Spaces:
Paused
Paused
| import streamlit as st | |
| import pandas as pd | |
| import pickle | |
| import json | |
| from utils import create_new_features, normalize, bucketize, init_new_pred | |
| 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) | |
| # 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') | |
| 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) | |
| bathrooms = st.slider('Bathrooms', min_value=min_dict['bathrooms'], max_value=max_dict['bathrooms'], value=2) | |
| sqft_living = st.slider('Square Feet (Living)', min_value=min_dict['sqft_living'], max_value=max_dict['sqft_living'], value=1000) | |
| sqft_lot = st.slider('Square Feet (Lot)', min_value=min_dict['sqft_lot'], max_value=max_dict['sqft_lot'], value=2000) | |
| floors = st.slider('Floors', min_value=min_dict['floors'], max_value=max_dict['floors'], value=1) | |
| view = st.slider('View', min_value=min_dict['view'], max_value=max_dict['view'], value=0) | |
| condition = st.slider('Condition', min_value=min_dict['condition'], max_value=max_dict['condition'], value=3) | |
| sqft_above = st.slider('Square Feet (Above)', min_value=min_dict['sqft_above'], max_value=max_dict['sqft_above'], value=1000) | |
| sqft_basement = st.slider('Square Feet (Basement)', min_value=min_dict['sqft_basement'], max_value=max_dict['sqft_basement'], value=0) | |
| yr_built = st.slider('Year Built', min_value=min_dict['yr_built'], max_value=max_dict['yr_built'], value=2000) | |
| yr_renovated = st.slider('Year Renovated', min_value=min_dict['yr_renovated'], max_value=max_dict['yr_renovated'], value=2010) | |
| st.markdown('</div>', unsafe_allow_html=True) | |
| 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 = bucketize(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: | |
| # Placeholder for displaying the predicted price at the top | |
| price_placeholder = st.empty() | |
| price_placeholder.markdown( | |
| f"<h1 style='font-size: 24px;'>Predicted Price: ${predicted_price[0][0]:,.2f}</h1>", | |
| unsafe_allow_html=True) | |
| map_data = pd.DataFrame(cities_geo[city]) | |
| st.map(map_data, zoom=11) |