gusdelact

Sitio Xinu

https://xinu.cs.purdue.edu/

En Safari

https://learning.oreilly.com/library/view/operating-system-design/9781439881118/

https://learning.oreilly.com/library/view/operating-system-design/9781498712439/

gusdelact

/*
   https://t.me/RustMX
*/
use std::collections::HashMap;

fn d1(x:i32)->Result<i32,u32>
{
    if x >0 
    {
       Ok(x + 5)

gusdelact

/*
   https://play.rust-lang.org
   https://t.me/RustMX
   
*/
fn f1( p0:i32) -> i32
{
    let p1 = p0 +1;
    p1*2
}

gusdelact

use nalgebra::{DMatrix, DVector};

fn main() {
    // Datos de ejemplo
    let x_data = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let y_data = vec![2.0, 4.0, 6.0, 8.0, 10.0];

    // Crear una matriz de diseño X
    let mut x_matrix = DMatrix::from_fn(x_data.len(), 2, |i, j| if j == 0 { 1.0 } else { x_data[i] });

gusdelact

use std::collections::HashMap;

fn f1(x:i32) -> Option<bool>
{
   
    if  x>=0 && x<=100
    {
        Some(true)
    } 
    else if x>=101 && x<=200

gusdelact

#[derive(Debug,Clone)]
struct Estr01 
{
    a : i32,
    b : char,
    s:  String
}
#[derive(Debug,Clone)]
struct Estr02<'a>
{

gusdelact

import streamlit as st
import boto3
from datetime import datetime
import time
import requests

###manejo de estado
if 'transcribiendo' not in st.session_state:
    st.session_state['transcribiendo'] = False
### Layout inicial

gusdelact

import boto3
client = boto3.client('rekognition')
id ='4abb12e92635247020465ea9ff0e86206096975f378c6954f105d4d5a6b785a7'
response = client.get_label_detection(JobId=id)
etiquetas=response['Labels']

for i,etiqueta in enumerate(etiquetas,1) :
    print(f"Etiqueta {i}")
    print(f"Nombre {etiqueta['Label']['Name']}")
    print(f"Timestamp {etiqueta['Timestamp']}")

gusdelact

import boto3
client = boto3.client('rekognition')
response = client.start_label_detection(
    Video={
        'S3Object': {
            'Bucket': 'aiasaservicetranscribe740327929864',
            'Name': 'video_of_funny_cat.mp4',
         }
    }
)

gusdelact

import boto3
client = boto3.client('rekognition')
censurado = 'img/tedcannabis.jpg'
with open(censurado,"rb") as desnudo :
    imagen = desnudo.read() 
response=client.detect_moderation_labels(Image = {'Bytes':imagen} )
print(response['ModerationLabels'])