fancellu

import zio._
import scala.{ Console => SConsole }
import scala.io.StdIn
import java.io.{ BufferedReader, IOException }
import scala.util.Try

object InterruptableReadLine extends ZIOAppDefault {

  def altReadLine(reader: BufferedReader = SConsole.in) =
    ZIO

fancellu

import zio._

object FakeConsoleExample extends ZIOAppDefault {

  private val program = ZIO.serviceWithZIO[Console] { console =>
    for {
      _     <- console.printLine("Going to the grocery store")
      input <- console.readLine("How are you? ")
      _     <- console.printLine(s"You said: $input")
    } yield ()

fancellu

package main

import (
	"fmt"
	"math/rand"
	"sync"
	"time"
	"unsafe"
)

fancellu

package main

import (
	"fmt"
	"time"
)

func greet(st string, done chan bool) {
	fmt.Println(st)
	done <- true

fancellu

package main

import (
	"fmt"
	"strconv"
)

// Maps over collection of T and applies function f to each item
// Returns a new slice with the transformed items
func mapf[T any, U any](items []T, f func(T) U) []U {

fancellu

import cats.effect.{IO, IOApp}
import fs2.{Stream, text}
import fs2.io.file.{Files, Path}
import fs2.io.{stdout, stderr}

import scala.util.Try

object WindowedAverage extends IOApp.Simple:

  private object Fahrenheit:

fancellu

import scala.annotation.targetName
import scala.util.boundary
import scala.util.boundary.{Label, break}

// Works in Scala 3.4.1

def firstIndex[T](xs: List[T], p: T): Int = {
  boundary:
    for (x, i) <- xs.zipWithIndex do if (x == p) break(i)
    -1

fancellu

def add_dicts_list(dict_list):
  """
  This function adds a list of dictionaries element-wise.

  Args:
      dict_list: A list of dictionaries with k->int elements.

  Returns:
      A new dictionary with the sum of the corresponding elements from all dictionaries in dict_list.
  """

fancellu

https://huggingface.co/spaces/Fancellu/streamlit-demo

fancellu

from PIL import Image
from transformers import BeitImageProcessor, BeitForImageClassification
import os

# Define the directory containing images
image_dir = "e:/media"

# Load the model and processor
processor = BeitImageProcessor.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')
model = BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224-pt22k-ft22k')