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import pandas as pd
import requests

response  = requests.get('https://servicodados.ibge.gov.br/api/v1/localidades/municipios')
df = pd.read_json(response.text)
cidades = df['nome']
cidades.to_csv("cidades.csv", index=False)

idcesares

# Imports
import tensorflow as tf
import numpy as np
from tensorflow import keras

# Define and compile the Neural Network (NN)
model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])]) # Only 1 neuron

# Compile the NN
model.compile(optimizer='sgd', loss='mean_squared_error')

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from datetime import datetime
day_of_year = datetime.now().timetuple().tm_yday
print(f"What's the Current Day Number? {day_of_year} days have passed since the beginning of the year.")

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from textblob import TextBlob

def sentiment(text):
  base_text = TextBlob(text)
  sentiment_score = base_text.sentiment.polarity
  if sentiment_score < 0:
    return 'Negative'
  elif sentiment_score == 0:
    return 'Neutral'
  return 'Positive'

idcesares

# Basic libraries
import requests
import pandas as pd
import json

# Define API URL
url = 'API_link'

# Consume API
response = requests.get(url).json()

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Get-ChildItem -Filter *.csv | Select-Object -ExpandProperty FullName | Import-Csv | Export-Csv .\combinedcsvs.csv -NoTypeInformation -Encoding UTF8 -Append

idcesares

# Instalar o pyttsx3 com o comando abaixo:
# pip install pyttsx3

import pyttsx3

# Iniciando a biblioteca na variável engine
engine = pyttsx3.init()

# Função pronta para uso
def speak(speech):