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)

# 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 two columns: one for the city and one for the map
col1, col2 = st.columns([1, 2])  # Adjust the width ratios as needed

# Display city dropdown in the first column
with col1:
    st.subheader('Features')
    
    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'],
    )
    waterfront = st.checkbox('Waterfront', value=False)
    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'])
    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'])
        
    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 map in the second column
with col2:
    st.subheader('Map')
    if city == 'Seattle':
        map_data = pd.DataFrame({
            'latitude': [47.6097, 47.6205, 47.6762],
            'longitude': [-122.3331, -122.3493, -122.3198]
        })
    elif city == 'Bellevue':
        map_data = pd.DataFrame({
            'latitude': [47.6101, 47.6183],
            'longitude': [-122.2015, -122.2046]
        })
    elif city == 'Algona':
        map_data = pd.DataFrame({
            'latitude': [47.3162],
            'longitude': [-122.2295]
        })
    elif city == 'Auburn':
        map_data = pd.DataFrame({
            'latitude': [47.3073],
            'longitude': [-122.2284]
        })
    elif city == 'Beaux Arts Village':
        map_data = pd.DataFrame({
            'latitude': [47.6141],
            'longitude': [-122.2125]
        })
    elif city == 'Black Diamond':
        map_data = pd.DataFrame({
            'latitude': [47.3465],
            'longitude': [-121.9877]
        })
    elif city == 'Bothell':
        map_data = pd.DataFrame({
            'latitude': [47.7595],
            'longitude': [-122.2056]
        })
    elif city == 'Burien':
        map_data = pd.DataFrame({
            'latitude': [47.4702],
            'longitude': [-122.3359]
        })
    elif city == 'Carnation':
        map_data = pd.DataFrame({
            'latitude': [47.6460],
            'longitude': [-121.9758]
        })
    elif city == 'Clyde Hill':
        map_data = pd.DataFrame({
            'latitude': [47.6330],
            'longitude': [-122.2107]
        })
    elif city == 'Covington':
        map_data = pd.DataFrame({
            'latitude': [47.3765],
            'longitude': [-122.0288]
        })
    elif city == 'Des Moines':
        map_data = pd.DataFrame({
            'latitude': [47.3840],
            'longitude': [-122.3061]
        })
    elif city == 'Duvall':
        map_data = pd.DataFrame({
            'latitude': [47.7332],
            'longitude': [-121.9916]
        })
    elif city == 'Enumclaw':
        map_data = pd.DataFrame({
            'latitude': [47.2059],
            'longitude': [-121.9876]
        })
    elif city == 'Fall City':
        map_data = pd.DataFrame({
            'latitude': [47.5980],
            'longitude': [-121.8896]
        })
    elif city == 'Federal Way':
        map_data = pd.DataFrame({
            'latitude': [47.3220],
            'longitude': [-122.3126]
        })
    elif city == 'Inglewood-Finn Hill':
        map_data = pd.DataFrame({
            'latitude': [47.7338],
            'longitude': [-122.2780]
        })
    elif city == 'Issaquah':
        map_data = pd.DataFrame({
            'latitude': [47.5410],
            'longitude': [-122.0311]
        })
    elif city == 'Kenmore':
        map_data = pd.DataFrame({
            'latitude': [47.7557],
            'longitude': [-122.2416]
        })
    elif city == 'Kent':
        map_data = pd.DataFrame({
            'latitude': [47.3809],
            'longitude': [-122.2348]
        })
    elif city == 'Kirkland':
        map_data = pd.DataFrame({
            'latitude': [47.6810],
            'longitude': [-122.2087]
        })
    elif city == 'Lake Forest Park':
        map_data = pd.DataFrame({
            'latitude': [47.7318],
            'longitude': [-122.2764]
        })
    elif city == 'Maple Valley':
        map_data = pd.DataFrame({
            'latitude': [47.3610],
            'longitude': [-122.0240]
        })
    elif city == 'Medina':
        map_data = pd.DataFrame({
            'latitude': [47.6357],
            'longitude': [-122.2169]
        })
    elif city == 'Mercer Island':
        map_data = pd.DataFrame({
            'latitude': [47.5703],
            'longitude': [-122.2264]
        })
    elif city == 'Milton':
        map_data = pd.DataFrame({
            'latitude': [47.2335],
            'longitude': [-122.2730]
        })
    elif city == 'Newcastle':
        map_data = pd.DataFrame({
            'latitude': [47.5477],
            'longitude': [-122.1711]
        })
    elif city == 'Normandy Park':
        map_data = pd.DataFrame({
            'latitude': [47.4051],
            'longitude': [-122.3376]
        })
    elif city == 'North Bend':
        map_data = pd.DataFrame({
            'latitude': [47.4904],
            'longitude': [-121.7852]
        })
    elif city == 'Pacific':
        map_data = pd.DataFrame({
            'latitude': [47.3197],
            'longitude': [-122.2786]
        })
    elif city == 'Preston':
        map_data = pd.DataFrame({
            'latitude': [47.5420],
            'longitude': [-121.9214]
        })
    elif city == 'Ravensdale':
        map_data = pd.DataFrame({
            'latitude': [47.3485],
            'longitude': [-121.9807]
        })
    elif city == 'Redmond':
        map_data = pd.DataFrame({
            'latitude': [47.6734],
            'longitude': [-122.1215]
        })
    elif city == 'Renton':
        map_data = pd.DataFrame({
            'latitude': [47.4829],
            'longitude': [-122.2170]
        })
    elif city == 'Sammamish':
        map_data = pd.DataFrame({
            'latitude': [47.6162],
            'longitude': [-122.0394]
        })
    elif city == 'SeaTac':
        map_data = pd.DataFrame({
            'latitude': [47.4484],
            'longitude': [-122.3085]
        })
    elif city == 'Shoreline':
        map_data = pd.DataFrame({
            'latitude': [47.7554],
            'longitude': [-122.3410]
        })
    elif city == 'Skykomish':
        map_data = pd.DataFrame({
            'latitude': [47.7054],
            'longitude': [-121.4848]
        })
    elif city == 'Snoqualmie':
        map_data = pd.DataFrame({
            'latitude': [47.5410],
            'longitude': [-121.8340]
        })
    elif city == 'Snoqualmie Pass':
        map_data = pd.DataFrame({
            'latitude': [47.4286],
            'longitude': [-121.4420]
        })
    elif city == 'Tukwila':
        map_data = pd.DataFrame({
            'latitude': [47.4835],
            'longitude': [-122.2585]
        })
    elif city == 'Vashon':
        map_data = pd.DataFrame({
            'latitude': [47.4337],
            'longitude': [-122.4660]
        })
    elif city == 'Woodinville':
        map_data = pd.DataFrame({
            'latitude': [47.7524],
            'longitude': [-122.1576]
        })
    elif city == 'Yarrow Point':
        map_data = pd.DataFrame({
            'latitude': [47.6348],
            'longitude': [-122.2218]
        })

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

    # Display the predicted price at the top of the app
    # price_placeholder.write(f"Predicted Price: ${predicted_price[0][0]:,.2f}")
    price_placeholder.markdown(
        f"<h1 style='font-size: 24px;'>Predicted Price: ${predicted_price[0][0]:,.2f}</h1>",
        unsafe_allow_html=True
    )