chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,97 @@
|
||||
<!DOCTYPE html>
|
||||
<html>
|
||||
<header>
|
||||
<title>Cuisine Matcher</title>
|
||||
</header>
|
||||
<body>
|
||||
<h1>Check your refrigerator. What can you create?</h1>
|
||||
<div id="wrapper">
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="4" class="checkbox">
|
||||
<label>apple</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="247" class="checkbox">
|
||||
<label>pear</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="77" class="checkbox">
|
||||
<label>cherry</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="126" class="checkbox">
|
||||
<label>fenugreek</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="302" class="checkbox">
|
||||
<label>sake</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="327" class="checkbox">
|
||||
<label>soy sauce</label>
|
||||
</div>
|
||||
|
||||
<div class="boxCont">
|
||||
<input type="checkbox" value="112" class="checkbox">
|
||||
<label>cumin</label>
|
||||
</div>
|
||||
</div>
|
||||
<div style="padding-top:10px">
|
||||
<button onClick="startInference()">What kind of cuisine can you make?</button>
|
||||
</div>
|
||||
<!-- import ONNXRuntime Web from CDN -->
|
||||
<script src="https://cdn.jsdelivr.net/npm/onnxruntime-web@1.9.0/dist/ort.min.js"></script>
|
||||
<script>
|
||||
const ingredients = Array(380).fill(0);
|
||||
|
||||
const checks = [...document.querySelectorAll('.checkbox')];
|
||||
|
||||
checks.forEach(check => {
|
||||
check.addEventListener('change', function() {
|
||||
// toggle the state of the ingredient
|
||||
// based on the checkbox's value (1 or 0)
|
||||
ingredients[check.value] = check.checked ? 1 : 0;
|
||||
});
|
||||
});
|
||||
|
||||
function testCheckboxes() {
|
||||
// validate if at least one checkbox is checked
|
||||
return checks.some(check => check.checked);
|
||||
}
|
||||
|
||||
async function startInference() {
|
||||
|
||||
let atLeastOneChecked = testCheckboxes()
|
||||
|
||||
if (!atLeastOneChecked) {
|
||||
alert('Please select at least one ingredient.');
|
||||
return;
|
||||
}
|
||||
try {
|
||||
// create a new session and load the model.
|
||||
|
||||
const session = await ort.InferenceSession.create('./model.onnx');
|
||||
|
||||
const input = new ort.Tensor(new Float32Array(ingredients), [1, 380]);
|
||||
const feeds = { float_input: input };
|
||||
|
||||
// feed inputs and run
|
||||
const results = await session.run(feeds);
|
||||
|
||||
// read from results
|
||||
alert('You can enjoy ' + results.label.data[0] + ' cuisine today!')
|
||||
|
||||
} catch (e) {
|
||||
console.log(`failed to inference ONNX model`);
|
||||
console.error(e);
|
||||
}
|
||||
}
|
||||
|
||||
</script>
|
||||
</body>
|
||||
</html>
|
||||
Binary file not shown.
@@ -0,0 +1,279 @@
|
||||
{
|
||||
"metadata": {
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.0"
|
||||
},
|
||||
"orig_nbformat": 2,
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.0 64-bit ('3.7')"
|
||||
},
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
|
||||
}
|
||||
},
|
||||
"interpreter": {
|
||||
"hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2,
|
||||
"cells": [
|
||||
{
|
||||
"source": [
|
||||
"# Build a cuisine recommender"
|
||||
],
|
||||
"cell_type": "markdown",
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"Requirement already satisfied: skl2onnx in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (1.8.0)\n",
|
||||
"Requirement already satisfied: protobuf in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (3.8.0)\n",
|
||||
"Requirement already satisfied: numpy>=1.15 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.19.2)\n",
|
||||
"Requirement already satisfied: onnx>=1.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.9.0)\n",
|
||||
"Requirement already satisfied: six in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from skl2onnx) (1.12.0)\n",
|
||||
"Requirement already satisfied: onnxconverter-common<1.9,>=1.6.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.8.1)\n",
|
||||
"Requirement already satisfied: scikit-learn>=0.19 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (0.24.2)\n",
|
||||
"Requirement already satisfied: scipy>=1.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from skl2onnx) (1.4.1)\n",
|
||||
"Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from protobuf->skl2onnx) (45.1.0)\n",
|
||||
"Requirement already satisfied: typing-extensions>=3.6.2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from onnx>=1.2.1->skl2onnx) (3.10.0.0)\n",
|
||||
"Requirement already satisfied: threadpoolctl>=2.0.0 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (2.1.0)\n",
|
||||
"Requirement already satisfied: joblib>=0.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from scikit-learn>=0.19->skl2onnx) (0.16.0)\n",
|
||||
"\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.1.2 is available.\n",
|
||||
"You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
|
||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"!pip install skl2onnx"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 59,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd \n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
|
||||
"0 0 indian 0 0 0 0 0 \n",
|
||||
"1 1 indian 1 0 0 0 0 \n",
|
||||
"2 2 indian 0 0 0 0 0 \n",
|
||||
"3 3 indian 0 0 0 0 0 \n",
|
||||
"4 4 indian 0 0 0 0 0 \n",
|
||||
"\n",
|
||||
" apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
|
||||
"0 0 0 0 ... 0 0 0 \n",
|
||||
"1 0 0 0 ... 0 0 0 \n",
|
||||
"2 0 0 0 ... 0 0 0 \n",
|
||||
"3 0 0 0 ... 0 0 0 \n",
|
||||
"4 0 0 0 ... 0 0 0 \n",
|
||||
"\n",
|
||||
" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
|
||||
"0 0 0 0 0 0 0 0 \n",
|
||||
"1 0 0 0 0 0 0 0 \n",
|
||||
"2 0 0 0 0 0 0 0 \n",
|
||||
"3 0 0 0 0 0 0 0 \n",
|
||||
"4 0 0 0 0 0 1 0 \n",
|
||||
"\n",
|
||||
"[5 rows x 382 columns]"
|
||||
],
|
||||
"text/html": "<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>Unnamed: 0</th>\n <th>cuisine</th>\n <th>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>indian</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>4</td>\n <td>indian</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 382 columns</p>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 60
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data = pd.read_csv('../../data/cleaned_cuisines.csv')\n",
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
|
||||
"0 0 0 0 0 0 0 0 \n",
|
||||
"1 1 0 0 0 0 0 0 \n",
|
||||
"2 0 0 0 0 0 0 0 \n",
|
||||
"3 0 0 0 0 0 0 0 \n",
|
||||
"4 0 0 0 0 0 0 0 \n",
|
||||
"\n",
|
||||
" armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n",
|
||||
"0 0 0 0 ... 0 0 0 \n",
|
||||
"1 0 0 0 ... 0 0 0 \n",
|
||||
"2 0 0 0 ... 0 0 0 \n",
|
||||
"3 0 0 0 ... 0 0 0 \n",
|
||||
"4 0 0 0 ... 0 0 0 \n",
|
||||
"\n",
|
||||
" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
|
||||
"0 0 0 0 0 0 0 0 \n",
|
||||
"1 0 0 0 0 0 0 0 \n",
|
||||
"2 0 0 0 0 0 0 0 \n",
|
||||
"3 0 0 0 0 0 0 0 \n",
|
||||
"4 0 0 0 0 0 1 0 \n",
|
||||
"\n",
|
||||
"[5 rows x 380 columns]"
|
||||
],
|
||||
"text/html": "<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>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>artemisia</th>\n <th>artichoke</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 61
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"X = data.iloc[:,2:]\n",
|
||||
"X.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" cuisine\n",
|
||||
"0 indian\n",
|
||||
"1 indian\n",
|
||||
"2 indian\n",
|
||||
"3 indian\n",
|
||||
"4 indian"
|
||||
],
|
||||
"text/html": "<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>cuisine</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>indian</td>\n </tr>\n <tr>\n <th>1</th>\n <td>indian</td>\n </tr>\n <tr>\n <th>2</th>\n <td>indian</td>\n </tr>\n <tr>\n <th>3</th>\n <td>indian</td>\n </tr>\n <tr>\n <th>4</th>\n <td>indian</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 62
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"y = data[['cuisine']]\n",
|
||||
"y.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.svm import SVC\n",
|
||||
"from sklearn.model_selection import cross_val_score\n",
|
||||
"from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"SVC(C=10, kernel='linear', probability=True, random_state=0)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 65
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"model = SVC(kernel='linear', C=10, probability=True,random_state=0)\n",
|
||||
"model.fit(X_train,y_train.values.ravel())\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_pred = model.predict(X_test)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
" precision recall f1-score support\n\n chinese 0.72 0.70 0.71 236\n indian 0.91 0.88 0.89 243\n japanese 0.80 0.75 0.77 240\n korean 0.80 0.81 0.81 230\n thai 0.76 0.85 0.80 250\n\n accuracy 0.80 1199\n macro avg 0.80 0.80 0.80 1199\nweighted avg 0.80 0.80 0.80 1199\n\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(classification_report(y_test,y_pred))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 68,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from skl2onnx import convert_sklearn\n",
|
||||
"from skl2onnx.common.data_types import FloatTensorType\n",
|
||||
"\n",
|
||||
"initial_type = [('float_input', FloatTensorType([None, 380]))]\n",
|
||||
"options = {id(model): {'nocl': True, 'zipmap': False}}\n",
|
||||
"onx = convert_sklearn(model, initial_types=initial_type, options=options)\n",
|
||||
"with open(\"./model.onnx\", \"wb\") as f:\n",
|
||||
" f.write(onx.SerializeToString())\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
Reference in New Issue
Block a user