app.py

import streamlit as st
import pandas as pd
import pickle
from utils import create_new_features, normalize, init_new_pred

with open('./trained_model.pkl', 'rb') as file:
    model = pickle.load(file)

new_pred = st.text_area('Enter text')

if new_pred:
    new_pred = init_new_pred()
    new_pred['bedrooms'] = 5
    new_pred['bathrooms'] = 3
    new_pred['sqft_living'] = 10000
    new_pred['sqft_lot'] = 1000
    new_pred['floors'] = 2
    new_pred['waterfront'] = 1
    new_pred['view'] = 3
    new_pred['condition'] = 5
    new_pred['sqft_above'] = 500
    new_pred['sqft_basement'] = 500
    new_pred['yr_built'] = 2012
    new_pred['yr_renovated'] = 2013
    new_pred['city_Bellevue'] = 1
    new_pred = pd.DataFrame([new_pred])
    
    new_pred = create_new_features(new_pred)
    new_pred = normalize(new_pred)
    
    predicted_price = model.predict(new_pred)
    st.json(predicted_price[0][0])