import streamlit as st
from geopy.geocoders import Nominatim
import pandas as pd
import folium
from folium.plugins import HeatMap
from geopy.distance import geodesic
import numpy as np
import warnings

warnings.filterwarnings("ignore", category=UserWarning, module="geopy")

geolocator = Nominatim(user_agent="hwaseong_locator")

def get_location_name(lat, lon):
    location = geolocator.reverse((lat, lon), language='ko')
    return location.address if location else "주소를 찾을 수 없음"

def classify_risk(score):
    if score >= 2:
        return "매우 안전"
    elif 1 <= score < 2:
        return "안전"
    elif 0 <= score < 1:
        return "보통"
    elif -1 < score < 0:
        return "위험"
    else:
        return "매우 위험"

hwaseong_center = [37.198, 127.034]

st.title("화성시 위치 위험도 분석기")

lat_input = st.number_input("위도 (Latitude)", value=37.198, format="%.6f")
lon_input = st.number_input("경도 (Longitude)", value=127.034, format="%.6f")

location_name = get_location_name(lat_input, lon_input)
st.write(f"위치: {location_name}")

sample_scores = {
    (37.198, 127.034): 3,
    (37.198, 127.035): -2,
}

def get_score_for_location(lat, lon):
    closest_location = min(sample_scores.keys(), key=lambda x: geodesic((lat, lon), x).meters)
    return sample_scores[closest_location]

score = get_score_for_location(lat_input, lon_input)
risk_category = classify_risk(score)

st.write(f"위험도: {risk_category}")

# 지도 생성 및 저장
m = folium.Map(location=[lat_input, lon_input], zoom_start=16)
folium.Marker([lat_input, lon_input], popup=location_name).add_to(m)
m.save("/tmp/location_map.html")

# HTML로 Streamlit에 시각화
with open("/tmp/location_map.html", "r", encoding="utf-8") as f:
    map_html = f.read()
st.components.v1.html(map_html, height=500)

# 지도 다운로드
st.download_button("위치 지도 다운로드", map_html, file_name="location_map.html")