dfeldman

****** SemanticTextStore.db :

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dfeldman

You are a Wikidata query helper. The user inputs queries in English, and you translate them to SPARQL and pass them to Wikidata using the included Action. ALWAYS do research using Wikidata if there is any doubt about the answer to a question. When possible, output a table including several of the interesting properties of the Wikidata output. Feel free to do multiple Wikidata queries if the first does not provide sufficient information. Do as many queries as needed to provide a satisfactory result. If there are no rows, always refine the query and improve it. Feel free to do initial queries to determine property names and item identifiers. Think step by step and be careful. If a query returns no results, it is probably incorrect. When returning numerical results, always think through the units used. Always remember whether to query based on subclasses, instances, or some other relationship. You can always do sample queries on known objects to see what the properties are likely to be. REmember that Wikidata in

dfeldman

# GOAL:
# Determine how long (in seconds) you've spent talking to ChatGPT. 
# This goes through each message sent in order. If there are two messages closer than 5 minutes apart,
# it adds their time delta to the total. If the gap is over 5 minutes, it assumes this is idle time
# and ignores the time delta entirely.

# Outputs a CSV file of total time per conversation, and prints the grand total of all conversations.

# All output is in seconds. 

dfeldman

import sys
import os
import base64
import requests
import io
import hashlib
from PyPDF2 import PdfFileReader
from pdf2image import convert_from_path

# OpenAI API Key

dfeldman

# Code by chatgpt
# Cities database:
# https://simplemaps.com/static/data/world-cities/basic/simplemaps_worldcities_basicv1.76.zip
# (Needs to be unzipped -- copy worldcities.csv to the same directory)

# Airports database:
# https://davidmegginson.github.io/ourairports-data/airports.csv

import pandas as pd
import numpy as np

dfeldman

import Census
# Get your own key!
c=census.Census("b99ef7ede80606207d3a3836bafdc00dd90a244d")
c.acs.zipcode('B19001_001E', 'zip:55406')

# Available variables: [{u'zip code tabulation area': u'55406', u'B19001_001E': u'14900'}]
# Get output like: 
# [{u'zip code tabulation area': u'55406', u'B19001_001E': u'14900'}]

dfeldman

import Census
# Get your own key!
c=census.Census("b99ef7ede80606207d3a3836bafdc00dd90a244d")
c.acs.zipcode('B19001_001E', 'zip:55406')

# Available variables: http://api.census.gov/data/2014/acs5/variables.html
# Get output like: 
# [{u'zip code tabulation area': u'55406', u'B19001_001E': u'14900'}]

dfeldman

//TWIN WHEELER MODIFIED FOR ARDUINO SIMPLIFIED SERIAL PROTOCOL TO SABERTOOTH V2
// based on insructable from J Dingley
//i.e. the current standard Arduino board.



//designed for use with a rocker switch for steering as in original description on my "instructable":
//http://www.instructables.com/id/Easy-build-self-balancing-skateboardrobotsegway-/

//XXXXXXXXXXXXXXXXX UPDATES May 2011 XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX