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| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| import json | |
| import pydeck as pdk | |
| import utils | |
| st.set_page_config(layout="wide") | |
| # Load model and files | |
| with open('./trained_model.pkl', 'rb') as file: | |
| model = pickle.load(file) | |
| with open("./min_dict.json", "r") as f: | |
| min_dict = json.load(f) | |
| with open("./max_dict.json", "r") as f: | |
| max_dict = json.load(f) | |
| with open("./cities_geo.json", "r") as f: | |
| cities_geo = json.load(f) | |
| help_dict = { | |
| 'bedrooms': "Select the number of bedrooms in the house. The value ranges from the minimum to the maximum available in the dataset.", | |
| 'bathrooms': "Select the number of bathrooms in the house. The value ranges from the minimum to the maximum available in the dataset.", | |
| 'yr_built': "Select the year the house was built. The value ranges from the earliest to the latest year available in the dataset.", | |
| 'yr_renovated': "Select the year the house was renovated. This value cannot be earlier than the year the house was built and ranges up to the maximum renovation year in the dataset.", | |
| 'floors': "Select the number of floors in the house. The value ranges from the minimum to the maximum number of floors available in the dataset.", | |
| 'view': "Select the quality of the view from the house. The value ranges from 0 (no view) to the highest view quality available in the dataset.", | |
| 'condition': "Select the overall condition of the house. The value ranges from the poorest to the best condition available in the dataset.", | |
| 'sqft_living': "Select the total square footage of the living space. The value ranges from the minimum to the maximum square footage available in the dataset.", | |
| 'sqft_lot': "Select the total square footage of the lot. The value ranges from the minimum to the maximum lot size available in the dataset.", | |
| 'sqft_above': "Select the square footage of the living space above ground level. The value ranges from the minimum to the maximum above-ground square footage available in the dataset.", | |
| 'sqft_basement': "Select the square footage of the basement. The value ranges from the minimum to the maximum basement square footage available in the dataset." | |
| } | |
| # Custom CSS to adjust the slider width | |
| st.markdown(""" | |
| <style> | |
| .stSlider [data-baseweb=slider]{ | |
| width: 95%; | |
| } | |
| .stCheckbox, .stSelectbox { | |
| width: 95%px; | |
| } | |
| </style> | |
| """,unsafe_allow_html=True) | |
| # Create two columns: one for the city and one for the map | |
| col1, col2 = st.columns([1, 2]) # Adjust the width ratios as needed | |
| with col1: | |
| st.subheader('Features') | |
| with st.container(height=550, border=True): | |
| city = st.selectbox('City', list(cities_geo.keys())) # Display city dropdown in the first column | |
| waterfront = st.checkbox('Waterfront', value=False) | |
| bedrooms = st.slider('Bedrooms', min_value=min_dict['bedrooms'], max_value=max_dict['bedrooms'], value=3, help=help_dict['bedrooms']) | |
| bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=2, help=help_dict['bathrooms']) | |
| yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=2000, help=help_dict['yr_built']) | |
| yr_renovated = st.slider('Year Renovated', min_value=yr_built, max_value=max_dict['yr_renovated'], value=yr_built, help=help_dict['yr_renovated']) | |
| floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=1, help=help_dict['floors']) | |
| view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=0, help=help_dict['view']) | |
| condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=3, help=help_dict['condition']) | |
| sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=1000, help=help_dict['sqft_living']) | |
| sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=2000, help=help_dict['sqft_lot']) | |
| sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=1000, help=help_dict['sqft_above']) | |
| sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=0, help=help_dict['sqft_basement']) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| new_pred = utils.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 = utils.create_new_features(new_pred) | |
| new_pred = utils.bucketize(new_pred) | |
| new_pred = utils.normalize(new_pred) | |
| # Predict the price | |
| predicted_price = model.predict(new_pred) | |
| with col2: | |
| # Placeholder for displaying the predicted price at the top | |
| price_placeholder = st.empty() | |
| price_placeholder.subheader(f'Predicted Price: ${predicted_price[0][0]:,.2f}') | |
| map_data = pd.DataFrame(cities_geo[city]) | |
| with st.container(height=550, border=True): | |
| st.map(map_data, zoom=11) | |