app.py

import streamlit as st
import pandas as pd
import joblib

@st.cache_resource
def load_model():
    data = joblib.load("model.pkl")
    return data["model"], data["features"]

model, feature_names = load_model()

st.title("PROPERTY PRICE PREDICTION TOOL (Streamlit Version)")
st.markdown("Predict the price of a new property based on District, Longitude, Latitude, Floor, Unit, Area, Year, and Week Number.")

district = st.selectbox("District (1 = Taikoo Shing, 2 = Mei Foo Sun Chuen, 3 = South Horizons, 4 = Whampoa Garden)", list(range(1, 9)))
longitude = st.number_input("Longitude", value=114.200)
latitude = st.number_input("Latitude", value=22.300)
floor = st.selectbox("Floor", list(range(1, 71)))
unit = st.selectbox("Unit (e.g., A=1, B=2, C=3, ...)", list(range(1, 31)))
area = st.slider("Area (in sq. feet)", min_value=137, max_value=5000, value=300)
year = st.selectbox("Year", [2024, 2025])
weeknumber = st.selectbox("Week Number", list(range(1, 53)))

if st.button("Predict"):
    new_data = [district, longitude, latitude, floor, unit, area, year, weeknumber]
    df_new = pd.DataFrame([new_data], columns=feature_names)
    prediction = model.predict(df_new)
    st.success(f"🏠 Estimated Price: **${prediction[0]:,.2f} Million**")