'twitter.com', 'twitter_handle'] = df[df.domain == 'twitter.com'].link.str.split('/').str[3].str.lower()\n",
    "df['twitter_handle'].fillna(np.nan, inplace=True)\n",
    "\n",
    "# Top 10 twitter handles\n",
    "df.value_counts('twitter_handle', normalize=True, ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fix html markup in the title field\n",
    "df.title = df.title.str.replace('\\r', ' ').str.replace('\\n', ' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function exporting a dataframe into an html table\n",
    "def save_interactive_table(dataframe, save_filename):\n",
    "    html_content = dataframe.to_html(render_links=True, \n",
    "                                         justify='center', \n",
    "                                         escape=False,\n",
    "                                         classes='table table-striped table-hover', \n",
    "                                         table_id='my_table')\n",
    "    with open(f'viz/{save_filename}.html', \"w\") as html_file:\n",
    "         html_file.write(f\"\"\"\n",
    "                <html>\n",
    "                <head>\n",
    "                <link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.datatables.net/1.10.21/css/jquery.dataTables.css\">\n",
    "                <script type=\"text/javascript\" charset=\"utf8\" src=\"https://code.jquery.com/jquery-3.5.1.js\"></script>\n",
    "                <script type=\"text/javascript\" charset=\"utf8\" src=\"https://cdn.datatables.net/1.10.21/js/jquery.dataTables.js\"></script>\n",
    "                <script>\n",
    "                $(document).ready( function () {{\n",
    "                    $('#my_table').DataTable({{\n",
    "                        pageLength: 25  // Set the number of entries to display per page\n",
    "                    }});\n",
    "                }} );\n",
    "                </script>\n",
    "                </head>\n",
    "                <body>\n",
    "                {html_content}\n",
    "                </body>\n",
    "                </html>\n",
    "            \"\"\")\n",
    "         \n",
    "         return html_content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning the top posts in interactive format\n",
    "def top_posts_lastNdays(datafarme, num, Ndays, save_filename=None):\n",
    "    end_date = dt.now()\n",
    "    start_date = end_date - timedelta(hours = nHours)\n",
    "    top_posts = datafarme[datafarme.created_date >= start_date].nlargest(num,'score_up')\n",
    "    top_posts['post_link'] = \"https://patriots.win/p/\" + top_posts['uuid']\n",
    "    top_posts['title'] = top_posts.apply(lambda row: f'<a href=\"{row[\"post_link\"]}\" target=\"_blank\">{row[\"title\"]}</a>', axis=1)\n",
    "    top_posts = top_posts[['title', \n",
    "                           'created_date',\n",
    "                           'score_up', \n",
    "                           'comments', \n",
    "                           'author'\n",
    "                           ]]\n",
    "    # Rename column headers\n",
    "    top_posts = top_posts.rename(columns={'score_up': 'upvote count',\n",
    "                                         'comments' : 'comment count'})\n",
    "    \n",
    "    \n",
    "    top_posts = top_posts.reset_index().drop(columns='index')\n",
    "    top_posts.index = np.arange(1, len(top_posts)+1)\n",
    "    \n",
    "    if save_filename:\n",
    "        save_interactive_table(top_posts, save_filename)\n",
    "    \n",
    "    return top_posts"
   ]
  },
  {
   "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>title</th>\n",
       "      <th>created_date</th>\n",
       "      <th>upvote count</th>\n",
       "      <th>comment count</th>\n",
       "      <th>author</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OhqzoCF\" target=\"_blank\"&gt;This guy worked harder to win PA for Trump than anyone else.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>3098</td>\n",
       "      <td>255</td>\n",
       "      <td>forth2win</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr1jL4\" target=\"_blank\"&gt;GRENNELL: Any state that can’t count their votes and report the results Tuesday night is corrupt and incompetent.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2931</td>\n",
       "      <td>165</td>\n",
       "      <td>Sunnybats2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OhsA7ar\" target=\"_blank\"&gt;I've been here since the r/The_Donald days. What a ride it's been pedes!&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>2776</td>\n",
       "      <td>592</td>\n",
       "      <td>DCSucks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr20SG\" target=\"_blank\"&gt;HOLY SHIT! Elon Podesta PIZZAGATE Post&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2623</td>\n",
       "      <td>304</td>\n",
       "      <td>CaptainCheekClapper</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr1B6g\" target=\"_blank\"&gt;BREAKING: The Georgia Supreme Court just ruled that ballots cannot be accepted and counted AFTER the deadline by election officials anywhere in the state. Democrats in Cobb County wanted to accept 3,000 mail-in ballots AFTER Election Day deadline, but the Court just shut down their plans. &lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2550</td>\n",
       "      <td>143</td>\n",
       "      <td>Mr_Rightside</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OhsA7vV\" target=\"_blank\"&gt;Rogan endorses Trump!&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>2499</td>\n",
       "      <td>278</td>\n",
       "      <td>WontForgetWhoDied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKa31j\" target=\"_blank\"&gt;HE'S COMING.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2495</td>\n",
       "      <td>114</td>\n",
       "      <td>trees65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr0u7m\" target=\"_blank\"&gt;BREAKING: Almost 14,000 mail-in ballots in Nevada currently don't have adequate signatures and will NOT be counted unless they're cured. This is devastating news for Kamala Harris.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2495</td>\n",
       "      <td>147</td>\n",
       "      <td>drendotwin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohs8kDQ\" target=\"_blank\"&gt;Kevin Sorbo: If a state can’t finish counting votes by Wednesday morning, it’s cheating. &lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2328</td>\n",
       "      <td>73</td>\n",
       "      <td>I-am-Orlando</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKab3d\" target=\"_blank\"&gt;PA Supreme Court says mail-in ballots with missing or wrong dates cannot be tallied&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2303</td>\n",
       "      <td>129</td>\n",
       "      <td>Sea_Still</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKZDc5\" target=\"_blank\"&gt;Team Trump posted this on TikTok one day ago. It has 30M views and 4.2M likes.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2231</td>\n",
       "      <td>163</td>\n",
       "      <td>trees65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr1S1T\" target=\"_blank\"&gt;Mike Tyson: \"I'm voting Trump. What are you gonna do about it?\"&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2212</td>\n",
       "      <td>148</td>\n",
       "      <td>trees65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohs9INc\" target=\"_blank\"&gt;Tomorrow is our last chance to defeat the corrupt establishment.&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>2153</td>\n",
       "      <td>63</td>\n",
       "      <td>Trump_Is_My_Daddy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OhtGau1\" target=\"_blank\"&gt;THERE IS NO TRUMP LEAD GREAT ENOUGH TO LET UP. IT IS TIME FOR RUTHLESSNESS, NOT CELEBRATION. DO NOT GET COMPLACENT. DRAG 10 PEOPLE WITH YOU TO VOTE TRUMP. DO NOT LIFT YOUR BOOT UNTIL THERE IS NO PULSE. IT’S MAGA OR THE DEATH OF OUR GREAT NATION.&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>2035</td>\n",
       "      <td>119</td>\n",
       "      <td>O__Q</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKZDgD\" target=\"_blank\"&gt;‼️THIS IS EXTREMELY IMPORTANT‼️ If you voted by mail in Pennsylvania — (including in-person) — check your ballot status ➡️RIGHT NOW⬅️. ⚠️If it says, “Ballot Returned,” that means your ballot was RECEIVED&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>2029</td>\n",
       "      <td>82</td>\n",
       "      <td>Whenagain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKa2lD\" target=\"_blank\"&gt;Anyone with an IQ of above room temperature knew this&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>1970</td>\n",
       "      <td>249</td>\n",
       "      <td>FBIAREFAGGOTS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdKaJTY\" target=\"_blank\"&gt;You don’t have to convince me, I’m already voting for Trump &lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>1910</td>\n",
       "      <td>183</td>\n",
       "      <td>IHeartMyDoggy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199OdIJFzB\" target=\"_blank\"&gt;Trump's rally schedule over the final few days reveals a brilliant strategy: He is not going to North Carolina, Georgia, and Pennsylvania because he's worried about any of them. It's quite the opposite. &lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>1882</td>\n",
       "      <td>214</td>\n",
       "      <td>RuprechtThaMonkeyBoy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohr0M5q\" target=\"_blank\"&gt;TRUMP TOOK A BULLET FOR YOU. YOU CAN WAIT IN LINE TO VOTE FOR HIM.&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>1842</td>\n",
       "      <td>62</td>\n",
       "      <td>WontForgetWhoDied</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>&lt;a href=\"https://patriots.win/p/199Ohs9IVl\" target=\"_blank\"&gt;The Exodus has begun.&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>1813</td>\n",
       "      <td>211</td>\n",
       "      <td>MemeWarsVet</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                                                                                                                                                                                                                                 title  \\\n",
       "1                                                                                                                                                                                                                                         <a href=\"https://patriots.win/p/199OhqzoCF\" target=\"_blank\">This guy worked harder to win PA for Trump than anyone else.</a>   \n",
       "2                                                                                                                                                                                    <a href=\"https://patriots.win/p/199Ohr1jL4\" target=\"_blank\">GRENNELL: Any state that can’t count their votes and report the results Tuesday night is corrupt and incompetent.</a>   \n",
       "3                                                                                                                                                                                                                             <a href=\"https://patriots.win/p/199OhsA7ar\" target=\"_blank\">I've been here since the r/The_Donald days. What a ride it's been pedes!</a>   \n",
       "4                                                                                                                                                                                                                                                               <a href=\"https://patriots.win/p/199Ohr20SG\" target=\"_blank\">HOLY SHIT! Elon Podesta PIZZAGATE Post</a>   \n",
       "5   <a href=\"https://patriots.win/p/199Ohr1B6g\" target=\"_blank\">BREAKING: The Georgia Supreme Court just ruled that ballots cannot be accepted and counted AFTER the deadline by election officials anywhere in the state. Democrats in Cobb County wanted to accept 3,000 mail-in ballots AFTER Election Day deadline, but the Court just shut down their plans. </a>   \n",
       "6                                                                                                                                                                                                                                                                                <a href=\"https://patriots.win/p/199OhsA7vV\" target=\"_blank\">Rogan endorses Trump!</a>   \n",
       "7                                                                                                                                                                                                                                                                                         <a href=\"https://patriots.win/p/199OdKa31j\" target=\"_blank\">HE'S COMING.</a>   \n",
       "8                                                                                                                 <a href=\"https://patriots.win/p/199Ohr0u7m\" target=\"_blank\">BREAKING: Almost 14,000 mail-in ballots in Nevada currently don't have adequate signatures and will NOT be counted unless they're cured. This is devastating news for Kamala Harris.</a>   \n",
       "9                                                                                                                                                                                                            <a href=\"https://patriots.win/p/199Ohs8kDQ\" target=\"_blank\">Kevin Sorbo: If a state can’t finish counting votes by Wednesday morning, it’s cheating. </a>   \n",
       "10                                                                                                                                                                                                                 <a href=\"https://patriots.win/p/199OdKab3d\" target=\"_blank\">PA Supreme Court says mail-in ballots with missing or wrong dates cannot be tallied</a>   \n",
       "11                                                                                                                                                                                                                      <a href=\"https://patriots.win/p/199OdKZDc5\" target=\"_blank\">Team Trump posted this on TikTok one day ago. It has 30M views and 4.2M likes.</a>   \n",
       "12                                                                                                                                                                                                                                     <a href=\"https://patriots.win/p/199Ohr1S1T\" target=\"_blank\">Mike Tyson: \"I'm voting Trump. What are you gonna do about it?\"</a>   \n",
       "13                                                                                                                                                                                                                                    <a href=\"https://patriots.win/p/199Ohs9INc\" target=\"_blank\">Tomorrow is our last chance to defeat the corrupt establishment.</a>   \n",
       "14                                               <a href=\"https://patriots.win/p/199OhtGau1\" target=\"_blank\">THERE IS NO TRUMP LEAD GREAT ENOUGH TO LET UP. IT IS TIME FOR RUTHLESSNESS, NOT CELEBRATION. DO NOT GET COMPLACENT. DRAG 10 PEOPLE WITH YOU TO VOTE TRUMP. DO NOT LIFT YOUR BOOT UNTIL THERE IS NO PULSE. IT’S MAGA OR THE DEATH OF OUR GREAT NATION.</a>   \n",
       "15                                                                                         <a href=\"https://patriots.win/p/199OdKZDgD\" target=\"_blank\">‼️THIS IS EXTREMELY IMPORTANT‼️ If you voted by mail in Pennsylvania — (including in-person) — check your ballot status ➡️RIGHT NOW⬅️. ⚠️If it says, “Ballot Returned,” that means your ballot was RECEIVED</a>   \n",
       "16                                                                                                                                                                                                                                               <a href=\"https://patriots.win/p/199OdKa2lD\" target=\"_blank\">Anyone with an IQ of above room temperature knew this</a>   \n",
       "17                                                                                                                                                                                                                                        <a href=\"https://patriots.win/p/199OdKaJTY\" target=\"_blank\">You don’t have to convince me, I’m already voting for Trump </a>   \n",
       "18                                                                                         <a href=\"https://patriots.win/p/199OdIJFzB\" target=\"_blank\">Trump's rally schedule over the final few days reveals a brilliant strategy: He is not going to North Carolina, Georgia, and Pennsylvania because he's worried about any of them. It's quite the opposite. </a>   \n",
       "19                                                                                                                                                                                                                                  <a href=\"https://patriots.win/p/199Ohr0M5q\" target=\"_blank\">TRUMP TOOK A BULLET FOR YOU. YOU CAN WAIT IN LINE TO VOTE FOR HIM.</a>   \n",
       "20                                                                                                                                                                                                                                                                               <a href=\"https://patriots.win/p/199Ohs9IVl\" target=\"_blank\">The Exodus has begun.</a>   \n",
       "\n",
       "   created_date  upvote count  comment count                author  \n",
       "1    2024-11-04          3098            255             forth2win  \n",
       "2    2024-11-04          2931            165            Sunnybats2  \n",
       "3    2024-11-05          2776            592               DCSucks  \n",
       "4    2024-11-04          2623            304   CaptainCheekClapper  \n",
       "5    2024-11-04          2550            143          Mr_Rightside  \n",
       "6    2024-11-05          2499            278     WontForgetWhoDied  \n",
       "7    2024-11-04          2495            114               trees65  \n",
       "8    2024-11-04          2495            147            drendotwin  \n",
       "9    2024-11-04          2328             73          I-am-Orlando  \n",
       "10   2024-11-04          2303            129             Sea_Still  \n",
       "11   2024-11-04          2231            163               trees65  \n",
       "12   2024-11-04          2212            148               trees65  \n",
       "13   2024-11-05          2153             63     Trump_Is_My_Daddy  \n",
       "14   2024-11-05          2035            119                  O__Q  \n",
       "15   2024-11-04          2029             82             Whenagain  \n",
       "16   2024-11-04          1970            249         FBIAREFAGGOTS  \n",
       "17   2024-11-04          1910            183         IHeartMyDoggy  \n",
       "18   2024-11-04          1882            214  RuprechtThaMonkeyBoy  \n",
       "19   2024-11-04          1842             62     WontForgetWhoDied  \n",
       "20   2024-11-05          1813            211           MemeWarsVet  "
      ]
     },
     "execution_count": 249,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datafarme = df\n",
    "num = 20\n",
    "nHours = 36\n",
    "save_filename= f'top_10_posts_from_the_{nHours} hours'\n",
    "\n",
    "# Returning a table of top posts from the last nHours\n",
    "top_posts_lastNdays(datafarme, num, nHours, save_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/keenansmacbookairm3/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "import nltk\n",
    "nltk.download('wordnet')\n",
    "from nltk import bigrams, trigrams\n",
    "nlp = spacy.load('en_core_web_lg')\n",
    "stopwords = list(nlp.Defaults.stop_words) \n",
    "stopwords.extend([\"ago\", 'old'])\n",
    "\n",
    "import contractions\n",
    "import emoji\n",
    "import re\n",
    "\n",
    "\n",
    "\n",
    "def clean_it_all_up (text, extra_stops):\n",
    "    text = emoji.replace_emoji(text)\n",
    "    text = contractions.fix(text)\n",
    "    text = re.sub(r'\\W+', ' ', text)\n",
    "    text = re.sub(r'\\n', ' ', text)\n",
    "    doc = nlp(text)\n",
    "    lemmatized_tokens = []\n",
    "    \n",
    "    for token in doc:\n",
    "        if (\n",
    "            token.text.lower() not in stopwords \n",
    "            and (extra_stops is None or token.text.lower() not in extra_stops) \n",
    "            # and token.pos_ in {'NOUN', 'PROPN', 'VERB', 'ADJ'} \n",
    "            # and token.ent_type_ not in {'GPE', 'LOC'}\n",
    "            and token.pos_ not in {\"AUX\",\n",
    "                                   \"ADP\",\n",
    "                                   \"CONJ\", \n",
    "                                   \"CCONJ\", \n",
    "                                   \"DET\", \n",
    "                                   \"INTJ\", \n",
    "                                   \"PART\", \n",
    "                                   \"PUNCT\",\n",
    "                                   \"SCONJ\", \n",
    "                                   \"SYM\", \n",
    "                                   \"X\", \n",
    "                                   \"SPACE\"}\n",
    "        ):\n",
    "          \n",
    "            \n",
    "            lemmatized_tokens.append(token.lemma_.lower())\n",
    "            \n",
    "    return \" \".join(lemmatized_tokens)   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning a list of dates \n",
    "def date_selection(start_date, end_date):\n",
    "    start_date = datetime.datetime.strptime(start_date, \"%Y-%m-%d\")\n",
    "    end_date = end_date = datetime.datetime.strptime(end_date, \"%Y-%m-%d\")\n",
    "\n",
    "    date_list = []\n",
    "    current_date = start_date\n",
    "    date_list.append(current_date.strftime('%Y-%m-%d'))\n",
    "\n",
    "    while current_date < end_date:\n",
    "        next_date = current_date + datetime.timedelta(days=1)\n",
    "        next_date = next_date\n",
    "        if next_date >= end_date:\n",
    "            next_date = end_date\n",
    "        date_list.append(next_date.strftime('%Y-%m-%d'))\n",
    "        current_date = next_date\n",
    "\n",
    "    return date_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning top ngrams between two given dates\n",
    "def ngram_generator(dataframe, num, start_date, end_date, extra_stops, top_n=None):\n",
    "    \n",
    "    date_list = date_selection(start_date, end_date)\n",
    "\n",
    "    cleaned_tokens = ' '.join(dataframe[dataframe.created_date.isin(date_list)]['title'].apply(lambda x: clean_it_all_up(x, extra_stops)).tolist())\n",
    "\n",
    "    if num == 1:\n",
    "        ngram = pd.Series(cleaned_tokens.split()).value_counts()\n",
    "    elif num == 2:\n",
    "        ngram = pd.Series(list(bigrams(cleaned_tokens.split()))).value_counts()\n",
    "    elif num == 3:\n",
    "        ngram = pd.Series(list(trigrams(cleaned_tokens.split()))).value_counts()\n",
    "    else:\n",
    "        raise ValueError('Invalid number. Must choose between 1, 2, or 3.')\n",
    "    \n",
    "    ngram = ngram[ngram > 1].head(top_n)\n",
    "    return ngram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning top ngrams from the past 24 hours\n",
    "def ngram_generator_24hrs(dataframe, num, extra_stops, top_n=None):\n",
    "    end_date = datetime.datetime.now()\n",
    "    start_date = end_date - datetime.timedelta(days=1)\n",
    "    \n",
    "    start_date = start_date.strftime(\"%Y-%m-%d\")\n",
    "    end_date = end_date.strftime(\"%Y-%m-%d\")\n",
    "    \n",
    "    return ngram_generator(dataframe, num, start_date, end_date, extra_stops, top_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_33493/2909344436.py:7: FutureWarning: The behavior of 'isin' with dtype=datetime64[ns] and castable values (e.g. strings) is deprecated. In a future version, these will not be considered matching by isin. Explicitly cast to the appropriate dtype before calling isin instead.\n",
      "  cleaned_tokens = ' '.join(dataframe[dataframe.created_date.isin(date_list)]['title'].apply(lambda x: clean_it_all_up(x, extra_stops)).tolist())\n"
     ]
    }
   ],
   "source": [
    "# Run the ngram_generator_24hrs function \n",
    "dataframe = df\n",
    "num = 2\n",
    "top_n = 50\n",
    "extra_stops=['trump']\n",
    "\n",
    "top_50_bigrams_24hours = ngram_generator_24hrs(dataframe= dataframe, num=num, top_n=top_n, extra_stops=extra_stops )"
   ]
  },
  {
   "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>bigrams</th>\n",
       "      <th>frequncy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(vote, vote)</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(election, day)</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(joe, rogan)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(kamala, harris)</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(elon, musk)</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(supreme, court)</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(donald, j)</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(4, year)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(swing, state)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(new, hampshire)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(mail, ballot)</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(new, york)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(fight, fight)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(save, country)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(rogan, endorse)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(vote, donald)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(election, integrity)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(united, states)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(maga, hat)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>(sunday, night)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>(president, donald)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>(hold, rally)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>(live, president)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>(early, voting)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>(endorse, donald)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>(georgia, supreme)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>(white, house)</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>(ballot, accept)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>(president, hold)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>(11, 4)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>(4, 24)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>(election, office)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>(ballot, receive)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>(nbc, force)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>(equal, time)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>(attorney, general)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>(jd, vance)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>(ballot, election)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>(3, 000)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>(night, football)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>(want, learn)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>(cobb, county)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>(kamala, supporter)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>(god, bless)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>(donald, president)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>(time, vote)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>(today, today)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>(illegal, alien)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>(election, night)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>(say, ballot)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  bigrams  frequncy\n",
       "1            (vote, vote)        54\n",
       "2         (election, day)        18\n",
       "3            (joe, rogan)        16\n",
       "4        (kamala, harris)        16\n",
       "5            (elon, musk)        13\n",
       "6        (supreme, court)         9\n",
       "7             (donald, j)         9\n",
       "8               (4, year)         8\n",
       "9          (swing, state)         8\n",
       "10       (new, hampshire)         7\n",
       "11         (mail, ballot)         7\n",
       "12            (new, york)         6\n",
       "13         (fight, fight)         6\n",
       "14        (save, country)         6\n",
       "15       (rogan, endorse)         6\n",
       "16         (vote, donald)         6\n",
       "17  (election, integrity)         6\n",
       "18       (united, states)         6\n",
       "19            (maga, hat)         6\n",
       "20        (sunday, night)         5\n",
       "21    (president, donald)         5\n",
       "22          (hold, rally)         5\n",
       "23      (live, president)         5\n",
       "24        (early, voting)         5\n",
       "25      (endorse, donald)         5\n",
       "26     (georgia, supreme)         5\n",
       "27         (white, house)         5\n",
       "28       (ballot, accept)         4\n",
       "29      (president, hold)         4\n",
       "30                (11, 4)         4\n",
       "31                (4, 24)         4\n",
       "32     (election, office)         4\n",
       "33      (ballot, receive)         4\n",
       "34           (nbc, force)         4\n",
       "35          (equal, time)         4\n",
       "36    (attorney, general)         4\n",
       "37            (jd, vance)         4\n",
       "38     (ballot, election)         4\n",
       "39               (3, 000)         4\n",
       "40      (night, football)         4\n",
       "41          (want, learn)         4\n",
       "42         (cobb, county)         4\n",
       "43    (kamala, supporter)         4\n",
       "44           (god, bless)         4\n",
       "45    (donald, president)         4\n",
       "46           (time, vote)         4\n",
       "47         (today, today)         4\n",
       "48       (illegal, alien)         4\n",
       "49      (election, night)         4\n",
       "50          (say, ballot)         3"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save the result in a new dataframe\n",
    "top_50_bigrams_24hours_df = pd.DataFrame(top_50_bigrams_24hours).reset_index()\n",
    "\n",
    "# Adjust the index so that it starts at 1 instead of 0\n",
    "top_50_bigrams_24hours_df.index = np.arange(1, len(top_50_bigrams_24hours_df)+1)\n",
    "\n",
    "# Rename column headers\n",
    "top_50_bigrams_24hours_df.rename(columns={\"index\":'bigrams', 'count':'frequncy'}, inplace=True)\n",
    "\n",
    "# Create a top 50 bigrams table\n",
    "dataframe= top_50_bigrams_24hours_df\n",
    "save_filename= 'top_bigrams_24hrs_table'\n",
    "save_interactive_table(dataframe, save_filename)\n",
    "\n",
    "top_50_bigrams_24hours_df.head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning a wordcloud chart covering a specific period of dates\n",
    "from wordcloud import WordCloud\n",
    "\n",
    "def ngram_cloud(dataframe, num, start_date, end_date, extra_stops, top_n=None):\n",
    "    ngram = ngram_generator(dataframe, num, start_date, end_date, extra_stops, top_n)\n",
    "    words_cloud = pd.DataFrame(ngram, columns=['count']).reset_index().rename(columns={'index': 'phrases'})\n",
    "    words_cloud['phrases'] = words_cloud['phrases'].apply(lambda x: ', '.join(x) if isinstance(x,tuple) else x)\n",
    "    words_cloud['phrases'] = words_cloud['phrases'].str.replace(',', '')\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap='gist_stern').\\\n",
    "    generate_from_frequencies(words_cloud.set_index('phrases')['count'].to_dict())\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A function returning a wordcloud chart covering ONLY the past 24 hours\n",
    "\n",
    "def ngram_24hrs_cloud(dataframe, num, save_viz, extra_stops, set_title=False, top_n=None):\n",
    "    ngram = ngram_generator_24hrs(dataframe, num, extra_stops, top_n)\n",
    "    words_cloud = pd.DataFrame(ngram, columns=['count']).reset_index().rename(columns={'index': 'phrases'})\n",
    "    words_cloud['phrases'] = words_cloud['phrases'].apply(lambda x: ', '.join(x) if isinstance(x,tuple) else x)\n",
    "    words_cloud['phrases'] = words_cloud['phrases'].str.replace(',', '')\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap='Dark2').\\\n",
    "    generate_from_frequencies(words_cloud.set_index('phrases')['count'].to_dict())\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.imshow(wordcloud, \n",
    "               interpolation='nearest',\n",
    "               aspect=\"auto\"\n",
    "               )\n",
    "    \n",
    "    if set_title:\n",
    "        plt.title(f\"The chart was last updated on {datetime.datetime.now():%Y-%m-%d %H:%M} (UTC)\",\n",
    "                fontsize=20,\n",
    "                pad=10,\n",
    "                style= \"italic\",\n",
    "                loc='center'\n",
    "                )\n",
    "    plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "\n",
    "    if save_viz is not None:\n",
    "        plt.savefig(f\"viz/{save_viz}.jpeg\",\n",
    "                    dpi=600\n",
    "                    )\n",
    "        plt.show()\n",
    "    else:\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_33493/2909344436.py:7: FutureWarning: The behavior of 'isin' with dtype=datetime64[ns] and castable values (e.g. strings) is deprecated. In a future version, these will not be considered matching by isin. Explicitly cast to the appropriate dtype before calling isin instead.\n",
      "  cleaned_tokens = ' '.join(dataframe[dataframe.created_date.isin(date_list)]['title'].apply(lambda x: clean_it_all_up(x, extra_stops)).tolist())\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and save the top 50 bigrams from the last 24 hours\n",
    "dataframe = df\n",
    "num = 2\n",
    "top_n = 50\n",
    "save_viz = 'bigrams_wordcloud_24hrs'\n",
    "extra_stops=['trump']\n",
    "set_title=True\n",
    "\n",
    "ngram_24hrs_cloud(dataframe = dataframe, num=num, top_n=top_n, save_viz=save_viz, set_title=set_title, extra_stops=extra_stops)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from transformers import pipeline\n",
    "\n",
    "# Load the deberta-v3-large-zeroshot-v2.0 model directly from Huggingface\n",
    "moritzlauerer_pipe = pipeline(\"zero-shot-classification\", \n",
    "                              model=\"MoritzLaurer/deberta-v3-large-zeroshot-v2.0\", \n",
    "                              # set device at -1 since Github Actions provide no GPU\n",
    "                              device=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "# Set up labels\n",
    "labels = ['claim of fraud or irregularity', 'no claim of fraud or irregularity']\n",
    "\n",
    "\n",
    "\n",
    "def ml_pred(dataframe, column, batch_size=20):\n",
    "    results = []\n",
    "    # Speed up by implementing batch processing t\n",
    "    for i in tqdm(range(0, len(dataframe), batch_size)):\n",
    "        batch = dataframe[column].iloc[i:i + batch_size].tolist()\n",
    "        batch_results = moritzlauerer_pipe(batch, candidate_labels=labels, multi_label=False)\n",
    "        results.extend(batch_results)\n",
    "\n",
    "    dataframe['pred_label'] = [result['labels'][0] for result in results]\n",
    "    dataframe['pred_score'] = [result['scores'][0] for result in results]\n",
    "\n",
    "    dataframe['pred_label'].replace({'claim of fraud or irregularity': 1, \n",
    "                                      'no claim of fraud or irregularity': 0},\n",
    "                                      inplace=True)\n",
    "\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4d6e93adabc3405f9e5f5f843f91ea7a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1755 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[261], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m election_df \u001b[38;5;241m=\u001b[39m election_df[election_df\u001b[38;5;241m.\u001b[39mcreated_date \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m dt\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m timedelta(days\u001b[38;5;241m=\u001b[39mndays)]\n\u001b[1;32m     12\u001b[0m \u001b[38;5;66;03m# Creating another dataframe on election fraud claims\u001b[39;00m\n\u001b[0;32m---> 13\u001b[0m fraud_df \u001b[38;5;241m=\u001b[39m \u001b[43mml_pred\u001b[49m\u001b[43m(\u001b[49m\u001b[43melection_df\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtitle\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     14\u001b[0m potential_fraud \u001b[38;5;241m=\u001b[39m fraud_df[fraud_df\u001b[38;5;241m.\u001b[39mpred_label\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m     15\u001b[0m potential_fraud\n",
      "Cell \u001b[0;32mIn[260], line 10\u001b[0m, in \u001b[0;36mml_pred\u001b[0;34m(dataframe, column)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mml_pred\u001b[39m(dataframe, column):\n\u001b[0;32m---> 10\u001b[0m     results \u001b[38;5;241m=\u001b[39m \u001b[43mdataframe\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogress_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mmoritzlauerer_pipe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m     dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_label\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     13\u001b[0m     dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_score\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscores\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/tqdm/std.py:917\u001b[0m, in \u001b[0;36mtqdm.pandas.<locals>.inner_generator.<locals>.inner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;66;03m# Apply the provided function (in **kwargs)\u001b[39;00m\n\u001b[1;32m    915\u001b[0m \u001b[38;5;66;03m# on the df using our wrapper (which provides bar updating)\u001b[39;00m\n\u001b[1;32m    916\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_function\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwrapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    919\u001b[0m     t\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/series.py:4924\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m   4789\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m   4790\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   4791\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4796\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   4797\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m   4798\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4799\u001b[0m \u001b[38;5;124;03m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4800\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4915\u001b[0m \u001b[38;5;124;03m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4916\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m   4917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4918\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4920\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4922\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4924\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/apply.py:1427\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1424\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1427\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/apply.py:1507\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1501\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m   1504\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m   1505\u001b[0m \u001b[38;5;66;03m#  Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m   1506\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1507\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1508\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m   1509\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1512\u001b[0m     \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1513\u001b[0m     \u001b[38;5;66;03m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1514\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m    919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/algorithms.py:1743\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m   1741\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1743\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1745\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m   1746\u001b[0m         values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m   1747\u001b[0m     )\n",
      "File \u001b[0;32mlib.pyx:2972\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/tqdm/std.py:912\u001b[0m, in \u001b[0;36mtqdm.pandas.<locals>.inner_generator.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    906\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    907\u001b[0m     \u001b[38;5;66;03m# update tbar correctly\u001b[39;00m\n\u001b[1;32m    908\u001b[0m     \u001b[38;5;66;03m# it seems `pandas apply` calls `func` twice\u001b[39;00m\n\u001b[1;32m    909\u001b[0m     \u001b[38;5;66;03m# on the first column/row to decide whether it can\u001b[39;00m\n\u001b[1;32m    910\u001b[0m     \u001b[38;5;66;03m# take a fast or slow code path; so stop when t.total==t.n\u001b[39;00m\n\u001b[1;32m    911\u001b[0m     t\u001b[38;5;241m.\u001b[39mupdate(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mn \u001b[38;5;241m<\u001b[39m t\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 912\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "Cell \u001b[0;32mIn[260], line 10\u001b[0m, in \u001b[0;36mml_pred.<locals>.<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mml_pred\u001b[39m(dataframe, column):\n\u001b[0;32m---> 10\u001b[0m     results \u001b[38;5;241m=\u001b[39m dataframe[column]\u001b[38;5;241m.\u001b[39mprogress_apply(\u001b[38;5;28;01mlambda\u001b[39;00m x: \u001b[43mmoritzlauerer_pipe\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcandidate_labels\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m)\n\u001b[1;32m     12\u001b[0m     dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_label\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlabels\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     13\u001b[0m     dataframe[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpred_score\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m results\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: x[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscores\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/zero_shot_classification.py:206\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline.__call__\u001b[0;34m(self, sequences, *args, **kwargs)\u001b[0m\n\u001b[1;32m    203\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    204\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUnable to understand extra arguments \u001b[39m\u001b[38;5;132;01m{\u001b[39;00margs\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 206\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msequences\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/zero_shot_classification.py:229\u001b[0m, in \u001b[0;36mZeroShotClassificationPipeline._forward\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m inspect\u001b[38;5;241m.\u001b[39msignature(model_forward)\u001b[38;5;241m.\u001b[39mparameters\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[1;32m    228\u001b[0m     model_inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muse_cache\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m--> 229\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    231\u001b[0m model_outputs \u001b[38;5;241m=\u001b[39m {\n\u001b[1;32m    232\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcandidate_label\u001b[39m\u001b[38;5;124m\"\u001b[39m: candidate_label,\n\u001b[1;32m    233\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msequence\u001b[39m\u001b[38;5;124m\"\u001b[39m: sequence,\n\u001b[1;32m    234\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m: inputs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mis_last\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[1;32m    235\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moutputs,\n\u001b[1;32m    236\u001b[0m }\n\u001b[1;32m    237\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model_outputs\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1297\u001b[0m, in \u001b[0;36mDebertaV2ForSequenceClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1290\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):\u001b[39;00m\n\u001b[1;32m   1291\u001b[0m \u001b[38;5;124;03m    Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,\u001b[39;00m\n\u001b[1;32m   1292\u001b[0m \u001b[38;5;124;03m    config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;124;03m    `config.num_labels > 1` a classification loss is computed (Cross-Entropy).\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1295\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1297\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdeberta\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1302\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1303\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1304\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1305\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1306\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1308\u001b[0m encoder_layer \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1309\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(encoder_layer)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:1063\u001b[0m, in \u001b[0;36mDebertaV2Model.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, inputs_embeds, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1053\u001b[0m     token_type_ids \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mzeros(input_shape, dtype\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mlong, device\u001b[38;5;241m=\u001b[39mdevice)\n\u001b[1;32m   1055\u001b[0m embedding_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39membeddings(\n\u001b[1;32m   1056\u001b[0m     input_ids\u001b[38;5;241m=\u001b[39minput_ids,\n\u001b[1;32m   1057\u001b[0m     token_type_ids\u001b[38;5;241m=\u001b[39mtoken_type_ids,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1060\u001b[0m     inputs_embeds\u001b[38;5;241m=\u001b[39minputs_embeds,\n\u001b[1;32m   1061\u001b[0m )\n\u001b[0;32m-> 1063\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1064\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1065\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1066\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1067\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1068\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1069\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1070\u001b[0m encoded_layers \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m1\u001b[39m]\n\u001b[1;32m   1072\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mz_steps \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:507\u001b[0m, in \u001b[0;36mDebertaV2Encoder.forward\u001b[0;34m(self, hidden_states, attention_mask, output_hidden_states, output_attentions, query_states, relative_pos, return_dict)\u001b[0m\n\u001b[1;32m    497\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    498\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    499\u001b[0m         next_kv,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    504\u001b[0m         output_attentions,\n\u001b[1;32m    505\u001b[0m     )\n\u001b[1;32m    506\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 507\u001b[0m     output_states \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    508\u001b[0m \u001b[43m        \u001b[49m\u001b[43mnext_kv\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    509\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    510\u001b[0m \u001b[43m        \u001b[49m\u001b[43mquery_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mquery_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    511\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrelative_pos\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrelative_pos\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    512\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrel_embeddings\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrel_embeddings\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    513\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    517\u001b[0m     output_states, att_m \u001b[38;5;241m=\u001b[39m output_states\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:365\u001b[0m, in \u001b[0;36mDebertaV2Layer.forward\u001b[0;34m(self, hidden_states, attention_mask, query_states, relative_pos, rel_embeddings, output_attentions)\u001b[0m\n\u001b[1;32m    363\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n\u001b[1;32m    364\u001b[0m     attention_output, att_matrix \u001b[38;5;241m=\u001b[39m attention_output\n\u001b[0;32m--> 365\u001b[0m intermediate_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mintermediate\u001b[49m\u001b[43m(\u001b[49m\u001b[43mattention_output\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    366\u001b[0m layer_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(intermediate_output, attention_output)\n\u001b[1;32m    367\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m output_attentions:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/deberta_v2/modeling_deberta_v2.py:317\u001b[0m, in \u001b[0;36mDebertaV2Intermediate.forward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m torch\u001b[38;5;241m.\u001b[39mTensor:\n\u001b[0;32m--> 317\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdense\u001b[49m\u001b[43m(\u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m     hidden_states \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mintermediate_act_fn(hidden_states)\n\u001b[1;32m    319\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m hidden_states\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/linear.py:116\u001b[0m, in \u001b[0;36mLinear.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    115\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 116\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# This chunck of codes measure posts claiming election frauds\n",
    "\n",
    "# Isolate posts mentioniong the election\n",
    "election_keywords = ['election', 'vote', 'voting', 'ballot', 'poll']\n",
    "election_df = df[df.title.str.contains(\"|\".join(election_keywords), case=False, na=False)]\n",
    "\n",
    "# Focus on the last ndays days\n",
    "ndays=15\n",
    "election_df = election_df[election_df.created_date >= dt.now() - timedelta(days=ndays)]\n",
    "\n",
    "# Create another dataframe on election fraud claims\n",
    "fraud_df = ml_pred(election_df, 'title')\n",
    "potential_fraud = fraud_df[fraud_df.pred_label==1]\n",
    "potential_fraud"
   ]
  },
  {
   "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>election fraud</th>\n",
       "      <th>election</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2024-10-07</th>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-08</th>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-09</th>\n",
       "      <td>22</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-10</th>\n",
       "      <td>18</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-11</th>\n",
       "      <td>15</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-12</th>\n",
       "      <td>13</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-13</th>\n",
       "      <td>15</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-14</th>\n",
       "      <td>10</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-15</th>\n",
       "      <td>24</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-16</th>\n",
       "      <td>25</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-17</th>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-18</th>\n",
       "      <td>34</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-19</th>\n",
       "      <td>48</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-20</th>\n",
       "      <td>37</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-21</th>\n",
       "      <td>26</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-22</th>\n",
       "      <td>38</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-23</th>\n",
       "      <td>36</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-24</th>\n",
       "      <td>41</td>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-25</th>\n",
       "      <td>42</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-26</th>\n",
       "      <td>34</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-27</th>\n",
       "      <td>13</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-28</th>\n",
       "      <td>37</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-29</th>\n",
       "      <td>52</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-30</th>\n",
       "      <td>86</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-10-31</th>\n",
       "      <td>81</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-01</th>\n",
       "      <td>78</td>\n",
       "      <td>148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-02</th>\n",
       "      <td>69</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-03</th>\n",
       "      <td>63</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-04</th>\n",
       "      <td>43</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024-11-05</th>\n",
       "      <td>21</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              election fraud  election\n",
       "created_date                          \n",
       "2024-10-07                22        65\n",
       "2024-10-08                17        49\n",
       "2024-10-09                22        72\n",
       "2024-10-10                18        62\n",
       "2024-10-11                15        73\n",
       "2024-10-12                13        61\n",
       "2024-10-13                15        50\n",
       "2024-10-14                10        45\n",
       "2024-10-15                24        59\n",
       "2024-10-16                25        77\n",
       "2024-10-17                21        73\n",
       "2024-10-18                34       105\n",
       "2024-10-19                48        97\n",
       "2024-10-20                37        87\n",
       "2024-10-21                26        81\n",
       "2024-10-22                38       110\n",
       "2024-10-23                36        85\n",
       "2024-10-24                41       115\n",
       "2024-10-25                42       109\n",
       "2024-10-26                34        83\n",
       "2024-10-27                13        65\n",
       "2024-10-28                37        94\n",
       "2024-10-29                52       114\n",
       "2024-10-30                86       160\n",
       "2024-10-31                81       144\n",
       "2024-11-01                78       148\n",
       "2024-11-02                69       146\n",
       "2024-11-03                63       144\n",
       "2024-11-04                43       145\n",
       "2024-11-05                21        93"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fraud_counts = potential_fraud.created_date.value_counts(dropna=False).sort_index()\n",
    "election_counts = election_df.created_date.value_counts(dropna=False).sort_index()\n",
    "\n",
    "# Merge two series into a dataframe\n",
    "result_df = pd.DataFrame([fraud_counts, election_counts]).T\n",
    "# Rename the column headers\n",
    "result_df.columns = ['election fraud', 'election']\n",
    "\n",
    "result_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def election_claims(dataframe, save_filename):\n",
    "    # Define the size of the figure\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    \n",
    "    # Create a stacked bar plot\n",
    "    plt.bar(result_df.index, result_df['election fraud'], color='red', alpha=0.7, label='Election Fraud')\n",
    "    plt.bar(result_df.index, result_df['election'], bottom=result_df['election fraud'], color='teal', alpha=0.4, label='Election')\n",
    "    \n",
    "    # Set x-axis labels\n",
    "    x_label_date_range = pd.date_range(fraud_df['created_date'].min(), fraud_df['created_date'].max(), freq='2D')\n",
    "    plt.xlim([fraud_df['created_date'].min(), fraud_df['created_date'].max()])\n",
    "    plt.xticks(ticks=x_label_date_range, rotation=45)\n",
    "    \n",
    "    # Label and title\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel('Post count')\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.title(\n",
    "        label=f\"The chart was last updated on {dt.now():%Y-%m-%d %H:%M} (UTC)\",\n",
    "        fontdict={\"fontsize\": 15, \"color\": 'firebrick'}\n",
    "    )\n",
    "\n",
    "    plt.tight_layout()\n",
    "    \n",
    "    # Save the figure\n",
    "    plt.savefig(f\"viz/{save_filename}.jpeg\", dpi=600)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "datafarme =result_df\n",
    "save_filename = 'election_vs_election_fraud_new'\n",
    "election_claims(datafarme, save_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def expand_state_names(abbreviations):\n",
    "    #  State abbreviation dictionary\n",
    "    states = {\n",
    "        \"Alabama\": \"AL\", \"Alaska\": \"AK\", \"Arizona\": \"AZ\", \"Arkansas\": \"AR\",\n",
    "        \"California\": \"CA\", \"Colorado\": \"CO\", \"Connecticut\": \"CT\", \"Delaware\": \"DE\",\n",
    "        \"Florida\": \"FL\", \"Georgia\": \"GA\", \"Hawaii\": \"HI\", \"Idaho\": \"ID\",\n",
    "        \"Illinois\": \"IL\", \"Indiana\": \"IN\", \"Iowa\": \"IA\", \"Kansas\": \"KS\",\n",
    "        \"Kentucky\": \"KY\", \"Louisiana\": \"LA\", \"Maine\": \"ME\", \"Maryland\": \"MD\",\n",
    "        \"Massachusetts\": \"MA\", \"Michigan\": \"MI\", \"Minnesota\": \"MN\", \"Mississippi\": \"MS\",\n",
    "        \"Missouri\": \"MO\", \"Montana\": \"MT\", \"Nebraska\": \"NE\", \"Nebraska\": \"NB\", \"Nevada\": \"NV\",\n",
    "        \"New Hampshire\": \"NH\", \"New Jersey\": \"NJ\", \"New Mexico\": \"NM\", \"New York\": \"NY\",\n",
    "        \"North Carolina\": \"NC\", \"North Dakota\": \"ND\", \"Ohio\": \"OH\", \"Oklahoma\": \"OK\",\n",
    "        \"Oregon\": \"OR\", \"Pennsylvania\": \"PA\", \"Rhode Island\": \"RI\", \"South Carolina\": \"SC\",\n",
    "        \"South Dakota\": \"SD\", \"Tennessee\": \"TN\", \"Texas\": \"TX\", \"Utah\": \"UT\",\n",
    "        \"Vermont\": \"VT\", \"Virginia\": \"VA\", \"Washington\": \"WA\", \"West Virginia\": \"WV\",\n",
    "        \"Wisconsin\": \"WI\", \"Wyoming\": \"WY\"\n",
    "    }\n",
    "\n",
    "    updated_states = []\n",
    "\n",
    "    for item in abbreviations:\n",
    "        # Check if the abbreviation matches any value in the states dictionary\n",
    "        found = False  # Flag to check if a match was found\n",
    "        for key, value in states.items():\n",
    "            if value.lower() == item.lower():\n",
    "                updated_states.append(key.title())  # Append the corresponding state name\n",
    "                found = True\n",
    "                break  # Break to avoid unnecessary checks once a match is found\n",
    "        if not found:\n",
    "            updated_states.append(item.title())  # Keep the original abbreviation if no match is found\n",
    "\n",
    "    return updated_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "tqdm.pandas()\n",
    "\n",
    "import contractions\n",
    "import emoji\n",
    "import re\n",
    "\n",
    "def ner_function(dataframe, column):\n",
    "    def process_text(text):\n",
    "        if text:\n",
    "            text = emoji.replace_emoji(text)\n",
    "            text = contractions.fix(text)\n",
    "            text = re.sub(r'\\W+', ' ', text)  \n",
    "            text = re.sub(r'\\n', ' ', text)   \n",
    "        return text.strip()\n",
    "\n",
    "    dataframe[column] = dataframe[column].apply(process_text)\n",
    "    \n",
    "    # Initialize NER pipeline\n",
    "    nlp = pipeline('ner', model='dslim/bert-large-NER', device=-1)\n",
    "\n",
    "    # Initialize lists to collect unique entities\n",
    "    per_list = []  # List for person entities\n",
    "    loc_list = []  # List for location entities\n",
    "    \n",
    "    # Extract NER results\n",
    "    results = dataframe[column].progress_apply(nlp)  \n",
    "    for result in results:\n",
    "        current_name = [] \n",
    "        current_loc = []  \n",
    "        unique_locs = set()  # To track unique locations found in this cell\n",
    "\n",
    "        for each_item in result:\n",
    "            entity = each_item.get('entity')\n",
    "            word = each_item.get('word')\n",
    "\n",
    "            # Adjust for subword tokens\n",
    "            if word.startswith(\"##\"):\n",
    "                word = word.replace(\"##\", \"\")\n",
    "                if entity in ['B-PER', 'I-PER']:\n",
    "                    if current_name:\n",
    "                        current_name[-1] += word  # Append to the last name part\n",
    "                elif entity in ['B-LOC', 'I-LOC']:\n",
    "                    if current_loc:\n",
    "                        current_loc[-1] += word  # Append to the last location part\n",
    "            else:\n",
    "                # Handle person entities\n",
    "                if entity == 'B-PER':\n",
    "                    if current_name:\n",
    "                        per_list.append(\" \".join(current_name))  # Save previous name\n",
    "                        current_name = []  # Reset current name list\n",
    "                    current_name.append(word)  # Start a new name\n",
    "                \n",
    "                elif entity == 'I-PER' and current_name:\n",
    "                    current_name.append(word)  # Continue adding to the name\n",
    "                \n",
    "                # Handle location entities\n",
    "                if entity == 'B-LOC':\n",
    "                    if current_loc:\n",
    "                        # Save the current location to the set of unique locations\n",
    "                        unique_locs.add(\" \".join(current_loc))  # Save previous location\n",
    "                        current_loc = []  # Reset current location list\n",
    "                    current_loc.append(word)  # Start a new location\n",
    "                \n",
    "                elif entity == 'I-LOC' and current_loc:\n",
    "                    current_loc.append(word)  # Continue adding to the location\n",
    "\n",
    "        # After processing, save the remaining entities\n",
    "        if current_name:\n",
    "            per_list.append(\" \".join(current_name))\n",
    "        \n",
    "        # Save unique locations at the end of processing the cell\n",
    "        if current_loc:\n",
    "            unique_locs.add(\" \".join(current_loc))  # Add the last location if it exists\n",
    "\n",
    "        # Add unique locations to the final loc_list\n",
    "        loc_list.extend(unique_locs)\n",
    "    \n",
    "    # Remove duplicates from loc_list\n",
    "    loc_list = list(set(loc_list))\n",
    "    loc_list = expand_state_names(loc_list)\n",
    "    \n",
    "    return loc_list, per_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nest the NER feature in this function\n",
    "\n",
    "def ner_location_lastnDays(dataframe, column, nDays):\n",
    "    dataframe = dataframe[dataframe.created_date>= dt.now() - timedelta(days=nDays)].reset_index()\n",
    "    loc_list, name_list = ner_function(dataframe, column)\n",
    "\n",
    "    loc_df = pd.DataFrame(loc_list, columns=['location'])\n",
    "    loc_df['location'] = loc_df['location'].str.replace(\"county\", '', case=False).str.strip()\n",
    "\n",
    "    loc_count = loc_df.location.value_counts(dropna=False)\n",
    "\n",
    "    return loc_count, dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at dslim/bert-large-NER were not used when initializing BertForTokenClassification: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n",
      "- This IS expected if you are initializing BertForTokenClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0b4e0946cbb437d87028982bc5896ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/127 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[236], line 5\u001b[0m\n\u001b[1;32m      2\u001b[0m column \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      3\u001b[0m nDays \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n\u001b[0;32m----> 5\u001b[0m loc_count, fraud_lastnDay_df\u001b[38;5;241m=\u001b[39m \u001b[43mner_location_lastnDays\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnDays\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      6\u001b[0m loc_count\n",
      "Cell \u001b[0;32mIn[217], line 3\u001b[0m, in \u001b[0;36mner_location_lastnDays\u001b[0;34m(dataframe, column, nDays)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mner_location_lastnDays\u001b[39m(dataframe, column, nDays):\n\u001b[1;32m      2\u001b[0m     dataframe \u001b[38;5;241m=\u001b[39m dataframe[dataframe\u001b[38;5;241m.\u001b[39mcreated_date\u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m dt\u001b[38;5;241m.\u001b[39mnow() \u001b[38;5;241m-\u001b[39m timedelta(days\u001b[38;5;241m=\u001b[39mnDays)]\u001b[38;5;241m.\u001b[39mreset_index()\n\u001b[0;32m----> 3\u001b[0m     loc_list, name_list \u001b[38;5;241m=\u001b[39m \u001b[43mner_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdataframe\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m     loc_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame(loc_list, columns\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[1;32m      6\u001b[0m     loc_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m loc_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mreplace(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcounty\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m, case\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\u001b[38;5;241m.\u001b[39mstr\u001b[38;5;241m.\u001b[39mstrip()\n",
      "Cell \u001b[0;32mIn[216], line 27\u001b[0m, in \u001b[0;36mner_function\u001b[0;34m(dataframe, column)\u001b[0m\n\u001b[1;32m     24\u001b[0m loc_list \u001b[38;5;241m=\u001b[39m []  \u001b[38;5;66;03m# List for location entities\u001b[39;00m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;66;03m# Extract NER results\u001b[39;00m\n\u001b[0;32m---> 27\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mdataframe\u001b[49m\u001b[43m[\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprogress_apply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnlp\u001b[49m\u001b[43m)\u001b[49m  \n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m result \u001b[38;5;129;01min\u001b[39;00m results:\n\u001b[1;32m     29\u001b[0m     current_name \u001b[38;5;241m=\u001b[39m [] \n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/tqdm/std.py:917\u001b[0m, in \u001b[0;36mtqdm.pandas.<locals>.inner_generator.<locals>.inner\u001b[0;34m(df, func, *args, **kwargs)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[38;5;66;03m# Apply the provided function (in **kwargs)\u001b[39;00m\n\u001b[1;32m    915\u001b[0m \u001b[38;5;66;03m# on the df using our wrapper (which provides bar updating)\u001b[39;00m\n\u001b[1;32m    916\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdf_function\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwrapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    919\u001b[0m     t\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/series.py:4924\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m   4789\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mapply\u001b[39m(\n\u001b[1;32m   4790\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   4791\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4796\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m   4797\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m DataFrame \u001b[38;5;241m|\u001b[39m Series:\n\u001b[1;32m   4798\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   4799\u001b[0m \u001b[38;5;124;03m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4800\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4915\u001b[0m \u001b[38;5;124;03m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4916\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m   4917\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mSeriesApply\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4918\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4919\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4920\u001b[0m \u001b[43m        \u001b[49m\u001b[43mconvert_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4921\u001b[0m \u001b[43m        \u001b[49m\u001b[43mby_row\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mby_row\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4922\u001b[0m \u001b[43m        \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4923\u001b[0m \u001b[43m        \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m-> 4924\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/apply.py:1427\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1424\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mapply_compat()\n\u001b[1;32m   1426\u001b[0m \u001b[38;5;66;03m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1427\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mapply_standard\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/apply.py:1507\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1501\u001b[0m \u001b[38;5;66;03m# row-wise access\u001b[39;00m\n\u001b[1;32m   1502\u001b[0m \u001b[38;5;66;03m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m   1503\u001b[0m \u001b[38;5;66;03m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m   1504\u001b[0m \u001b[38;5;66;03m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m   1505\u001b[0m \u001b[38;5;66;03m#  Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m   1506\u001b[0m action \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mignore\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj\u001b[38;5;241m.\u001b[39mdtype, CategoricalDtype) \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m-> 1507\u001b[0m mapped \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_values\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1508\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmapper\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcurried\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maction\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_dtype\u001b[49m\n\u001b[1;32m   1509\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1511\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(mapped) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(mapped[\u001b[38;5;241m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1512\u001b[0m     \u001b[38;5;66;03m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1513\u001b[0m     \u001b[38;5;66;03m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1514\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m obj\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\u001b[38;5;28mlist\u001b[39m(mapped), index\u001b[38;5;241m=\u001b[39mobj\u001b[38;5;241m.\u001b[39mindex)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m    918\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m    919\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mmap(mapper, na_action\u001b[38;5;241m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43malgorithms\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43marr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mna_action\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mna_action\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/algorithms.py:1743\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m   1741\u001b[0m values \u001b[38;5;241m=\u001b[39m arr\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mobject\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m   1742\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m na_action \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1743\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mlib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap_infer\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmapper\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconvert\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1744\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1745\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m lib\u001b[38;5;241m.\u001b[39mmap_infer_mask(\n\u001b[1;32m   1746\u001b[0m         values, mapper, mask\u001b[38;5;241m=\u001b[39misna(values)\u001b[38;5;241m.\u001b[39mview(np\u001b[38;5;241m.\u001b[39muint8), convert\u001b[38;5;241m=\u001b[39mconvert\n\u001b[1;32m   1747\u001b[0m     )\n",
      "File \u001b[0;32mlib.pyx:2972\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/tqdm/std.py:912\u001b[0m, in \u001b[0;36mtqdm.pandas.<locals>.inner_generator.<locals>.inner.<locals>.wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    906\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    907\u001b[0m     \u001b[38;5;66;03m# update tbar correctly\u001b[39;00m\n\u001b[1;32m    908\u001b[0m     \u001b[38;5;66;03m# it seems `pandas apply` calls `func` twice\u001b[39;00m\n\u001b[1;32m    909\u001b[0m     \u001b[38;5;66;03m# on the first column/row to decide whether it can\u001b[39;00m\n\u001b[1;32m    910\u001b[0m     \u001b[38;5;66;03m# take a fast or slow code path; so stop when t.total==t.n\u001b[39;00m\n\u001b[1;32m    911\u001b[0m     t\u001b[38;5;241m.\u001b[39mupdate(n\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m t\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;129;01mor\u001b[39;00m t\u001b[38;5;241m.\u001b[39mn \u001b[38;5;241m<\u001b[39m t\u001b[38;5;241m.\u001b[39mtotal \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;241m0\u001b[39m)\n\u001b[0;32m--> 912\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/token_classification.py:250\u001b[0m, in \u001b[0;36mTokenClassificationPipeline.__call__\u001b[0;34m(self, inputs, **kwargs)\u001b[0m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m offset_mapping:\n\u001b[1;32m    248\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moffset_mapping\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m offset_mapping\n\u001b[0;32m--> 250\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/base.py:1294\u001b[0m, in \u001b[0;36mPipeline.__call__\u001b[0;34m(self, inputs, num_workers, batch_size, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1292\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39miterate(inputs, preprocess_params, forward_params, postprocess_params)\n\u001b[1;32m   1293\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframework \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ChunkPipeline):\n\u001b[0;32m-> 1294\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43miter\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_iterator\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m                \u001b[49m\u001b[43m[\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_workers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreprocess_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mforward_params\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpostprocess_params\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1300\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1302\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_single(inputs, preprocess_params, forward_params, postprocess_params)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py:124\u001b[0m, in \u001b[0;36mPipelineIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    121\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_item()\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# We're out of items within a batch\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    125\u001b[0m processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfer(item, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mparams)\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# We now have a batch of \"inferred things\".\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/pt_utils.py:269\u001b[0m, in \u001b[0;36mPipelinePackIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    266\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m accumulator\n\u001b[1;32m    268\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_last:\n\u001b[0;32m--> 269\u001b[0m     processed \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minfer\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    270\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mloader_batch_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    271\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(processed, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/base.py:1209\u001b[0m, in \u001b[0;36mPipeline.forward\u001b[0;34m(self, model_inputs, **forward_params)\u001b[0m\n\u001b[1;32m   1207\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m inference_context():\n\u001b[1;32m   1208\u001b[0m         model_inputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_inputs, device\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[0;32m-> 1209\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mforward_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1210\u001b[0m         model_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ensure_tensor_on_device(model_outputs, device\u001b[38;5;241m=\u001b[39mtorch\u001b[38;5;241m.\u001b[39mdevice(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcpu\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m   1211\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/pipelines/token_classification.py:287\u001b[0m, in \u001b[0;36mTokenClassificationPipeline._forward\u001b[0;34m(self, model_inputs)\u001b[0m\n\u001b[1;32m    285\u001b[0m     logits \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmodel(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_inputs)[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    286\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 287\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mmodel_inputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    288\u001b[0m     logits \u001b[38;5;241m=\u001b[39m output[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogits\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, \u001b[38;5;28mdict\u001b[39m) \u001b[38;5;28;01melse\u001b[39;00m output[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m {\n\u001b[1;32m    291\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlogits\u001b[39m\u001b[38;5;124m\"\u001b[39m: logits,\n\u001b[1;32m    292\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecial_tokens_mask\u001b[39m\u001b[38;5;124m\"\u001b[39m: special_tokens_mask,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    296\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmodel_inputs,\n\u001b[1;32m    297\u001b[0m }\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/bert/modeling_bert.py:1862\u001b[0m, in \u001b[0;36mBertForTokenClassification.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1856\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1857\u001b[0m \u001b[38;5;124;03mlabels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):\u001b[39;00m\n\u001b[1;32m   1858\u001b[0m \u001b[38;5;124;03m    Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.\u001b[39;00m\n\u001b[1;32m   1859\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1860\u001b[0m return_dict \u001b[38;5;241m=\u001b[39m return_dict \u001b[38;5;28;01mif\u001b[39;00m return_dict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39muse_return_dict\n\u001b[0;32m-> 1862\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbert\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1863\u001b[0m \u001b[43m    \u001b[49m\u001b[43minput_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1864\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1865\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken_type_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken_type_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1866\u001b[0m \u001b[43m    \u001b[49m\u001b[43mposition_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mposition_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1867\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1868\u001b[0m \u001b[43m    \u001b[49m\u001b[43minputs_embeds\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs_embeds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1869\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1870\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1871\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1872\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1874\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1876\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout(sequence_output)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/bert/modeling_bert.py:1142\u001b[0m, in \u001b[0;36mBertModel.forward\u001b[0;34m(self, input_ids, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1135\u001b[0m \u001b[38;5;66;03m# Prepare head mask if needed\u001b[39;00m\n\u001b[1;32m   1136\u001b[0m \u001b[38;5;66;03m# 1.0 in head_mask indicate we keep the head\u001b[39;00m\n\u001b[1;32m   1137\u001b[0m \u001b[38;5;66;03m# attention_probs has shape bsz x n_heads x N x N\u001b[39;00m\n\u001b[1;32m   1138\u001b[0m \u001b[38;5;66;03m# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]\u001b[39;00m\n\u001b[1;32m   1139\u001b[0m \u001b[38;5;66;03m# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]\u001b[39;00m\n\u001b[1;32m   1140\u001b[0m head_mask \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_head_mask(head_mask, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconfig\u001b[38;5;241m.\u001b[39mnum_hidden_layers)\n\u001b[0;32m-> 1142\u001b[0m encoder_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1143\u001b[0m \u001b[43m    \u001b[49m\u001b[43membedding_output\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1144\u001b[0m \u001b[43m    \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mextended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1145\u001b[0m \u001b[43m    \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1146\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1147\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoder_extended_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1148\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpast_key_values\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpast_key_values\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1149\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_cache\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_cache\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1150\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1151\u001b[0m \u001b[43m    \u001b[49m\u001b[43moutput_hidden_states\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1152\u001b[0m \u001b[43m    \u001b[49m\u001b[43mreturn_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_dict\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1153\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1154\u001b[0m sequence_output \u001b[38;5;241m=\u001b[39m encoder_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m   1155\u001b[0m pooled_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler(sequence_output) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpooler \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/bert/modeling_bert.py:695\u001b[0m, in \u001b[0;36mBertEncoder.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_values, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m    684\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gradient_checkpointing_func(\n\u001b[1;32m    685\u001b[0m         layer_module\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__call__\u001b[39m,\n\u001b[1;32m    686\u001b[0m         hidden_states,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    692\u001b[0m         output_attentions,\n\u001b[1;32m    693\u001b[0m     )\n\u001b[1;32m    694\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 695\u001b[0m     layer_outputs \u001b[38;5;241m=\u001b[39m \u001b[43mlayer_module\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    696\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    697\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    698\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlayer_head_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    699\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    700\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    701\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    702\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    703\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    705\u001b[0m hidden_states \u001b[38;5;241m=\u001b[39m layer_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    706\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m use_cache:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/bert/modeling_bert.py:585\u001b[0m, in \u001b[0;36mBertLayer.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    573\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    574\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    575\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    582\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[1;32m    583\u001b[0m     \u001b[38;5;66;03m# decoder uni-directional self-attention cached key/values tuple is at positions 1,2\u001b[39;00m\n\u001b[1;32m    584\u001b[0m     self_attn_past_key_value \u001b[38;5;241m=\u001b[39m past_key_value[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m past_key_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m--> 585\u001b[0m     self_attention_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mattention\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    586\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    587\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    588\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    589\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    590\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mself_attn_past_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    591\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    592\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m self_attention_outputs[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m    594\u001b[0m     \u001b[38;5;66;03m# if decoder, the last output is tuple of self-attn cache\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/transformers/models/bert/modeling_bert.py:515\u001b[0m, in \u001b[0;36mBertAttention.forward\u001b[0;34m(self, hidden_states, attention_mask, head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions)\u001b[0m\n\u001b[1;32m    505\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\n\u001b[1;32m    506\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m    507\u001b[0m     hidden_states: torch\u001b[38;5;241m.\u001b[39mTensor,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    513\u001b[0m     output_attentions: Optional[\u001b[38;5;28mbool\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m    514\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[torch\u001b[38;5;241m.\u001b[39mTensor]:\n\u001b[0;32m--> 515\u001b[0m     self_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mself\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    516\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[43m        \u001b[49m\u001b[43mattention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    518\u001b[0m \u001b[43m        \u001b[49m\u001b[43mhead_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    519\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_hidden_states\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[43m        \u001b[49m\u001b[43mencoder_attention_mask\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    521\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpast_key_value\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    522\u001b[0m \u001b[43m        \u001b[49m\u001b[43moutput_attentions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    523\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    524\u001b[0m     attention_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(self_outputs[\u001b[38;5;241m0\u001b[39m], hidden_states)\n\u001b[1;32m    525\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (attention_output,) \u001b[38;5;241m+\u001b[39m self_outputs[\u001b[38;5;241m1\u001b[39m:]  \u001b[38;5;66;03m# add attentions if we output them\u001b[39;00m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1532\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1531\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1532\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/torch/nn/modules/module.py:1541\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1536\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1537\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1538\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1539\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1540\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1544\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "dataframe = potential_fraud\n",
    "column = 'title'\n",
    "nDays = 2\n",
    "\n",
    "loc_count, fraud_lastnDay_df= ner_location_lastnDays(dataframe, column, nDays)\n",
    "loc_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'uuid'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
      "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32mpandas/_libs/hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'uuid'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[237], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m fraud_lastnDay_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlink\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mhttps://patriots.win/p/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m+\u001b[39m \u001b[43mfraud_lastnDay_df\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43muuid\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m      3\u001b[0m fraud_lastnDay_df \u001b[38;5;241m=\u001b[39m fraud_lastnDay_df\u001b[38;5;241m.\u001b[39mrename(columns \u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mscore_up\u001b[39m\u001b[38;5;124m'\u001b[39m:\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mupvote count\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m      4\u001b[0m                                                        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcomments\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcomment count\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m      5\u001b[0m                                                        })\n\u001b[1;32m      8\u001b[0m fraud_lastnDay_df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m=\u001b[39m fraud_lastnDay_df\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m row: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m<a href=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrow[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlink\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m target=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_blank\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m>\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrow[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m</a>\u001b[39m\u001b[38;5;124m'\u001b[39m, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m   3810\u001b[0m     ):\n\u001b[1;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[1;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
      "\u001b[0;31mKeyError\u001b[0m: 'uuid'"
     ]
    }
   ],
   "source": [
    "fraud_lastnDay_df['link'] = 'https://patriots.win/p/'+ fraud_lastnDay_df['uuid']\n",
    "\n",
    "fraud_lastnDay_df = fraud_lastnDay_df.rename(columns ={'score_up':'upvote count',\n",
    "                                                       'comments': 'comment count'\n",
    "                                                       })\n",
    "\n",
    "\n",
    "fraud_lastnDay_df['title']= fraud_lastnDay_df.apply(lambda row: f'<a href=\"{row[\"link\"]}\" target=\"_blank\">{row[\"title\"]}</a>', axis=1)\n",
    "fraud_lastnDay_df = fraud_lastnDay_df[['title', 'created_date', 'upvote count','comment count']].sort_values('created_date', ascending=False)\n",
    "\n",
    "fraud_lastnDay_df.index +=1 \n",
    "fraud_lastnDay_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"my_table\">\\n  <thead>\\n    <tr style=\"text-align: center;\">\\n      <th></th>\\n      <th>title</th>\\n      <th>created_date</th>\\n      <th>upvote count</th>\\n      <th>comment count</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>127</th>\\n      <td><a href=\"https://patriots.win/p/199OhuQMfR\" target=\"_blank\">Roseanne Barr Wish every state had a KenPaxtonTX We would actually have free and fair and legitimate elections and Dems would dissolve as a party entirely When you have to cheat it means you cannot win on ideas because your ideas suck</a></td>\\n      <td>2024-11-05</td>\\n      <td>40</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>117</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs91b4\" target=\"_blank\">This is why Trump said he can win New Jersey An army of Jewish voters flooded the polls in Lakewood to vote for Trump</a></td>\\n      <td>2024-11-05</td>\\n      <td>155</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>107</th>\\n      <td><a href=\"https://patriots.win/p/199OhsA7es\" target=\"_blank\">So Democrats Are not Voting in the Same Numbers They Did in 2020 Because They NEVER EXISTED IN THE FIRST PLACE Remember We are Watching Everywhere</a></td>\\n      <td>2024-11-05</td>\\n      <td>736</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>109</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9ImK\" target=\"_blank\">SHOULD TRUMP BE CHEATED OUT OF THIS ELECTION I WILL BE LOGGING OFF AND SAYING GOODBYE AS IT WILL BE TIME TO GET BUSY SHOULD THIS BE MY LAST POST I LOVE YOU ALL FRENS IF IT GETS SQUIGGLY GRAB THE DIDDLY AND BE COURAGEOUS DO NOT LET THEM TAKE AMERICA WITHOUT A FIGHT GOBBLESS</a></td>\\n      <td>2024-11-05</td>\\n      <td>313</td>\\n      <td>28</td>\\n    </tr>\\n    <tr>\\n      <th>110</th>\\n      <td><a href=\"https://patriots.win/p/199OhsA7Wa\" target=\"_blank\">34 535 voting illegals discovered in Michigan Dems zooming in their cheat on this state</a></td>\\n      <td>2024-11-05</td>\\n      <td>293</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>111</th>\\n      <td><a href=\"https://patriots.win/p/199OhtIFWv\" target=\"_blank\">Trump Legal Team James O Keefe Philadelphia City Commissioners Office Tells Non Citizens They Can Vote if They Are Philly Residents NGO Ceiba Across the Street Sends Non Citizen Voter with an ITIN</a></td>\\n      <td>2024-11-05</td>\\n      <td>253</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>112</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9IqT\" target=\"_blank\">The math did not even add up Stolen election 2020</a></td>\\n      <td>2024-11-05</td>\\n      <td>229</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>113</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9ZUn\" target=\"_blank\">Elon brings Saying Justice is coming got Pizzagate Democrat Pedos Balls of steal VOTE FOR JUSTICE</a></td>\\n      <td>2024-11-05</td>\\n      <td>214</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>114</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9qwU\" target=\"_blank\">Two Maine Citizens Allege Many Counties Have Implausible and Impossible Voter Registration Rates Threaten to Sue Secretary of State</a></td>\\n      <td>2024-11-05</td>\\n      <td>182</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>115</th>\\n      <td><a href=\"https://patriots.win/p/199OhtHy10\" target=\"_blank\">Never again Get out and vote stay in line block the 3am dropoffs</a></td>\\n      <td>2024-11-05</td>\\n      <td>175</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>116</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9qPQ\" target=\"_blank\">Trump campaign update Democrat s early votes count collapsed</a></td>\\n      <td>2024-11-05</td>\\n      <td>172</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>108</th>\\n      <td><a href=\"https://patriots.win/p/199OhsAP2f\" target=\"_blank\">BREAKINGFORSYTH COUNTY NC Early voting poll worker Just found out that our tabulator was opened mid week the USB stick taken out replaced No chain of custody no idea when it went back in the machine</a></td>\\n      <td>2024-11-05</td>\\n      <td>314</td>\\n      <td>21</td>\\n    </tr>\\n    <tr>\\n      <th>118</th>\\n      <td><a href=\"https://patriots.win/p/199OhuPGjw\" target=\"_blank\">James O Keefe Caught Philadelphia City Commissioners Office Tells Non Citizens They Can Vote if They Are Philly Residents</a></td>\\n      <td>2024-11-05</td>\\n      <td>134</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>123</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs91Sn\" target=\"_blank\">The reason why early Democrat voters the urban voters and the low propensity voters seem to not be voting in numbers like 2020 is because those voters never existed and we are watching the count</a></td>\\n      <td>2024-11-05</td>\\n      <td>52</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>119</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs9ZpM\" target=\"_blank\">KEEP YOUR EYES OPEN This little dot will void a trump vote</a></td>\\n      <td>2024-11-05</td>\\n      <td>122</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>125</th>\\n      <td><a href=\"https://patriots.win/p/199OhuPXmy\" target=\"_blank\">Non citizens being told they can vote in Philly</a></td>\\n      <td>2024-11-05</td>\\n      <td>51</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>124</th>\\n      <td><a href=\"https://patriots.win/p/199OhuPopx\" target=\"_blank\">On Election Eve Matthew Graves Kamala s want to be attorney general submitted an unhinged sentencing recommendation for Thomas Caldwell whose only conviction after 1512 conviction was vacated is tampering with evidence for deleting FB messages DOJ wants 4 years for Caldwell 70 who never went</a></td>\\n      <td>2024-11-05</td>\\n      <td>52</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>126</th>\\n      <td><a href=\"https://patriots.win/p/199OhuQ5cS\" target=\"_blank\">Mail In Ballots Sent to Uninhabited Locations Across Pennsylvania Including Power Plants and a Baseball Field</a></td>\\n      <td>2024-11-05</td>\\n      <td>42</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>122</th>\\n      <td><a href=\"https://patriots.win/p/199OhuPodX\" target=\"_blank\">whatever you do today do not post exit polls they are wrong and have a demonrat bias</a></td>\\n      <td>2024-11-05</td>\\n      <td>61</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>121</th>\\n      <td><a href=\"https://patriots.win/p/199OhuOz9l\" target=\"_blank\">PSA If you live in a Midwest or Western State Pay Zero Attention to East Coast Returns Also Ignore Early Exit Polls Pushed by MSM as They Will be Designed to Suppress Mountain and West Coast Vote</a></td>\\n      <td>2024-11-05</td>\\n      <td>84</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>120</th>\\n      <td><a href=\"https://patriots.win/p/199OhuPGGy\" target=\"_blank\">Clarks county election office was caught being secretly open yesterday and they resumed counting to cut Trump s lead It went unnoticed RNC still does not know about it</a></td>\\n      <td>2024-11-05</td>\\n      <td>92</td>\\n      <td>7</td>\\n    </tr>\\n    <tr>\\n      <th>84</th>\\n      <td><a href=\"https://patriots.win/p/199OdJRNG2\" target=\"_blank\">Democrats Have Challenged the Presidential Election Every Time They Lost Over the Past 24 Years When Trump Won They Objected to the Electoral Count with Raskin D MD Himself Registering an Objection</a></td>\\n      <td>2024-11-04</td>\\n      <td>285</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>76</th>\\n      <td><a href=\"https://patriots.win/p/199OdKaan9\" target=\"_blank\">I will never trust elections until they are all in person on paper and counted by early evening</a></td>\\n      <td>2024-11-04</td>\\n      <td>450</td>\\n      <td>13</td>\\n    </tr>\\n    <tr>\\n      <th>83</th>\\n      <td><a href=\"https://patriots.win/p/199OdKYfaB\" target=\"_blank\">I am a Polish citizen but since the US does not ask for voter ID I thought why not vote for Donald Trump while I am on vacation You are welcome</a></td>\\n      <td>2024-11-04</td>\\n      <td>285</td>\\n      <td>29</td>\\n    </tr>\\n    <tr>\\n      <th>82</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1S5c\" target=\"_blank\">BFREAKING Appeals Court Rejects Arizona Democrat Secretary of State s Request to Withhold List of 218 000 Registered Voters Who Did Not Provide Proof of Citizenship He Must Produce List Today</a></td>\\n      <td>2024-11-04</td>\\n      <td>286</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>81</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr0uG0\" target=\"_blank\">HUGE election integrity victory in Georgia Michael Whatley</a></td>\\n      <td>2024-11-04</td>\\n      <td>293</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>80</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1AqH\" target=\"_blank\">Allegheny County Pittsburgh PA Election workers will stop counting between 2 and 3 am so they can go home and get some rest Please get people on the ground at the City County Building They are going to steal Allegheny County</a></td>\\n      <td>2024-11-04</td>\\n      <td>330</td>\\n      <td>17</td>\\n    </tr>\\n    <tr>\\n      <th>79</th>\\n      <td><a href=\"https://patriots.win/p/199OdKaarA\" target=\"_blank\">Georgia Files Suit In Federal Court After SEVEN Blue Counties Ignore Election Law And Accept Absentee Ballots</a></td>\\n      <td>2024-11-04</td>\\n      <td>330</td>\\n      <td>23</td>\\n    </tr>\\n    <tr>\\n      <th>78</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1B6k\" target=\"_blank\">BREAKING The Georgia Supreme Court just ruled that ballots cannot be accepted and counted AFTER</a></td>\\n      <td>2024-11-04</td>\\n      <td>338</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>77</th>\\n      <td><a href=\"https://patriots.win/p/199OdJPixr\" target=\"_blank\">MY KIDS PRANKED THEIR RIVAL HIGH SCHOOL BY VOTING IN THEIR STUDENT GOV ELECTION EVEN THOUGH THEY DO NOT GO TO SCHOOL THERE THEY GOT CAUGHT AND THE PRINCIPAL LECTURED US ON HOW THIS UNDERMINES THE VOTES OF THE KIDS WHO ACTUALLY GO THERE WHILE HE UNIRONICALLY HAD A HARRIS WALZ SIGN ON HIS BOOKSHELF</a></td>\\n      <td>2024-11-04</td>\\n      <td>406</td>\\n      <td>16</td>\\n    </tr>\\n    <tr>\\n      <th>71</th>\\n      <td><a href=\"https://patriots.win/p/199OdJPihJ\" target=\"_blank\">Breaking Shady Election Group Dumps 90 000 Ballot Registrations in Maricopa County Before Sign Up Deadline Ends At Least 40 000 Damaged Thousands Incomplete</a></td>\\n      <td>2024-11-04</td>\\n      <td>799</td>\\n      <td>75</td>\\n    </tr>\\n    <tr>\\n      <th>75</th>\\n      <td><a href=\"https://patriots.win/p/199OdJRe6j\" target=\"_blank\">Judge Allows Iowa to Challenge Voters It Suspects of Being Noncitizens</a></td>\\n      <td>2024-11-04</td>\\n      <td>541</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>74</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr0uON\" target=\"_blank\">Huge election integrity victory in Georgia RNC just stopped 3 000 ballots received after election day from being counted No more cheating allowed</a></td>\\n      <td>2024-11-04</td>\\n      <td>626</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>73</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr0tvV\" target=\"_blank\">BREAKING Almost 14 000 mail in ballots in Nevada currently do not have adequate signatures and will NOT be counted unless they are cured This is devastating news for Kamala Harris</a></td>\\n      <td>2024-11-04</td>\\n      <td>655</td>\\n      <td>16</td>\\n    </tr>\\n    <tr>\\n      <th>72</th>\\n      <td><a href=\"https://patriots.win/p/199OdIJo5G\" target=\"_blank\">STEVE BANNON The Facts Are On Our Side Joe Biden Never Got 15 Million More Votes Than Barack Obama IMPOSSIBLE We Did not Pick Up Net 12 House Seats When We Lost the Presidential IMPOSSIBLE We Did not Win 19 20 Bellwethers And Lose The Presidency IMPOSSIBLE You Generated Phony Ballots Of Folks</a></td>\\n      <td>2024-11-04</td>\\n      <td>668</td>\\n      <td>21</td>\\n    </tr>\\n    <tr>\\n      <th>70</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr04uf\" target=\"_blank\">I am still PISSED about 2020 being blatantly STOLEN but guess what I STILL VOTED</a></td>\\n      <td>2024-11-04</td>\\n      <td>884</td>\\n      <td>30</td>\\n    </tr>\\n    <tr>\\n      <th>69</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr04ub\" target=\"_blank\">Nevada does not stop counting ballots until Friday That means that after the election they are still going to count ballots for an entire three days Three days where they know exactly how many ballots they need in order to push Harris over the top</a></td>\\n      <td>2024-11-04</td>\\n      <td>936</td>\\n      <td>43</td>\\n    </tr>\\n    <tr>\\n      <th>68</th>\\n      <td><a href=\"https://patriots.win/p/199OdIJFO5\" target=\"_blank\">If he loses the plan is to jail him quickly so that he is not in a position to challenge any irregularities Tuesday is very important He is got to win you got to vote</a></td>\\n      <td>2024-11-04</td>\\n      <td>1539</td>\\n      <td>187</td>\\n    </tr>\\n    <tr>\\n      <th>67</th>\\n      <td><a href=\"https://patriots.win/p/199OdKab3d\" target=\"_blank\">PA Supreme Court says mail in ballots with missing or wrong dates cannot be tallied</a></td>\\n      <td>2024-11-04</td>\\n      <td>2303</td>\\n      <td>129</td>\\n    </tr>\\n    <tr>\\n      <th>86</th>\\n      <td><a href=\"https://patriots.win/p/199OdIJWZM\" target=\"_blank\">Election fuckery in Kentucky They are finding ways to turn normal ballots inadmissible only when voting for Trump</a></td>\\n      <td>2024-11-04</td>\\n      <td>240</td>\\n      <td>18</td>\\n    </tr>\\n    <tr>\\n      <th>85</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr052p\" target=\"_blank\">Iowa What is Happening Illegals Swarming Iowa Ballots ____ Keep Fighting VOTE VOTE VOTE</a></td>\\n      <td>2024-11-04</td>\\n      <td>240</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>97</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1SQK\" target=\"_blank\">BREAKING GA Supreme Court says NO ballots accepted after Election day</a></td>\\n      <td>2024-11-04</td>\\n      <td>137</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>87</th>\\n      <td><a href=\"https://patriots.win/p/199Ohqznay\" target=\"_blank\">Slight correction our force is 230 000 poll observers poll workers strong AND 500 attorneys in EVERY BATTLEGROUND STATE Our message is crystal clear DO NOT CHEAT If you do we will find you and prosecute you to the FULLEST EXTENT OF THE LAW</a></td>\\n      <td>2024-11-04</td>\\n      <td>237</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>98</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1jTL\" target=\"_blank\">This is crazy The 2020 Dem voters simply are not turning out Everywhere especially in urban areas Aside from being good news for Trump it will also make Biden s 81 million votes so suspect</a></td>\\n      <td>2024-11-04</td>\\n      <td>137</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>106</th>\\n      <td><a href=\"https://patriots.win/p/199OdJQoxU\" target=\"_blank\">BREAKING EMERGENCY Trump Warns That The Democrats Are Actively Trying To Steal The 2024 Election Massive Evidence of Confirmed Fraud Flooding All Battleground States Trump Told A Massive PA Crowd That Everyone Else Is Too Scared To Tell You This</a></td>\\n      <td>2024-11-04</td>\\n      <td>81</td>\\n      <td>14</td>\\n    </tr>\\n    <tr>\\n      <th>105</th>\\n      <td><a href=\"https://patriots.win/p/199OdJQY6v\" target=\"_blank\">76 of PA ballots received Trump was up 55 Then all of a sudden an incredible 74 of the uncounted ballots went to Biden</a></td>\\n      <td>2024-11-04</td>\\n      <td>83</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>104</th>\\n      <td><a href=\"https://patriots.win/p/199OdIJWu0\" target=\"_blank\">Voter fraud in California</a></td>\\n      <td>2024-11-04</td>\\n      <td>84</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>103</th>\\n      <td><a href=\"https://patriots.win/p/199OdJR64l\" target=\"_blank\">Faggot Marc Elias is in Nevada trying to RIG the elections Republicans are ready for him When marceelias opened an office in NV I knew we had a problem Expect challenges on everything We are ready for guerrilla warfare from 3AM ballot drops to every tactic under the sun</a></td>\\n      <td>2024-11-04</td>\\n      <td>85</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>102</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr2H6T\" target=\"_blank\">Voting machines voting without ID mail in ballots drop boxes and voting day becoming voting month There is only one reason for that Cheating</a></td>\\n      <td>2024-11-04</td>\\n      <td>86</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>101</th>\\n      <td><a href=\"https://patriots.win/p/199OdJQXiA\" target=\"_blank\">It is Election Eve 2024 Four years in the making Our guy has been spied on impeached raided arrested mugshotted and nearly killed All we have to do is vote a vote that will become historic the second it is casted LFG</a></td>\\n      <td>2024-11-04</td>\\n      <td>99</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>100</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1BJ3\" target=\"_blank\">Federal Judge Rules Iowa Officials Can Challenge Ballots Of Potential Noncitizen Voters The Iowa Secretary of State has hailed the court s ruling as a win for Iowa s election integrity</a></td>\\n      <td>2024-11-04</td>\\n      <td>108</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>99</th>\\n      <td><a href=\"https://patriots.win/p/199OdKYOX6\" target=\"_blank\">If they are calling you this before an election what do you think they will do to you after they steal it</a></td>\\n      <td>2024-11-04</td>\\n      <td>113</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>65</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr0u7m\" target=\"_blank\">BREAKING Almost 14 000 mail in ballots in Nevada currently do not have adequate signatures and will NOT be counted unless they are cured This is devastating news for Kamala Harris</a></td>\\n      <td>2024-11-04</td>\\n      <td>2495</td>\\n      <td>147</td>\\n    </tr>\\n    <tr>\\n      <th>88</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr20SD\" target=\"_blank\">THE FIX IS IN Georgia Supreme Court DENIES relief for Cobb County voters who requested absentee ballots on time but did not receive them on time due to the county s failure to mail them promptly MAKE SURE YOU TURN IN YOUR BALLOT IN PERSON IN Cobb County</a></td>\\n      <td>2024-11-04</td>\\n      <td>230</td>\\n      <td>12</td>\\n    </tr>\\n    <tr>\\n      <th>96</th>\\n      <td><a href=\"https://patriots.win/p/199OdJSCGn\" target=\"_blank\">Location Maryland Report Citizen s vote was switched from Donald Trump to RFK Jr When she brought it to the attention of the election judge she was reprimanded for capturing proof Resolve If this happens to you immediately file a complaint</a></td>\\n      <td>2024-11-04</td>\\n      <td>140</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>95</th>\\n      <td><a href=\"https://patriots.win/p/199OdKY75L\" target=\"_blank\">Cat Lady says Iowa goes Lib gets exposed as hacktivist with no polling or data background</a></td>\\n      <td>2024-11-04</td>\\n      <td>142</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>94</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1AyX\" target=\"_blank\">HUGE election integrity victory in Georgia Democrat run Cobb County wanted to accept 3 000 absentee ballots AFTER the Election Day deadline We took this case to the Georgia Supreme Court We just got word that we WON the case Election Day is Election Day not the week after</a></td>\\n      <td>2024-11-04</td>\\n      <td>144</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>93</th>\\n      <td><a href=\"https://patriots.win/p/199OdIJngT\" target=\"_blank\">In addition to illegally boosting KamalaHarris NBC s Saturday Night Live last night did a skit literally aimed at boosting the name recognition of Sen timkaine Why Perhaps because Kaine s challenger HungCao_VA just closed the gap could win It is election interference</a></td>\\n      <td>2024-11-04</td>\\n      <td>150</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>92</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1iwG\" target=\"_blank\">Dominion Voting has been forced to admit IN WRITING that its machines in Michigan do not work properly</a></td>\\n      <td>2024-11-04</td>\\n      <td>151</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>91</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr0csO\" target=\"_blank\">IRREFUTABLE EVIDENCE your government was overthrown on November 3 2020 an Obama activist wishing Trump for Christmas in handcuffs now in charge of Dominion voting machines Jim savage held up in his hand a fairly large vDrive key and said You can overthrow the United States government</a></td>\\n      <td>2024-11-04</td>\\n      <td>182</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>90</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs8CFY\" target=\"_blank\">My democrat co workers all wrote in biden on their ballot they feel Kamala stole his nomination love all the wasted votes</a></td>\\n      <td>2024-11-04</td>\\n      <td>195</td>\\n      <td>12</td>\\n    </tr>\\n    <tr>\\n      <th>89</th>\\n      <td><a href=\"https://patriots.win/p/199OdKYfNj\" target=\"_blank\">Illegal aliens being counted in the census also distorts the electoral college It is estimated that about 20 electoral college votes and members of Congress would shift to Red states if we did not count illegal aliens in the census</a></td>\\n      <td>2024-11-04</td>\\n      <td>225</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>66</th>\\n      <td><a href=\"https://patriots.win/p/199Ohs8kDQ\" target=\"_blank\">Kevin Sorbo If a state cannot finish counting votes by Wednesday morning it is cheating</a></td>\\n      <td>2024-11-04</td>\\n      <td>2328</td>\\n      <td>73</td>\\n    </tr>\\n    <tr>\\n      <th>64</th>\\n      <td><a href=\"https://patriots.win/p/199Ohr1jL4\" target=\"_blank\">GRENNELL Any state that cannot count their votes and report the results Tuesday night is corrupt and incompetent</a></td>\\n      <td>2024-11-04</td>\\n      <td>2931</td>\\n      <td>165</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td><a href=\"https://patriots.win/p/199OdEuLCx\" target=\"_blank\">Only 16 ballots were collected at one site because of the pressure Keep the pressure on</a></td>\\n      <td>2024-11-03</td>\\n      <td>1899</td>\\n      <td>142</td>\\n    </tr>\\n    <tr>\\n      <th>17</th>\\n      <td><a href=\"https://patriots.win/p/199OdHBPYv\" target=\"_blank\">2 libtards placed huge bets on PA election to sway the odds and try to spook MAGA</a></td>\\n      <td>2024-11-03</td>\\n      <td>278</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>30</th>\\n      <td><a href=\"https://patriots.win/p/199OdII9K4\" target=\"_blank\">Elons PAC America is going to be bussing PA Republicans to the polls on Tuesday much like the PA Dems did in 2022 for Fetterman</a></td>\\n      <td>2024-11-03</td>\\n      <td>148</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>29</th>\\n      <td><a href=\"https://patriots.win/p/199OdHB7uo\" target=\"_blank\">NUMBER OF PATRIOTS WHO ALREADY VOTED If you have not voted yet then vote like your life depends on it They tried to kill Trump TWICE That is means they are not confident they can steal the election a 2nd time So VOTE</a></td>\\n      <td>2024-11-03</td>\\n      <td>148</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>28</th>\\n      <td><a href=\"https://patriots.win/p/199OdII9OD\" target=\"_blank\">LEAKED AUDIO OF LEGACY MEDIA EXEC STAFF STRATEGIZING TO MANIPULATE BETTING ODDS MARKETS TO AFFECT PRESIDENTIAL ELECTION</a></td>\\n      <td>2024-11-03</td>\\n      <td>150</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>27</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9Tgj\" target=\"_blank\">BREAKING Military service members have complained that they have not received enough absentee ballots to vote before Election Day Republican lawmakers have sent a letter to the Pentagon demanding answers</a></td>\\n      <td>2024-11-03</td>\\n      <td>165</td>\\n      <td>9</td>\\n    </tr>\\n    <tr>\\n      <th>26</th>\\n      <td><a href=\"https://patriots.win/p/199OdEuc3V\" target=\"_blank\">Steal interrupted The 5 Fulton County offices that were illegally open today to receive ballots without R s being allowed to observe collected only 105 ballots TOTAL after R s were allowed in</a></td>\\n      <td>2024-11-03</td>\\n      <td>166</td>\\n      <td>20</td>\\n    </tr>\\n    <tr>\\n      <th>25</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2BUu\" target=\"_blank\">An FCC Commissioner suggested that VP Kamala Harris s surprise appearance on SNL days before the November 5 election may have violated federal law stating that NBC scheduled Harris s SNL appearance to evade the Equal Time rule</a></td>\\n      <td>2024-11-03</td>\\n      <td>168</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>24</th>\\n      <td><a href=\"https://patriots.win/p/199OdEs8Y8\" target=\"_blank\">Bannon It is all about low prop voters So where are yours I will tell you they do not exist You generated those ballots that is the reality And I do not care if you do not like hearing it Here is what I care about saving our country That is the only thing that matters you scumbags</a></td>\\n      <td>2024-11-03</td>\\n      <td>178</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>23</th>\\n      <td><a href=\"https://patriots.win/p/199OdEsPfE\" target=\"_blank\">The obviously fake and gay Iowa poll is just as expected Dem propaganda from Pritzker Ignore the dooming and charge FULL STEAM AHEAD</a></td>\\n      <td>2024-11-03</td>\\n      <td>178</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>22</th>\\n      <td><a href=\"https://patriots.win/p/199OdHA1v5\" target=\"_blank\">In the late Roman Republic legal tampering with voting rolls caused the great First Civil War This happened by sudden massive additions of new citizens by legislative fiat Here is what happened</a></td>\\n      <td>2024-11-03</td>\\n      <td>186</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>21</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9Cm1\" target=\"_blank\">Don Jr Win lose or fraud everyone needs to be all in for the next 3 days Get out and vote Make it too big to rig Make your voice heard and end the insanity of radical leftism now</a></td>\\n      <td>2024-11-03</td>\\n      <td>189</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>20</th>\\n      <td><a href=\"https://patriots.win/p/199OdHAJIf\" target=\"_blank\">Notice to Election Riggers If you are caught STEALING this election there will be no forgiveness there will be no mercy and you WILL face Justice</a></td>\\n      <td>2024-11-03</td>\\n      <td>194</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>19</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHbgz\" target=\"_blank\">member when president trump magically LOST 673 495 votes in an instant and cnn completely brushed it off i member</a></td>\\n      <td>2024-11-03</td>\\n      <td>213</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>18</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2BIX\" target=\"_blank\">When Trump wins he needs to implement a way for people to see their ballot AFTER its been counted for all elections moving forward</a></td>\\n      <td>2024-11-03</td>\\n      <td>270</td>\\n      <td>30</td>\\n    </tr>\\n    <tr>\\n      <th>16</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1MYA\" target=\"_blank\">NYT pollster just admitted that polls could yet again underestimate Trump just like 2020 SWAMP THE VOTE AND WE WIN</a></td>\\n      <td>2024-11-03</td>\\n      <td>312</td>\\n      <td>7</td>\\n    </tr>\\n    <tr>\\n      <th>32</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHK2i\" target=\"_blank\">Vote like you are three BALLOT MULEs Behind</a></td>\\n      <td>2024-11-03</td>\\n      <td>147</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>15</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1dX1\" target=\"_blank\">Democrats cheating in Nebraska Ballot MULE ON TAPE</a></td>\\n      <td>2024-11-03</td>\\n      <td>433</td>\\n      <td>22</td>\\n    </tr>\\n    <tr>\\n      <th>14</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9kjm\" target=\"_blank\">Trump is pissed Just minutes ago in Pennsylvania 45 broke down the election fraud being openly carried out by the deep state Democrats He basically repeated everything I said last night AJNlive</a></td>\\n      <td>2024-11-03</td>\\n      <td>467</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>13</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHKJN\" target=\"_blank\">The RNC has won 4 election integrity lawsuits in 3 days in 4 states</a></td>\\n      <td>2024-11-03</td>\\n      <td>569</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>12</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9kfd\" target=\"_blank\">President Trump In Lancaster they found 2600 ballots all done by the same exact hand The same pen the same penmanship the same everything Then they try to say this is a conspiracy theory The whole world watches this Even third world countries say they take fingerprints before they vote</a></td>\\n      <td>2024-11-03</td>\\n      <td>589</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>11</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1uRp\" target=\"_blank\">I have LEAKED AUDIO of NBC execs talking about using polling and media coverage to MANIPULATE election betting markets As soon as I get the noise cleaned up I will post it Stay tuned</a></td>\\n      <td>2024-11-03</td>\\n      <td>596</td>\\n      <td>21</td>\\n    </tr>\\n    <tr>\\n      <th>10</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2BhH\" target=\"_blank\">MASSIVE ELECTION FRAUD IN PENNSYLVANIA</a></td>\\n      <td>2024-11-03</td>\\n      <td>652</td>\\n      <td>16</td>\\n    </tr>\\n    <tr>\\n      <th>9</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1MgO\" target=\"_blank\">How rigged are American Elections VOTER ROLLS Tom Fitton Judicial Watch We sued</a></td>\\n      <td>2024-11-03</td>\\n      <td>703</td>\\n      <td>16</td>\\n    </tr>\\n    <tr>\\n      <th>8</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1dWz\" target=\"_blank\">Election fuckery in CT Voters were lied to by poll watchers If you did early voting in North Stonington CT follow advice</a></td>\\n      <td>2024-11-03</td>\\n      <td>793</td>\\n      <td>31</td>\\n    </tr>\\n    <tr>\\n      <th>7</th>\\n      <td><a href=\"https://patriots.win/p/199OdHB835\" target=\"_blank\">BREAKING ELECTORAL NEWS It looks like Elons PAC America is going to be bussing PA Republicans to the polls on Tuesday much like the PA Dems did in 2022 for Fetterman Dems will likely be bussing their own voters as well but the fact that the GOP has responded is big</a></td>\\n      <td>2024-11-03</td>\\n      <td>880</td>\\n      <td>13</td>\\n    </tr>\\n    <tr>\\n      <th>6</th>\\n      <td><a href=\"https://patriots.win/p/199OdIIQRN\" target=\"_blank\">They are already switching Trump ballots to RFK Jr and election day riggers have not even started adjudicating ballots yet</a></td>\\n      <td>2024-11-03</td>\\n      <td>987</td>\\n      <td>35</td>\\n    </tr>\\n    <tr>\\n      <th>5</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2BhJ\" target=\"_blank\">SCOOP LEAKED MSM ELECTION NIGHT MOCKUP SHOWS KAMALA HARRIS LOSING ELECTION IN SWING STATES mockup footage of election night reported to use Harris internal polling shows Trump leading in PA by a small margin winning in MI winning in NB winning in GA and in AZ by strong margins</a></td>\\n      <td>2024-11-03</td>\\n      <td>1012</td>\\n      <td>169</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2jSe\" target=\"_blank\">If you already voted go find three people who have not</a></td>\\n      <td>2024-11-03</td>\\n      <td>1095</td>\\n      <td>37</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtmqJ\" target=\"_blank\">Trump predicted on Joe Rogan that fake polls would come out right before the election to suppress MAGA voters Ignore and Vote Vote Vote</a></td>\\n      <td>2024-11-03</td>\\n      <td>1664</td>\\n      <td>54</td>\\n    </tr>\\n    <tr>\\n      <th>31</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHbQW\" target=\"_blank\">7 Counties STOLE The USA Election 2020 JUST 7 Fulton County GA one of the OG 7 Fulton County At It Again NOW Attention GA Peds RNC Lara Trump Full Article Link Gateway Pundit</a></td>\\n      <td>2024-11-03</td>\\n      <td>148</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>33</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtEkK\" target=\"_blank\">STEVE BANNON The Facts Are On Our Side Joe Biden Never Got 15 Million More Votes Than Barack Obama IMPOSSIBLE</a></td>\\n      <td>2024-11-03</td>\\n      <td>147</td>\\n      <td>7</td>\\n    </tr>\\n    <tr>\\n      <th>63</th>\\n      <td><a href=\"https://patriots.win/p/199OdIIQZY\" target=\"_blank\">BREAKING A federal judge has ruled that Iowa can continue challenging the validity of hundreds of ballots from potential noncitizens AP</a></td>\\n      <td>2024-11-03</td>\\n      <td>64</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>49</th>\\n      <td><a href=\"https://patriots.win/p/199OdII9Fy\" target=\"_blank\">Reddit allowing fake tweets to convince people they will get 1000 to vote against Trump Election interference</a></td>\\n      <td>2024-11-03</td>\\n      <td>92</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>62</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtn2f\" target=\"_blank\">The betting markets and the Selzer Iowa poll were coordinated as a Saturday night drive by to demoralize MAGA and pump up the communists But the Emerson and Atlas Intel polls fucked up the plan And now the betting markets are scrambled mess And Selzer is being accused of a pay for play</a></td>\\n      <td>2024-11-03</td>\\n      <td>65</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>61</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9kjn\" target=\"_blank\">I voted for Kamala 36 thousand up votes I voted for Trump Banned from r pics</a></td>\\n      <td>2024-11-03</td>\\n      <td>66</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>60</th>\\n      <td><a href=\"https://patriots.win/p/199OdG0oBd\" target=\"_blank\">Fulton County Dems Have Broken The Law By Opening Voting Centers On The Weekend</a></td>\\n      <td>2024-11-03</td>\\n      <td>66</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>59</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtmmC\" target=\"_blank\">Trans Influencer busted pretending to be a Conservative Christian who is voting for Kamala</a></td>\\n      <td>2024-11-03</td>\\n      <td>68</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>58</th>\\n      <td><a href=\"https://patriots.win/p/199OdG0oaJ\" target=\"_blank\">Bannon Let me say this so Timodc and MSNBC can understand those low propensity voters people who supposedly voted in 2020 and 2022 are not showing up now because they did not exist</a></td>\\n      <td>2024-11-03</td>\\n      <td>70</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>57</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtWC4\" target=\"_blank\">Kamala voter Arrested for Burning Political Signs</a></td>\\n      <td>2024-11-03</td>\\n      <td>75</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>56</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtW3o\" target=\"_blank\">BREAKING Democratic nominee Kamala Harris will appear on SNL in the last episode before the presidential election This is a clear and blatant effort to evade the FCC s Equal Time rule</a></td>\\n      <td>2024-11-03</td>\\n      <td>77</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>55</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHsk3\" target=\"_blank\">Republican Georgia State Senator Who Descended on Fulton County to Monitor Ballot Counting Says It is Worse Than Previously Reported</a></td>\\n      <td>2024-11-03</td>\\n      <td>77</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>54</th>\\n      <td><a href=\"https://patriots.win/p/199OdG15VB\" target=\"_blank\">URGENT BREAKING Four Georgia Counties Decide at the Last Minute to Accept Ballots Over the Weekend in Violation of the Law Refuse to Allow Republican Poll Watchers Observe Process Updated</a></td>\\n      <td>2024-11-03</td>\\n      <td>79</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>53</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHbgw\" target=\"_blank\">Repost for the doomerfags DO NOT FALL FOR OPERATION DEMORALIZATION FAKE POLLS AND MARKET MANIPULATION JUST LIKE THE FIRST WEEKS OF HER CAMPAIGN FAKE AND GAY THEN FAKER AND GAYER NOW VOTE</a></td>\\n      <td>2024-11-03</td>\\n      <td>84</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>52</th>\\n      <td><a href=\"https://patriots.win/p/199OdG15ZF\" target=\"_blank\">voting machines have been programmed to cheat you</a></td>\\n      <td>2024-11-03</td>\\n      <td>85</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>51</th>\\n      <td><a href=\"https://patriots.win/p/199OdEu45h\" target=\"_blank\">Trump on fake polls They are a form of cheating</a></td>\\n      <td>2024-11-03</td>\\n      <td>86</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>50</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1dCK\" target=\"_blank\">Swing states Pedes America is counting on you to not only document election fraud but DO SOMETHING ABOUT IT</a></td>\\n      <td>2024-11-03</td>\\n      <td>89</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>48</th>\\n      <td><a href=\"https://patriots.win/p/199OdEsPfM\" target=\"_blank\">Joe Rogan nails Fetterman on the Dems illegal migrant voter conspiracy to rig all future elections in their favor Undeniably says Fetterman</a></td>\\n      <td>2024-11-03</td>\\n      <td>92</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>34</th>\\n      <td><a href=\"https://patriots.win/p/199OdH9l8Y\" target=\"_blank\">President Trump slams the swing states that have announced Election Results may not be announced on election night</a></td>\\n      <td>2024-11-03</td>\\n      <td>136</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>47</th>\\n      <td><a href=\"https://patriots.win/p/199OdEs8Tw\" target=\"_blank\">How do I know its desperation not setting up the steal A Selzer poll has Harris winning by 3 in FUCKING IOWA There is nothing calculated about this they just lost their minds</a></td>\\n      <td>2024-11-03</td>\\n      <td>99</td>\\n      <td>12</td>\\n    </tr>\\n    <tr>\\n      <th>46</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHKZn\" target=\"_blank\">The egregious and unlawful actions this weekend of certain Georgia counties have compromised the election Courts cannot act quickly enough to remedy this From the Republican Party lawsuit filed in Federal court full post in comments</a></td>\\n      <td>2024-11-03</td>\\n      <td>100</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>45</th>\\n      <td><a href=\"https://patriots.win/p/199OdIHs8p\" target=\"_blank\">In Addition To Not Being Funny SNL May Have Violated Election Law With Kamala Cameo</a></td>\\n      <td>2024-11-03</td>\\n      <td>101</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>44</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1ueG\" target=\"_blank\">Why are the swing states the only ones who cannot count vote on time</a></td>\\n      <td>2024-11-03</td>\\n      <td>106</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>43</th>\\n      <td><a href=\"https://patriots.win/p/199OdEuc7f\" target=\"_blank\">To everyone saying why vote if it is rigged I present the case of President Milei of Argentina</a></td>\\n      <td>2024-11-03</td>\\n      <td>108</td>\\n      <td>3</td>\\n    </tr>\\n    <tr>\\n      <th>42</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtWKP\" target=\"_blank\">Georgia caught Collecting Illegal Ballots</a></td>\\n      <td>2024-11-03</td>\\n      <td>110</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>41</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtn2e\" target=\"_blank\">Laura Trump and RNC just CAUGHT and STOPPED 164 568 DUPLICATE BALLOTS in Key Swing State</a></td>\\n      <td>2024-11-03</td>\\n      <td>112</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>40</th>\\n      <td><a href=\"https://patriots.win/p/199OdG1umX\" target=\"_blank\">NBC Final Poll Trump 49 Harris 49 Tied NBC Final 2020 poll was Biden up 10 Lmao These fucks fixed nothing in their methodology if anything Trump voters are even more averse to speaking with Fake News pollsters after 4 years of being vilified LANDSLIDE INCOMING Get the vote out</a></td>\\n      <td>2024-11-03</td>\\n      <td>113</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>39</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtn6m\" target=\"_blank\">Californians showing up to vote dressed in trash bags</a></td>\\n      <td>2024-11-03</td>\\n      <td>113</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>38</th>\\n      <td><a href=\"https://patriots.win/p/199OdII9KB\" target=\"_blank\">Spoke with an undecided voter in PA who was concerned about threats to democracy Here is what I told him By the end he was wearing a MAGA hat</a></td>\\n      <td>2024-11-03</td>\\n      <td>116</td>\\n      <td>15</td>\\n    </tr>\\n    <tr>\\n      <th>37</th>\\n      <td><a href=\"https://patriots.win/p/199OdHAa16\" target=\"_blank\">Attention Election Thieves We have eyes EVERYWHERE We are embedded in your organizations We are watching your EVERY move Are you feeling lucky</a></td>\\n      <td>2024-11-03</td>\\n      <td>116</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>36</th>\\n      <td><a href=\"https://patriots.win/p/199OdEtmqO\" target=\"_blank\">Fuck polls claiming Kamala has a chance in Iowa Fake and Gay</a></td>\\n      <td>2024-11-03</td>\\n      <td>118</td>\\n      <td>5</td>\\n    </tr>\\n    <tr>\\n      <th>35</th>\\n      <td><a href=\"https://patriots.win/p/199OdHAaHg\" target=\"_blank\">Reminder that Trump currently has a sentencing date of November 26 on the 34 felony convictions So if he loses the plan is to jail him quickly so that he is not in a position to challenge any irregularities So Tuesday is very important He is got to win you got to vote</a></td>\\n      <td>2024-11-03</td>\\n      <td>125</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>1</th>\\n      <td><a href=\"https://patriots.win/p/199OdG2Btc\" target=\"_blank\">Rasmussen Reminder Election Cheaters you are fully expendable NOBODY is coming to your aid They are disavowing ever knowing you Your legal defense will bankrupt you</a></td>\\n      <td>2024-11-03</td>\\n      <td>2182</td>\\n      <td>165</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "save_interactive_table(fraud_lastnDay_df, f'posts containing fraud claims from the last {nDays} days')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"my_table\">\\n  <thead>\\n    <tr style=\"text-align: center;\">\\n      <th></th>\\n      <th>location</th>\\n      <th>count</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>1</th>\\n      <td>Nevada</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td>Nebraska</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td>Pennsylvania</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>5</th>\\n      <td>Fulton</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>6</th>\\n      <td>Iowa</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>7</th>\\n      <td>Arizona</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>8</th>\\n      <td>Georgia</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td>Michigan</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>28</th>\\n      <td>Western State</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>24</th>\\n      <td>Argentina</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>25</th>\\n      <td>Connecticut</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>26</th>\\n      <td>Us</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>27</th>\\n      <td>North Stonington</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>31</th>\\n      <td>New Jersey</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>29</th>\\n      <td>Lakewood</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>30</th>\\n      <td>Midwest</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>22</th>\\n      <td>Key Swing State</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>32</th>\\n      <td>East Coast</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>33</th>\\n      <td>Maine</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>23</th>\\n      <td>Kentucky</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>18</th>\\n      <td>America</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>21</th>\\n      <td>Cobb</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>20</th>\\n      <td>California</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>19</th>\\n      <td>Clark</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>17</th>\\n      <td>United States</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>16</th>\\n      <td>Allegheny  Pittsburgh</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>15</th>\\n      <td>Pennsyvania</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>14</th>\\n      <td>City  Building</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>13</th>\\n      <td>Allegheny</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>12</th>\\n      <td>Lancaster</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>11</th>\\n      <td>West Coast</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>10</th>\\n      <td>Maricopa</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>9</th>\\n      <td>Philly</td>\\n      <td>1</td>\\n    </tr>\\n    <tr>\\n      <th>34</th>\\n      <td>Mountain</td>\\n      <td>1</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe= pd.DataFrame(loc_count).reset_index().sort_values('count', ascending=False)\n",
    "dataframe.index +=1\n",
    "save_filename = \"location_potential_fraud_claim_table\"\n",
    "\n",
    "\n",
    "save_interactive_table(dataframe, save_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud\n",
    "\n",
    "def location_cloud(dataframe, top_n, save_filename):\n",
    "    words_cloud = loc_count.reset_index().rename(columns={'index': 'location'})\n",
    "    words_cloud['location'] = words_cloud['location'].apply(lambda x: ', '.join(x) if isinstance(x,tuple) else x)\n",
    "    words_cloud['location'] = words_cloud['location'].str.replace(',', '')\n",
    "    wordcloud = WordCloud(width=800, height=400, background_color='white', colormap='tab20b').\\\n",
    "    generate_from_frequencies(words_cloud.set_index('location')['count'].to_dict())\n",
    "    plt.figure(figsize=(10,8))\n",
    "    plt.imshow(wordcloud, interpolation='bilinear')\n",
    "    plt.axis('off')\n",
    "\n",
    "    plt.title(\n",
    "        f\"The chart was last updated on {dt.now():%Y-%m-%d %H:%M} (UTC)\",\n",
    "        fontsize=25, color='darkorange', pad=45, loc='center'\n",
    "    )\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"viz/{save_filename}.jpeg\",\n",
    "                    dpi=600\n",
    "                    )\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe = loc_count,\n",
    "top_n= 100\n",
    "save_filename = \"location_potential_fraud_claim_wordcloud\"\n",
    "fraud_location_cloud = location_cloud(dataframe, top_n, save_filename)\n",
    "\n",
    "fraud_location_cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(824, 18)\n"
     ]
    }
   ],
   "source": [
    "# Create a separate dataframe that include only youtube-linked posts\n",
    "youtube_df = df[df.domain=='youtube.com']\n",
    "\n",
    "# Return number of posts linked to youtube's domain in this dataset \n",
    "print(youtube_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_33493/3888369381.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  youtube_df['youtube_id'] = youtube_df['link'].apply(youtube_parser)\n"
     ]
    }
   ],
   "source": [
    "# A functiont extracting youtube ids from urls\n",
    "def youtube_parser(url):\n",
    "    # Regular expression to capture YouTube video IDs from various URL formats\n",
    "    id_pattern = r'(?:https?:\\/\\/)?(?:[0-9A-Z-]+\\.)?(?:youtube|youtu|youtube-nocookie)\\.(?:com|be)\\/(?:watch\\?v=|watch\\?.+&v=|embed\\/|v\\/|.+\\?v=)?([^&=\\n%\\?]{11})'    \n",
    "    match = re.search(id_pattern, url)\n",
    "    return match.group(1) if match else np.nan\n",
    "\n",
    "# Run the function on the link field \n",
    "youtube_df['youtube_id'] = youtube_df['link'].apply(youtube_parser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function returns the most posted domains from the last nDays\n",
    "def youtube_last_ndays(dataframe, nDays):\n",
    "    end_date = datetime.datetime.now()\n",
    "    start_date = end_date - datetime.timedelta(days=nDays)\n",
    "    \n",
    "    start_date = start_date.strftime(\"%Y-%m-%d\")\n",
    "    end_date = end_date.strftime(\"%Y-%m-%d\")\n",
    "\n",
    "    date_list = date_selection(start_date, end_date)\n",
    "    dataframe = dataframe[dataframe['created_date'].isin(date_list)].sort_values('score_up')\n",
    "    return dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/f2/xbsrplkj23z71l_fw6890_hr0000gn/T/ipykernel_33493/1263139798.py:10: FutureWarning: The behavior of 'isin' with dtype=datetime64[ns] and castable values (e.g. strings) is deprecated. In a future version, these will not be considered matching by isin. Explicitly cast to the appropriate dtype before calling isin instead.\n",
      "  dataframe = dataframe[dataframe['created_date'].isin(date_list)].sort_values('score_up')\n"
     ]
    },
    {
     "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",
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       "        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>file_name</th>\n",
       "      <th>uuid</th>\n",
       "      <th>title</th>\n",
       "      <th>content</th>\n",
       "      <th>post_flair_text</th>\n",
       "      <th>author</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>created</th>\n",
       "      <th>comments</th>\n",
       "      <th>score_up</th>\n",
       "      <th>preview</th>\n",
       "      <th>link</th>\n",
       "      <th>domain</th>\n",
       "      <th>is_image</th>\n",
       "      <th>is_video</th>\n",
       "      <th>video_link</th>\n",
       "      <th>created_date</th>\n",
       "      <th>twitter_handle</th>\n",
       "      <th>youtube_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53273</th>\n",
       "      <td>20241105-071743_page11.json</td>\n",
       "      <td>199OhuPXmy</td>\n",
       "      <td>Non-citizens being told they can vote in Philly</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Tacitus1963</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-05 09:22:59.020000-05:00</td>\n",
       "      <td>2</td>\n",
       "      <td>51</td>\n",
       "      <td>https://media.patriots.win/preview/199OhuPXmy.png</td>\n",
       "      <td>https://www.youtube.com/watch?v=OWLY3R85nDo</td>\n",
       "      <td>youtube.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>https://www.youtube.com/embed/OWLY3R85nDo?start=0</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OWLY3R85nDo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52458</th>\n",
       "      <td>20241103-011438_page11.json</td>\n",
       "      <td>199OdEtF8y</td>\n",
       "      <td>Wizened, senile Harrison Ford, whose carbon footprint includes flying to France 20 yrs ago to hand an Academy Award to exiled pedophile Roman Polanski, endorses Kamala. He's a lying POS, too, b/c he's been farting abt Trump for yrs. Like someone asked in the YT comments, \"Whose list is HE on?\"🤔</td>\n",
       "      <td></td>\n",
       "      <td>STUPID  S.O.B.</td>\n",
       "      <td>mateus</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 02:29:10.029000-05:00</td>\n",
       "      <td>10</td>\n",
       "      <td>67</td>\n",
       "      <td>https://media.patriots.win/preview/199OdEtF8y.png</td>\n",
       "      <td>https://www.youtube.com/watch?v=0Biws4eiwOk</td>\n",
       "      <td>youtube.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>https://www.youtube.com/embed/0Biws4eiwOk?start=0</td>\n",
       "      <td>2024-11-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0Biws4eiwOk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52434</th>\n",
       "      <td>20241103-194623_page8.json</td>\n",
       "      <td>199OdIHbh0</td>\n",
       "      <td>riot fences are going up around the white house.</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>carmella</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 22:22:45.006000-05:00</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>https://media.patriots.win/preview/199OdIHbh0.png</td>\n",
       "      <td>https://www.youtube.com/watch?v=Gbld1FiH2oA</td>\n",
       "      <td>youtube.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>https://www.youtube.com/embed/Gbld1FiH2oA?start=0</td>\n",
       "      <td>2024-11-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gbld1FiH2oA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52998</th>\n",
       "      <td>20241104-011504_page10.json</td>\n",
       "      <td>199OdIJWtv</td>\n",
       "      <td>I Got Kicked OUT Of A Kamala ‘Rally!’ Campaign Called COPS On Me For EXPOSING Their EMPTY Event</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Colorado-Living</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-04 02:19:16.340000-05:00</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>https://media.patriots.win/preview/199OdIJWtv.png</td>\n",
       "      <td>https://m.youtube.com/watch?v=3NRIegaoTgU&amp;t=329s&amp;pp=2AHJApACAQ%3D%3D</td>\n",
       "      <td>youtube.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>https://www.youtube.com/embed/3NRIegaoTgU?start=0</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3NRIegaoTgU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52345</th>\n",
       "      <td>20241103-194623_page6.json</td>\n",
       "      <td>199OdIHbMN</td>\n",
       "      <td>Kim Driscoll Democrat Lt. Governor of Massachusetts asks residents to house illegal Aliens if they have room. How many illegal aliens do you think she has living with her in her house?</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>PaperBoy47</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 22:00:45.485000-05:00</td>\n",
       "      <td>10</td>\n",
       "      <td>96</td>\n",
       "      <td>https://media.patriots.win/preview/199OdIHbMN.png</td>\n",
       "      <td>https://m.youtube.com/watch?v=0Oo4M0CPMlA&amp;pp=ygU8Q2l0eSBjb3VuY2lsIGFza3MgcHVibGljIHRvIGhvdXNlIG1pZ3JhbnRzIGlmIHRoZXkgaGF2ZSByb29t</td>\n",
       "      <td>youtube.com</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>https://www.youtube.com/embed/0Oo4M0CPMlA?start=0</td>\n",
       "      <td>2024-11-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0Oo4M0CPMlA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         file_name        uuid  \\\n",
       "53273  20241105-071743_page11.json  199OhuPXmy   \n",
       "52458  20241103-011438_page11.json  199OdEtF8y   \n",
       "52434   20241103-194623_page8.json  199OdIHbh0   \n",
       "52998  20241104-011504_page10.json  199OdIJWtv   \n",
       "52345   20241103-194623_page6.json  199OdIHbMN   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                              title  \\\n",
       "53273                                                                                                                                                                                                                                                               Non-citizens being told they can vote in Philly   \n",
       "52458  Wizened, senile Harrison Ford, whose carbon footprint includes flying to France 20 yrs ago to hand an Academy Award to exiled pedophile Roman Polanski, endorses Kamala. He's a lying POS, too, b/c he's been farting abt Trump for yrs. Like someone asked in the YT comments, \"Whose list is HE on?\"🤔        \n",
       "52434                                                                                                                                                                                                                                                              riot fences are going up around the white house.   \n",
       "52998                                                                                                                                                                                                               I Got Kicked OUT Of A Kamala ‘Rally!’ Campaign Called COPS On Me For EXPOSING Their EMPTY Event   \n",
       "52345                                                                                                                      Kim Driscoll Democrat Lt. Governor of Massachusetts asks residents to house illegal Aliens if they have room. How many illegal aliens do you think she has living with her in her house?   \n",
       "\n",
       "      content              post_flair_text           author author_flair_text  \\\n",
       "53273                                           Tacitus1963                     \n",
       "52458                     STUPID  S.O.B.             mateus                     \n",
       "52434                                              carmella                     \n",
       "52998                                       Colorado-Living                     \n",
       "52345                                            PaperBoy47                     \n",
       "\n",
       "                               created  comments  score_up  \\\n",
       "53273 2024-11-05 09:22:59.020000-05:00         2        51   \n",
       "52458 2024-11-03 02:29:10.029000-05:00        10        67   \n",
       "52434 2024-11-03 22:22:45.006000-05:00        11        73   \n",
       "52998 2024-11-04 02:19:16.340000-05:00         2        77   \n",
       "52345 2024-11-03 22:00:45.485000-05:00        10        96   \n",
       "\n",
       "                                                 preview  \\\n",
       "53273  https://media.patriots.win/preview/199OhuPXmy.png   \n",
       "52458  https://media.patriots.win/preview/199OdEtF8y.png   \n",
       "52434  https://media.patriots.win/preview/199OdIHbh0.png   \n",
       "52998  https://media.patriots.win/preview/199OdIJWtv.png   \n",
       "52345  https://media.patriots.win/preview/199OdIHbMN.png   \n",
       "\n",
       "                                                                                                                                    link  \\\n",
       "53273                                                                                        https://www.youtube.com/watch?v=OWLY3R85nDo   \n",
       "52458                                                                                        https://www.youtube.com/watch?v=0Biws4eiwOk   \n",
       "52434                                                                                        https://www.youtube.com/watch?v=Gbld1FiH2oA   \n",
       "52998                                                               https://m.youtube.com/watch?v=3NRIegaoTgU&t=329s&pp=2AHJApACAQ%3D%3D   \n",
       "52345  https://m.youtube.com/watch?v=0Oo4M0CPMlA&pp=ygU8Q2l0eSBjb3VuY2lsIGFza3MgcHVibGljIHRvIGhvdXNlIG1pZ3JhbnRzIGlmIHRoZXkgaGF2ZSByb29t   \n",
       "\n",
       "            domain  is_image  is_video  \\\n",
       "53273  youtube.com     False      True   \n",
       "52458  youtube.com     False      True   \n",
       "52434  youtube.com     False      True   \n",
       "52998  youtube.com     False      True   \n",
       "52345  youtube.com     False      True   \n",
       "\n",
       "                                              video_link created_date  \\\n",
       "53273  https://www.youtube.com/embed/OWLY3R85nDo?start=0   2024-11-05   \n",
       "52458  https://www.youtube.com/embed/0Biws4eiwOk?start=0   2024-11-03   \n",
       "52434  https://www.youtube.com/embed/Gbld1FiH2oA?start=0   2024-11-03   \n",
       "52998  https://www.youtube.com/embed/3NRIegaoTgU?start=0   2024-11-04   \n",
       "52345  https://www.youtube.com/embed/0Oo4M0CPMlA?start=0   2024-11-03   \n",
       "\n",
       "      twitter_handle   youtube_id  \n",
       "53273            NaN  OWLY3R85nDo  \n",
       "52458            NaN  0Biws4eiwOk  \n",
       "52434            NaN  Gbld1FiH2oA  \n",
       "52998            NaN  3NRIegaoTgU  \n",
       "52345            NaN  0Oo4M0CPMlA  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run the youtube_last_ndays function\n",
    "youtube_3days_df = youtube_last_ndays(youtube_df, 3)\n",
    "youtube_3days_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"/Users/keenansmacbookairm3/Documents/GitHub/SearchTube/\")\n",
    "\n",
    "#import\n",
    "import SearchTube"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Video metadata list_1 fetched\n",
      "---------\n",
      "Finished fetching ALL 1 chunks of video metadata\n"
     ]
    }
   ],
   "source": [
    "video_id = youtube_3days_df.youtube_id.to_list()\n",
    "api_key = 'AIzaSyARKEvPpE5oed00lMD5rK8kJtaOJBHngIE'\n",
    "\n",
    "video_meta =  SearchTube.tube_meta(video_id, api_key)\n",
    "video_meta_df = pd.DataFrame(video_meta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>file_name</th>\n",
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       "      <th>title</th>\n",
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       "      <th>score_up</th>\n",
       "      <th>...</th>\n",
       "      <th>video_defaultAudioLanguage</th>\n",
       "      <th>video_categoryId</th>\n",
       "      <th>video_duration</th>\n",
       "      <th>video_caption</th>\n",
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       "      <th>published_date_on_youtube</th>\n",
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       "      <td>20241105-071743_page11.json</td>\n",
       "      <td>199OhuPXmy</td>\n",
       "      <td>Non-citizens being told they can vote in Philly</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Tacitus1963</td>\n",
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       "      <td>2024-11-05 09:22:59.020000-05:00</td>\n",
       "      <td>2</td>\n",
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       "      <td>20</td>\n",
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       "      <td>False</td>\n",
       "      <td>32963</td>\n",
       "      <td>6912</td>\n",
       "      <td>1101</td>\n",
       "      <td>In a recent conversation with an undercover O’Keefe Media Group journalist, Milton Jamerson, Election And Voter Registration Clerk from the Philadelphia City Commissioners Office, stated that non-citizens can vote in local elections as long as they are residents of Philadelphia. This claim is echoed by the nongovernmental organization (NGO) “Ceiba,” located just across the street, which asserts that individuals with an Individual Taxpayer Identification Number (ITIN) are eligible to vote.</td>\n",
       "      <td>2024-11-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20241103-011438_page11.json</td>\n",
       "      <td>199OdEtF8y</td>\n",
       "      <td>Wizened, senile Harrison Ford, whose carbon footprint includes flying to France 20 yrs ago to hand an Academy Award to exiled pedophile Roman Polanski, endorses Kamala. He's a lying POS, too, b/c he's been farting abt Trump for yrs. Like someone asked in the YT comments, \"Whose list is HE on?\"🤔</td>\n",
       "      <td></td>\n",
       "      <td>STUPID  S.O.B.</td>\n",
       "      <td>mateus</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 02:29:10.029000-05:00</td>\n",
       "      <td>10</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>en</td>\n",
       "      <td>24</td>\n",
       "      <td>PT1M39S</td>\n",
       "      <td>false</td>\n",
       "      <td>True</td>\n",
       "      <td>314880</td>\n",
       "      <td>24826</td>\n",
       "      <td>6491</td>\n",
       "      <td>Harrison Ford has endorsed Kamala Harris and Tim Walz. In a pair of black-and-white clips, he explained why, at age 64, he has decided to tell “people I’ve never met who I’m voting for and why” for this election.</td>\n",
       "      <td>2024-11-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20241103-194623_page8.json</td>\n",
       "      <td>199OdIHbh0</td>\n",
       "      <td>riot fences are going up around the white house.</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>carmella</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 22:22:45.006000-05:00</td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>en</td>\n",
       "      <td>25</td>\n",
       "      <td>PT9M30S</td>\n",
       "      <td>false</td>\n",
       "      <td>True</td>\n",
       "      <td>230072</td>\n",
       "      <td>9532</td>\n",
       "      <td>4732</td>\n",
       "      <td>Fences are going up around the White House and the Vice President's house this weekend, along with more buildings in DC being secured from possible election violence.</td>\n",
       "      <td>2024-11-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20241104-011504_page10.json</td>\n",
       "      <td>199OdIJWtv</td>\n",
       "      <td>I Got Kicked OUT Of A Kamala ‘Rally!’ Campaign Called COPS On Me For EXPOSING Their EMPTY Event</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Colorado-Living</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-04 02:19:16.340000-05:00</td>\n",
       "      <td>2</td>\n",
       "      <td>77</td>\n",
       "      <td>...</td>\n",
       "      <td>en-US</td>\n",
       "      <td>25</td>\n",
       "      <td>PT15M33S</td>\n",
       "      <td>false</td>\n",
       "      <td>True</td>\n",
       "      <td>565880</td>\n",
       "      <td>59451</td>\n",
       "      <td>10846</td>\n",
       "      <td>Kamala Harris’ campaign is so panicked that I was kicked out just for recording the tiny crowd for her event with Hillary</td>\n",
       "      <td>2024-11-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20241103-194623_page6.json</td>\n",
       "      <td>199OdIHbMN</td>\n",
       "      <td>Kim Driscoll Democrat Lt. Governor of Massachusetts asks residents to house illegal Aliens if they have room. How many illegal aliens do you think she has living with her in her house?</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>PaperBoy47</td>\n",
       "      <td></td>\n",
       "      <td>2024-11-03 22:00:45.485000-05:00</td>\n",
       "      <td>10</td>\n",
       "      <td>96</td>\n",
       "      <td>...</td>\n",
       "      <td>en</td>\n",
       "      <td>25</td>\n",
       "      <td>PT58S</td>\n",
       "      <td>true</td>\n",
       "      <td>True</td>\n",
       "      <td>36529</td>\n",
       "      <td>199</td>\n",
       "      <td>1006</td>\n",
       "      <td>Mass. Gov. asking families to house migrants</td>\n",
       "      <td>2023-08-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
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      ],
      "text/plain": [
       "                     file_name        uuid  \\\n",
       "0  20241105-071743_page11.json  199OhuPXmy   \n",
       "1  20241103-011438_page11.json  199OdEtF8y   \n",
       "2   20241103-194623_page8.json  199OdIHbh0   \n",
       "3  20241104-011504_page10.json  199OdIJWtv   \n",
       "4   20241103-194623_page6.json  199OdIHbMN   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                          title  \\\n",
       "0                                                                                                                                                                                                                                                               Non-citizens being told they can vote in Philly   \n",
       "1  Wizened, senile Harrison Ford, whose carbon footprint includes flying to France 20 yrs ago to hand an Academy Award to exiled pedophile Roman Polanski, endorses Kamala. He's a lying POS, too, b/c he's been farting abt Trump for yrs. Like someone asked in the YT comments, \"Whose list is HE on?\"🤔        \n",
       "2                                                                                                                                                                                                                                                              riot fences are going up around the white house.   \n",
       "3                                                                                                                                                                                                               I Got Kicked OUT Of A Kamala ‘Rally!’ Campaign Called COPS On Me For EXPOSING Their EMPTY Event   \n",
       "4                                                                                                                      Kim Driscoll Democrat Lt. Governor of Massachusetts asks residents to house illegal Aliens if they have room. How many illegal aliens do you think she has living with her in her house?   \n",
       "\n",
       "  content              post_flair_text           author author_flair_text  \\\n",
       "0                                           Tacitus1963                     \n",
       "1                     STUPID  S.O.B.             mateus                     \n",
       "2                                              carmella                     \n",
       "3                                       Colorado-Living                     \n",
       "4                                            PaperBoy47                     \n",
       "\n",
       "                           created  comments  score_up  ...  \\\n",
       "0 2024-11-05 09:22:59.020000-05:00         2        51  ...   \n",
       "1 2024-11-03 02:29:10.029000-05:00        10        67  ...   \n",
       "2 2024-11-03 22:22:45.006000-05:00        11        73  ...   \n",
       "3 2024-11-04 02:19:16.340000-05:00         2        77  ...   \n",
       "4 2024-11-03 22:00:45.485000-05:00        10        96  ...   \n",
       "\n",
       "  video_defaultAudioLanguage video_categoryId video_duration  video_caption  \\\n",
       "0                        NaN               20        PT2M10S          false   \n",
       "1                         en               24        PT1M39S          false   \n",
       "2                         en               25        PT9M30S          false   \n",
       "3                      en-US               25       PT15M33S          false   \n",
       "4                         en               25          PT58S           true   \n",
       "\n",
       "   video_licensedContent video_viewCount video_likeCount video_commentCount  \\\n",
       "0                  False           32963            6912               1101   \n",
       "1                   True          314880           24826               6491   \n",
       "2                   True          230072            9532               4732   \n",
       "3                   True          565880           59451              10846   \n",
       "4                   True           36529             199               1006   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                brief_desription  \\\n",
       "0  In a recent conversation with an undercover O’Keefe Media Group journalist, Milton Jamerson, Election And Voter Registration Clerk from the Philadelphia City Commissioners Office, stated that non-citizens can vote in local elections as long as they are residents of Philadelphia. This claim is echoed by the nongovernmental organization (NGO) “Ceiba,” located just across the street, which asserts that individuals with an Individual Taxpayer Identification Number (ITIN) are eligible to vote.   \n",
       "1                                                                                                                                                                                                                                                                                           Harrison Ford has endorsed Kamala Harris and Tim Walz. In a pair of black-and-white clips, he explained why, at age 64, he has decided to tell “people I’ve never met who I’m voting for and why” for this election.   \n",
       "2                                                                                                                                                                                                                                                                                                                                         Fences are going up around the White House and the Vice President's house this weekend, along with more buildings in DC being secured from possible election violence.   \n",
       "3                                                                                                                                                                                                                                                                                                                                                                                      Kamala Harris’ campaign is so panicked that I was kicked out just for recording the tiny crowd for her event with Hillary   \n",
       "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                   Mass. Gov. asking families to house migrants   \n",
       "\n",
       "  published_date_on_youtube  \n",
       "0                2024-11-05  \n",
       "1                2024-11-02  \n",
       "2                2024-11-03  \n",
       "3                2024-11-03  \n",
       "4                2023-08-10  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A funciton extracting first paragraph from a longer chunck of text\n",
    "def find_first_paragraph(text):\n",
    "    first_paragraph = re.search(r'(.+?)(?:\\n\\s*\\n|$)', text, re.DOTALL)\n",
    "    return first_paragraph.group(1).strip() if first_paragraph else ''\n",
    "\n",
    "# Run the function on the full description\n",
    "video_meta_df['brief_desription'] = video_meta_df.full_description.apply(find_first_paragraph).str.replace('\\n', ' ').str.replace('\\r', ' ')\n",
    "\n",
    "\n",
    "# Merge the two youtube dataframes\n",
    "youtube_3days_df = youtube_3days_df.merge(video_meta_df, left_on='youtube_id', right_on='video_id')\n",
    "\n",
    "# Rename publishedAt column and fix format\n",
    "youtube_3days_df['publishedAt'] = pd.to_datetime(youtube_3days_df['publishedAt'])\n",
    "youtube_3days_df['published_date_on_youtube']  = youtube_3days_df['publishedAt'].dt.date\n",
    "\n",
    "youtube_3days_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>video title (Youtube)</th>\n",
       "      <th>published date (Youtube)</th>\n",
       "      <th>brief desription (Youtube)</th>\n",
       "      <th>channel_title (Youtube)</th>\n",
       "      <th>shared date (The Donald)</th>\n",
       "      <th>upvote count (The Donald)</th>\n",
       "      <th>comment count (The Donald)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>&lt;a href=\"https://www.youtube.com/watch?v=OWLY3R85nDo\" target=\"_blank\"&gt;Philadelphia City Commissioners' Office Tells O'Keefe Non-Citizens Can Vote&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>In a recent conversation with an undercover O’Keefe Media Group journalist, Milton Jamerson, Election And Voter Registration Clerk from the Philadelphia City Commissioners Office, stated that non-citizens can vote in local elections as long as they are residents of Philadelphia. This claim is echoed by the nongovernmental organization (NGO) “Ceiba,” located just across the street, which asserts that individuals with an Individual Taxpayer Identification Number (ITIN) are eligible to vote.</td>\n",
       "      <td>O’Keefe Media Group</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>51</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>&lt;a href=\"https://www.youtube.com/watch?v=DWVev5-2Db0\" target=\"_blank\"&gt;Elon Musk Pleads \"Vote Like Your Life Depends on It!\" and Speaks on Why He Became Politically Active&lt;/a&gt;</td>\n",
       "      <td>2024-11-04</td>\n",
       "      <td>JRE #2223 w/Elon Musk YouTube: https://youtu.be/7qZl_5xHoBw JRE on Spotify: https://open.spotify.com/show/4rOoJ6Egrf8K2IrywzwOMk</td>\n",
       "      <td>JRE Clips</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>133</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>&lt;a href=\"https://www.youtube.com/watch?v=oKM_L1dSeQw\" target=\"_blank\"&gt;LIVE: President Trump Holds a Rally in Pittsburgh, PA - 11/4/24&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Pittsburgh, Pennsylvania on Monday, November 4, 2024, at 6:00PM EST.</td>\n",
       "      <td>Right Side Broadcasting Network</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>1115</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>&lt;a href=\"https://www.youtube.com/watch?v=j41NvXcls9c\" target=\"_blank\"&gt;LIVE: President Trump Holds Final 2024 Campaign Rally in Grand Rapids, MI - 11/4/24&lt;/a&gt;</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Grand Rapids, Michigan on Monday, November 4, 2024, at 10:30PM EST.</td>\n",
       "      <td>Right Side Broadcasting Network</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>973</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>&lt;a href=\"https://youtu.be/WGomGRZIDIE\" target=\"_blank\"&gt;In The Hall Of the Trumpen King&lt;/a&gt;</td>\n",
       "      <td>2016-11-11</td>\n",
       "      <td>The General Election Version of The Trumpen King.  Congratulations President Elect Donald Trump!  We're going to Make America Great Again!  Subscribe, as I may make a version covering his entire run, start to finish!</td>\n",
       "      <td>SMB Productions</td>\n",
       "      <td>2024-11-05</td>\n",
       "      <td>1710</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                                                                            video title (Youtube)  \\\n",
       "1                           <a href=\"https://www.youtube.com/watch?v=OWLY3R85nDo\" target=\"_blank\">Philadelphia City Commissioners' Office Tells O'Keefe Non-Citizens Can Vote</a>   \n",
       "2  <a href=\"https://www.youtube.com/watch?v=DWVev5-2Db0\" target=\"_blank\">Elon Musk Pleads \"Vote Like Your Life Depends on It!\" and Speaks on Why He Became Politically Active</a>   \n",
       "3                                       <a href=\"https://www.youtube.com/watch?v=oKM_L1dSeQw\" target=\"_blank\">LIVE: President Trump Holds a Rally in Pittsburgh, PA - 11/4/24</a>   \n",
       "4                   <a href=\"https://www.youtube.com/watch?v=j41NvXcls9c\" target=\"_blank\">LIVE: President Trump Holds Final 2024 Campaign Rally in Grand Rapids, MI - 11/4/24</a>   \n",
       "5                                                                                      <a href=\"https://youtu.be/WGomGRZIDIE\" target=\"_blank\">In The Hall Of the Trumpen King</a>   \n",
       "\n",
       "  published date (Youtube)  \\\n",
       "1               2024-11-05   \n",
       "2               2024-11-04   \n",
       "3               2024-11-05   \n",
       "4               2024-11-05   \n",
       "5               2016-11-11   \n",
       "\n",
       "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      brief desription (Youtube)  \\\n",
       "1  In a recent conversation with an undercover O’Keefe Media Group journalist, Milton Jamerson, Election And Voter Registration Clerk from the Philadelphia City Commissioners Office, stated that non-citizens can vote in local elections as long as they are residents of Philadelphia. This claim is echoed by the nongovernmental organization (NGO) “Ceiba,” located just across the street, which asserts that individuals with an Individual Taxpayer Identification Number (ITIN) are eligible to vote.   \n",
       "2                                                                                                                                                                                                                                                                                                                                                                               JRE #2223 w/Elon Musk YouTube: https://youtu.be/7qZl_5xHoBw JRE on Spotify: https://open.spotify.com/show/4rOoJ6Egrf8K2IrywzwOMk   \n",
       "3                                                                                                                                                                                                                                                                                                                             President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Pittsburgh, Pennsylvania on Monday, November 4, 2024, at 6:00PM EST.   \n",
       "4                                                                                                                                                                                                                                                                                                                              President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Grand Rapids, Michigan on Monday, November 4, 2024, at 10:30PM EST.   \n",
       "5                                                                                                                                                                                                                                                                                       The General Election Version of The Trumpen King.  Congratulations President Elect Donald Trump!  We're going to Make America Great Again!  Subscribe, as I may make a version covering his entire run, start to finish!   \n",
       "\n",
       "           channel_title (Youtube) shared date (The Donald)  \\\n",
       "1              O’Keefe Media Group               2024-11-05   \n",
       "2                        JRE Clips               2024-11-05   \n",
       "3  Right Side Broadcasting Network               2024-11-05   \n",
       "4  Right Side Broadcasting Network               2024-11-05   \n",
       "5                  SMB Productions               2024-11-05   \n",
       "\n",
       "   upvote count (The Donald)  comment count (The Donald)  \n",
       "1                         51                           2  \n",
       "2                        133                           0  \n",
       "3                       1115                         235  \n",
       "4                        973                         525  \n",
       "5                       1710                         159  "
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a dataframe specifically for the html\n",
    "display_df = youtube_3days_df[['uuid',\n",
    "                               'full_title', \n",
    "                               'published_date_on_youtube',\n",
    "                               'brief_desription', \n",
    "                               'channel_title', \n",
    "                               'link', \n",
    "                               'created_date',\n",
    "                               'score_up',\n",
    "                               'comments'\n",
    "                               ]].sort_values('created_date', ascending=False)\n",
    "\n",
    "# drop duplicated posts\n",
    "display_df.drop_duplicates(subset='uuid', inplace=True)\n",
    "# drop uuid field\n",
    "display_df.drop(columns='uuid', inplace=True)\n",
    "\n",
    "\n",
    "# reset index \n",
    "display_df = display_df.reset_index(drop=True)\n",
    "display_df.index +=1\n",
    "\n",
    "# embed youtube to title text in the table\n",
    "display_df['full_title']= display_df.apply(lambda row: f'<a href=\"{row[\"link\"]}\" target=\"_blank\">{row[\"full_title\"]}</a>', axis=1)\n",
    "\n",
    "# drop link field\n",
    "display_df = display_df.drop(columns = 'link')\n",
    "\n",
    "# rename some column headers\n",
    "display_df = display_df.rename(columns ={'full_title':'video title (Youtube)',\n",
    "                                         'brief_desription': 'brief desription (Youtube)',\n",
    "                                         'published_date_on_youtube': 'published date (Youtube)',\n",
    "                                         'channel_title' : 'channel_title (Youtube)',\n",
    "                                         \"created_date\": 'shared date (The Donald)',\n",
    "                                         'score_up': 'upvote count (The Donald)',\n",
    "                                         'comments' : 'comment count (The Donald)'\n",
    "                                         })\n",
    "\n",
    "\n",
    "display_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'<table border=\"1\" class=\"dataframe table table-striped table-hover\" id=\"my_table\">\\n  <thead>\\n    <tr style=\"text-align: center;\">\\n      <th></th>\\n      <th>video title (Youtube)</th>\\n      <th>published date (Youtube)</th>\\n      <th>brief desription (Youtube)</th>\\n      <th>channel_title (Youtube)</th>\\n      <th>shared date (The Donald)</th>\\n      <th>upvote count (The Donald)</th>\\n      <th>comment count (The Donald)</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <th>1</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=OWLY3R85nDo\" target=\"_blank\">Philadelphia City Commissioners\\' Office Tells O\\'Keefe Non-Citizens Can Vote</a></td>\\n      <td>2024-11-05</td>\\n      <td>In a recent conversation with an undercover O’Keefe Media Group journalist, Milton Jamerson, Election And Voter Registration Clerk from the Philadelphia City Commissioners Office, stated that non-citizens can vote in local elections as long as they are residents of Philadelphia. This claim is echoed by the nongovernmental organization (NGO) “Ceiba,” located just across the street, which asserts that individuals with an Individual Taxpayer Identification Number (ITIN) are eligible to vote.</td>\\n      <td>O’Keefe Media Group</td>\\n      <td>2024-11-05</td>\\n      <td>51</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>2</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=DWVev5-2Db0\" target=\"_blank\">Elon Musk Pleads \"Vote Like Your Life Depends on It!\" and Speaks on Why He Became Politically Active</a></td>\\n      <td>2024-11-04</td>\\n      <td>JRE #2223 w/Elon Musk YouTube: https://youtu.be/7qZl_5xHoBw JRE on Spotify: https://open.spotify.com/show/4rOoJ6Egrf8K2IrywzwOMk</td>\\n      <td>JRE Clips</td>\\n      <td>2024-11-05</td>\\n      <td>133</td>\\n      <td>0</td>\\n    </tr>\\n    <tr>\\n      <th>3</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=oKM_L1dSeQw\" target=\"_blank\">LIVE: President Trump Holds a Rally in Pittsburgh, PA - 11/4/24</a></td>\\n      <td>2024-11-05</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Pittsburgh, Pennsylvania on Monday, November 4, 2024, at 6:00PM EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-05</td>\\n      <td>1115</td>\\n      <td>235</td>\\n    </tr>\\n    <tr>\\n      <th>4</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=j41NvXcls9c\" target=\"_blank\">LIVE: President Trump Holds Final 2024 Campaign Rally in Grand Rapids, MI - 11/4/24</a></td>\\n      <td>2024-11-05</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Grand Rapids, Michigan on Monday, November 4, 2024, at 10:30PM EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-05</td>\\n      <td>973</td>\\n      <td>525</td>\\n    </tr>\\n    <tr>\\n      <th>5</th>\\n      <td><a href=\"https://youtu.be/WGomGRZIDIE\" target=\"_blank\">In The Hall Of the Trumpen King</a></td>\\n      <td>2016-11-11</td>\\n      <td>The General Election Version of The Trumpen King.  Congratulations President Elect Donald Trump!  We\\'re going to Make America Great Again!  Subscribe, as I may make a version covering his entire run, start to finish!</td>\\n      <td>SMB Productions</td>\\n      <td>2024-11-05</td>\\n      <td>1710</td>\\n      <td>159</td>\\n    </tr>\\n    <tr>\\n      <th>6</th>\\n      <td><a href=\"https://youtu.be/sI4APm2p-pI?si=hM06G5Tnjw4OktmV\" target=\"_blank\">Secure the border. Stop the CCP! #lilytangwilliams #lily4congress #nh02</a></td>\\n      <td>2024-08-21</td>\\n      <td>A life without freedom. That’s the China I survived.</td>\\n      <td>Lily Tang Williams</td>\\n      <td>2024-11-04</td>\\n      <td>453</td>\\n      <td>6</td>\\n    </tr>\\n    <tr>\\n      <th>7</th>\\n      <td><a href=\"https://m.youtube.com/watch?v=3NRIegaoTgU&t=329s&pp=2AHJApACAQ%3D%3D\" target=\"_blank\">I Got Kicked OUT Of A Kamala ‘Rally!’ Campaign Called COPS On Me For EXPOSING Their EMPTY Event 🤣</a></td>\\n      <td>2024-11-03</td>\\n      <td>Kamala Harris’ campaign is so panicked that I was kicked out just for recording the tiny crowd for her event with Hillary</td>\\n      <td>Benny Johnson</td>\\n      <td>2024-11-04</td>\\n      <td>77</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>8</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=7qZl_5xHoBw\" target=\"_blank\">Joe Rogan Experience #2223 - Elon Musk</a></td>\\n      <td>2024-11-04</td>\\n      <td>Elon Musk is a business magnate, designer, and engineer. His portfolio of businesses include Tesla, Inc., SpaceX, Neuralink, X, and many others.</td>\\n      <td>PowerfulJRE</td>\\n      <td>2024-11-04</td>\\n      <td>1659</td>\\n      <td>112</td>\\n    </tr>\\n    <tr>\\n      <th>9</th>\\n      <td><a href=\"https://youtu.be/L4hJLVZoJOg?t=171\" target=\"_blank\">\\'MESSAGE OF PEACE\\': Muslim American mayor explains Trump endorsement</a></td>\\n      <td>2024-11-03</td>\\n      <td>Hamtramck, Michigan Mayor Amer Ghalib, a Democrat, joins \\'Sunday Morning Futures\\' to discuss his Trump endorsement and share the reactions he has received since backing the GOP candidate for president. #FoxNews</td>\\n      <td>Fox News</td>\\n      <td>2024-11-04</td>\\n      <td>157</td>\\n      <td>4</td>\\n    </tr>\\n    <tr>\\n      <th>10</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=7qZl_5xHoBw\" target=\"_blank\">Joe Rogan Experience #2223 - Elon Musk</a></td>\\n      <td>2024-11-04</td>\\n      <td>Elon Musk is a business magnate, designer, and engineer. His portfolio of businesses include Tesla, Inc., SpaceX, Neuralink, X, and many others.</td>\\n      <td>PowerfulJRE</td>\\n      <td>2024-11-04</td>\\n      <td>169</td>\\n      <td>8</td>\\n    </tr>\\n    <tr>\\n      <th>11</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=DF1fxZwrZCI\" target=\"_blank\">LIVE: President Trump Holds a Rally in Reading, PA - 11/4/24</a></td>\\n      <td>2024-11-04</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Reading, Pennsylvania on Monday, November 4, 2024, at 2:00PM EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-04</td>\\n      <td>1069</td>\\n      <td>76</td>\\n    </tr>\\n    <tr>\\n      <th>12</th>\\n      <td><a href=\"https://youtu.be/3GrwdUhqLpY\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Macon, GA - 11/3/24</a></td>\\n      <td>2024-11-04</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Macon, Georgia, on Sunday, November 3, 2024, at 6:30 p.m. EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-04</td>\\n      <td>1017</td>\\n      <td>137</td>\\n    </tr>\\n    <tr>\\n      <th>13</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=Gbld1FiH2oA\" target=\"_blank\">Secret Service Seals Up White House Campus with riot fences today as election nears.</a></td>\\n      <td>2024-11-03</td>\\n      <td>Fences are going up around the White House and the Vice President\\'s house this weekend, along with more buildings in DC being secured from possible election violence.</td>\\n      <td>penguinsix</td>\\n      <td>2024-11-03</td>\\n      <td>73</td>\\n      <td>11</td>\\n    </tr>\\n    <tr>\\n      <th>14</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=kwMaQeW4Ugc\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Kinston, NC - 11/3/24</a></td>\\n      <td>2024-11-03</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Kinston, North Carolina, on Sunday, November 3, 2024, at 2:00 p.m. EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-03</td>\\n      <td>903</td>\\n      <td>73</td>\\n    </tr>\\n    <tr>\\n      <th>15</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=-cQ9sdkSF5s\" target=\"_blank\">Laura Trump and RNC just CAUGHT and STOPPED 164,568 DUPLICATE BALLOTS in Key Swing State!</a></td>\\n      <td>2024-11-02</td>\\n      <td>Lara Trump, the daughter-in-law of Donald Trump and co-chair of the Republican National Committee, said the RNC\\'s election integrity team had received reports of \"thousands of duplicate ballots\" in the key swing state, which will now no longer be counted.</td>\\n      <td>PatriotTwins</td>\\n      <td>2024-11-03</td>\\n      <td>112</td>\\n      <td>2</td>\\n    </tr>\\n    <tr>\\n      <th>16</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=ONnZ5A1PgA8\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Lititz, PA - 11/3/24</a></td>\\n      <td>2024-11-03</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Lititz, Pennsylvania, on Sunday, November 3, 2024, at 10:00 a.m. EST.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-03</td>\\n      <td>617</td>\\n      <td>70</td>\\n    </tr>\\n    <tr>\\n      <th>17</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=0Biws4eiwOk\" target=\"_blank\">Harrison Ford Endorses Harris: ‘We Need a President Who Works for Us All’</a></td>\\n      <td>2024-11-02</td>\\n      <td>Harrison Ford has endorsed Kamala Harris and Tim Walz. In a pair of black-and-white clips, he explained why, at age 64, he has decided to tell “people I’ve never met who I’m voting for and why” for this election.</td>\\n      <td>Rolling Stone</td>\\n      <td>2024-11-03</td>\\n      <td>67</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>18</th>\\n      <td><a href=\"https://youtu.be/opxwiTXtXls?feature=shared\" target=\"_blank\">CNN is amazed at Biden\\'s miraculous comeback in Wisconsin (HD)</a></td>\\n      <td>2020-11-30</td>\\n      <td>The infamous 120,000 vote turnaround as reported live by CNN. I tracked down a HD copy to supplement the original video:</td>\\n      <td>Amy Adams</td>\\n      <td>2024-11-03</td>\\n      <td>213</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>19</th>\\n      <td><a href=\"https://m.youtube.com/watch?v=0Oo4M0CPMlA&pp=ygU8Q2l0eSBjb3VuY2lsIGFza3MgcHVibGljIHRvIGhvdXNlIG1pZ3JhbnRzIGlmIHRoZXkgaGF2ZSByb29t\" target=\"_blank\">Mass. Gov. asking families to house migrants</a></td>\\n      <td>2023-08-10</td>\\n      <td>Mass. Gov. asking families to house migrants</td>\\n      <td>WTEN</td>\\n      <td>2024-11-03</td>\\n      <td>96</td>\\n      <td>10</td>\\n    </tr>\\n    <tr>\\n      <th>20</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=ZS0r_W8Txvo\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Milwaukee, WI - 11/1/24</a></td>\\n      <td>2024-11-02</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Milwaukee, Wisconsin, on Friday, November 1, 2024, at 8:00 p.m. EDT.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-02</td>\\n      <td>942</td>\\n      <td>158</td>\\n    </tr>\\n    <tr>\\n      <th>21</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=dqeiHakzjb0\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Salem, VA - 11/2/24</a></td>\\n      <td>2024-11-02</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Salem, Virginia, on Saturday, November 2, 2024, at 4:00 p.m. EDT.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-02</td>\\n      <td>999</td>\\n      <td>191</td>\\n    </tr>\\n    <tr>\\n      <th>22</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=8Bi4NlJBa0c\" target=\"_blank\">LIVE REPLAY: President Trump Holds a Rally in Gastonia, NC - 11/2/24</a></td>\\n      <td>2024-11-02</td>\\n      <td>President Donald J. Trump, 45th President of the United States of America, will deliver remarks at a rally in Gastonia, North Carolina, on Saturday, November 2, 2024, at 12:00 p.m. EDT.</td>\\n      <td>Right Side Broadcasting Network</td>\\n      <td>2024-11-02</td>\\n      <td>885</td>\\n      <td>54</td>\\n    </tr>\\n    <tr>\\n      <th>23</th>\\n      <td><a href=\"https://www.youtube.com/watch?v=sJITu_a3--g\" target=\"_blank\">Tensions boil over at South Carolina polling station</a></td>\\n      <td>2024-10-31</td>\\n      <td>A fight broke out at an early voting location in Orangeburg County, SC between a man wearing a ‘Let\\'s go Brandon’ hat and polling workers.</td>\\n      <td>New York Post</td>\\n      <td>2024-11-02</td>\\n      <td>101</td>\\n      <td>16</td>\\n    </tr>\\n  </tbody>\\n</table>'"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create a table for the top youtube videos and save it as \"top_youtube_table\"\n",
    "dataframe = display_df\n",
    "save_filename = \"top_youtube_table_new\"\n",
    "save_interactive_table(display_df, save_filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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