Upload kaggle_data_and_huggingface.ipynb

kaggle_data_and_huggingface.ipynb

@@ -0,0 +1,813 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "https://www.kdnuggets.com/deploying-your-first-machine-learning-model"
+      ],
+      "metadata": {
+        "id": "MP7O1gtliL6n"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "try:\n",
+        "    import opendatasets as od\n",
+        "    import pandas as pd\n",
+        "except:\n",
+        "    !pip install opendatasets\n",
+        "    import opendatasets as od\n",
+        "from os import path\n",
+        "\n",
+        "url = \"https://www.kaggle.com/datasets/uciml/glass\"  ### kaggle dataset url here\n",
+        "data_dir = \"/content/\"  ### directory where you want to save data\n",
+        "\n",
+        "# Go to the account tab and under API section, click Create New API Token.\n",
+        "\n",
+        "# A JSON file will be downloaded, open it locally or you can also use any online JSON viewer and upload it there.\n",
+        "\n",
+        "# On opening this file, you will find the username and key in it. Copy the username and password and paste it into the prompted Notebook cell.\n",
+        "# The content of the downloaded file would look like this.\n",
+        "\n",
+        "#               {\"username\":<KAGGLE USERNAME>,\"key\":<KAGGLE KEY>}\n",
+        "\n",
+        "\n",
+        "def download_data(url, data_dir):\n",
+        "    od.download(url, data_dir)"
+      ],
+      "metadata": {
+        "id": "5ewudtMkfnPL"
+      },
+      "execution_count": 4,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#  comment out below if you already have the data downloaded\n",
+        "# download_data(url, data_dir)"
+      ],
+      "metadata": {
+        "id": "y-gTjPFggtAM"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 143
+        },
+        "id": "lIYdn1woOS1n",
+        "outputId": "405db65f-b99a-4643-b8b0-2e06bcf6ea53"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          RI     Na    Mg    Al     Si     K     Ca   Ba    Fe  Type\n",
+              "55   1.51769  12.45  2.71  1.29  73.70  0.56   9.06  0.0  0.24     1\n",
+              "184  1.51115  17.38  0.00  0.34  75.41  0.00   6.65  0.0  0.00     6\n",
+              "103  1.52725  13.80  3.15  0.66  70.57  0.08  11.64  0.0  0.00     2"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b2950a69-76d4-46ec-8a3d-96971bd2b1f1\" 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>RI</th>\n",
+              "      <th>Na</th>\n",
+              "      <th>Mg</th>\n",
+              "      <th>Al</th>\n",
+              "      <th>Si</th>\n",
+              "      <th>K</th>\n",
+              "      <th>Ca</th>\n",
+              "      <th>Ba</th>\n",
+              "      <th>Fe</th>\n",
+              "      <th>Type</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>55</th>\n",
+              "      <td>1.51769</td>\n",
+              "      <td>12.45</td>\n",
+              "      <td>2.71</td>\n",
+              "      <td>1.29</td>\n",
+              "      <td>73.70</td>\n",
+              "      <td>0.56</td>\n",
+              "      <td>9.06</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.24</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>184</th>\n",
+              "      <td>1.51115</td>\n",
+              "      <td>17.38</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>0.34</td>\n",
+              "      <td>75.41</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>6.65</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>6</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>103</th>\n",
+              "      <td>1.52725</td>\n",
+              "      <td>13.80</td>\n",
+              "      <td>3.15</td>\n",
+              "      <td>0.66</td>\n",
+              "      <td>70.57</td>\n",
+              "      <td>0.08</td>\n",
+              "      <td>11.64</td>\n",
+              "      <td>0.0</td>\n",
+              "      <td>0.00</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b2950a69-76d4-46ec-8a3d-96971bd2b1f1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\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",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\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-b2950a69-76d4-46ec-8a3d-96971bd2b1f1 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-b2950a69-76d4-46ec-8a3d-96971bd2b1f1');\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",
+              "\n",
+              "\n",
+              "<div id=\"df-c39206fc-c582-432b-bf27-e108ba1cc6c6\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c39206fc-c582-432b-bf27-e108ba1cc6c6')\"\n",
+              "            title=\"Suggest charts\"\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",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-c39206fc-c582-432b-bf27-e108ba1cc6c6 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "#  use path below for colab\n",
+        "# glass_df = pd.read_csv(\"/content/glass/glass.csv\")\n",
+        "glass_df = pd.read_csv(\"glass.csv\")\n",
+        "\n",
+        "glass_df = glass_df.sample(frac = 1)\n",
+        "glass_df.head(3)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X = glass_df.drop(\"Type\",axis=1)\n",
+        "y = glass_df.Type\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=125)"
+      ],
+      "metadata": {
+        "id": "7_eWUKS6hV2o"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "\n",
+        "\n",
+        "pipe = Pipeline(\n",
+        "    steps=[\n",
+        "        (\"imputer\", SimpleImputer()),\n",
+        "        (\"scaler\", StandardScaler()),\n",
+        "        (\"model\", RandomForestClassifier(n_estimators=100, random_state=125)),\n",
+        "    ]\n",
+        ")\n",
+        "pipe.fit(X_train, y_train)\n",
+        "\n",
+        "pipe.score(X_test, y_test)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MTMLGHGuhvAA",
+        "outputId": "d4c7a6b6-6774-47d7-d288-2d1a29dbd9c5"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "0.7846153846153846"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.metrics import classification_report\n",
+        "\n",
+        "y_pred = pipe.predict(X_test)\n",
+        "print(classification_report(y_test,y_pred))"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "EREHPUy_h0Zq",
+        "outputId": "2a4255fb-c2b4-4fc8-cec8-f07bd619cbe0"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           1       0.70      0.91      0.79        23\n",
+            "           2       0.87      0.80      0.83        25\n",
+            "           3       1.00      0.33      0.50         6\n",
+            "           5       0.67      1.00      0.80         2\n",
+            "           6       1.00      1.00      1.00         2\n",
+            "           7       0.80      0.57      0.67         7\n",
+            "\n",
+            "    accuracy                           0.78        65\n",
+            "   macro avg       0.84      0.77      0.77        65\n",
+            "weighted avg       0.81      0.78      0.77        65\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install skops"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "56jjXsBxiAiB",
+        "outputId": "27f71a89-8eec-4e8a-b23b-f3f1f7329cbe"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting skops\n",
+            "  Downloading skops-0.9.0-py3-none-any.whl (120 kB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m120.7/120.7 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.10/dist-packages (from skops) (1.2.2)\n",
+            "Requirement already satisfied: huggingface-hub>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from skops) (0.19.4)\n",
+            "Requirement already satisfied: tabulate>=0.8.8 in /usr/local/lib/python3.10/dist-packages (from skops) (0.9.0)\n",
+            "Requirement already satisfied: packaging>=17.0 in /usr/local/lib/python3.10/dist-packages (from skops) (23.2)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (3.13.1)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (2023.6.0)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (4.66.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (6.0.1)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->skops) (4.5.0)\n",
+            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.23.5)\n",
+            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.11.4)\n",
+            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (1.3.2)\n",
+            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.24->skops) (3.2.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->skops) (2023.11.17)\n",
+            "Installing collected packages: skops\n",
+            "Successfully installed skops-0.9.0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import skops.io as sio\n",
+        "sio.dump(pipe, \"glass_pipeline.skops\")"
+      ],
+      "metadata": {
+        "id": "wZARmF26h4S9"
+      },
+      "execution_count": 9,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sio.load(\"glass_pipeline.skops\", trusted=True)\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 161
+        },
+        "id": "DQ1zj-mjiIRL",
+        "outputId": "b93c6edf-c16f-403c-ef69-38970b7c2b4f"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Pipeline(steps=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),\n",
+              "                ('model', RandomForestClassifier(random_state=125))])"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;model&#x27;, RandomForestClassifier(random_state=125))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer()), (&#x27;scaler&#x27;, StandardScaler()),\n",
+              "                (&#x27;model&#x27;, RandomForestClassifier(random_state=125))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SimpleImputer</label><div class=\"sk-toggleable__content\"><pre>SimpleImputer()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=125)</pre></div></div></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 10
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install gradio"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "beFfVpBQiWMo",
+        "outputId": "13434ca2-9b7e-433a-b805-b565805b936b"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gradio in /usr/local/lib/python3.10/dist-packages (4.12.0)\n",
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+            "Requirement already satisfied: fastapi in /usr/local/lib/python3.10/dist-packages (from gradio) (0.108.0)\n",
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+            "Requirement already satisfied: gradio-client==0.8.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.8.0)\n",
+            "Requirement already satisfied: httpx in /usr/local/lib/python3.10/dist-packages (from gradio) (0.26.0)\n",
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+            "Requirement already satisfied: uvicorn>=0.14.0 in /usr/local/lib/python3.10/dist-packages (from gradio) (0.25.0)\n",
+            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.8.0->gradio) (2023.6.0)\n",
+            "Requirement already satisfied: websockets<12.0,>=10.0 in /usr/local/lib/python3.10/dist-packages (from gradio-client==0.8.0->gradio) (11.0.3)\n",
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+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->gradio) (2.31.0)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.19.3->gradio) (4.66.1)\n",
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+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (0.12.1)\n",
+            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (4.46.0)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (1.4.5)\n",
+            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib~=3.0->gradio) (3.1.1)\n",
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+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx->gradio) (2023.11.17)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx->gradio) (1.0.2)\n",
+            "Requirement already satisfied: idna in /usr/local/lib/python3.10/dist-packages (from httpx->gradio) (3.6)\n",
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+            "Requirement already satisfied: attrs>=22.2.0 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (23.1.0)\n",
+            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (2023.11.2)\n",
+            "Requirement already satisfied: referencing>=0.28.4 in /usr/local/lib/python3.10/dist-packages (from jsonschema>=3.0->altair<6.0,>=4.2.0->gradio) (0.32.0)\n",
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+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib~=3.0->gradio) (1.16.0)\n",
+            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-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 /usr/local/lib/python3.10/dist-packages (from rich<14.0.0,>=10.11.0->typer[all]<1.0,>=0.9->gradio) (2.16.1)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio->httpx->gradio) (1.2.0)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (3.3.2)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.19.3->gradio) (2.0.7)\n",
+            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-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"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install --upgrade typing\n",
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hkRt-nm-i7n3",
+        "outputId": "fb8b64cf-1033-4ac3-a37b-6c2b47651645"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: typing in /usr/local/lib/python3.10/dist-packages (3.7.4.3)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import gradio as gr\n",
+        "import skops.io as sio\n",
+        "\n",
+        "pipe = sio.load(\"glass_pipeline.skops\", trusted=True)\n",
+        "\n",
+        "classes = [\n",
+        "    \"None\",\n",
+        "    \"Building Windows Float Processed\",\n",
+        "    \"Building Windows Non Float Processed\",\n",
+        "    \"Vehicle Windows Float Processed\",\n",
+        "    \"Vehicle Windows Non Float Processed\",\n",
+        "    \"Containers\",\n",
+        "    \"Tableware\",\n",
+        "    \"Headlamps\",\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def classifier(RI, Na, Mg, Al, Si, K, Ca, Ba, Fe):\n",
+        "    pred_glass = pipe.predict([[RI, Na, Mg, Al, Si, K, Ca, Ba, Fe]])[0]\n",
+        "    label = f\"Predicted Glass label: **{classes[pred_glass]}**\"\n",
+        "    return label\n",
+        "\n",
+        "\n",
+        "inputs = [\n",
+        "    gr.Slider(1.51, 1.54, step=0.01, label=\"Refractive Index\"),\n",
+        "    gr.Slider(10, 17, step=1, label=\"Sodium\"),\n",
+        "    gr.Slider(0, 4.5, step=0.5, label=\"Magnesium\"),\n",
+        "    gr.Slider(0.3, 3.5, step=0.1, label=\"Aluminum\"),\n",
+        "    gr.Slider(69.8, 75.4, step=0.1, label=\"Silicon\"),\n",
+        "    gr.Slider(0, 6.2, step=0.1, label=\"Potassium\"),\n",
+        "    gr.Slider(5.4, 16.19, step=0.1, label=\"Calcium\"),\n",
+        "    gr.Slider(0, 3, step=0.1, label=\"Barium\"),\n",
+        "    gr.Slider(0, 0.5, step=0.1, label=\"Iron\"),\n",
+        "]\n",
+        "outputs = [gr.Label(num_top_classes=7)]\n",
+        "\n",
+        "title = \"Glass Classification\"\n",
+        "description = \"Enter the details to correctly identify glass type?\"\n",
+        "\n",
+        "gr.Interface(\n",
+        "    fn=classifier,\n",
+        "    inputs=inputs,\n",
+        "    outputs=outputs,\n",
+        "    title=title,\n",
+        "    description=description,\n",
+        ").launch()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "A8KXp_EFiS1U",
+        "outputId": "c021cdbf-b938-4951-f5e7-8bc0988e9d8a"
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Exception in thread Thread-5 (attachment_entry):\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 237, in listen\n",
+            "    sock, _ = endpoints_listener.accept()\n",
+            "  File \"/usr/lib/python3.10/socket.py\", line 293, in accept\n",
+            "    fd, addr = self._accept()\n",
+            "TimeoutError: timed out\n",
+            "\n",
+            "During handling of the above exception, another exception occurred:\n",
+            "\n",
+            "Traceback (most recent call last):\n",
+            "  File \"/usr/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\n",
+            "    self.run()\n",
+            "  File \"/usr/lib/python3.10/threading.py\", line 953, in run\n",
+            "    self._target(*self._args, **self._kwargs)\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/google/colab/_debugpy.py\", line 52, in attachment_entry\n",
+            "    debugpy.listen(_dap_port)\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/debugpy/public_api.py\", line 31, in wrapper\n",
+            "    return wrapped(*args, **kwargs)\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 143, in debug\n",
+            "    log.reraise_exception(\"{0}() failed:\", func.__name__, level=\"info\")\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 141, in debug\n",
+            "    return func(address, settrace_kwargs, **kwargs)\n",
+            "  File \"/usr/local/lib/python3.10/dist-packages/debugpy/server/api.py\", line 251, in listen\n",
+            "    raise RuntimeError(\"timed out waiting for adapter to connect\")\n",
+            "RuntimeError: timed out waiting for adapter to connect\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
+            "\n",
+            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
+            "Running on public URL: https://efa6ecf31e4b5a440c.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"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "<div><iframe src=\"https://efa6ecf31e4b5a440c.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": []
+          },
+          "metadata": {},
+          "execution_count": 1
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}