toxicity_detection.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "88fdc6e7-cb2f-4dbf-acfa-a613147b0548",
   "metadata": {},
   "source": [
    "Toxicity Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e2ddebe9-e59c-435f-b144-e49a4a44ae59",
   "metadata": {
    "is_executing": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Requirement already satisfied: pyasn1<0.6.0,>=0.4.6 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (0.5.1)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow-intel==2.15.0->tensorflow) (3.2.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow pandas matplotlib scikit-learn \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ace4cf52-9745-4e60-a78c-3c611bbddc8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_6708\\621415305.py:2: DeprecationWarning: \n",
      "Pyarrow will become a required dependency of pandas in the next major release of pandas (pandas 3.0),\n",
      "(to allow more performant data types, such as the Arrow string type, and better interoperability with other libraries)\n",
      "but was not found to be installed on your system.\n",
      "If this would cause problems for you,\n",
      "please provide us feedback at https://github.com/pandas-dev/pandas/issues/54466\n",
      "        \n",
      "  import pandas as pd\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import pandas as pd \n",
    "import tensorflow as tf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ffdc9418-7086-441a-b2af-93c31c1f4137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function tensorflow.python.ops.losses.losses_impl.sparse_softmax_cross_entropy(labels, logits, weights=1.0, scope=None, loss_collection='losses', reduction='weighted_sum_by_nonzero_weights')>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.compat.v1.losses.sparse_softmax_cross_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c63aa5b0-60fd-46aa-8a52-f4ded188d95f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[1;31mSignature:\u001b[0m       \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mCall signature:\u001b[0m  \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mType:\u001b[0m            _ArrayFunctionDispatcher\n",
       "\u001b[1;31mString form:\u001b[0m     <function expand_dims at 0x0000016DC0353F60>\n",
       "\u001b[1;31mFile:\u001b[0m            c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages\\numpy\\lib\\shape_base.py\n",
       "\u001b[1;31mSource:\u001b[0m         \n",
       "\u001b[1;33m@\u001b[0m\u001b[0marray_function_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_expand_dims_dispatcher\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;32mdef\u001b[0m \u001b[0mexpand_dims\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;34m\"\"\"\n",
       "    Expand the shape of an array.\n",
       "\n",
       "    Insert a new axis that will appear at the `axis` position in the expanded\n",
       "    array shape.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    a : array_like\n",
       "        Input array.\n",
       "    axis : int or tuple of ints\n",
       "        Position in the expanded axes where the new axis (or axes) is placed.\n",
       "\n",
       "        .. deprecated:: 1.13.0\n",
       "            Passing an axis where ``axis > a.ndim`` will be treated as\n",
       "            ``axis == a.ndim``, and passing ``axis < -a.ndim - 1`` will\n",
       "            be treated as ``axis == 0``. This behavior is deprecated.\n",
       "\n",
       "        .. versionchanged:: 1.18.0\n",
       "            A tuple of axes is now supported.  Out of range axes as\n",
       "            described above are now forbidden and raise an `AxisError`.\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    result : ndarray\n",
       "        View of `a` with the number of dimensions increased.\n",
       "\n",
       "    See Also\n",
       "    --------\n",
       "    squeeze : The inverse operation, removing singleton dimensions\n",
       "    reshape : Insert, remove, and combine dimensions, and resize existing ones\n",
       "    doc.indexing, atleast_1d, atleast_2d, atleast_3d\n",
       "\n",
       "    Examples\n",
       "    --------\n",
       "    >>> x = np.array([1, 2])\n",
       "    >>> x.shape\n",
       "    (2,)\n",
       "\n",
       "    The following is equivalent to ``x[np.newaxis, :]`` or ``x[np.newaxis]``:\n",
       "\n",
       "    >>> y = np.expand_dims(x, axis=0)\n",
       "    >>> y\n",
       "    array([[1, 2]])\n",
       "    >>> y.shape\n",
       "    (1, 2)\n",
       "\n",
       "    The following is equivalent to ``x[:, np.newaxis]``:\n",
       "\n",
       "    >>> y = np.expand_dims(x, axis=1)\n",
       "    >>> y\n",
       "    array([[1],\n",
       "           [2]])\n",
       "    >>> y.shape\n",
       "    (2, 1)\n",
       "\n",
       "    ``axis`` may also be a tuple:\n",
       "\n",
       "    >>> y = np.expand_dims(x, axis=(0, 1))\n",
       "    >>> y\n",
       "    array([[[1, 2]]])\n",
       "\n",
       "    >>> y = np.expand_dims(x, axis=(2, 0))\n",
       "    >>> y\n",
       "    array([[[1],\n",
       "            [2]]])\n",
       "\n",
       "    Note that some examples may use ``None`` instead of ``np.newaxis``.  These\n",
       "    are the same objects:\n",
       "\n",
       "    >>> np.newaxis is None\n",
       "    True\n",
       "\n",
       "    \"\"\"\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmatrix\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
       "\u001b[0m        \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
       "\u001b[0m        \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\n",
       "\u001b[0m        \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mout_ndim\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnormalize_axis_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout_ndim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mshape_it\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0miter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mnext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape_it\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0max\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mout_ndim\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\n",
       "\u001b[0m\u001b[1;33m\n",
       "\u001b[0m    \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;31mClass docstring:\u001b[0m\n",
       "Class to wrap functions with checks for __array_function__ overrides.\n",
       "\n",
       "All arguments are required, and can only be passed by position.\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "dispatcher : function or None\n",
       "    The dispatcher function that returns a single sequence-like object\n",
       "    of all arguments relevant.  It must have the same signature (except\n",
       "    the default values) as the actual implementation.\n",
       "    If ``None``, this is a ``like=`` dispatcher and the\n",
       "    ``_ArrayFunctionDispatcher`` must be called with ``like`` as the\n",
       "    first (additional and positional) argument.\n",
       "implementation : function\n",
       "    Function that implements the operation on NumPy arrays without\n",
       "    overrides.  Arguments passed calling the ``_ArrayFunctionDispatcher``\n",
       "    will be forwarded to this (and the ``dispatcher``) as if using\n",
       "    ``*args, **kwargs``.\n",
       "\n",
       "Attributes\n",
       "----------\n",
       "_implementation : function\n",
       "    The original implementation passed in."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.expand_dims??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8f14e401-8510-4ea4-a96e-f5bad866f19a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(os.path.join(r'C:\\Users\\Admin\\Desktop\\New folder\\train.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58b65205-8ffb-4d05-971b-09b1773a77d6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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     "execution_count": 7,
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    "df.head()"
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   "cell_type": "code",
   "execution_count": 8,
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5f8a29c3-41af-48ca-9930-c4c40aa2b351",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Explanation\\nWhy the edits made under my username Hardcore Metallica Fan were reverted? They weren't vandalisms, just closure on some GAs after I voted at New York Dolls FAC. And please don't remove the template from the talk page since I'm retired now.89.205.38.27\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0]['comment_text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e429c4ca-b750-4fa8-82f6-8226da62d0d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "toxic            1\n",
       "severe_toxic     0\n",
       "obscene          1\n",
       "threat           0\n",
       "insult           0\n",
       "identity_hate    0\n",
       "Name: 58, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[2:]].iloc[58]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5dfa044-df2f-4b8d-9fa7-54a3aad4393e",
   "metadata": {},
   "source": [
    "1 Preprocesses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d592632e-57bd-41e2-ac29-3e7b0fd514f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import TextVectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7b7fab33-3b73-44bb-9e03-58de729849e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df['comment_text']\n",
    "y = df[df.columns[2:]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5220af14-9288-42be-b195-e07f496cbe69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'comment_text', 'toxic', 'severe_toxic', 'obscene', 'threat',\n",
       "       'insult', 'identity_hate'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "acaea118-439b-42a4-954e-65dba1f2104a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       ...,\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.columns[2:]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d4188ceb-9111-46ac-b91c-3661c45707fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_FEATURES =200000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "af236e8a-35f0-4c38-afca-6ed30bd85a7e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vectorizer = TextVectorization(max_tokens= MAX_FEATURES,\n",
    "                               output_sequence_length =1800,\n",
    "                               output_mode = 'int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "927e939b-326f-4603-bd58-1b3052d35154",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "vectorizer.adapt(X.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b1fed3f-47f9-4f04-abff-5c81287d6f87",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_vocabulary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "373b2bef-a517-4103-bc00-cf9ce9fa677b",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorized_text = vectorizer(X.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "54c43b61-031c-472e-9538-51a45bd1ef59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(159571, 1800), dtype=int64, numpy=\n",
       "array([[  645,    76,     2, ...,     0,     0,     0],\n",
       "       [    1,    54,  2489, ...,     0,     0,     0],\n",
       "       [  425,   441,    70, ...,     0,     0,     0],\n",
       "       ...,\n",
       "       [32445,  7392,   383, ...,     0,     0,     0],\n",
       "       [    5,    12,   534, ...,     0,     0,     0],\n",
       "       [    5,     8,   130, ...,     0,     0,     0]], dtype=int64)>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorized_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "34499711-f62b-436f-8c00-8bb20daf2e0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function tensorflow.python.data.ops.dataset_ops.DatasetV2.list_files(file_pattern, shuffle=None, seed=None, name=None) -> 'DatasetV2'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.data.Dataset.list_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c45983e0-d9b4-4aca-9a56-9beb9a100f42",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.from_tensor_slices((vectorized_text,y))\n",
    "dataset = dataset.cache()\n",
    "dataset = dataset.shuffle(160000)\n",
    "dataset = dataset.batch(16)\n",
    "dataset = dataset.prefetch(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5718c198-19d0-4b45-9faa-579d7fed8300",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_x,batch_y = dataset.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b7c00098-2974-454f-9efa-eb3981f203bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6981"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(len(dataset)*.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f1afff8f-177c-4e60-b893-842e7e9cd54d",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = dataset.take(int(len(dataset)*.7))\n",
    "val = dataset.skip(int(len(dataset)*.7)).take(int(len(dataset)*.2))\n",
    "test = dataset.skip(int(len(dataset)*.9)).take(int(len(dataset)*.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5508ea57-8af7-4bcd-a065-b9b3b0fd21c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1994"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dde9bf18-bbff-42ff-884f-986712b15ae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator = train.as_numpy_iterator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b4b8a4ce-d080-4042-bbfb-9d844dafe56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[    8,  2276,  8433, ...,     0,     0,     0],\n",
       "        [   49,     2,   416, ...,     0,     0,     0],\n",
       "        [65442,     8,   457, ...,     0,     0,     0],\n",
       "        ...,\n",
       "        [ 1109,   983,   844, ...,     0,     0,     0],\n",
       "        [65321,    27,    70, ...,     0,     0,     0],\n",
       "        [  433,    49, 17884, ...,     0,     0,     0]], dtype=int64),\n",
       " array([[0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0],\n",
       "        [0, 0, 0, 0, 0, 0]], dtype=int64))"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_generator.next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a3b45fe9-5ea3-47a3-a922-06ded25a02c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM,Dropout,Bidirectional,Dense,Embedding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8109235b-d747-47b1-8d76-daf80a823430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(159571, 6)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c098be2a-76c1-42e8-8b28-82116bf957cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(MAX_FEATURES+1,32))\n",
    "# make bidirectional\n",
    "model.add(Bidirectional(LSTM(32,activation= 'tanh')))\n",
    "#fearture extractor fully connected layers\n",
    "\n",
    "model.add(Dense(128,activation ='relu'))\n",
    "model.add(Dense(256,activation ='relu'))\n",
    "model.add(Dense(128,activation ='relu'))\n",
    "#final layer\n",
    "model.add(Dense(6,activation ='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4cf7aa7f-14b0-40c0-b512-8c419cc36ef3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Admin\\AppData\\Local\\Temp\\ipykernel_6708\\452966030.py:1: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "tensorflow.python.training.optimizer.Optimizer"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.compat.v1.train.Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "b3b20867-8361-4cbb-ad8c-8f2e48b102fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='BinaryCrossentropy',optimizer='Adam')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "77b03707-eb43-47af-9012-32cf480e68d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, None, 32)          6400032   \n",
      "                                                                 \n",
      " bidirectional (Bidirection  (None, 64)                16640     \n",
      " al)                                                             \n",
      "                                                                 \n",
      " dense (Dense)               (None, 128)               8320      \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               33024     \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 6)                 774       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 6491686 (24.76 MB)\n",
      "Trainable params: 6491686 (24.76 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "10767e47-7851-496f-931e-09fe682071ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6981/6981 [==============================] - 5784s 828ms/step - loss: 0.0628 - val_loss: 0.0452\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train, epochs=1 , validation_data=val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "21b14f51-cf88-4736-90e4-c0e2155b1da1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a33cb2dc-f8a8-44ef-b3cf-72e003d50f29",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e78533b7-c321-4ea1-a9f3-2202bbe7d66c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "pd.DataFrame(history.history).plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea83114b-f8f5-457d-9139-c0e4fbbc54b8",
   "metadata": {},
   "source": [
    "##3. Make Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "0e0fe1a1-e89b-4416-933c-8a91f0ced36c",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_text = vectorizer('You freaking suck! I am going to hit you.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "f3d7b422-2d63-4144-997e-aa29ca241110",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "in user code:\n\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2440, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2425, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2413, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2381, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\lstm.py\", line 616, in call\n        timesteps = input_shape[0] if self.time_major else input_shape[1]\n\n    TypeError: Exception encountered when calling layer 'forward_lstm' (type LSTM).\n    \n    'NoneType' object is not subscriptable\n    \n    Call arguments received by layer 'forward_lstm' (type LSTM):\n      • inputs=tf.Tensor(shape=<unknown>, dtype=float32)\n      • mask=None\n      • training=False\n      • initial_state=None\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[88], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_text\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_fileez37aiyl.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__predict_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "\u001b[1;31mTypeError\u001b[0m: in user code:\n\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2440, in predict_function  *\n        return step_function(self, iterator)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2425, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2413, in run_step  **\n        outputs = model.predict_step(data)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py\", line 2381, in predict_step\n        return self(x, training=False)\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n    File \"C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\lstm.py\", line 616, in call\n        timesteps = input_shape[0] if self.time_major else input_shape[1]\n\n    TypeError: Exception encountered when calling layer 'forward_lstm' (type LSTM).\n    \n    'NoneType' object is not subscriptable\n    \n    Call arguments received by layer 'forward_lstm' (type LSTM):\n      • inputs=tf.Tensor(shape=<unknown>, dtype=float32)\n      • mask=None\n      • training=False\n      • initial_state=None\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(input_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "31134cf5-b2a7-434a-b1b5-3281afb70900",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'res' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[62], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m (\u001b[43mres\u001b[49m \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0.5\u001b[39m)\u001b[38;5;241m.\u001b[39mastype(\u001b[38;5;28mint\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'res' is not defined"
     ]
    }
   ],
   "source": [
    "(res > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "be23f707-00aa-4097-8039-c38039c7f351",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_X, batch_y = test.as_numpy_iterator().next()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "98fbb935-66f5-4908-8d46-3155842b5ce7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 262ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [1, 0, 1, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0],\n",
       "       [0, 0, 0, 0, 0, 0]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(model.predict(batch_X) > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b8465b7f-6882-45cb-a5ef-43badd2b69a3",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'res' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[68], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mres\u001b[49m\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[1;31mNameError\u001b[0m: name 'res' is not defined"
     ]
    }
   ],
   "source": [
    "res.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5eb16a51-eeda-40a8-be26-392c5ad7ac9a",
   "metadata": {},
   "source": [
    "4. Evaluate Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "7325961d-ed2f-47f4-9d58-a3f320da2983",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.metrics import Precision, Recall, CategoricalAccuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "8b09db7f-644b-4db4-9e74-79d655234368",
   "metadata": {},
   "outputs": [],
   "source": [
    "pre = Precision()\n",
    "re = Recall()\n",
    "acc = CategoricalAccuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "30005935-ff75-4765-9abf-d6d85353de53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "\n",
    "for batch in test.as_numpy_iterator(): \n",
    "    # Unpack the batch \n",
    "    X_true, y_true = batch\n",
    "    # Make a prediction \n",
    "    yhat = model.predict(X_true)\n",
    "    \n",
    "    # Flatten the predictions\n",
    "    y_true = y_true.flatten()\n",
    "    yhat = yhat.flatten()\n",
    "    \n",
    "    pre.update_state(y_true, yhat)\n",
    "    re.update_state(y_true, yhat)\n",
    "    acc.update_state(y_true, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "4f256d65-a6ca-4098-82b5-8cdb15436d57",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7679455280303955, Recall:0.7045131921768188, Accuracy:0.4974924921989441\n"
     ]
    }
   ],
   "source": [
    "print(f'Precision: {pre.result().numpy()}, Recall:{re.result().numpy()}, Accuracy:{acc.result().numpy()}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "696abba3-c4b3-48ff-b7a5-58bef2acff20",
   "metadata": {},
   "source": [
    "5. Test and Gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7056a5de-e481-41ee-8500-5caa1733293b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: gradio in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (4.16.0)\n",
      "Requirement already satisfied: jinja2 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (3.1.3)\n",
      "Requirement already satisfied: aiofiles<24.0,>=22.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (23.2.1)\n",
      "Requirement already satisfied: altair<6.0,>=4.2.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (5.2.0)\n",
      "Requirement already satisfied: fastapi in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.109.0)\n",
      "Requirement already satisfied: ffmpy in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.3.1)\n",
      "Requirement already satisfied: gradio-client==0.8.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.8.1)\n",
      "Requirement already satisfied: httpx in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.26.0)\n",
      "Requirement already satisfied: huggingface-hub>=0.19.3 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.20.3)\n",
      "Requirement already satisfied: importlib-resources<7.0,>=1.3 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (6.1.1)\n",
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      "Requirement already satisfied: matplotlib~=3.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (3.8.2)\n",
      "Requirement already satisfied: numpy~=1.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (1.26.3)\n",
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      "Requirement already satisfied: packaging in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (23.2)\n",
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      "Requirement already satisfied: typing-extensions~=4.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (4.9.0)\n",
      "Requirement already satisfied: uvicorn>=0.14.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio) (0.27.0)\n",
      "Requirement already satisfied: fsspec in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio-client==0.8.1->gradio) (2023.12.2)\n",
      "Requirement already satisfied: websockets<12.0,>=10.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from gradio-client==0.8.1->gradio) (11.0.3)\n",
      "Requirement already satisfied: jsonschema>=3.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from altair<6.0,>=4.2.0->gradio) (4.21.1)\n",
      "Requirement already satisfied: toolz in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from altair<6.0,>=4.2.0->gradio) (0.12.1)\n",
      "Requirement already satisfied: filelock in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from huggingface-hub>=0.19.3->gradio) (3.13.1)\n",
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      "Requirement already satisfied: tqdm>=4.42.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from huggingface-hub>=0.19.3->gradio) (4.66.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib~=3.0->gradio) (1.2.0)\n",
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      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib~=3.0->gradio) (1.4.5)\n",
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      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib~=3.0->gradio) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas<3.0,>=1.0->gradio) (2023.3.post1)\n",
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      "Requirement already satisfied: annotated-types>=0.4.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pydantic>=2.0->gradio) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.14.6 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pydantic>=2.0->gradio) (2.14.6)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from typer<1.0,>=0.9->typer[all]<1.0,>=0.9->gradio) (8.1.7)\n",
      "Requirement already satisfied: colorama<0.5.0,>=0.4.3 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (0.4.6)\n",
      "Requirement already satisfied: shellingham<2.0.0,>=1.3.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (1.5.4)\n",
      "Requirement already satisfied: rich<14.0.0,>=10.11.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from typer[all]<1.0,>=0.9->gradio) (13.7.0)\n",
      "Requirement already satisfied: h11>=0.8 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from uvicorn>=0.14.0->gradio) (0.14.0)\n",
      "Requirement already satisfied: starlette<0.36.0,>=0.35.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from fastapi->gradio) (0.35.1)\n",
      "Requirement already satisfied: anyio in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from httpx->gradio) (4.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from httpx->gradio) (2023.11.17)\n",
      "Requirement already satisfied: httpcore==1.* in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from httpx->gradio) (1.0.2)\n",
      "Requirement already satisfied: idna in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from httpx->gradio) (3.6)\n",
      "Requirement already satisfied: sniffio in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from httpx->gradio) (1.3.0)\n",
      "Requirement already satisfied: attrs>=22.2.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (23.2.0)\n",
      "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (2023.12.1)\n",
      "Requirement already satisfied: referencing>=0.28.4 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.32.1)\n",
      "Requirement already satisfied: rpds-py>=0.7.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.17.1)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
      "Requirement already satisfied: markdown-it-py>=2.2.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (3.0.0)\n",
      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (2.17.2)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from requests->huggingface-hub>=0.19.3->gradio) (2.1.0)\n",
      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\admin\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from markdown-it-py>=2.2.0->rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (0.1.2)\n"
     ]
    }
   ],
   "source": [
    "!pip install gradio jinja2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "2ecce999-d4eb-4dfb-8a15-7d5e06690466",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import gradio as gr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "0548bb7d-f9ec-4595-bfab-4c3b4f302d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6af2ea04-e44c-49a2-9e04-8aa7fdfe3f4c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.models.load_model('toxicity.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "46085b9a-6e08-4dec-89a6-223853b4aff5",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_str = vectorizer('hey i freaken hate you!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "cd3533c4-4c4f-4ceb-a0bf-bc0bdc9a365d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 637ms/step\n"
     ]
    }
   ],
   "source": [
    "res = model.predict(np.expand_dims(input_str,0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5b2ac00-ad32-46bc-af8e-65b54ce419cc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa4d52b2-6c5e-4cc9-b62a-7f7a0e083747",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "b3c5bbe0-5f96-40b4-a5b3-3c66e29f73b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9326273 , 0.08088681, 0.75304425, 0.04792411, 0.56650794,\n",
       "        0.13886681]], dtype=float32)"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "72173ca0-de7d-4575-b9a5-f5eeca00d831",
   "metadata": {},
   "outputs": [],
   "source": [
    "def score_comment(comment):\n",
    "    vectorized_comment = vectorizer([comment])\n",
    "    results = model.predict(vectorized_comment)\n",
    "    \n",
    "    text = ''\n",
    "    for idx, col in enumerate(df.columns[2:]):\n",
    "        text += '{}: {}\\n'.format(col, results[0][idx]>0.5)\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f585437e-751e-4c13-950b-c8b3eb30df34",
   "metadata": {},
   "outputs": [],
   "source": [
    "interface = gr.Interface(fn=score_comment, \n",
    "                         inputs=gr.Textbox(lines=2, placeholder='Comment to score'),\n",
    "                        outputs='text')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "d1c5a557-55d3-4427-9295-f8559d0199eb",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running on local URL:  http://127.0.0.1:7862\n",
      "Running on public URL: https://f007e952fc2968e328.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://f007e952fc2968e328.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": []
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 79ms/step\n",
      "1/1 [==============================] - 0s 96ms/step\n"
     ]
    }
   ],
   "source": [
    "interface.launch(share=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "a2dad0ef-0c3b-4375-be5f-464a4795c83f",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3470978507.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[115], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    gradio deploy\u001b[0m\n\u001b[1;37m           ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "gradio deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "0803ac98-69dd-4874-8c15-642a8e12bab5",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1404216386.py, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[116], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    gr deploy\u001b[0m\n\u001b[1;37m       ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "gr deploy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2a94ab5-4c0f-4cf1-989d-613cf6c080c7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "158ca0cd-5d0c-4ba5-9a58-dbf54fdb0656",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b37a63c5-ef2c-4398-8061-10de98102237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80b6d0c3-16eb-4ed8-8b47-46592c8e199f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ML-KERNEL",
   "language": "python",
   "name": "ml-kernel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}