Jon Perl jperl

Prompt for Migrating a Java/Selenium/RestAssured Framework to Playwright/TypeScript

I have a Java-based automation framework using Selenium for UI testing and RestAssured for API testing. The framework's structure and test cases are contained within a directory named [Source Directory Name] at the root level of this project. Please migrate this entire framework to use Playwright with TypeScript for both UI and API testing. The new framework should be placed in a new directory named [Destination Directory Name] at the root level.

The current framework includes:

UI Tests: Implemented using Selenium, likely with a Page Object Model (POM) or similar design pattern. These tests cover various user interactions with the web application.
API Tests: Implemented using RestAssured, covering the testing of RESTful APIs. These tests verify API functionality, data integrity, and response codes.
Test Framework: Likely using JUnit or TestNG for test execution, reporting, and assertion.

This notes is written by Sheldon. You can find me with #iOSBySheldon in Github, Youtube, Facebook, etc.

Need

Convert .mov/.MP4 to .gif

Reason

As a developer, I feel better to upload a short video when I create the pull request to show other viewers what I did in this PR. I tried .mov format directly got after finishing recording screen using Quicktime, however, gif offers preview in most web pages, and has smaller file size.

This is not limited to developer, anyone has this need can use this method to convert the files.

	-- original code: https://github.com/ericelliott/cuid

	-- Add the "plv8" extension
	create extension if not exists "plv8";

	-- Add the "pgcrypto" extension
	create extension if not exists "pgcrypto";
	\dx

	-- Connect a database

	import keras.backend as K
	import tensorflow as tf
	from tensorflow.keras.layers import Layer

	"""Not tested, I'll play around with GANs soon with it."""
	from tensorflow.keras.layers import Conv2D
	import numpy as np


	class CoordConv2D(Layer):

	import six
	import tensorflow as tf
	from tensorflow.python.framework import ops
	from tensorflow.python.training import training_util
	from tensorflow.python.training.session_run_hook import SessionRunArgs


	class RayTuneReportingHook(tf.train.SessionRunHook):
	def __init__(self, params, reporter):
	self.reporter = reporter

	#!/usr/bin/python

	from PIL import Image
	import math

	test_image_list = '/home/tianwei/Data/sfm_data/test_image_list' # path to the image list
	with open(test_image_list, 'r') as f:
	test_images = f.readlines()
	test_images = map(str.strip, test_images)

	"""An example of how to use tf.Dataset in Keras Model"""
	import tensorflow as tf # only work from tensorflow==1.9.0-rc1 and after

	_EPOCHS = 5
	_NUM_CLASSES = 10
	_BATCH_SIZE = 128

	def training_pipeline():
	# #############
	# Load Dataset

	# Transmit a x264 compressed low latency feed using udp
	server: gst-launch-1.0 videotestsrc is-live=true ! videoconvert ! x264enc speed-preset=ultrafast tune=zerolatency byte-stream=true threads=1 key-int-max=15 intra-refresh=true ! rtph264pay ! udpsink host=%d port=9002
	client: gst-launch-1.0 udpsrc port=9002 caps='application/x-rtp' ! rtph264depay ! avdec_h264 ! videoconvert ! autovideosink sync=false async=false

	"""Script to illustrate usage of tf.estimator.Estimator in TF v1.3"""
	import tensorflow as tf

	from tensorflow.examples.tutorials.mnist import input_data as mnist_data
	from tensorflow.contrib import slim
	from tensorflow.contrib.learn import ModeKeys
	from tensorflow.contrib.learn import learn_runner


	# Show debugging output

	"""
	Clean and simple Keras implementation of network architectures described in:
	- (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
	- (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).

	Python 3.
	"""

	from keras import layers
	from keras import models