diff --git "a/stresstest_ner_service.ipynb" "b/stresstest_ner_service.ipynb"
--- "a/stresstest_ner_service.ipynb"
+++ "b/stresstest_ner_service.ipynb"
@@ -2,7 +2,31 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: pydantic in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (2.5.2)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (0.6.0)\n",
+      "Requirement already satisfied: pydantic-core==2.14.5 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (2.14.5)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.1 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (4.10.0)\n",
+      "Requirement already satisfied: pydantic in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (2.5.2)\n",
+      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (0.6.0)\n",
+      "Requirement already satisfied: pydantic-core==2.14.5 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (2.14.5)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.1 in c:\\users\\soura\\anaconda3\\envs\\py311_genai\\lib\\site-packages (from pydantic) (4.10.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%pip install pydantic datasets locust gradio "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -22,9 +46,87 @@
     "\n",
     "class NERResponse(BaseModel):\n",
     "    entities: List[Entity]\n",
-    "\n"
+    "\n",
+    "NER_API_URL = \"http://127.0.0.1:8000\"\n",
+    "NER_API_URL = \"https://lampofsocrates-hf-gradio-plodcw-group27:7860\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Found cached dataset parquet (C:/Users/soura/.cache/huggingface/datasets/surrey-nlp___parquet/surrey-nlp--PLOD-CW-843ef47e3e665cc1/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "089d97a26277479d8685bc27bc8c952d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset , DownloadMode\n",
+    "import random \n",
+    "datCW = load_dataset(\"surrey-nlp/PLOD-CW\", download_mode=DownloadMode.REUSE_DATASET_IF_EXISTS)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "( left panel ) Spectrophotometric reading value ( A450 ) represents NF - κB p65 activity .\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'( left panel ) Spectrophotometric reading value ( A450 ) represents NF - κB p65 activity .'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "def pick_random_payload():\n",
+    "    global datCW\n",
+    "    idx = random.randint(0, len(datCW[\"test\"])-1)\n",
+    "    tokens = datCW[\"test\"][idx][\"tokens\"]\n",
+    "    payload = ' '.join(tokens)\n",
+    "    print(payload)\n",
+    "    return payload\n",
+    "\n",
+    "pick_random_payload()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -34,25 +136,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "entities=[Entity(entity='B-O', score=0.7138232588768005, start=0, end=5, word='Hello'), Entity(entity='B-O', score=0.8602567911148071, start=5, end=6, word=','), Entity(entity='B-O', score=0.7045656442642212, start=7, end=12, word='world'), Entity(entity='B-O', score=0.9600160717964172, start=12, end=13, word='!')]\n"
+      "HPD , human population density .\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\soura\\AppData\\Local\\Temp\\ipykernel_8020\\832059799.py:8: PydanticDeprecatedSince20: The `json` method is deprecated; use `model_dump_json` instead. Deprecated in Pydantic V2.0 to be removed in V3.0. See Pydantic V2 Migration Guide at https://errors.pydantic.dev/2.5/migration/\n",
+      "  request_json = request_data.json()\n"
+     ]
+    },
+    {
+     "ename": "ConnectTimeout",
+     "evalue": "HTTPConnectionPool(host='10.25.188.70', port=80): Max retries exceeded with url: /predict (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x000001A11B8D4E90>, 'Connection to 10.25.188.70 timed out. (connect timeout=None)'))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:174\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 174\u001b[0m     conn \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m    175\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mextra_kw\n\u001b[0;32m    176\u001b[0m     )\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\connection.py:95\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m err \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---> 95\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m socket\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetaddrinfo returns an empty list\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     84\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[1;32m---> 85\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sock\n",
+      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m    716\u001b[0m     conn,\n\u001b[0;32m    717\u001b[0m     method,\n\u001b[0;32m    718\u001b[0m     url,\n\u001b[0;32m    719\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m    720\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    721\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    722\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    723\u001b[0m )\n\u001b[0;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:416\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    415\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 416\u001b[0m         conn\u001b[38;5;241m.\u001b[39mrequest(method, url, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhttplib_request_kw)\n\u001b[0;32m    418\u001b[0m \u001b[38;5;66;03m# We are swallowing BrokenPipeError (errno.EPIPE) since the server is\u001b[39;00m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;66;03m# legitimately able to close the connection after sending a valid response.\u001b[39;00m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;66;03m# With this behaviour, the received response is still readable.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:244\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers)\u001b[0m\n\u001b[0;32m    243\u001b[0m     headers[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUser-Agent\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m _get_default_user_agent()\n\u001b[1;32m--> 244\u001b[0m \u001b[38;5;28msuper\u001b[39m(HTTPConnection, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39mrequest(method, url, body\u001b[38;5;241m=\u001b[39mbody, headers\u001b[38;5;241m=\u001b[39mheaders)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\http\\client.py:1286\u001b[0m, in \u001b[0;36mHTTPConnection.request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1285\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Send a complete request to the server.\"\"\"\u001b[39;00m\n\u001b[1;32m-> 1286\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_request(method, url, body, headers, encode_chunked)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\http\\client.py:1332\u001b[0m, in \u001b[0;36mHTTPConnection._send_request\u001b[1;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[0;32m   1331\u001b[0m     body \u001b[38;5;241m=\u001b[39m _encode(body, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbody\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m-> 1332\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mendheaders(body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\http\\client.py:1281\u001b[0m, in \u001b[0;36mHTTPConnection.endheaders\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1280\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m CannotSendHeader()\n\u001b[1;32m-> 1281\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_send_output(message_body, encode_chunked\u001b[38;5;241m=\u001b[39mencode_chunked)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\http\\client.py:1041\u001b[0m, in \u001b[0;36mHTTPConnection._send_output\u001b[1;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[0;32m   1040\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_buffer[:]\n\u001b[1;32m-> 1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(msg)\n\u001b[0;32m   1043\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m message_body \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[0;32m   1044\u001b[0m \n\u001b[0;32m   1045\u001b[0m     \u001b[38;5;66;03m# create a consistent interface to message_body\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\http\\client.py:979\u001b[0m, in \u001b[0;36mHTTPConnection.send\u001b[1;34m(self, data)\u001b[0m\n\u001b[0;32m    978\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mauto_open:\n\u001b[1;32m--> 979\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[0;32m    980\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:205\u001b[0m, in \u001b[0;36mHTTPConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    204\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 205\u001b[0m     conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[0;32m    206\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prepare_conn(conn)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:179\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n\u001b[1;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[0;32m    180\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    181\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    182\u001b[0m         \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout),\n\u001b[0;32m    183\u001b[0m     )\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPConnection object at 0x000001A11B8D4E90>, 'Connection to 10.25.188.70 timed out. (connect timeout=None)')",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m    487\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m    488\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m    489\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m    490\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m    491\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    492\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    493\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    494\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    495\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m    496\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m    497\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    498\u001b[0m     )\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:799\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    797\u001b[0m     e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[1;32m--> 799\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[0;32m    800\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39me, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m    801\u001b[0m )\n\u001b[0;32m    802\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\retry.py:592\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_retry\u001b[38;5;241m.\u001b[39mis_exhausted():\n\u001b[1;32m--> 592\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause))\n\u001b[0;32m    594\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPConnectionPool(host='10.25.188.70', port=80): Max retries exceeded with url: /predict (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x000001A11B8D4E90>, 'Connection to 10.25.188.70 timed out. (connect timeout=None)'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[16], line 11\u001b[0m\n\u001b[0;32m      8\u001b[0m request_json \u001b[38;5;241m=\u001b[39m request_data\u001b[38;5;241m.\u001b[39mjson()\n\u001b[0;32m     10\u001b[0m \u001b[38;5;66;03m# Make the POST request\u001b[39;00m\n\u001b[1;32m---> 11\u001b[0m response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mpost(url, data\u001b[38;5;241m=\u001b[39mrequest_json, headers\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mContent-Type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mapplication/json\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m     14\u001b[0m \u001b[38;5;66;03m# Check if the request was successful\u001b[39;00m\n\u001b[0;32m     15\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m200\u001b[39m:\n\u001b[0;32m     16\u001b[0m     \u001b[38;5;66;03m# Parse the response JSON to the NERResponse model\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\api.py:115\u001b[0m, in \u001b[0;36mpost\u001b[1;34m(url, data, json, **kwargs)\u001b[0m\n\u001b[0;32m    103\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpost\u001b[39m(url, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, json\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m    104\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a POST request.\u001b[39;00m\n\u001b[0;32m    105\u001b[0m \n\u001b[0;32m    106\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    112\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m    113\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpost\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, data\u001b[38;5;241m=\u001b[39mdata, json\u001b[38;5;241m=\u001b[39mjson, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m    587\u001b[0m }\n\u001b[0;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\adapters.py:507\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[0;32m    505\u001b[0m     \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n\u001b[0;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[1;32m--> 507\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m    509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n\u001b[0;32m    510\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RetryError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPConnectionPool(host='10.25.188.70', port=80): Max retries exceeded with url: /predict (Caused by ConnectTimeoutError(<urllib3.connection.HTTPConnection object at 0x000001A11B8D4E90>, 'Connection to 10.25.188.70 timed out. (connect timeout=None)'))"
      ]
     }
    ],
    "source": [
-    "NER_API_URL = \"http://127.0.0.1:8000\"\n",
+    "\n",
     "\n",
     "# URL of the FastAPI server\n",
-    "url = f\"{NER_API_URL}/ner\"\n",
+    "url = f\"https://huggingface.co/spaces/LampOfSocrates/hf_gradio_plodcw_group27/predict\"\n",
     "\n",
     "# Create an instance of NERRequest\n",
-    "request_data = NERRequest(text=\"Hello, world!\") # Pick from PLOD-CW\n",
+    "request_data = NERRequest(text=pick_random_payload()) # Pick from PLOD-CW\n",
     "\n",
     "# Convert the request data to a JSON string\n",
     "request_json = request_data.json()\n",
@@ -79,14 +230,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'message': 'Hello, World!'}\n"
+     "ename": "ConnectTimeout",
+     "evalue": "HTTPSConnectionPool(host='lampofsocrates-hf_gradio_plodcw_group27.hf.space', port=7860): Max retries exceeded with url: /hello (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x000001A11B91A2D0>, 'Connection to lampofsocrates-hf_gradio_plodcw_group27.hf.space timed out. (connect timeout=None)'))",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTimeoutError\u001b[0m                              Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:174\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    173\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 174\u001b[0m     conn \u001b[38;5;241m=\u001b[39m connection\u001b[38;5;241m.\u001b[39mcreate_connection(\n\u001b[0;32m    175\u001b[0m         (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dns_host, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mport), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mextra_kw\n\u001b[0;32m    176\u001b[0m     )\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\connection.py:95\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     94\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m err \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---> 95\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n\u001b[0;32m     97\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m socket\u001b[38;5;241m.\u001b[39merror(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgetaddrinfo returns an empty list\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\connection.py:85\u001b[0m, in \u001b[0;36mcreate_connection\u001b[1;34m(address, timeout, source_address, socket_options)\u001b[0m\n\u001b[0;32m     84\u001b[0m     sock\u001b[38;5;241m.\u001b[39mbind(source_address)\n\u001b[1;32m---> 85\u001b[0m sock\u001b[38;5;241m.\u001b[39mconnect(sa)\n\u001b[0;32m     86\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m sock\n",
+      "\u001b[1;31mTimeoutError\u001b[0m: [WinError 10060] A connection attempt failed because the connected party did not properly respond after a period of time, or established connection failed because connected host has failed to respond",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m                       Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:715\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    714\u001b[0m \u001b[38;5;66;03m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[1;32m--> 715\u001b[0m httplib_response \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_make_request(\n\u001b[0;32m    716\u001b[0m     conn,\n\u001b[0;32m    717\u001b[0m     method,\n\u001b[0;32m    718\u001b[0m     url,\n\u001b[0;32m    719\u001b[0m     timeout\u001b[38;5;241m=\u001b[39mtimeout_obj,\n\u001b[0;32m    720\u001b[0m     body\u001b[38;5;241m=\u001b[39mbody,\n\u001b[0;32m    721\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[0;32m    722\u001b[0m     chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    723\u001b[0m )\n\u001b[0;32m    725\u001b[0m \u001b[38;5;66;03m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[0;32m    726\u001b[0m \u001b[38;5;66;03m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;66;03m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;66;03m# mess.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:404\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[1;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[0;32m    403\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 404\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_validate_conn(conn)\n\u001b[0;32m    405\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (SocketTimeout, BaseSSLError) \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    406\u001b[0m     \u001b[38;5;66;03m# Py2 raises this as a BaseSSLError, Py3 raises it as socket timeout.\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:1058\u001b[0m, in \u001b[0;36mHTTPSConnectionPool._validate_conn\u001b[1;34m(self, conn)\u001b[0m\n\u001b[0;32m   1057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(conn, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msock\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m):  \u001b[38;5;66;03m# AppEngine might not have  `.sock`\u001b[39;00m\n\u001b[1;32m-> 1058\u001b[0m     conn\u001b[38;5;241m.\u001b[39mconnect()\n\u001b[0;32m   1060\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m conn\u001b[38;5;241m.\u001b[39mis_verified:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:363\u001b[0m, in \u001b[0;36mHTTPSConnection.connect\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    361\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mconnect\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    362\u001b[0m     \u001b[38;5;66;03m# Add certificate verification\u001b[39;00m\n\u001b[1;32m--> 363\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msock \u001b[38;5;241m=\u001b[39m conn \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_new_conn()\n\u001b[0;32m    364\u001b[0m     hostname \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connection.py:179\u001b[0m, in \u001b[0;36mHTTPConnection._new_conn\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    178\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketTimeout:\n\u001b[1;32m--> 179\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeoutError(\n\u001b[0;32m    180\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    181\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection to \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m timed out. (connect timeout=\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    182\u001b[0m         \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhost, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimeout),\n\u001b[0;32m    183\u001b[0m     )\n\u001b[0;32m    185\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m SocketError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "\u001b[1;31mConnectTimeoutError\u001b[0m: (<urllib3.connection.HTTPSConnection object at 0x000001A11B91A2D0>, 'Connection to lampofsocrates-hf_gradio_plodcw_group27.hf.space timed out. (connect timeout=None)')",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m                             Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    485\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 486\u001b[0m     resp \u001b[38;5;241m=\u001b[39m conn\u001b[38;5;241m.\u001b[39murlopen(\n\u001b[0;32m    487\u001b[0m         method\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mmethod,\n\u001b[0;32m    488\u001b[0m         url\u001b[38;5;241m=\u001b[39murl,\n\u001b[0;32m    489\u001b[0m         body\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mbody,\n\u001b[0;32m    490\u001b[0m         headers\u001b[38;5;241m=\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders,\n\u001b[0;32m    491\u001b[0m         redirect\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    492\u001b[0m         assert_same_host\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    493\u001b[0m         preload_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    494\u001b[0m         decode_content\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    495\u001b[0m         retries\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmax_retries,\n\u001b[0;32m    496\u001b[0m         timeout\u001b[38;5;241m=\u001b[39mtimeout,\n\u001b[0;32m    497\u001b[0m         chunked\u001b[38;5;241m=\u001b[39mchunked,\n\u001b[0;32m    498\u001b[0m     )\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (ProtocolError, \u001b[38;5;167;01mOSError\u001b[39;00m) \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\connectionpool.py:799\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[0;32m    797\u001b[0m     e \u001b[38;5;241m=\u001b[39m ProtocolError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConnection aborted.\u001b[39m\u001b[38;5;124m\"\u001b[39m, e)\n\u001b[1;32m--> 799\u001b[0m retries \u001b[38;5;241m=\u001b[39m retries\u001b[38;5;241m.\u001b[39mincrement(\n\u001b[0;32m    800\u001b[0m     method, url, error\u001b[38;5;241m=\u001b[39me, _pool\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m, _stacktrace\u001b[38;5;241m=\u001b[39msys\u001b[38;5;241m.\u001b[39mexc_info()[\u001b[38;5;241m2\u001b[39m]\n\u001b[0;32m    801\u001b[0m )\n\u001b[0;32m    802\u001b[0m retries\u001b[38;5;241m.\u001b[39msleep()\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\urllib3\\util\\retry.py:592\u001b[0m, in \u001b[0;36mRetry.increment\u001b[1;34m(self, method, url, response, error, _pool, _stacktrace)\u001b[0m\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m new_retry\u001b[38;5;241m.\u001b[39mis_exhausted():\n\u001b[1;32m--> 592\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m MaxRetryError(_pool, url, error \u001b[38;5;129;01mor\u001b[39;00m ResponseError(cause))\n\u001b[0;32m    594\u001b[0m log\u001b[38;5;241m.\u001b[39mdebug(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mIncremented Retry for (url=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m): \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m, url, new_retry)\n",
+      "\u001b[1;31mMaxRetryError\u001b[0m: HTTPSConnectionPool(host='lampofsocrates-hf_gradio_plodcw_group27.hf.space', port=7860): Max retries exceeded with url: /hello (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x000001A11B91A2D0>, 'Connection to lampofsocrates-hf_gradio_plodcw_group27.hf.space timed out. (connect timeout=None)'))",
+      "\nDuring handling of the above exception, another exception occurred:\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[21], line 7\u001b[0m\n\u001b[0;32m      4\u001b[0m url \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://lampofsocrates-hf_gradio_plodcw_group27.hf.space:7860/hello\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# Make the GET request\u001b[39;00m\n\u001b[1;32m----> 7\u001b[0m response \u001b[38;5;241m=\u001b[39m requests\u001b[38;5;241m.\u001b[39mget(url)\n\u001b[0;32m      9\u001b[0m \u001b[38;5;66;03m# Check if the request was successful\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m200\u001b[39m:\n\u001b[0;32m     11\u001b[0m     \u001b[38;5;66;03m# Print the response JSON\u001b[39;00m\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\api.py:73\u001b[0m, in \u001b[0;36mget\u001b[1;34m(url, params, **kwargs)\u001b[0m\n\u001b[0;32m     62\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget\u001b[39m(url, params\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m     63\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124;03m\"\"\"Sends a GET request.\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \n\u001b[0;32m     65\u001b[0m \u001b[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     70\u001b[0m \u001b[38;5;124;03m    :rtype: requests.Response\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m---> 73\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m request(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget\u001b[39m\u001b[38;5;124m\"\u001b[39m, url, params\u001b[38;5;241m=\u001b[39mparams, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\api.py:59\u001b[0m, in \u001b[0;36mrequest\u001b[1;34m(method, url, **kwargs)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001b[39;00m\n\u001b[0;32m     56\u001b[0m \u001b[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001b[39;00m\n\u001b[0;32m     57\u001b[0m \u001b[38;5;66;03m# cases, and look like a memory leak in others.\u001b[39;00m\n\u001b[0;32m     58\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m sessions\u001b[38;5;241m.\u001b[39mSession() \u001b[38;5;28;01mas\u001b[39;00m session:\n\u001b[1;32m---> 59\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m session\u001b[38;5;241m.\u001b[39mrequest(method\u001b[38;5;241m=\u001b[39mmethod, url\u001b[38;5;241m=\u001b[39murl, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[0;32m    584\u001b[0m send_kwargs \u001b[38;5;241m=\u001b[39m {\n\u001b[0;32m    585\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtimeout\u001b[39m\u001b[38;5;124m\"\u001b[39m: timeout,\n\u001b[0;32m    586\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mallow_redirects\u001b[39m\u001b[38;5;124m\"\u001b[39m: allow_redirects,\n\u001b[0;32m    587\u001b[0m }\n\u001b[0;32m    588\u001b[0m send_kwargs\u001b[38;5;241m.\u001b[39mupdate(settings)\n\u001b[1;32m--> 589\u001b[0m resp \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msend(prep, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39msend_kwargs)\n\u001b[0;32m    591\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m resp\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[1;34m(self, request, **kwargs)\u001b[0m\n\u001b[0;32m    700\u001b[0m start \u001b[38;5;241m=\u001b[39m preferred_clock()\n\u001b[0;32m    702\u001b[0m \u001b[38;5;66;03m# Send the request\u001b[39;00m\n\u001b[1;32m--> 703\u001b[0m r \u001b[38;5;241m=\u001b[39m adapter\u001b[38;5;241m.\u001b[39msend(request, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    705\u001b[0m \u001b[38;5;66;03m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[0;32m    706\u001b[0m elapsed \u001b[38;5;241m=\u001b[39m preferred_clock() \u001b[38;5;241m-\u001b[39m start\n",
+      "File \u001b[1;32mc:\\Users\\soura\\anaconda3\\envs\\py311_genai\\Lib\\site-packages\\requests\\adapters.py:507\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[0;32m    504\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ConnectTimeoutError):\n\u001b[0;32m    505\u001b[0m     \u001b[38;5;66;03m# TODO: Remove this in 3.0.0: see #2811\u001b[39;00m\n\u001b[0;32m    506\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, NewConnectionError):\n\u001b[1;32m--> 507\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m ConnectTimeout(e, request\u001b[38;5;241m=\u001b[39mrequest)\n\u001b[0;32m    509\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(e\u001b[38;5;241m.\u001b[39mreason, ResponseError):\n\u001b[0;32m    510\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m RetryError(e, request\u001b[38;5;241m=\u001b[39mrequest)\n",
+      "\u001b[1;31mConnectTimeout\u001b[0m: HTTPSConnectionPool(host='lampofsocrates-hf_gradio_plodcw_group27.hf.space', port=7860): Max retries exceeded with url: /hello (Caused by ConnectTimeoutError(<urllib3.connection.HTTPSConnection object at 0x000001A11B91A2D0>, 'Connection to lampofsocrates-hf_gradio_plodcw_group27.hf.space timed out. (connect timeout=None)'))"
      ]
     }
    ],
@@ -94,7 +274,7 @@
     "import requests\n",
     "\n",
     "# URL of the FastAPI server\n",
-    "url = f\"{NER_API_URL}/hello\"\n",
+    "url = f\"https://lampofsocrates-hf_gradio_plodcw_group27.hf.space:7860/hello\"\n",
     "\n",
     "# Make the GET request\n",
     "response = requests.get(url)\n",
@@ -116,7 +296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -125,13 +305,13 @@
      "text": [
       "       Time Taken\n",
       "count  100.000000\n",
-      "mean     0.017934\n",
-      "std      0.008877\n",
-      "min      0.000000\n",
-      "25%      0.012228\n",
-      "50%      0.017015\n",
-      "75%      0.021086\n",
-      "max      0.047817\n"
+      "mean     0.018420\n",
+      "std      0.008319\n",
+      "min      0.002018\n",
+      "25%      0.012080\n",
+      "50%      0.018285\n",
+      "75%      0.021183\n",
+      "max      0.050951\n"
      ]
     }
    ],
@@ -200,27 +380,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0     0.020945\n",
-       "1     0.015174\n",
-       "2     0.008024\n",
-       "3     0.006214\n",
-       "4     0.012195\n",
+       "0     0.010957\n",
+       "1     0.013984\n",
+       "2     0.008043\n",
+       "3     0.013479\n",
+       "4     0.007442\n",
        "        ...   \n",
-       "95    0.011356\n",
-       "96    0.012191\n",
-       "97    0.013172\n",
-       "98    0.014342\n",
-       "99    0.016271\n",
+       "95    0.029726\n",
+       "96    0.032254\n",
+       "97    0.028206\n",
+       "98    0.030225\n",
+       "99    0.020141\n",
        "Name: Time Taken, Length: 100, dtype: float64"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -231,12 +411,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -246,7 +426,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]