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def check_validity(my_url):
    try:
        urlopen(my_url)
        print("Valid URL")
    except IOError:
        print ("Invalid URL")
        sys.exit()

nhammad

import * as ml5 from "ml5";
import p5Types from "p5"; 

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	let capture: any = useRef<any>();
	let classifier: any = useRef<any>();
 
	  //setting up the webcam from p5, and featureExtractor/classifier from ml5
	  const setup = (p5: p5Types, canvasParentRef: Element) => {
		capture.current = p5.createCapture(p5.VIDEO).parent(canvasParentRef);
		const featureExtractor = ml5.featureExtractor("MobileNet", {epochs: props.numberOfEpochs}, modelReady);
		classifier.current = featureExtractor.classification(
		  capture.current,
		  videoReady

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	  function train() {
		classifier.current.train((lossValue: number ) => {
		  console.log("Loss is", lossValue);
		  if (lossValue) {
			  setLossValue(lossValue)
			}
		  if (lossValue == null) {
			setTrainingComplete(true);
			console.log("training complete");
		  }

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      function gotResult() {
       classifier.current.classify(capture.current, (err: any, result: any) => {
           console.log('result', result)
         setPrediction(result[0].label);
         setConfidence(result[0].confidence);
       });
     }

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export default function VideoComponent(
	props: VideoComponentProps
  ){
	//console.log('NUMBER OF EPOCHS', props.numberOfEpochs)
	const [prediction, setPrediction] = useState<string>();
	const [confidence, setConfidence] = useState<string>();
	const [firstImages, setFirstImages] = useState<number>(0);
	const [secondImages, setSecondImages] = useState<number>(0);
	const [trainingComplete, setTrainingComplete] = useState<boolean>();
	const [lossValue, setLossValue] = useState<number>();

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import logging
import zipfile
from io import BytesIO
from boto3 import resource
import gzip
import io

logger = logging.getLogger()
logger.setLevel(logging.INFO)

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dbt_project:
  target: dev
  outputs:
    dev:
      type: snowflake
      account: zzzz.eu-east-1

      user: DEV_USER
      password: "{{ env_var('DBT_PASSWORD') }}"

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image: fishtownanalytics/dbt:1.0.0

pipelines:
  branches:
    master:
      - step:
          name: 'setup and compile dbt'
          script:
            - cd dbt_project
            - dbt deps --profiles-dir ./profiles

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resource "aws_s3_bucket" "incoming" {
  bucket = local.bucket_name
  acl    = "private"

  server_side_encryption_configuration {
    rule {
      apply_server_side_encryption_by_default {
        sse_algorithm     = "aws:kms"
        kms_master_key_id = aws_kms_key.ingest.id
      }