Periklis Papanikolaou admariner

Grouping results and removing unwanted ones

Here we want to scrape product name, price and rating from ebay product pages:

url = 'https://www.ebay.com/itm/Sony-PlayStation-4-PS4-Pro-1TB-4K-Console-Black/203084236670' 

wanted_list = ['Sony PlayStation 4 PS4 Pro 1TB 4K Console - Black', 'US $349.99', '4.8'] 

scraper.build(url, wanted_list)

	// Google Apps Script for one-way data sync from a table in a Google Doc to Coda table from multiple Google Docs in a Google Drive folder
	// Author: Al Chen ([email protected])
	// Last Updated: August 6th, 2020
	// Notes: Assumes you are using the V8 runtime (https://developers.google.com/apps-script/guides/v8-runtime)
	// Coda's library for Google Apps Script: 15IQuWOk8MqT50FDWomh57UqWGH23gjsWVWYFms3ton6L-UHmefYHS9Vl

	//////////////// Setup and global variables ////////////////////////////////

	CodaAPI.authenticate('YOUR_API_KEY')

	new MutationObserver(() => {
	let filtered = Array.prototype.filter.call(
	document.querySelectorAll(".roam-body a"),
	(a) => {
	return a.hostname && !a.hostname.includes("roamresearch.com");
	}
	);
	Array.prototype.forEach.call(filtered, (a) => {
	if (a.text == "*") {
	a.style.background = `url(https://www.google.com/s2/favicons?sz=16&domain=${a.hostname}) right center no-repeat`;

	#!/usr/bin/env bash
	###
	# NB: You probably don't want this gist any more.
	# Instead, use this version from `fastsetup`:
	# https://github.com/fastai/fastsetup/blob/master/setup-conda.sh
	###

	set -e

	cd

	#################################################################################
	# Title: Create Daily Journal Entry in Devonthink, with Daily Events, Quote of the Day, World & Local News and Weather Condtion
	#################################################################################

	# Iain Dunn
	# Logic2design.com
	# [email protected]

	# Contributors, inspration and sources
	# Based on script by Chuck Lane October 2, 2013

	yaml_config="""
	input_features:
	-
	name: Original_Title
	type: text
	level: word
	encoder: t5
	reduce_output: null
	-
	name: Keyword

	import streamlit as st
	from ludwig.api import LudwigModel
	import pandas as pd

	st.cache(show_spinner=False)
	def load_model():
	#Update with the path to the Ludwig trained model
	model = LudwigModel.load("results/experiment_run_1/model/")
	return model

	import torch
	import streamlit as st
	from transformers import BartTokenizer, BartForConditionalGeneration
	from transformers import T5Tokenizer, T5ForConditionalGeneration

	st.title('Text Summarization Demo')
	st.markdown('Using BART and T5 transformer model')

	model = st.selectbox('Select the model', ('BART', 'T5'))

	id,Title,Year,Department,Description,Directory
	A01-lantel-OEM,Lantel at the Boston Office of Emergency Management,2019,Boston Office of Emergency Management,"Since 2003, the City of Boston and surrounding cities and towns have been designated as an “Urban Areas Security Initiative” (UASI) region by the federal Department of Homeland Security. This UASI designation enables the City and the larger Metro Boston Homeland Security Region (MBHSR) to apply for and obtain federal grants for information sharing, surveillance, emergency preparedness, police and first responder communications, and other technologies and staffing.

	The cities and towns included in the MBHSR are Boston, Brookline, Cambridge, Chelsea, Everett, Quincy, Revere, Somerville and Winthrop. Since 2003, the region has received hundreds of millions of dollars through the UASI grant program. These funds are distributed through the Office of Emergency Management in Boston, which also coordinates regional planning meetings, technology acquisitions and

	{
	"final_space": true,
	"console_title": true,
	"console_title_style": "folder",
	"blocks": [
	{
	"type": "prompt",
	"alignment": "left",
	"horizontal_offset": 0,
	"vertical_offset": 0,