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

min_dict = {'bedrooms': 0,
 'bathrooms': 0,
 'sqft_living': 370,
 'sqft_lot': 638,
 'floors': 1,
 'waterfront': 0,
 'view': 0,
 'condition': 1,
 'sqft_above': 370,
 'sqft_basement': 0,
 'yr_built': 1900,
 'yr_renovated': 0,
 'house_age': 0,
 'years_since_renovation': 0}

max_dict = {'bedrooms': 9,
 'bathrooms': 8,
 'sqft_living': 13540,
 'sqft_lot': 1074218,
 'floors': 3,
 'waterfront': 1,
 'view': 4,
 'condition': 5,
 'sqft_above': 9410,
 'sqft_basement': 4820,
 'yr_built': 2014,
 'yr_renovated': 2014,
 'house_age': 114,
 'years_since_renovation': 2014}

columns = ['bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot', 'floors',
       'waterfront', 'view', 'condition', 'sqft_above', 'sqft_basement',
       'yr_built', 'yr_renovated', 'house_age', 'years_since_renovation',
       'has_basement', 'city_Algona', 'city_Auburn', 'city_Beaux Arts Village',
       'city_Bellevue', 'city_Black Diamond', 'city_Bothell', 'city_Burien',
       'city_Carnation', 'city_Clyde Hill', 'city_Covington',
       'city_Des Moines', 'city_Duvall', 'city_Enumclaw', 'city_Fall City',
       'city_Federal Way', 'city_Inglewood-Finn Hill', 'city_Issaquah',
       'city_Kenmore', 'city_Kent', 'city_Kirkland', 'city_Lake Forest Park',
       'city_Maple Valley', 'city_Medina', 'city_Mercer Island', 'city_Milton',
       'city_Newcastle', 'city_Normandy Park', 'city_North Bend',
       'city_Pacific', 'city_Preston', 'city_Ravensdale', 'city_Redmond',
       'city_Renton', 'city_Sammamish', 'city_SeaTac', 'city_Seattle',
       'city_Shoreline', 'city_Skykomish', 'city_Snoqualmie',
       'city_Snoqualmie Pass', 'city_Tukwila', 'city_Vashon',
       'city_Woodinville', 'city_Yarrow Point']

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

new_pred = st.text_area('Enter text')

if new_pred:
    new_pred = {key:0 for key in columns}
    new_pred['date'] = pd.to_datetime('2014-07-10') # do not change
    
    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)
    for col in numerical_features:
        new_pred[col] = normalize(new_pred, col, min_dict, max_dict)
    
    predicted_price = model.predict(new_pred)
    st.json(predicted_price[0][0])