Miguel07Alm
/ create_postman_collection_nestjs.py
Last active
March 3, 2024 18:19
It creates automatically your postman collection based in NestJS endpoints
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| import json | |
| # Absolute path of your file .ts where the endpoints are located (example: '/home/dev/project/app/driver/src/driver.controller.ts') | |
| file_path = '' | |
| postman_collection = { | |
| "info": { | |
| "name": "NestJS Endpoints Collection", | |
| "schema": "https://schema.getpostman.com/json/collection/v2.1.0/collection.json" | |
| }, | |
| "item": [] | |
| } |
Miguel07Alm
/ actions.ts
Created
March 4, 2024 22:07
Simple script for adding files to AWS S3 Bucket
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| "use server"; | |
| import { cookies } from "next/headers"; | |
| import { S3Client, PutObjectCommand } from "@aws-sdk/client-s3"; | |
| import { getSignedUrl } from "@aws-sdk/s3-request-presigner"; | |
| import crypto from "crypto"; | |
| import verifyJwtToken from "@/utils/api/verifyJwtToken"; | |
| const generateFileName = (bytes = 32) => | |
| crypto.randomBytes(bytes).toString("hex"); |
Miguel07Alm
/ clasificador_imagenes.py
Last active
May 30, 2024 20:14
Tutorial sobre el desarrollo de un modelo con Machine Learning (IA), el cual utiliza un modelo de clasificación de imagenes (convolutional model) para predecir si una imagen es x según los labels del dataset.
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Importar bibliotecas necesarias | |
| import tensorflow as tf | |
| from tensorflow.keras import datasets, layers, models | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| # Cargar y preprocesar los datos | |
| (train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data() | |
| train_images, test_images = train_images / 255.0, test_images / 255.0 |