CharlyWargnier

from zipfile import ZipFile
from io import BytesIO

zipObj = ZipFile("sample.zip", "w")
# Add multiple files to the zip
zipObj.write("checkpoint")
zipObj.write("model_hyperparameters.json")
# close the Zip File
zipObj.close()

CharlyWargnier

function getSummary(text){
  
  payload = `text=${text}`;
  
  payload = encodeURI(payload);
  
  console.log(payload);
  
  var url = "https://us-central1-hamlet-title-descr-generator.cloudfunctions.net/t5_post";
  

CharlyWargnier

function ConnectButton(){
    console.log("Connect pushed"); 
    document.querySelector("#top-toolbar > colab-connect-button").shadowRoot.querySelector("#connect").click() 
}
setInterval(ConnectButton,60000);

CharlyWargnier

import asyncio

import streamlit as st
from httpx_oauth.clients.google import GoogleOAuth2

st.title("Google OAuth2 flow")

"## Configuration"

client_id = st.text_input("Client ID")

CharlyWargnier

import streamlit as st
import pandas as pd
import pandas as pd
import requests
import base64
import os


st.markdown('## **Bulk HTTP Status Code Checker**')
st.markdown('*Made with* :heart: *with [Streamlit](https://www.streamlit.io/)*')

CharlyWargnier

'''

If you have a lengthy training job running on Colab, why bother monitoring it when you can simply send yourself an email when it's finished? This code, credit again to Rohit Midha, can be placed after your training loop, and can send you a message once training has completed. You can likely envision some more creative ways to configure what types of emails are sent for failed tasks, completed tasks, etc, or checkpoint emails for different points along your machine learning pipeline, perhaps.

The code snippet uses the smtplib library which is included by default in your Colab environment. Just fill in the email addresses and passwords (use the same for both in order to send a message to yourself, for example) as well as the message and away you go.

'''

import smtplib

CharlyWargnier

dfLasMonth['FruitClass'] = pd.np.where(dfLasMonth.Title.str.contains("Pear"), "Pear",
  pd.np.where(dfLasMonth.Title.str.contains("Apple"), "Apple",
    pd.np.where(dfLasMonth.Title.str.contains("Banana"), "Banana",
      pd.np.where(dfLasMonth.Title.str.contains("Oranges"), "Oranges", "Else"))))
dfLasMonth

CharlyWargnier

dfFiltered = df[(df['Date'] > start_date) & (df['Date'] <= end_date) & (df['httpCodeClass'].isin(myNewList))]

csvName = "csvExport2" #@param {type:"string"}
csvName = csvName + '.csv'
dfFiltered.to_csv(csvName)

CharlyWargnier

myNewList = []

Code_2XX = False  # @param {type:"boolean"}
Code_3XX = True  # @param {type:"boolean"}
Code_4XX = True  # @param {type:"boolean"}
Code_5XX = True  # @param {type:"boolean"}

if Code_2XX == True:
    myNewList.append('Success (2XX)')
if Code_3XX == True:

CharlyWargnier

DfPivotCodes = df.groupby(['httpCode']).agg({'httpCode': ['count']})
DfPivotCodes.columns = ['_'.join(multi_index) for multi_index in DfPivotCodes.columns.ravel()]
DfPivotCodes = DfPivotCodes.reset_index()
DfPivotCodes