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import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
st.set_page_config(
page_title = 'FIFA 2022 - EDA',
layout = 'wide',
initial_sidebar_state = 'expanded'
)
def run():
st.title("FIFA 2022 Player Rating Prediction")
st.subheader('EDA untuk Analisis Dataset FIFA 2022')
st.image('https://e2e85xpajrr.exactdn.com/wp-content/uploads/2022/09/21190008/shutterstock_2190840355-scaled.jpg?strip=all&lossy=1&ssl=1',
caption='World Cup Champion')
st.write('Page ini dibuat oleh Vincent')
st.write('# Head')
st.write('## SubHeader')
st.write('### SubsubHeader')
st.markdown('---')
'''
Pada page ini, penulis akan melakukan eksplorasi sederhana, Dataset yang digunakan adalah dataset FIFA 2022.
Dataset ini berasal dari web [sofia.com](www.google.com)
'''
# Show dataframe
df = pd.read_csv('https://raw.githubusercontent.com/FTDS-learning-materials/phase-1/master/w1/P1W1D1PM%20-%20Machine%20Learning%20Problem%20Framing.csv')
st.dataframe(df)
st.write('### Plot AttackingWorkRate')
fig = plt.figure(figsize=(15,5))
sns.countplot(x= 'AttackingWorkRate', data=df)
st.pyplot(fig)
# Membuat histogram berdasarkan input user
st.write(' ### Histogram berdasarkan pilihan-mu')
pilihan = st.selectbox('Pilih feature: ',('Age','Height','Weight'))
fig = plt.figure(figsize= (15,5))
sns.histplot(df[pilihan], bins = 30, kde = True)
st.pyplot(fig)
# Membuat plotlt plot
st.write('### Plot antara ValueEur dengan Price')
fig = px.scatter(df,x='ValueEUR',y='Overall', hover_data=['Name','Age'])
st.plotly_chart(fig)
if __name__ == '__main__':
run()
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