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)

# Placeholder for displaying the predicted price at the top
price_placeholder = st.empty()

city = st.selectbox(
    'Select City', 
    ['Algona', 'Auburn', 'Beaux Arts Village', 'Bellevue',
     'Black Diamond', 'Bothell', 'Burien', 'Carnation', 'Clyde Hill',
     'Covington', 'Des Moines', 'Duvall', 'Enumclaw', 'Fall City',
     'Federal Way', 'Inglewood-Finn Hill', 'Issaquah', 'Kenmore',
     'Kent', 'Kirkland', 'Lake Forest Park', 'Maple Valley', 'Medina',
     'Mercer Island', 'Milton', 'Newcastle', 'Normandy Park',
     'North Bend', 'Pacific', 'Preston', 'Ravensdale', 'Redmond',
     'Renton', 'Sammamish', 'SeaTac', 'Seattle', 'Shoreline',
     'Skykomish', 'Snoqualmie', 'Snoqualmie Pass', 'Tukwila', 'Vashon',
     'Woodinville', 'Yarrow Point']
)

# Define min and max values from the dictionaries
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
}

# Create sliders for each item in the dictionaries
bedrooms = st.slider('Bedrooms', min_value=min_dict['bedrooms'], max_value=max_dict['bedrooms'], value=min_dict['bedrooms'])
bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=min_dict['bathrooms'])
sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=min_dict['sqft_living'])
sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=min_dict['sqft_lot'])
floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=min_dict['floors'])
waterfront = st.checkbox('Waterfront', value=False)
view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=min_dict['view'])
condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=min_dict['condition'])
sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=min_dict['sqft_above'])
sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=min_dict['sqft_basement'])
yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=min_dict['yr_built'])
yr_renovated = st.slider('Year Renovated', min_value=min_dict['yr_renovated'], max_value=max_dict['yr_renovated'], value=min_dict['yr_renovated'])

if (bedrooms != min_dict['bedrooms'] or
    bathrooms != min_dict['bathrooms'] or
    sqft_living != min_dict['sqft_living'] or
    sqft_lot != min_dict['sqft_lot'] or
    floors != min_dict['floors'] or
    waterfront != min_dict['waterfront'] or
    view != min_dict['view'] or
    condition != min_dict['condition'] or
    sqft_above != min_dict['sqft_above'] or
    sqft_basement != min_dict['sqft_basement'] or
    yr_built != min_dict['yr_built'] or
    yr_renovated != min_dict['yr_renovated']):
    
    new_pred = init_new_pred()
    new_pred['bedrooms'] = bedrooms
    new_pred['bathrooms'] = bathrooms
    new_pred['sqft_living'] = sqft_living
    new_pred['sqft_lot'] = sqft_lot
    new_pred['floors'] = floors
    new_pred['waterfront'] = int(waterfront)
    new_pred['view'] = view
    new_pred['condition'] = condition
    new_pred['sqft_above'] = sqft_above
    new_pred['sqft_basement'] = sqft_basement
    new_pred['yr_built'] = yr_built
    new_pred['yr_renovated'] = yr_renovated
    new_pred[f'city_{city}'] = 1

    # Process the prediction
    new_pred = pd.DataFrame([new_pred])
    new_pred = create_new_features(new_pred)
    new_pred = normalize(new_pred)

    # Predict the price
    predicted_price = model.predict(new_pred)
    
    # Display the predicted price at the top of the app
    price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")