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"""
Title: Neural style transfer
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2016/01/11
Last modified: 2020/05/02
Description: Transfering the style of a reference image to target image using gradient descent.
"""

# https://github.com/keras-team/keras-io/blob/master/examples/generative/neural_style_transfer.py

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{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "iam:AttachRolePolicy",
                "iam:GetRole",
                "iam:CreateRole",
                "iam:PassRole",

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FROM python:3.7
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV FLASK_APP=app.py
ENV FLASK_ENV=development
# install system dependencies
RUN apt-get update \
    && apt-get -y install gcc make \
    && rm -rf /var/lib/apt/lists/*s
RUN python3 --version

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def pre_process(df):
    cols_too_many_missing = ['new_tests',
                             'new_tests_per_thousand',
                             'total_tests_per_thousand',
                             'total_tests',
                             'tests_per_case',
                             'positive_rate',
                             'new_tests_smoothed',
                             'new_tests_smoothed_per_thousand',
                             'tests_units',

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@app.route('/predict',methods=['POST'])
def predict():
    input_val = [x for x in request.form.values()][0]
    rf = load_model(BUCKET_NAME, MODEL_FILE_NAME, MODEL_LOCAL_PATH)
    if input_val not in available_countries:
        return f'Country {input_val} is not in available list. Try one from the list! Go back in your browser', 400
    to_pred = get_prediction_params(input_val, url_to_covid)
    prediction = rf.predict(to_pred)[0]
    return render_template('home.html',pred=f'New cases will be {prediction}')

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import os
import glob
import shutil
import typing
import numpy as np
from PIL import Image, ImageStat, ImageChops
from pathlib import Path


def pre_processsing(input_dir: str, output_dir: str, logfile: str) -> int:

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import numpy as np

one_dim = np.ones(shape=(5), dtype=np.int)

# print(one_dim)
# [1 1 1 1 1]

two_dim = np.ones(shape=(5, 2), dtype=np.int)

# print(two_dim)