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import os
import subprocess
import platform

def convert_docx_to_pdf(input_folder, output_folder):
    """Converts all docx files in the input folder to pdf and saves them in the output folder.

    Args:
        input_folder: The path to the folder containing the docx files.
        output_folder: The path to the folder where the converted pdf files will be saved.

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# video_to_gif.py
# -------------------------------------------------
# 🏃 This script converts any video file to an HD GIF.
# 📚 It uses the moviepy library for video processing.
# 🎨 Includes options for resizing, trimming, and optimizing.
# -------------------------------------------------

# Step 1: Install dependencies (run these in your terminal if needed)
# pip install moviepy

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import sys
print(sys.version.split()[0])

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# Install the Rich library: ```pip install rich```

from rich.console import Console
from rich.text import Text
from time import sleep

console = Console()

# Create a Text object for the animated text
animated_text = Text("", style="bold")

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Enable-WindowsOptionalFeature -FeatureName "Containers-DisposableClientVM" -All -Online

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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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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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# 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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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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# 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')