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#import the necessary dependencies
import streamlit as st
import streamlit.components.v1 as cmpnts
import shap
import numpy as np
import pandas as pd
import predictor
st.set_option('deprecation.showPyplotGlobalUse', False)
#INTERFACE
#Creating an empty dataset to use in the model
data = pd.DataFrame(np.zeros((1,60)),columns=predictor.X_test.columns, index=[0])
st.title("*The Ames, Iowa Housing Price Predictor*")
st.header("Description:")
st.subheader("This is a showcase of the regression model, which has been trained using LightGBM and Optuna for hyperparameter tuning on the Ames, Iowa Housing Dataset. On the side are the top 20 features which this model deems the most relevant towards housing prices. Please try from a range of different inputs!")
OverallQual = st.sidebar.slider("Overall Quality", 1, 5)
with st.sidebar:
data["OverallQual"] = OverallQual
GrLivingArea = st.slider("Above ground Living Area (sqft):", 0, 5000)
data["GrLivArea"] = GrLivingArea
YearBuilt = st.number_input("Year Built: ", value=2000, min_value=1800, max_value=2023)
data["YearBuilt"] = YearBuilt
TotalBsmtSF = st.number_input("Total Basement Square Footage:", value=0, min_value=0)
data["TotalBsmtSF"] = TotalBsmtSF
BsmtFinSF1 = st.number_input("Square Footage of Type 1 Basement (same as total if one type):", value=0, min_value=0)
data["BsmtFinSF1"] = BsmtFinSF1
data["YearRemodAdd"] = st.number_input("Year of Remodeling/Additions (same as year built if no remodeling)", value = 2000, min_value= 1800,max_value=2023)
data["LotArea"] = st.slider("Lot Area:", 0, 5000)
data["OverallCond"] = st.slider("Overall Condition of the House (5 being the best):", 1, 5)
data["1stFlrSF"] = st.number_input("First Floor Square Footage:", min_value=0,value=0)
data["GarageCars"] = st.slider("Number of Cars the Garage Can Hold:", 0, 10)
data["2ndFlrSF"] = st.number_input("Second Floor Square Footage:", value=0, min_value=0)
data["Fireplaces"] = st.slider("Number of Fireplaces:", 0, 10)
data["Neighborhood"] = st.slider("Neighborhood:", 0, 24)
data["GarageArea"] = st.number_input("Garage Area (sqft):", value= 0, min_value=0)
data["BsmtFullBath"] = st.slider("Number of Full Bathrooms in the Basement:", 0, 5)
data["FullBath"] = st.slider("Number of Full Bathroooms Above Ground:", 0, 5)
data["HalfBath"] = st.slider("Number of Half Bathrooms Above Ground (no shower):", 0, 5)
data["KitchenQual"] = st.slider("Quality of the Kitchen", 1, 5)
data["OpenPorchSF"] = st.number_input("Open Porch Square Footage:", value=0, min_value=0)
data["MoSold"] = st.slider("Month Sold:", 1,12)
from streamlit import components
def st_shap(plot, height=None):
shap_html = f"<head>{shap.getjs()}</head><body>{plot.html()}</body>"
cmpnts.html(shap_html, height=height)
#The button
if st.button("Calculate Your House Price!"):
results = predictor.make_a_prediction(data)
opt_f = '{0:.2f}'.format(results[0][0])
unopt_f = '{0:.2f}'.format(results[1][0])
st.subheader('Optimized with optuna: $' + opt_f)
st.write("Below are the SHAP graphs which can be used to explain the decisions made by the model using the features provided for the *optimized* results.")
force_plot_1 = predictor.create_opt_shap_models(data)
st_shap(force_plot_1)
st.subheader('Unoptimized: $' + unopt_f)
force_plot_2 = predictor.create_unopt_shap_models(data)
st_shap(force_plot_2)
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