tuantranf

package auth

import (
	"encoding/json"
	"fmt"
	"io"
	"io/ioutil"
	"net"
	"net/http"
	"net/url"

tuantranf

// Requires the gpt library from https://github.com/hrishioa/socrate and the progress bar library.
// Created by Hrishi Olickel (hrishioa@gmail.com) (@hrishioa). Reach out if you have trouble running this.

import { ThunkQueue } from '../../utils/simplethrottler';
import {
  AcceptedModels,
  Messages,
  askChatGPT,
  getMessagesTokenCount,
  getProperJSONFromGPT,

tuantranf

This will create a proxy server in whatever your availability zone your VPC is in. For me, that's us-east-1b. For you, that may be something different. Steps 10+ should more or less work regardless of your provider since those steps cover the setup and configuration of TinyProxy.

1. Click the Launch Instance button.
2. Choose Ubuntu Server 14.04 LTS (HVM), SSD Volume Type. This isn't strictly necessary. If you choose another OS, check its documentation for how to install new packages.
3. On the Choose an Instance Type screen, select t2.micro. It's Free Tier eligible.
4. Click the Next: ... buttons until you reach the Configure Security Group screen.
   • You may wish to reduce the amount of storage on the Add Storage screen. This is optional.
   • You may wish to add a tag on the Tag Instance screen. This is also optional.
5. On the Configure Security Group screen:

• Select Create a new security group.

tuantranf

#include <M5Stack.h>
#include <WiFi.h>
#include <WiFiClientSecure.h>

WiFiClientSecure client;

//WiFi設定
const char* ssid = "******";
const char* password = "*****";

tuantranf

• 0.1. References
• 0.2. Other cheatsheets
• 0.3. Manage multiple gcloud config configurations
  • 0.3.1. Switch gcloud context with gcloud config
• 0.4. Credentials
• 0.5. info
• 0.6. projects
• 0.7. zones & regions
• 0.8. Organization
• 0.9. billing

tuantranf

JSON Puller - Google Apps Script

Transforms the data of a given Spreadsheet Sheet to JSON.

• The frozen rows are taken as keys for the JSON.
• The data taken for the values is only that after the frozen rows

Set up:

exportJSON(Spreadsheet) - transforms the data in the given sheet to JSON.

tuantranf

import sqlalchemy as sa
import sqlparse

metadata = sa.MetaData()

tasks = sa.Table(
    'tasks', metadata,
    sa.Column('id', sa.Integer, primary_key=True),
    sa.Column('bot', sa.String(255), nullable=False),
    sa.Column('task', sa.String(255), nullable=False),

tuantranf

# One liner for counting unique IP addresses from nginx logs
# Feel free to comment with better ideas - I'm sure it's not the best way of doing this (I'm no awk ninja!)
#
# Sample output:
# 
# $ cat example.com.access.log | awk -F " " '{a[$1]++ } END { for (b in a) { print b, "\t", a[b] } }'
# 66.65.145.220 49
# 92.63.28.68 126

cat example.com.access.log | awk -F " " '{a[$1]++ } END  { for (b in a) { print b, "\t", a[b] } }'

tuantranf

Using Python 3 + Google Cloud Vision API's OCR to extract text from photos and scanned documents

Just a quickie test in Python 3 (using Requests) to see if Google Cloud Vision can be used to effectively OCR a scanned data table and preserve its structure, in the way that products such as ABBYY FineReader can OCR an image and provide Excel-ready output.

The short answer: No. While Cloud Vision provides bounding polygon coordinates in its output, it doesn't provide it at the word or region level, which would be needed to then calculate the data delimiters.

On the other hand, the OCR quality is pretty good, if you just need to identify text anywhere in an image, without regards to its physical coordinates. I've included two examples:

####### 1. A low-resolution photo of road signs

tuantranf

"""
Author: Eric J. Ma  

Purpose: To merge PDFs together in an automated fashion.
"""


import os

from PyPDF2 import PdfFileReader, PdfFileMerger