jwlin’s gists

jwlin / function-app-deploy.tf

Created January 20, 2025 17:19

Minimum working Terraform example to create Azure Function app with a secure storage

	provider "azurerm" {
	features {}
	subscription_id = "<your-subscription-id>"
	}

	data "azurerm_resource_group" "your_resource_group" {
	name = "<your-resource-group-name>"
	}

	data "azurerm_virtual_network" "your_vnet" {

jwlin / arm-funtion-app-deploy-no-appinsight.json

Created January 20, 2025 17:16

ARM template creating a linux function app with secure storage - less resources created

	{
	"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
	"contentVersion": "1.0.0.0",
	"parameters": {
	"functionAppName": {
	"defaultValue": "",
	"type": "String",
	"metadata": {
	"description": "The name of the Azure Function app."
	}

jwlin / arm-funtion-app-deploy.json

Created January 20, 2025 17:14

ARM template creating a linux function app with secure storage

	{
	"$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#",
	"contentVersion": "1.0.0.0",
	"parameters": {
	"functionAppName": {
	"defaultValue": "YourFunctionAppName",
	"type": "String",
	"metadata": {
	"description": "The name of the Azure Function app."
	}

jwlin / views.py

Last active April 25, 2020 08:45

	from django.http import HttpResponse
	from django.shortcuts import get_object_or_404, render
	from .models import Account, Client

	def show(request):
	uname = request.POST["uname"]
	if "injection" in request.POST:
	# Use raw SQL and string concatenation, resulting in SQL injection
	accounts = Account.objects.raw(f'SELECT * FROM atm_account as a, atm_client as c WHERE a.client_id=c.id and c.username="{uname}"')
	else:

jwlin / extract_features.py

Last active October 20, 2018 00:44

	#!/usr/bin/python
	# -- coding: utf-8 --

	"""
	Extract features from htmls
	"""

	import sys, os, random, datetime
	from bs4 import BeautifulSoup
	# preprocess.py 檔案在這裡

jwlin / getTopic.py

Last active October 10, 2018 00:07

	def getTopic(self, ai, imgtopic, Dict):
	# JW:　features 是準備要被預測 topic 的變數, 應該要挪到下方 prediction 開始前, 程式比較連貫
	#extrat the features of the element
	features = str(re.sub(' +', ' ', ' '.join(self.extract_features(ai, imgtopic, Dict, 1))))
	#print (features)

	# JW:　從這裡開始可以獨立切一個 function, 只呼叫一次, 把 train 好的 model 存起來
	#open training data file
	current_dir = os.path.dirname(_file_)
	corpus_dir = os.path.join(current_dir, 'corpus', 'all-corpus')

jwlin / cs295-first-look.ipynb

Created February 14, 2018 00:40

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jwlin / blog_hahow_crawler_3.py

Created September 5, 2017 08:34

	with open('hahow_courses.json', 'r', encoding='utf-8') as f:
	courses = json.load(f)

	# 取出程式類課程的募資價/上線價/學生數，並顯示統計資料
	pre_order_prices = list()
	prices = list()
	tickets = list()
	lengths = list()
	for c in courses:
	if '55de81ac9d1fa51000f94770' in c['categories']:

jwlin / blog_hahow_crawler_2.json

Last active September 5, 2017 08:37

	{
	"_id": "58744feda8aae907000d06c0",
	"categories": [
	"55de81ac9d1fa51000f94770",
	"55de81929d1fa51000f94769"
	],
	"coverImage": {
	"_id": "588421e46ecf3a0700b7a31d",
	"url": "https://hahow.in/images/588421e46ecf3a0700b7a31d"
	},

jwlin / blog_hahow_crawler_1.py

Created September 5, 2017 08:31

	def crawl():
	# 初始 API: https://api.hahow.in/api/courses?limit=12&status=PUBLISHED
	# 接續 API: https://api.hahow.in/api/courses?latestId=54d5a117065a7e0e00725ac0&latestValue=2015-03-27T15:38:27.187Z&limit=30&status=PUBLISHED
	headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
	'AppleWebKit/537.36 (KHTML, like Gecko) '
	'Chrome/59.0.3071.115 Safari/537.36'}
	url = 'https://api.hahow.in/api/courses'
	courses = list()
	resp_courses = requests.get(url + '?limit=30&status=PUBLISHED', headers=headers).json()
	while resp_courses: # 有回傳資料則繼續下一輪擷取

Jun-Wei Lin jwlin