Fabio Magarelli fabiom91

The MIT License (MIT)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

	from flask import Flask

	app = Flask(__name__)

	@app.route("/")
	def hello_world():
	return "<p>Hello, World!</p>"

	from flask import Flask
	import pandas as pd

	app = Flask(__name__)

	@app.route("/")
	def hello_world():
	mydata = {
	"name":["John","Danny","Patricia","Rose"],
	"office":["HR","Sales","Sales","HR"],

	# Get image from docker hub
	FROM tiangolo/meinheld-gunicorn-flask:python3.9

	# If you need to install any apt package you can do it here e.g:
	# RUN apt-get update && apt-get install -y pandoc

	# Copy the requirements file into the container.
	# The requirements file is a list of all pip packages we need.
	COPY ./requirements.txt /app/requirements.txt

	version: "3"
	services:
	web:
	build: .
	ports:
	- "80:80"

	greenlet == 0.4.17
	flask_cors == 3.0.10
	flask == 2.0.2
	pandas == 1.3.4
	scikit-learn == 1.0
	flask_sqlalchemy == 2.5.1
	cherrypy == 18.6.1
	paste == 3.5.0

	import pandas as pd
	from sklearn.model_selection import train_test_split


	# import my data into a dataframe (df)
	df = pd.read_csv("mydata.csv")

	# random split 60/40% solution / public dataframe
	solution, public_df = train_test_split(df,test_size=0.40,random_state=101)

	from main import app

	if __name__ == "__main__":
	app.run()