#import the necessary dependencies
import streamlit as st
from transformers import pipeline
import lightgbm as lgb
import pandas as pd
import predictor

#INTERFACE

data = pd.DataFrame({})
for label in predictor.X_test.columns:
    data[label] = 0

OverallQual = st.slider("Overall Quality", 1, 5)
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)


if st.button("Calculate"):
    results = predictor.make_a_prediction(data)
    print(results)