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import numpy as np
import pickle
import pandas as pd
import streamlit as st
import json
from PIL import Image
import warnings
warnings.filterwarnings('ignore')
pickle_in = open("banglore_home_prices_model.pickle","rb")
classifier=pickle.load(pickle_in)
with open("columns.json", "r") as f:
__data_columns = json.load(f)['data_columns']
__locations = __data_columns[3:]
def welcome():
return "Welcome All"
def predict_note_authentication(sqft,bhk,bath,loc):
try:
loc_index = __data_columns.index(loc.lower())
except:
loc_index = -1
x = np.zeros(len(__data_columns))
x[0] = sqft
x[1] = bath
x[2] = bhk
if loc_index>=0:
x[loc_index] = 1
prediction=round(classifier.predict([x])[0],2)
return round(classifier.predict([x])[0],2)
def main():
html_temp = """
<div style="padding:10px">
<h2 style="color:white;text-align:center;">Banglore Home Price Predictor </h2>
</div>
"""
st.markdown(html_temp,unsafe_allow_html=True)
sqft = st.text_input("Area (Total Square Feet)","")
bhk = st.selectbox("BHK",('1','2','3','4','5'))
bath = st.selectbox("Bath",('1','2','3','4','5'))
loc = st.selectbox("Location",__locations)
result=""
if st.button("Estimate Price"):
result=predict_note_authentication(sqft,bhk,bath,loc)
st.success('Estimated Price is {} lakhs'.format(result))
if __name__=='__main__':
main()
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