import pandas as pd
import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns



# loading the data
df = pd.read_csv('train4.csv')


# Renaming columns
#df.rename(columns = {'MSSubClass':'import pandas as pd
import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import r2_score
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import seaborn as sns



# loading the data
df = pd.read_csv('train4.csv')


# Renaming columns
df.rename(columns = {'MSSubClass':'MSSubClass','LotArea':'LotArea', 'OverallQual':'OverallQual','OverallCond':'OverallCond', 'YearBuilt':'YearBuilt', 
	 'BsmtFinSF1':'BsmtFinSF1', 'BsmtFinSF2':'BsmtFinSF2',
	'BsmtUnfSF':'BsmtUnfSF','TotalBsmtSF':'TotalBsmtSF'},inplace = True)


# HEADINGS
st.title('House Price Prediction')
st.sidebar.header('Housing Data')
st.subheader('Training Data Stats')
st.write(df.describe())


# X AND Y DATA
x = df.drop(['SalePrice'], axis = 1)
y = df.iloc[:, -1]
x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.2, random_state = 0)


# FUNCTION
def user_report():
  MSSubClass = st.sidebar.slider('MSSubClass', 0,60, 200 )
  LotArea = st.sidebar.slider('LotArea', 0,5,10 )
  OverallQual = st.sidebar.slider('OverallQual', 0,5, 10 )
  OverallCond = st.sidebar.slider('OverallCond', 2,7, 3 )
  YearBuilt = st.sidebar.slider('YearBuilt', 1872,1900, 201 )
  YearRemodAdd = st.sidebar.slider('YearRemodAdd', 170,70000, 5000 )
  BsmtFinSF1 = st.sidebar.slider('BsmtFinSF1', 170,70000, 5000 )
  BsmtUnfSF = st.sidebar.slider('BsmtUnfSF', 170,70000, 5000 )
  BsmtFinSF2 = st.sidebar.slider('BsmtFinSF2', 170,70000, 5000 )
  TotalBsmtSF = st.sidebar.slider('TotalBsmtSF', 170,70000, 5000 )
  SalePrice = st.sidebar.slider('SalePrice', 170,70000, 5000 )  

  user_report_data = {
      'MSSubClass':MSSubClass,
      'LotArea':LotArea,
      'OverallQual':OverallQual,
      'OverallCond': OverallCond,
      'YearBuilt':YearBuilt,
      'YearRemodAdd': YearRemodAdd,
	  'BsmtFinSF1': BsmtFinSF1,
	  'BsmtUnfSF': BsmtUnfSF,
	  'BsmtFinSF2': BsmtFinSF2,
	  'TotalBsmtSF': TotalBsmtSF,
      'SalePrice': SalePrice,
  }
  
  report_data = pd.DataFrame(user_report_data, index=[0])
  return report_data




# Housing Data
user_data = user_report()
st.subheader('Housing Data')
st.write(user_data)


# MODEL
lr  = LinearRegression()
lr.fit(x_train, y_train)
user_result = lr.predict(user_data)



# VISUALISATIONS
st.title('Visualised Housing Data')



# COLOR FUNCTION
if user_result[0]==0:
  color = 'blue'
else:
  color = 'red'



# OUTPUT
st.subheader('Price of House is : ')
st.write(str(user_result))
st.title('output')
st.subheader('r2_score: ')
st.write(str(r2_score(y_test, lr.predict(x_test))*100)+'%')