{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7ce883ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Itsab\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'model.joblib'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 4\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgradio\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mgr\u001b[39;00m\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mjoblib\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m load\n\u001b[32m----> \u001b[39m\u001b[32m4\u001b[39m model = \u001b[43mload\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mmodel.joblib\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m      5\u001b[39m conv = load(\u001b[33m\"\u001b[39m\u001b[33mtfd.joblib\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m      7\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mprediction\u001b[39m(email):\n",
      "\u001b[36mFile \u001b[39m\u001b[32mc:\\Users\\Itsab\\AppData\\Local\\Programs\\Python\\Python313\\Lib\\site-packages\\joblib\\numpy_pickle.py:735\u001b[39m, in \u001b[36mload\u001b[39m\u001b[34m(filename, mmap_mode, ensure_native_byte_order)\u001b[39m\n\u001b[32m    733\u001b[39m         obj = _unpickle(fobj, ensure_native_byte_order=ensure_native_byte_order)\n\u001b[32m    734\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m735\u001b[39m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mrb\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[32m    736\u001b[39m         \u001b[38;5;28;01mwith\u001b[39;00m _validate_fileobject_and_memmap(f, filename, mmap_mode) \u001b[38;5;28;01mas\u001b[39;00m (\n\u001b[32m    737\u001b[39m             fobj,\n\u001b[32m    738\u001b[39m             validated_mmap_mode,\n\u001b[32m    739\u001b[39m         ):\n\u001b[32m    740\u001b[39m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fobj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[32m    741\u001b[39m                 \u001b[38;5;66;03m# if the returned file object is a string, this means we\u001b[39;00m\n\u001b[32m    742\u001b[39m                 \u001b[38;5;66;03m# try to load a pickle file generated with an version of\u001b[39;00m\n\u001b[32m    743\u001b[39m                 \u001b[38;5;66;03m# Joblib so we load it with joblib compatibility function.\u001b[39;00m\n",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: 'model.joblib'"
     ]
    }
   ],
   "source": [
    "import gradio as gr\n",
    "from joblib import load\n",
    "\n",
    "model = load(\"model.joblib\")\n",
    "conv = load(\"tfd.joblib\")\n",
    "\n",
    "def prediction(email):\n",
    "    \n",
    "    inp = [email]   # passing input\n",
    "    \n",
    "    inp_final = conv.transform(inp)  # converting input\n",
    "    \n",
    "    res = model.predict(inp_final)[0] # predicting output\n",
    "    \n",
    "    return \"Not Spam\" if res==1 else \"Spam\" # predicting result\n",
    "\n",
    "iface = gr.Interface(\n",
    "        fn = prediction,\n",
    "        inputs=[gr.Text(label=\"Email\")],\n",
    "        outputs= \"text\",\n",
    "        title=\"Spam Idetifier\",\n",
    "        description=\"This is used an app which can identify the email\")\n",
    "\n",
    "iface.launch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2c6c655",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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