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| # 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): |
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| Get-ChildItem -Filter *.csv | Select-Object -ExpandProperty FullName | Import-Csv | Export-Csv .\combinedcsvs.csv -NoTypeInformation -Encoding UTF8 -Append |
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| # 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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| 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' |
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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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| # 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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| 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) |
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| # Install poppler using chocolatey | |
| # choco install poppler | |
| # Imports | |
| import pdf2image | |
| import os | |
| # Function to convert PDF to image (JPEG) | |
| def to_image(path): | |
| images = convert_from_path(path) | |
| for i in range(len(images)): |
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| from urllib.request import urlopen | |
| import json | |
| import pprint | |
| url = 'https://ipinfo.io/json' | |
| result = urlopen(url) | |
| data = json.load(result) | |
| pprint.pprint(data) |
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| # Mount Google Drive | |
| from google.colab import drive | |
| drive.mount('/gdrive') | |
| # Script to download multiple files on Google Drive with Colab | |
| import requests | |
| import os | |
| def download_file_from_google_drive(id, destination): | |
| URL = "https://docs.google.com/uc?export=download" |
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