app.py

import streamlit as st
import pandas as pd
import numpy as np 
import time
import module.titanic as modu
from PIL import Image
from sklearn.preprocessing import StandardScaler
#st.set_page_config(
	#page_title="Survivor Classificator",
	#page_icon="🧊",
    #layout="wide",
 	#initial_sidebar_state="expanded",
#)

hide_footer_style = """
<style>
.reportview-container .main footer {visibility: hidden;}    
"""
st.markdown(hide_footer_style, unsafe_allow_html=True)

def show_footer():
	st.markdown("***")
	st.markdown("**© 2021 Developed by Daniele Grotti **") 
	st.markdown("**Like this tool?** Follow me on  "
				"[Linkedin](https://www.linkedin.com/in/daniele-grotti-38681146)")

def main():
	#streamlit
	st.title("Titanic Survivor Classificator")
################ css background #########################
	with open("style.css") as f:
		st.markdown('<style>{}</style>'.format(f.read()), unsafe_allow_html=True)
	# Add a placeholder
	latest_iteration = st.empty()
	bar = st.progress(0)

	for i in range(100):
	# Update the progress bar with each iteration.
		latest_iteration.text(f'Iteration {i+1}')
		bar.progress(i + 1)
		time.sleep(0.01)

	image = Image.open('titanic.jpg')
	st.image(image, caption='Titanic',use_column_width=True)

	zero_list=(0,0,0,0,0,0,0,0,0,0)
	test_df=pd.DataFrame([zero_list],columns=['Pclass_1','Pclass_2','Pclass_3','Fare','Sex','Age','SibSp','Parch','Child','Mother'])

	#pclass/fare,pclass=1&2&3  £2100 & £1900 & £600，£84/£21/£14
	fare_option=st.selectbox(' Welcome to Titanic ! Choose your class and start journey. ',
	('Economy Class-£600','Bussiness Class-£1900','First Class-£2100'))

	'You selected:', fare_option

	if (fare_option=='First Class-£2100'):
		test_df.loc[0,'Pclass_1']=1
		test_df.loc[0,'Pclass_2']=0
		test_df.loc[0,'Pclass_3']=0
		test_df.loc[0,'Fare']=84
	elif (fare_option=='Bussiness Class-£1900'):
		test_df.loc[0,'Pclass_2']=1
		test_df.loc[0,'Pclass_1']=0
		test_df.loc[0,'Pclass_3']=0
		test_df.loc[0,'Fare']=21
	else:
		test_df.loc[0,'Pclass_3']=1
		test_df.loc[0,'Pclass_1']=0
		test_df.loc[0,'Pclass_2']=0
		test_df.loc[0,'Fare']=14
		
	#gender
	gender_option = st.selectbox('Please select your gender.',('Male','Female'))

	'You selected:', gender_option

	if (gender_option=='Male'):
		test_df['Sex_male']=1
		test_df['Sex_female']=0
		
	else:
		test_df['Sex_male']=0
		test_df['Sex_female']=1
		

	#Age
	Age = st.slider('How old are you?', 0, 100, 1)
	st.write("I'm ", Age, 'years old')

	test_df.loc[0,'Age']=Age

	#SibSp
	spouse_option = st.selectbox('Are you married?',('No','Yes'))

	if (spouse_option=='Yes'):
		test_df['spouse']=1
	else:
		test_df['spouse']=0

	sibling = st.number_input('How many sibling do you have?',value=0,max_value=20,step=1)
	SibSp=test_df['spouse']+sibling

	#children
	children=st.number_input('How many children do you have?',value=0,max_value=20,step=1)
	st.write('The current number is ', children)

	#parents
	parent_option=st.selectbox('Do you want to bring your parents?',('No','Yes'))

	if (parent_option=='Yes'):
		test_df['parent']=2
		
	if (parent_option=='No'):
		parent_num=st.selectbox('Choose the number of parents that you would like to bring.',(' Ok , I will ask one of them. - 1' , 'No , I do NOT play with them! - 0' ))
		
		if (parent_num==' Ok , I will ask one of them. - 1'):
			test_df['parent']=1
		else:
			test_df['parent']=0

	test_df=modu.preprocessing(test_df)

	#parch
	test_df['Parch']=children+test_df['parent']

	#mother
	if (test_df.iloc[0]['Sex_female']==1) & (test_df.iloc[0]['Parch']>0):
		test_df['Mother']=int(1)

	#familysize	
	test_df['Familysize']=test_df['Parch']+test_df['SibSp']

	test_df.drop(['spouse','Sex','parent'],axis=1,inplace=True)

	st.write("Please check your data.")
	st.write(test_df)

	prob=modu.makeprediction(test_df)

	#Result
	if st.checkbox(' Show the Result ! '):
		st.subheader('Result')
		st.write('Your Survivability is %0.2f , Mortality is %0.2f'%(prob[1],prob[0]))
	
	show_footer()


if __name__ == '__main__':
	main()