Spaces:
Paused
Paused
File size: 3,352 Bytes
fd7e04e 9ae8083 6676090 7c90a6c 0934492 fd7e04e 1523bf0 5250b05 08625ae cd9721e 1fedf0d 5a66be2 86120d5 5a66be2 86120d5 45576df d4d3bd5 a9b4435 d4d3bd5 a9b4435 8f827eb 86120d5 68a1d92 1523bf0 1fedf0d a86821d 1523bf0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 |
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) |