import streamlit as st
import pandas as pd 
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px 
from PIL import Image 

st.title("Fifa 2022 Eda")
st.subheader("EDA for 2022")

# add image 
# image = image.open('')
# st.image(image, caption='Fifa 2022')


# create Outline 
st.write('# Outline A') 
st.write('## Outline A') 
st.write('### Outline A.1.a')
st.write('# Outline B')

# Create markdwon line 
st.markdown("----")


# Show datafreame
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.markdown("----")

# Visualization
st.write('# Visualization')

# Create Barplot
st.write('## Attacking Work Rate Plot')
fig = plt.figure(figsize=(15,5))
sns.countplot(x='AttackingWorkRate', data=df)
st.pyplot(fig)
st.write('Explanation')

# Create Histplot
st.write('## Histogram of Rating')
fig = plt.figure(figsize=(15,5))
sns.histplot(x='Overall',
            data=df,
            bins=50,
            kde=True)  # Add line
st.pyplot(fig)
st.write("Explanation")

# Create scatterplot
st.write('## Scatterplot of Weight and Height')
fig = plt.figure(figsize=(15,5))
sns.scatterplot(x='Weight',
                y='Height',
                data=df)
st.pyplot(fig)
st.write("Explanation")

# Create histogram by user input
st.write('# Visualization by User Input')
choice = st.selectbox('Select Feature: ',
                    ('Age','Weight','Height','ShootingTotal'),
                    help='Select the feature you want!')
fig = plt.figure(figsize=(15,5))
sns.histplot(df[choice],
            bins=35,
            kde=True)  # Add line
st.pyplot(fig)
st.write('Explaination')

# Create plotly plot
st.write('# Plotly Plot - Player Price againts Rating')
fig = px.scatter(df,
                x='ValueEUR',
                y='Overall',
                hover_data=['Name','Age'])
st.plotly_chart(fig)
st.write('Explain!')