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import streamlit as st
import json
from autogluon.multimodal import MultiModalPredictor
import pandas as pd
from geopy.geocoders import GoogleV3
import os
import tempfile
def predict_page():
if "price_text" not in st.session_state:
st.session_state.price_text = 0
@st.cache_resource
def load_mm_text_no_price_model():
return MultiModalPredictor.load("models/mm-text-no-price/", verbosity=0)
mm_text_no_price_predictor = load_mm_text_no_price_model()
@st.cache_resource
def load_city_map():
return json.load(open("city-map.json"))
city_map = load_city_map()
@st.cache_resource
def load_city_district_map():
return json.load(open("city-district-map.json"))
city_district_map = load_city_district_map()
CERT_STATUS = pd.CategoricalDtype(
categories=["Không có", "hợp đồng", "sổ đỏ / sổ hồng"], ordered=False
)
DIRECTION = pd.CategoricalDtype(
categories=[
"Không có",
"Tây - Nam",
"Đông - Nam",
"Đông - Bắc",
"Tây - Bắc",
"Nam",
"Tây",
"Bắc",
"Đông",
],
ordered=False,
)
CITY = pd.CategoricalDtype(categories=city_map.keys(), ordered=False)
DISTRICT = pd.CategoricalDtype(
categories=sum([list(map(int, v.keys())) for v in city_district_map.values()], []),
ordered=False,
)
location_options = st.columns([1, 1, 2, 1, 1])
with location_options[0]:
city = st.selectbox(
"Choose city", options=city_map.items(), format_func=lambda x: x[1]
)
with location_options[1]:
district = st.selectbox(
"Choose district",
options=city_district_map[city[0]].items(),
format_func=lambda x: x[1],
)
with location_options[2]:
location = st.text_input("Enter precise location")
location = (location + ", " if location else "") + city[1] + ", " + district[1]
geocode_result = geocoder.geocode(query=location, region="vn", language="vi")
latitude = float("nan")
longitude = float("nan")
with location_options[3]:
latitude = st.number_input(
"Enter latitude", value=latitude, step=1e-8, format="%.7f"
)
with location_options[4]:
longitude = st.number_input(
"Enter longitude", value=longitude, step=1e-8, format="%.7f"
)
numerical_options = st.columns(6)
with numerical_options[0]:
area = st.number_input("Area (m2)", min_value=1.0)
with numerical_options[1]:
bedrooms = st.number_input("Number of bedrooms", min_value=1, value=1)
with numerical_options[2]:
bathrooms = st.number_input("Number of bathrooms", min_value=1, value=1)
with numerical_options[3]:
floors = st.number_input("Number of floors", min_value=1, value=1)
with numerical_options[4]:
front_width = st.number_input(
"Front width, leave 0 for N/A", min_value=0.0, value=0.0, step=0.1
)
with numerical_options[5]:
road_width = st.number_input(
"Road width, leave 0 for N/A", min_value=0.0, value=0.0, step=0.1
)
cat_time_columns = st.columns(4)
with cat_time_columns[0]:
timestamp = st.date_input("Date posted", format="DD/MM/YYYY")
with cat_time_columns[1]:
cert_status = st.selectbox("Certification status", options=CERT_STATUS.categories)
with cat_time_columns[2]:
direction = st.selectbox("Direction", options=DIRECTION.categories)
with cat_time_columns[3]:
balcony_direction = st.selectbox("Balcony direction", options=DIRECTION.categories)
description = st.text_area("Description")
title = description.split(".", maxsplit=1)[0]
uploaded_image = st.file_uploader("Upload an image")
image_tmp = None
if uploaded_image:
image_tmp = tempfile.NamedTemporaryFile(suffix=uploaded_image.name)
image_tmp.write(uploaded_image.read())
print(image_tmp.name)
df = pd.DataFrame(
[
{
"Title": title,
"Area": area,
"Location": location,
"Time stamp": timestamp,
"Certification status": cert_status,
"Direction": direction,
"Bedrooms": bedrooms,
"Bathrooms": bathrooms,
"Front width": front_width or float("nan"),
"Floor": floors,
"Description": description,
"Image URL": image_tmp.name if image_tmp else None,
"Road width": road_width or float("nan"),
"City_code": city[0],
"DistrictId": int(district[0]),
"Lattitude": latitude,
"Longitude": longitude,
"Balcony_Direction": balcony_direction,
}
]
).astype(
{
"Title": "str",
"Area": "float",
"Location": "str",
"Time stamp": "datetime64[ns]",
"Certification status": CERT_STATUS,
"Direction": DIRECTION,
"Bedrooms": "int",
"Bathrooms": "int",
"Front width": "float",
"Floor": "int",
"Description": "str",
"Image URL": "str",
"Road width": "float",
"City_code": CITY,
"DistrictId": DISTRICT,
"Lattitude": "float",
"Longitude": "float",
"Balcony_Direction": DIRECTION,
}
)
if st.button("Get estimated price with text"):
st.session_state.price_text = mm_text_no_price_predictor.predict(
df, as_pandas=False
).item()
st.text(
"Estimated price: {0:,} VND".format(int(st.session_state.price_text * 1e6))
if st.session_state.price_text
else "No price estimated."
)
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