app.py

import os
os.environ["PGEOCODE_CACHE_DIR"] = "/tmp/pgeocode"

import streamlit as st
import pandas as pd
import joblib

def create_dataframe_from_user_input():
    """
    Collects user input for house features using Streamlit and
    returns a Pandas DataFrame.  The input fields are organized
    into categorized sections for better usability.
    """

    # Define the lists of possible values for dropdown selections
    type_list = ['HOUSE', 'APARTMENT']
    subtype_list = ['HOUSE', 'APARTMENT', 'VILLA', 'APARTMENT_BLOCK', 'APARTMENT_GROUP',
                     'MIXED_USE_BUILDING', 'GROUND_FLOOR', 'DUPLEX', 'HOUSE_GROUP',
                     'FLAT_STUDIO', 'PENTHOUSE', 'EXCEPTIONAL_PROPERTY', 'MANSION',
                     'TOWN_HOUSE', 'SERVICE_FLAT', 'BUNGALOW', 'KOT', 'COUNTRY_COTTAGE',
                     'FARMHOUSE', 'LOFT', 'CHALET', 'TRIPLEX', 'CASTLE', 'OTHER_PROPERTY',
                     'MANOR_HOUSE', 'PAVILION']
    province_list = ['West Flanders', 'Antwerp', 'East Flanders', 'Brussels', 'Hainaut',
                     'Liège', 'Flemish Brabant', 'Limburg', 'Walloon Brabant', 'Namur',
                     'Luxembourg']
    building_condition_list = ['GOOD', 'AS_NEW', 'TO_RENOVATE', 'TO_BE_DONE_UP',
                               'JUST_RENOVATED', 'TO_RESTORE']
    flood_zone_type_list = ['NON_FLOOD_ZONE', 'POSSIBLE_FLOOD_ZONE', 'RECOGNIZED_FLOOD_ZONE',
                            'RECOGNIZED_N_CIRCUMSCRIBED_FLOOD_ZONE', 'CIRCUMSCRIBED_WATERSIDE_ZONE',
                            'CIRCUMSCRIBED_FLOOD_ZONE', 'POSSIBLE_N_CIRCUMSCRIBED_FLOOD_ZONE',
                            'POSSIBLE_N_CIRCUMSCRIBED_WATERSIDE_ZONE', 'RECOGNIZED_N_CIRCUMSCRIBED_WATERSIDE_ZONE']
    heating_type_list = ['GAS', 'FUELOIL', 'ELECTRIC', 'PELLET', 'WOOD', 'SOLAR', 'CARBON']
    kitchen_type_list = ['INSTALLED', 'HYPER_EQUIPPED', 'SEMI_EQUIPPED', 'NOT_INSTALLED',
                         'USA_HYPER_EQUIPPED', 'USA_INSTALLED', 'USA_SEMI_EQUIPPED',
                         'USA_UNINSTALLED']
    garden_orientation_list = ['SOUTH', 'SOUTH_WEST', 'SOUTH_EAST', 'WEST', 'EAST',
                               'NORTH_WEST', 'NORTH_EAST', 'NORTH']
    terrace_orientation_list = ['SOUTH', 'SOUTH_WEST', 'SOUTH_EAST', 'WEST', 'EAST',
                               'NORTH_WEST', 'NORTH_EAST', 'NORTH']
    epc_score_list = ['B', 'C', 'D', 'A', 'F', 'E', 'G', 'A+', 'A++']

    # Create Streamlit input fields
    st.header("Enter House Information")

    # --- Property Details ---
    st.subheader("Property Details")
    col1, col2 = st.columns(2)
    with col1:
        property_type = st.selectbox("Property Type", type_list, key='type')
        property_subtype = st.selectbox("Subtype", subtype_list, key='subtype')
        province = st.selectbox("Province", province_list, key='province')
        locality = st.text_input("Locality", key='locality')
        post_code = st.number_input("Post Code", min_value=1000, max_value=9999, step=1, key='postCode')
    with col2:
        building_condition = st.selectbox("Building Condition", building_condition_list, key='buildingCondition')
        building_construction_year = st.number_input("Building Construction Year", min_value=1000, max_value=2024, step=1, key='buildingConstructionYear')
        facade_count = st.number_input("Facade Count", min_value=0, step=1, key='facadeCount')
        floor_count = st.number_input("Floor Count", min_value=0, step=1, key='floorCount')
        flood_zone_type = st.selectbox("Flood Zone Type", flood_zone_type_list, key='floodZoneType')
        epc_score = st.selectbox("EPC Score", epc_score_list, key='epcScore')

    # --- Room Information ---
    st.subheader("Room Information")
    col3, col4 = st.columns(2)
    with col3:
        bedroom_count = st.number_input("Bedroom Count", min_value=0, step=1, key='bedroomCount')
        bathroom_count = st.number_input("Bathroom Count", min_value=0, step=1, key='bathroomCount')
        room_count = st.number_input("Room Count", min_value=0, step=1, key='roomCount')
        has_attic = st.selectbox("Has Attic", ['Yes', 'No'], key='hasAttic')
        has_basement = st.selectbox("Has Basement", ['Yes', 'No'], key='hasBasement')
        has_dressing_room = st.selectbox("Has Dressing Room", ['Yes', 'No'], key='hasDressingRoom')
        has_dining_room = st.selectbox("Has Dining Room", ['Yes', 'No'], key='hasDiningRoom')
        dining_room_surface = st.number_input("Dining Room Surface (sqm)", min_value=0.0, key='diningRoomSurface')
    with col4:
        has_living_room = st.selectbox("Has Living Room", ['Yes', 'No'], key='hasLivingRoom')
        living_room_surface = st.number_input("Living Room Surface (sqm)", min_value=0.0, key='livingRoomSurface')
        kitchen_surface = st.number_input("Kitchen Surface (sqm)", min_value=0.0, key='kitchenSurface')
        kitchen_type = st.selectbox("Kitchen Type", kitchen_type_list, key='kitchenType')
        toilet_count = st.number_input("Toilet Count", min_value=0, step=1, key='toiletCount')
        has_office = st.selectbox("Has Office", ['Yes', 'No'], key='hasOffice')
        has_lift = st.selectbox("Has Lift", ['Yes', 'No'], key='hasLift')

    # --- Surface Areas ---
    st.subheader("Surface Areas")
    col5, col6 = st.columns(2)
    with col5:
        habitable_surface = st.number_input("Habitable Surface (sqm)", min_value=0.0, key='habitableSurface')
        land_surface = st.number_input("Land Surface (sqm)", min_value=0.0, key='landSurface')
        garden_surface = st.number_input("Garden Surface (sqm)", min_value=0.0, key='gardenSurface')
    with col6:
        terrace_surface = st.number_input("Terrace Surface (sqm)", min_value=0.0, key='terraceSurface')
        street_facade_width = st.number_input("Street Facade Width (m)", min_value=0.0, key='streetFacadeWidth')
        monthly_cost = st.number_input("Monthly Cost (€)", min_value=0.0, key='monthlyCost')

    # --- Outdoor Features ---
    st.subheader("Outdoor Features")
    col7, col8 = st.columns(2)
    with col7:
        has_garden = st.selectbox("Has Garden", ['Yes', 'No'], key='hasGarden')
        garden_orientation = st.selectbox("Garden Orientation", garden_orientation_list, key='gardenOrientation')
        has_balcony = st.selectbox("Has Balcony", ['Yes', 'No'], key='hasBalcony')
        has_terrace = st.selectbox("Has Terrace", ['Yes', 'No'], key='hasTerrace')
        terrace_orientation = st.selectbox("Terrace Orientation", terrace_orientation_list, key='terraceOrientation')
    with col8:
        parking_count_indoor = st.number_input("Indoor Parking Count", min_value=0, step=1, key='parkingCountIndoor')
        parking_count_outdoor = st.number_input("Outdoor Parking Count", min_value=0, step=1, key='parkingCountOutdoor')
        has_swimming_pool = st.selectbox("Has Swimming Pool", ['Yes', 'No'], key='hasSwimmingPool')

    # --- Additional Features ---
    st.subheader("Additional Features")
    col9, col10 = st.columns(2)
    with col9:
        heating_type = st.selectbox("Heating Type", heating_type_list, key='heatingType')
        has_heat_pump = st.selectbox("Has Heat Pump", ['Yes', 'No'], key='hasHeatPump')
        has_photovoltaic_panels = st.selectbox("Has Photovoltaic Panels", ['Yes', 'No'], key='hasPhotovoltaicPanels')
        has_thermic_panels = st.selectbox("Has Thermic Panels", ['Yes', 'No'], key='hasThermicPanels')
    with col10:
        has_air_conditioning = st.selectbox("Has Air Conditioning", ['Yes', 'No'], key='hasAirConditioning')
        has_armored_door = st.selectbox("Has Armored Door", ['Yes', 'No'], key='hasArmoredDoor')
        has_visiophone = st.selectbox("Has Visiophone", ['Yes', 'No'], key='hasVisiophone')
        has_fireplace = st.selectbox("Has Fireplace", ['Yes', 'No'], key='hasFireplace')
        accessible_disabled_people = st.selectbox("Accessible Disabled People", ['True', 'False'], key='accessibleDisabledPeople')

    # Create a button to trigger DataFrame creation
    if st.button("Predict"):
        # Create the DataFrame
        data = {
            'type': property_type,
            'subtype': property_subtype,
            'bedroomCount': bedroom_count,
            'bathroomCount': bathroom_count,
            'province': province,
            'locality': locality,
            'postCode': post_code,
            'habitableSurface': habitable_surface,
            'roomCount': room_count,
            'monthlyCost': monthly_cost,
            'hasAttic': has_attic == 'Yes',
            'hasBasement': has_basement == 'Yes',
            'hasDressingRoom': has_dressing_room == 'Yes',
            'diningRoomSurface': dining_room_surface,
            'hasDiningRoom': has_dining_room == 'Yes',
            'buildingCondition': building_condition,
            'buildingConstructionYear': building_construction_year,
            'facadeCount': facade_count,
            'floorCount': floor_count,
            'streetFacadeWidth': street_facade_width,
            'hasLift': has_lift == 'Yes',
            'floodZoneType': flood_zone_type,
            'heatingType': heating_type,
            'hasHeatPump': has_heat_pump == 'Yes',
            'hasPhotovoltaicPanels': has_photovoltaic_panels == 'Yes',
            'hasThermicPanels': has_thermic_panels == 'Yes',
            'kitchenSurface': kitchen_surface,
            'kitchenType': kitchen_type,
            'landSurface': land_surface,
            'hasLivingRoom': has_living_room == 'Yes',
            'livingRoomSurface': living_room_surface,
            'hasBalcony': has_balcony == 'Yes',
            'hasGarden': has_garden == 'Yes',
            'gardenSurface': garden_surface,
            'gardenOrientation': garden_orientation,
            'parkingCountIndoor': parking_count_indoor,
            'parkingCountOutdoor': parking_count_outdoor,
            'hasAirConditioning': has_air_conditioning == 'Yes',
            'hasArmoredDoor': has_armored_door == 'Yes',
            'hasVisiophone': has_visiophone == 'Yes',
            'hasOffice': has_office == 'Yes',
            'toiletCount': toilet_count,
            'hasSwimmingPool': has_swimming_pool == 'Yes',
            'hasFireplace': has_fireplace == 'Yes',
            'hasTerrace': has_terrace == 'Yes',
            'terraceSurface': terrace_surface,
            'terraceOrientation': terrace_orientation,
            'accessibleDisabledPeople': accessible_disabled_people == 'True',
            'epcScore': epc_score
        }
        df = pd.DataFrame(data, index=[0])
        pipeline = joblib.load('saved/pipeline.pkl')
        model = joblib.load('saved/model.pkl')
        expected_columns = joblib.load('saved/columns.pkl')
        df_test = pipeline.transform(df)
        for col in expected_columns:
            if col not in df_test.columns:
                df_test[col] = 0

        df_test = df_test[expected_columns]
        preds = model.predict(df_test)
        st.subheader("Price prediction")
        st.markdown(f"<h1 style='text-align: center; color: red;'>{preds[0]:.2f} €</h1>", unsafe_allow_html=True)
        return df

if __name__ == "__main__":
    create_dataframe_from_user_input()