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{
"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"
],
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" <th>Mg</th>\n",
" <th>Al</th>\n",
" <th>Si</th>\n",
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" <th>55</th>\n",
" <td>1.51769</td>\n",
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" .colab-df-container {\n",
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" background-color: #E8F0FE;\n",
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"\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",
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" 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",
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"</div>\n",
"\n",
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]
},
"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=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),\n",
" ('model', 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=[('imputer', SimpleImputer()), ('scaler', StandardScaler()),\n",
" ('model', 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": [
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]
}
]
},
{
"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"
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