import streamlit as st
from PIL import Image
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

st.set_page_config(
    page_title='Model EDA',
    layout='wide',
    initial_sidebar_state='expanded'
)

def run():
    # Title
    st.title('House Price Prediction - EDA')

    image = Image.open('image.jpeg')
    st.image(image, caption='US Suburbs')

    st.markdown('---')

    st.subheader('Dataset')

    df = pd.read_csv('USA_Housing.csv')
    cols = {'Avg. Area Income': 'Income',
        'Avg. Area House Age': 'Age',
        'Avg. Area Number of Rooms': 'Rooms',
        'Avg. Area Number of Bedrooms': 'Bedrooms',
        'Area Population': 'Population'}
    df.rename(columns=cols, inplace=True)
    st.dataframe(df)

    st.markdown('---')

    st.subheader('Area Income')

    def scat(df, x, y):
        fig = plt.figure(figsize=(15, 10))
        sns.scatterplot(data=df, x=x, y=y)
        st.pyplot(fig)

    scat(df, 'Income', 'Price')

    st.markdown('---')

    st.subheader('House Age')

    eda = df.copy()
    eda['Pricerange'] = 'Very High'
    eda.loc[eda['Price'].between(1.5e+06, 1.75e+06), 'Pricerange'] = 'High'
    eda.loc[eda['Price'].between(1.25e+06, 1.5e+06), 'Pricerange'] = 'Above Average'
    eda.loc[eda['Price'].between(1e+06, 1.25e+06), 'Pricerange'] = 'Below Average'
    eda.loc[eda['Price'].between(0.75e+06, 1e+06), 'Pricerange'] = 'Low'
    eda.loc[eda['Price']<0.75e+06, 'Pricerange'] = 'Very Low'

    def barc(df, x, y):
        plot = df.groupby(x).mean().reset_index()
        plot = plot.sort_values(y)
        fig = plt.figure(figsize=(15, 5))
        plt.bar(range(len(plot)), plot[y])
        plt.xticks(range(len(plot)), plot[x], rotation=45)
        plt.ylabel('Mean')
        st.pyplot(fig)

    barc(eda,'Pricerange','Age')

    st.markdown('---')

    st.subheader('Number of Rooms')

    barc(eda,'Pricerange','Rooms')

    st.markdown('---')

    st.subheader('Area Population')

    barc(eda,'Pricerange','Population')

    st.markdown('---')

    st.subheader('Connection Between Features')

    def scatp(df, x, y, hue):
        fig = plt.figure(figsize=(15, 10))
        sns.scatterplot(data=df, x=x, y=y, hue=hue)
        st.pyplot(fig)
    
    scatp(eda, "Income", "Rooms", 'Pricerange')

    scatp(eda, "Income", "Population", 'Pricerange')

    eda['Roomscat'] = 9
    eda.loc[eda['Rooms'].between(8, 9), 'Roomscat'] = 8
    eda.loc[eda['Rooms'].between(7, 8), 'Roomscat'] = 7
    eda.loc[eda['Rooms'].between(6, 7), 'Roomscat'] = 6
    eda.loc[eda['Rooms'].between(5, 6), 'Roomscat'] = 5
    eda.loc[eda['Rooms'].between(4, 5), 'Roomscat'] = 4
    eda.loc[eda['Rooms'].between(3, 4), 'Roomscat'] = 3
    eda.loc[eda['Rooms']<3, 'Roomscat'] = 2

    barc(eda,'Roomscat','Age')

if __name__ == '__main__':
    run()