15th_Sept_Semantic_text_Similarity.ipynb

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      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Mwadz/Sematic-Text-Similarity/blob/main/15th_Sept_Semantic_text_Similarity.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('requirements.txt', 'w') as writefile:\n",
        "    writefile.write(\"datasets\")\n",
        "    writefile.write(\"sentence_transformers\")\n",
        "    writefile.write(\"pandas\")\n",
        "    writefile.write(\"numpy\")\n",
        "    writefile.write(\"anaconda3\")\n",
        "    writefile.write(\"tqdm\")\n",
        "    writefile.write(\"sklearn\")\n",
        "    writefile.write(\"pickle\")\n",
        "    writefile.write(\"streamlit\")\n",
        "    writefile.write(\"pyngrok\")"
      ],
      "metadata": {
        "id": "XpmWLJROgbnv"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installations"
      ],
      "metadata": {
        "id": "Xe6RNkRU9eYt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "lJAFBf_V9iLF"
      }
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      "execution_count": 33,
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        "id": "TfFzQegbF2Oo",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Reusing dataset stsb_multi_mt (/root/.cache/huggingface/datasets/stsb_multi_mt/en/1.0.0/a5d260e4b7aa82d1ab7379523a005a366d9b124c76a5a5cf0c4c5365458b0ba9)\n"
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              "                                       sentence1  \\\n",
              "0                    A girl is styling her hair.   \n",
              "1       A group of men play soccer on the beach.   \n",
              "2  One woman is measuring another woman's ankle.   \n",
              "3                A man is cutting up a cucumber.   \n",
              "4                       A man is playing a harp.   \n",
              "\n",
              "                                          sentence2  similarity_score  \n",
              "0                      A girl is brushing her hair.               2.5  \n",
              "1  A group of boys are playing soccer on the beach.               3.6  \n",
              "2           A woman measures another woman's ankle.               5.0  \n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "#loading training set\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tqdm import tqdm\n",
        "tqdm.pandas()\n",
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "# Load the English STSB dataset\n",
        "stsb_dataset = load_dataset('stsb_multi_mt', 'en')\n",
        "stsb_train = pd.DataFrame(stsb_dataset['train'])\n",
        "stsb_test = pd.DataFrame(stsb_dataset['test'])\n",
        "\n",
        "# Check loaded data\n",
        "print(stsb_train.shape, stsb_test.shape)\n",
        "stsb_test.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating helper functions\n",
        "* The first function is to pre-process texts by lemmatizing, lowercasing, and removing numbers and stop words.\n",
        "* The second function takes in two columns of text embeddings and returns the row-wise cosine similarity between the two columns."
      ],
      "metadata": {
        "id": "N6eaoVvt0uJP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "import spacy\n",
        "nlp = spacy.load(\"en_core_web_sm\")\n",
        "\n",
        "def text_processing(sentence):\n",
        "    \"\"\"\n",
        "    Lemmatize, lowercase, remove numbers and stop words\n",
        "    \n",
        "    Args:\n",
        "      sentence: The sentence we want to process.\n",
        "    \n",
        "    Returns:\n",
        "      A list of processed words\n",
        "    \"\"\"\n",
        "    sentence = [token.lemma_.lower()\n",
        "                for token in nlp(sentence) \n",
        "                if token.is_alpha and not token.is_stop]\n",
        "    \n",
        "    return sentence\n",
        "\n",
        "\n",
        "def cos_sim(sentence1_emb, sentence2_emb):\n",
        "    \"\"\"\n",
        "    Cosine similarity between two columns of sentence embeddings\n",
        "    \n",
        "    Args:\n",
        "      sentence1_emb: sentence1 embedding column\n",
        "      sentence2_emb: sentence2 embedding column\n",
        "    \n",
        "    Returns:\n",
        "      The row-wise cosine similarity between the two columns.\n",
        "      For instance is sentence1_emb=[a,b,c] and sentence2_emb=[x,y,z]\n",
        "      Then the result is [cosine_similarity(a,x), cosine_similarity(b,y), cosine_similarity(c,z)]\n",
        "    \"\"\"\n",
        "    cos_sim = cosine_similarity(sentence1_emb, sentence2_emb)\n",
        "    return np.diag(cos_sim)"
      ],
      "metadata": {
        "id": "tkrRnbzw0inG"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Setup"
      ],
      "metadata": {
        "id": "ETy_exPj9rsM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = (pd.read_csv(\"/content/SBERT_data.csv\")).drop(['Unnamed: 0'], axis = 1)\n",
        "\n",
        "prompt = input(\"Enter prompt: \")\n",
        "data['prompt']= prompt\n",
        "data.rename(columns = {'target_text':'sentence2', 'prompt':'sentence1'}, inplace = True)\n",
        "data['sentence2'] = data['sentence2'].astype('str')\n",
        "data['sentence1']  = data['sentence1'].astype('str')\n",
        "\n",
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "xNXMOVdl1D2e",
        "outputId": "505bff37-07b6-4066-932a-0903ada0925f"
      },
      "execution_count": 35,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter prompt:  property name\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "                                          input_text  \\\n",
              "0  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "1  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "2  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "3  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "4  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "\n",
              "                                           sentence2       sentence1  \n",
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              "1  Specifies the creditor organization type exemp...   property name  \n",
              "2  A free-form text field used to collect additio...   property name  \n",
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              "          document.querySelector('#df-5632f474-3217-476d-8eb8-06045efd22da button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5632f474-3217-476d-8eb8-06045efd22da');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loop"
      ],
      "metadata": {
        "id": "tecT585c9xvt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import CrossEncoder\n",
        "XpathFinder = CrossEncoder(\"cross-encoder/stsb-roberta-base\")\n",
        "sentence_pairs = []\n",
        "for sentence1, sentence2 in zip(data['sentence1'],data['sentence2']):\n",
        "  sentence_pairs.append([sentence1, sentence2])\n",
        "\n",
        "data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs, show_progress_bar = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "1ab421911f57496a8a98c714547ff5d8",
            "907ebd28fd9f44fcb4bf349df9e1bcaf",
            "c322a267c7434351ba8da47daf09cbfb",
            "48cf7f9d4e3b47978178159ea6742619",
            "3d47cbefacaa4e2a8b48f3afe8f37ed8",
            "4bf41309e91c4fa2be1f6fe261e38bab",
            "2c047d602f3b4189a5c7a72711fee467",
            "b5b7269cb26a45b8ab8013bcdb3c590e",
            "6d7166ccfe1e453e8f1b3aed226f96ff",
            "9e0f49153daa4a1c9c86c476e3155786",
            "f216199500a14489a6dda843c07d3d0d"
          ]
        },
        "id": "IIObK22u2R6B",
        "outputId": "dd8441f9-2917-4410-aaf3-ea48c3221f4b"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/681 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ab421911f57496a8a98c714547ff5d8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Sort \n",
        "data.sort_values(by=['SBERT CrossEncoder_Score'], ascending=False)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "UZII2yb05QPu",
        "outputId": "ab8b60de-fd86-4607-95d1-490fa9573829"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              input_text  \\\n",
              "21158  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/ASSETS/...   \n",
              "21155  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/ASSETS/...   \n",
              "21160  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/COLLATE...   \n",
              "21157  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/COLLATE...   \n",
              "11725  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "...                                                  ...   \n",
              "20898  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "15790  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "14518  DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "91     DOCUMENT/DEAL_SETS/DEAL_SET/ACH/ACHPendingDraf...   \n",
              "8047   DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...   \n",
              "\n",
              "                                               sentence2       sentence1  \\\n",
              "21158  An unparsed legal description of a parcel of r...   property name   \n",
              "21155  An unparsed legal description of a parcel of r...   property name   \n",
              "21160  An unparsed legal description of a parcel of r...   property name   \n",
              "21157  An unparsed legal description of a parcel of r...   property name   \n",
              "11725  The monetary amount of a charge an adjustment ...   property name   \n",
              "...                                                  ...             ...   \n",
              "20898  The number of months the trail plan was extend...   property name   \n",
              "15790  The dollar amount of principal reduction as re...   property name   \n",
              "14518  How often described as number of times per yea...   property name   \n",
              "91     Identifies the date on which automated draftin...   property name   \n",
              "8047   The estimated dollar amount of the insurance p...   property name   \n",
              "\n",
              "       SBERT CrossEncoder_Score  \n",
              "21158                  0.724039  \n",
              "21155                  0.715632  \n",
              "21160                  0.701064  \n",
              "21157                  0.699485  \n",
              "11725                  0.698021  \n",
              "...                         ...  \n",
              "20898                  0.051092  \n",
              "15790                  0.050820  \n",
              "14518                  0.049390  \n",
              "91                     0.043005  \n",
              "8047                   0.037858  \n",
              "\n",
              "[21787 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-99462567-ff3a-4dc2-a13c-39b1f21603a3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>input_text</th>\n",
              "      <th>sentence2</th>\n",
              "      <th>sentence1</th>\n",
              "      <th>SBERT CrossEncoder_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>21158</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/ASSETS/...</td>\n",
              "      <td>An unparsed legal description of a parcel of r...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.724039</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21155</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/ASSETS/...</td>\n",
              "      <td>An unparsed legal description of a parcel of r...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.715632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21160</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/COLLATE...</td>\n",
              "      <td>An unparsed legal description of a parcel of r...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.701064</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21157</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/COLLATE...</td>\n",
              "      <td>An unparsed legal description of a parcel of r...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.699485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11725</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...</td>\n",
              "      <td>The monetary amount of a charge an adjustment ...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.698021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20898</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...</td>\n",
              "      <td>The number of months the trail plan was extend...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.051092</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15790</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...</td>\n",
              "      <td>The dollar amount of principal reduction as re...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.050820</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14518</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...</td>\n",
              "      <td>How often described as number of times per yea...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.049390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>91</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/ACH/ACHPendingDraf...</td>\n",
              "      <td>Identifies the date on which automated draftin...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.043005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8047</th>\n",
              "      <td>DOCUMENT/DEAL_SETS/DEAL_SET/DEALS/DEAL/LOANS/L...</td>\n",
              "      <td>The estimated dollar amount of the insurance p...</td>\n",
              "      <td>property name</td>\n",
              "      <td>0.037858</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>21787 rows × 4 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99462567-ff3a-4dc2-a13c-39b1f21603a3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-99462567-ff3a-4dc2-a13c-39b1f21603a3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-99462567-ff3a-4dc2-a13c-39b1f21603a3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download"
      ],
      "metadata": {
        "id": "x7t0aY4u98ZA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "\n",
        "filename = 'XpathFinder1.sav'\n",
        "pickle.dump(XpathFinder, open(filename, 'wb'))"
      ],
      "metadata": {
        "id": "xlGAQtYw5mDL"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# App"
      ],
      "metadata": {
        "id": "HnvB6a2t-AwV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile app.py\n",
        "import io\n",
        "import netrc\n",
        "import pickle\n",
        "import sys\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import streamlit as st\n",
        "# let's import sentence transformer\n",
        "import sentence_transformers\n",
        "import torch\n",
        "#######################################\n",
        "\n",
        "st.markdown(\n",
        "    f\"\"\"\n",
        "<style>\n",
        "    .reportview-container .main .block-container{{\n",
        "        max-width: 90%;\n",
        "        padding-top: 5rem;\n",
        "        padding-right: 5rem;\n",
        "        padding-left: 5rem;\n",
        "        padding-bottom: 5rem;\n",
        "    }}\n",
        "    img{{\n",
        "    \tmax-width:40%;\n",
        "    \tmargin-bottom:40px;\n",
        "    }}\n",
        "</style>\n",
        "\"\"\",\n",
        "    unsafe_allow_html=True,\n",
        ")\n",
        "\n",
        "# # let's load the saved model\n",
        "loaded_model = pickle.load(open('XpathFinder1.sav', 'rb'))\n",
        "#loaded_model = pickle.load('XpathFinder1.sav', map_location='cpu')\n",
        "\n",
        "\n",
        "#class CPU_Unpickler(pickle.Unpickler):\n",
        "#    def find_class(self, module, name):\n",
        "#        if module == 'torch.storage' and name == '_load_from_bytes':\n",
        "#            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n",
        "#        else:\n",
        "#            return super().find_class(module, name)\n",
        "#\n",
        "\n",
        "#loaded_model = CPU_Unpickler(open('XpathFinder1.sav', 'rb')).load()\n",
        "\n",
        "\n",
        "# Containers\n",
        "header_container = st.container()\n",
        "mod_container = st.container()\n",
        "\n",
        "# Header\n",
        "with header_container:\n",
        "\n",
        "    # different levels of text you can include in your app\n",
        "    st.title(\"Xpath Finder App\")\n",
        "\n",
        "\n",
        "# model container\n",
        "with mod_container:\n",
        "    # collecting input from user\n",
        "    prompt = st.text_input(\"Enter your description below ...\")\n",
        "\n",
        "    # Loading e data\n",
        "    data = (pd.read_csv(\"/content/SBERT_data.csv\")).drop(['Unnamed: 0'], axis = 1)\n",
        "\n",
        "    data['prompt']= prompt\n",
        "    data.rename(columns = {'target_text':'sentence2', 'prompt':'sentence1'}, inplace = True)\n",
        "    data['sentence2'] = data['sentence2'].astype('str')\n",
        "    data['sentence1']  = data['sentence1'].astype('str')\n",
        "\n",
        "    # let's pass the input to the loaded_model with torch compiled with cuda\n",
        "    if prompt:\n",
        "        # let's get the result\n",
        "        simscore = XpathFinder.predict([prompt])\n",
        "        from sentence_transformers import CrossEncoder\n",
        "        XpathFinder = CrossEncoder(\"cross-encoder/stsb-roberta-base\")\n",
        "        sentence_pairs = []\n",
        "        for sentence1, sentence2 in zip(data['sentence1'],data['sentence2']):\n",
        "          sentence_pairs.append([sentence1, sentence2])\n",
        "        \n",
        "        # sorting the df to get highest scoring xpath_container\n",
        "        data['SBERT CrossEncoder_Score'] = XpathFinder.predict(sentence_pairs)\n",
        "        most_acc = data.head(5)\n",
        "        # predictions\n",
        "        st.write(\"Highest Similarity score: \", simscore)\n",
        "        st.text(\"Is this one of these the Xpath you're looking for?\")\n",
        "        st.write(st.write(most_acc[\"input_text\"])) \n",
        "        "
      ],
      "metadata": {
        "id": "3CG67-aKnV2h",
        "outputId": "b708f116-b27f-4d0b-be1c-7d088c114815",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting app.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# from pyngrok import ngrok"
      ],
      "metadata": {
        "id": "TZlx9eRhmlHT"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ngrok.set_auth_token(\"29Mzs7BHkeeRGNZM41x0Rn4Xilq_7TYKeCLdR34nSS2qBCTzo\")"
      ],
      "metadata": {
        "id": "4-KM1-vgoPd_"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# !nohup streamlit run app.py --server.port 80 &\n",
        "# url = ngrok.connect(port = '80')\n",
        "# print(url)"
      ],
      "metadata": {
        "id": "wfmoyRmvonA7",
        "outputId": "b9622b4b-c8b2-469b-e686-93b2050608f3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        }
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nohup: appending output to 'nohup.out'\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pyngrok.process.ngrok:t=2022-09-15T21:52:17+0000 lvl=warn msg=\"failed to start tunnel\" pg=/api/tunnels id=5d3ef9186f18c63b err=\"Your account may not run more than 3 tunnels over a single ngrok agent session.\\nThe tunnels already running on this session are:\\ntn_2Ep3XRfLcUvTL481NFeFsAUHB6d, tn_2EozgPpAI8lfcpb4ILGz4PCNpAj, tn_2EozgSgZX3yVEhJrouYMpprEYph\\n\\r\\n\\r\\nERR_NGROK_324\\r\\n\"\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "PyngrokNgrokHTTPError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyngrok/ngrok.py\u001b[0m in \u001b[0;36mapi_request\u001b[0;34m(url, method, data, params, timeout)\u001b[0m\n\u001b[1;32m    439\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murlopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    441\u001b[0m         \u001b[0mresponse_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36murlopen\u001b[0;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0mopener\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mopen\u001b[0;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 531\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    532\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[0;34m(self, request, response)\u001b[0m\n\u001b[1;32m    640\u001b[0m             response = self.parent.error(\n\u001b[0;32m--> 641\u001b[0;31m                 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[1;32m    642\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36merror\u001b[0;34m(self, proto, *args)\u001b[0m\n\u001b[1;32m    568\u001b[0m             \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'default'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'http_error_default'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 569\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[0;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[1;32m    502\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 503\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.7/urllib/request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[0;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[1;32m    648\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 649\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    650\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mHTTPError\u001b[0m: HTTP Error 502: Bad Gateway",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mPyngrokNgrokHTTPError\u001b[0m                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-d00aef0b9c7b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msystem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'nohup streamlit run app.py --server.port 80 &'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mngrok\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'80'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyngrok/ngrok.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(addr, proto, name, pyngrok_config, **options)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    255\u001b[0m     tunnel = NgrokTunnel(api_request(\"{}/api/tunnels\".format(api_url), method=\"POST\", data=options,\n\u001b[0;32m--> 256\u001b[0;31m                                      timeout=pyngrok_config.request_timeout),\n\u001b[0m\u001b[1;32m    257\u001b[0m                          pyngrok_config, api_url)\n\u001b[1;32m    258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pyngrok/ngrok.py\u001b[0m in \u001b[0;36mapi_request\u001b[0;34m(url, method, data, params, timeout)\u001b[0m\n\u001b[1;32m    461\u001b[0m         raise PyngrokNgrokHTTPError(\"ngrok client exception, API returned {}: {}\".format(status_code, response_data),\n\u001b[1;32m    462\u001b[0m                                     \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 463\u001b[0;31m                                     status_code, e.msg, e.hdrs, response_data)\n\u001b[0m\u001b[1;32m    464\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mURLError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    465\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mPyngrokNgrokURLError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ngrok client exception, URLError: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreason\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mPyngrokNgrokHTTPError\u001b[0m: ngrok client exception, API returned 502: {\"error_code\":103,\"status_code\":502,\"msg\":\"failed to start tunnel\",\"details\":{\"err\":\"Your account may not run more than 3 tunnels over a single ngrok agent session.\\nThe tunnels already running on this session are:\\ntn_2Ep3XRfLcUvTL481NFeFsAUHB6d, tn_2EozgPpAI8lfcpb4ILGz4PCNpAj, tn_2EozgSgZX3yVEhJrouYMpprEYph\\n\\r\\n\\r\\nERR_NGROK_324\\r\\n\"}}\n"
          ]
        }
      ]
    }
  ]
}