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#!/bin/bash

# Define las opciones del menú
options=("Opción 1" "Opción 2" "Opción 3" "Salir")

# Establece la posición inicial del cursor en la primera opción
selected=0

# Función para mostrar el menú y resaltar la opción seleccionada
function show_menu() {

martingaido

from langchain.llms import Ollama
from langchain.document_loaders import WebBaseLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import GPT4AllEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
import gradio as gr

# Initialize variables to store the previous URL and its corresponding data and embeddings
prev_url = None

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#!/bin/bash

# Replace YOUR_API_KEY with your actual API key
API_KEY="OPEN_AI_API_KEY"

# Clear the screen
clear

# Function to check if the API key is set
check_api_key() {

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import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse, urljoin

def create_sitemap(url):
    # Parse the base URL
    parsed_url = urlparse(url)
    base_url = parsed_url.scheme + "://" + parsed_url.netloc

    # Initialize the set of visited URLs

martingaido

#!/bin/bash

# Replace YOUR_API_KEY with your actual API key
API_KEY="OPEN_AI_API_KEY"

# Get the message from the command line argument
MESSAGE=$1

# Set up the API endpoint
API_ENDPOINT="https://api.openai.com/v1/chat/completions"

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/*

Hamming distance is a measure of the difference between 
two same-length strings of characters. Simply put, it is 
defined as the number of positions at which corresponding 
characters in two strings of characters are different.

Formally, the Hamming distance between two same-length 
strings of characters, s and t, is defined as the number 
of positions at which the corresponding characters are 

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from datetime import datetime, timedelta
import math

def calculate_biorhythm(birthday, current_date, rhythm_type):

  days_difference = (current_date - birthday).days
  
  if rhythm_type == 'physical':
    cycle = 23
  elif rhythm_type == 'emotional':

martingaido

import json
import snscrape
import snscrape.modules.twitter as sntwitter
import snscrape.modules.facebook as snfacebbok
import snscrape.modules.instagram as sninstagram
import snscrape.modules.mastodon as snmastodon
import snscrape.modules.reddit as smreddit

def lambda_handler(event, context):
    

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martingaido

# pip install torch
# pip install "transformers==4.24.0"
# usage: python text-generator-transformers.py 'some text'

import sys
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel

arg = sys.argv