app.py

import streamlit as st
import requests
import xmltodict
import pandas as pd
from datetime import datetime, timedelta
import streamlit.components.v1 as components
import plotly.express as px
import time
import plotly.io as pio
import httpx
from openai import OpenAI

# plotly의 JSON 직렬화 엔진을 기본 json으로 설정
pio.json.config.default_engine = 'json'

# 페이지 설정
st.set_page_config(
    page_title="우리집 날씨 정보", 
    page_icon="🌤️", 
    layout="wide",
    menu_items={
        'Get Help': None,
        'Report a bug': None,
        'About': None
    }
)

# CSS 스타일
st.markdown("""
    <style>
        section[data-testid="stSidebar"] {
            display: none;
        }
        #MainMenu {
            display: none;
        }
        header {
            display: none;
        }
        .block-container {
            padding: 0 !important;
            max-width: 100% !important;
        }
        .element-container {
            margin: 0 !important;
        }
        .stApp > header {
            display: none;
        }
        #other-info {
            display: none;
        }
        .stPlotlyChart {
            width: 100%;
            margin: 0 !important;
            padding: 0 !important;
        }
        [data-testid="stMetricValue"] {
            font-size: 3rem;
        }
        .time-container {
            width: 100%;
            text-align: center;
            margin: 0 auto;
            padding: 15px 0;
        }
        .date-text {
            font-size: 8em !important;
            font-weight: bold !important;
            color: rgb(0, 0, 0) !important;
            font-family: Arial, sans-serif !important;
            text-shadow: none !important;
            background: transparent !important;
            display: block !important;
            line-height: 1.2 !important;
            margin-bottom: 0.5px !important;
        }
        h1, h2, h3, h4, h5, h6, p, .stMetric > div > div {
            color: black !important;
        }
        .plotly-graph-div {
            overflow-x: scroll !important;
            min-width: 100% !important;
        }
        div[data-testid="stVerticalBlock"] > div {
            padding: 0 !important;
        }
        .main {
            padding: 0 !important;
        }
        .stApp {
            margin: 0 !important;
        }
        [data-testid="stHeader"] {
            display: none;
        }
        .section-container {
            position: absolute;
            top: 0;
            left: 0;
            width: 100%;
            height: 100vh;
            padding: 1rem;
            box-sizing: border-box;
            background-color: inherit;
        }
        .graph-container {
            width: 100%;
            height: calc(100vh - 100px);
            display: flex;
            flex-direction: column;
            align-items: center;
        }
        iframe {
            margin: 0 !important;
            padding: 0 !important;
        }
        [data-testid="column"] {
            padding: 0 !important;
        }
        [data-testid="stVerticalBlock"] {
            padding: 0 !important;
            gap: 0 !important;
        }
        .dust-status {
            font-size: 2em;
            font-weight: bold;
            color: black;
            padding: 0.3rem 1rem;
            border-radius: 1rem;
            box-shadow: 0 2px 4px rgba(0,0,0,0.1);
            display: inline-block;
        }
        @keyframes scroll-text {
            from {
                transform: translateX(100%);
            }
            to {
                transform: translateX(-100%);
            }
        }
        .scroll-container {
            position: fixed;
            bottom: 20px;
            left: 0;
            width: 100%;
            overflow: hidden;
            background-color: rgba(255, 255, 255, 0.9);
            padding: 10px 0;
            z-index: 1000;
        }
        .scroll-text {
            display: inline-block;
            white-space: nowrap;
            animation: scrolling 60s linear infinite;
            font-size: 1.5em;
            font-weight: bold;
            color: #333;
        }
        @keyframes scrolling {
            0% {transform: translateX(100%);}
            100% {transform: translateX(-100%);}
        }
    </style>
""", unsafe_allow_html=True)


def get_korean_weekday(date):
    weekday = date.strftime('%a')
    weekday_dict = {
        'Mon': '월',
        'Tue': '화',
        'Wed': '수',
        'Thu': '목',
        'Fri': '금',
        'Sat': '토',
        'Sun': '일'
    }
    return weekday_dict[weekday]

@st.cache_data(ttl=300)  # 5분마다 캐시 갱신
def get_weather_data():
    url = "http://openapi.seoul.go.kr:8088/77544e69764a414d363647424a655a/xml/citydata/1/5/신림역"
    try:
        response = requests.get(url)
        response.raise_for_status()  # HTTPError에 대해 예외 발생
        if not response.text.strip():  # 빈 응답 처리
            raise ValueError("응답이 비어 있습니다.")
        data = xmltodict.parse(response.text)
        return data['SeoulRtd.citydata']['CITYDATA']['WEATHER_STTS']['WEATHER_STTS']
    except requests.exceptions.RequestException as e:
        st.error(f"API 호출 중 오류 발생: {e}")
    except Exception as e:
        st.error(f"데이터를 처리하는 중 오류가 발생했습니다: {e}")
    return None

def get_background_color(pm10_value):
    try:
        pm10 = float(pm10_value)
        if pm10 <= 30:
            return "#87CEEB"  # 파랑 (좋음)
        elif pm10 <= 80:
            return "#90EE90"  # 초록 (보통)
        elif pm10 <= 150:
            return "#FFD700"  # 노랑 (나쁨)
        else:
            return "#FF6B6B"  # 빨강 (매우 나쁨)
    except:
        return "#FFFFFF"  # 기본 흰색


def get_current_sky_status(data):
    current_time = datetime.utcnow() + timedelta(hours=9)
    current_hour = current_time.hour

    forecast_data = data['FCST24HOURS']['FCST24HOURS']
    if not isinstance(forecast_data, list):
        forecast_data = [forecast_data]
    
    closest_forecast = None
    min_time_diff = float('inf')

    for forecast in forecast_data:
        forecast_hour = int(forecast['FCST_DT'][8:10])
        time_diff = abs(forecast_hour - current_hour)
        if time_diff < min_time_diff:
            min_time_diff = time_diff
            closest_forecast = forecast

    return closest_forecast['SKY_STTS'] if closest_forecast else "정보없음"

def format_news_message(news_list):
    if not isinstance(news_list, list):
        news_list = [news_list]

    current_warnings = []
    for news in news_list:
        if not isinstance(news, dict):
            continue

        warn_val = news.get('WARN_VAL', '')
        warn_stress = news.get('WARN_STRESS', '')
        command = news.get('COMMAND', '')
        warn_msg = news.get('WARN_MSG', '')
        announce_time = news.get('ANNOUNCE_TIME', '')

        if announce_time and len(announce_time) == 12:
            year = announce_time[0:4]
            month = announce_time[4:6]
            day = announce_time[6:8]
            hour = announce_time[8:10]
            minute = announce_time[10:12]
            formatted_time = f"({year}년{month}월{day}일{hour}시{minute}분)"
        else:
            formatted_time = ""

        if command == '해제':
            warning_text = f"✅ {warn_val}{warn_stress} 해제 {formatted_time} {warn_msg}"
        else:
            warning_text = f"⚠️ {warn_val}{warn_stress} 발령 {formatted_time} {warn_msg}"
        current_warnings.append(warning_text)

    return ' | '.join(current_warnings)

def show_weather_info(data):
    st.markdown('<div class="section-container">', unsafe_allow_html=True)

    # Add update time display using the last API call timestamp (already in KST)
    refresh_time = datetime.fromtimestamp(st.session_state.last_api_call) if st.session_state.last_api_call else (datetime.utcnow() + timedelta(hours=9))
    st.markdown(f'''
        <div style="text-align: center; font-size: 0.8em; color: gray;">
            Data refreshed at: {refresh_time.strftime('%Y-%m-%d %H:%M:%S')}
        </div>
    ''', unsafe_allow_html=True)

    # Add this code to define formatted_date
    current_time = datetime.utcnow() + timedelta(hours=9)
    weekday = get_korean_weekday(current_time)
    formatted_date = f"{current_time.strftime('%Y-%m-%d')}({weekday})"

    pm10 = float(data['PM10'])
    if pm10 <= 30:
        dust_status = "좋음"
        dust_color = "#87CEEB"  # Blue
    elif pm10 <= 80:
        dust_status = "보통"
        dust_color = "#90EE90"  # Green
    elif pm10 <= 150:
        dust_status = "나쁨"
        dust_color = "#FFD700"  # Yellow
    else:
        dust_status = "매우나쁨"
        dust_color = "#FF6B6B"  # Red

    temp = data.get('TEMP', "정보없음")
    precip_type = data.get('PRECPT_TYPE', "정보없음")

    try:
        temp = f"{float(temp):.1f}°C"
    except:
        temp = "정보없음"

    # 현재 시간 기준으로 가장 가까운 06시 데이터 찾기
    morning_six_data = None
    current_time = datetime.utcnow() + timedelta(hours=9)  # KST
    
    forecast_data = data['FCST24HOURS']['FCST24HOURS']
    if not isinstance(forecast_data, list):
        forecast_data = [forecast_data]

    for fcst in forecast_data:
        fcst_hour = int(fcst['FCST_DT'][8:10])  # HH
        if fcst_hour == 6:
            fcst_datetime = datetime.strptime(fcst['FCST_DT'], '%Y%m%d%H%M')
            if fcst_datetime > current_time:
                morning_six_data = fcst
                break

    # 06시 날씨 정보 준비
    tomorrow_morning_weather = "없음"
    if morning_six_data:
        tomorrow_temp = morning_six_data['TEMP']
        weather_icon = ""
        
        # PRECPT_TYPE 먼저 확인
        precip_type = morning_six_data['PRECPT_TYPE']
        if precip_type == "비" or precip_type == "비/눈":
            weather_icon = "☔"
        elif precip_type == "눈":
            weather_icon = "❄"
        # PRECPT_TYPE이 '없음'이면 SKY_STTS 기반으로 아이콘 설정
        else:
            if morning_six_data['SKY_STTS'] == "맑음":
                weather_icon = "🌞"
            elif morning_six_data['SKY_STTS'] in ["구름", "구름많음"]:
                weather_icon = "⛅"
            elif morning_six_data['SKY_STTS'] == "흐림":
                weather_icon = "☁️"
        
        tomorrow_morning_weather = f"{tomorrow_temp}°C {weather_icon}"

    # 화면에 표시
    weather_icon = ""
    current_time_str = current_time.strftime('%Y%m%d%H')
    
    # Check current precipitation type first
    if data['PRECPT_TYPE'] in ["비", "눈", "비/눈", "빗방울"]:
        if data['PRECPT_TYPE'] in ["비", "빗방울"]:
            weather_icon = "☔"
        elif data['PRECPT_TYPE'] == "눈":
            weather_icon = "❄"
        elif data['PRECPT_TYPE'] == "비/눈":
            weather_icon = "☔❄"
    else:
        # Find nearest forecast time when no current precipitation
        nearest_forecast = None
        min_time_diff = float('inf')
        
        for forecast in forecast_data:
            forecast_time = datetime.strptime(forecast['FCST_DT'], '%Y%m%d%H%M')
            time_diff = abs((forecast_time - current_time).total_seconds())
            if time_diff < min_time_diff:
                min_time_diff = time_diff
                nearest_forecast = forecast
        
        if nearest_forecast:
            if nearest_forecast['PRECPT_TYPE'] in ["비", "눈", "비/눈", "빗방울"]:
                if nearest_forecast['PRECPT_TYPE'] in ["비", "빗방울"]:
                    weather_icon = "☔"
                elif nearest_forecast['PRECPT_TYPE'] == "눈":
                    weather_icon = "❄"
                elif nearest_forecast['PRECPT_TYPE'] == "비/눈":
                    weather_icon = "☔❄"
            else:
                # Use SKY_STTS when no precipitation
                sky_status = nearest_forecast['SKY_STTS']
                if sky_status == "맑음":
                    weather_icon = "🌞"
                elif sky_status in ["구름", "구름많음"]:
                    weather_icon = "⛅"
                elif sky_status == "흐림":
                    weather_icon = "☁"
    
    precip_mark = weather_icon
    st.markdown(f'''
        <div class="time-container">
            <div style="text-align: center; margin-bottom: 0.5rem; font-size: 6em; font-weight: bold; color: black;">
                {temp}{precip_mark} &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; {tomorrow_morning_weather}
            </div>
            <span class="date-text">{formatted_date}</span>
        </div>
    ''', unsafe_allow_html=True)


    clock_html = """
        <div style="width: 100%; max-width: 1200px; margin: 0 auto; padding: 0 20px;">
        <div style="text-align: center; height: 300px; display: flex; align-items: center; justify-content: center;">
        <span id="clock" style="font-size: 15em; font-weight: bold; color: black; line-height: 1.2; white-space: nowrap;"></span>
        </div>
        </div>
        <script>
        function updateClock() {
            const now = new Date();
            const options = {
                timeZone: 'Asia/Seoul',
                hour12: true,
                hour: 'numeric',
                minute: '2-digit'
            };
            document.getElementById('clock').textContent = now.toLocaleTimeString('ko-KR', options);
        }
        setInterval(updateClock, 1000);
        updateClock();
        </script>   
    """
    components.html(clock_html, height=300)

    st.button("시간대별 온도 보기", on_click=lambda: st.session_state.update({'current_section': 'temperature'}))

    st.markdown('</div>', unsafe_allow_html=True)


def show_temperature_graph(data):
    st.markdown('<div class="section-container">', unsafe_allow_html=True)
    st.markdown('<h1 style="text-align: center; margin-bottom: 1rem;">시간대별 온도</h1>', unsafe_allow_html=True)

    forecast_data = data['FCST24HOURS']['FCST24HOURS']
    if not isinstance(forecast_data, list):
        forecast_data = [forecast_data]
    
    # Sort forecast data by FCST_DT to ensure correct time ordering
    forecast_data = sorted(forecast_data, key=lambda x: x['FCST_DT'])
    
    # 현재 시간 기준으로 유효한 예보 데이터만 필터링
    current_time = datetime.utcnow() + timedelta(hours=9)  # KST
    current_date = current_time.strftime('%Y%m%d')
    next_date = (current_time + timedelta(days=1)).strftime('%Y%m%d')
    
    # 현재 시간 이후의 예보 데이터와 다음 날의 데이터 모두 포함
    valid_forecast_data = []
    for fcst in forecast_data:
        fcst_date = fcst['FCST_DT'][:8]  # YYYYMMDD
        fcst_hour = int(fcst['FCST_DT'][8:10])  # HH
        current_hour = current_time.hour
        
        # 현재 날짜의 현재 시간 이후 데이터 또는 다음 날의 데이터
        if (fcst_date == current_date and fcst_hour >= current_hour) or fcst_date == next_date:
            valid_forecast_data.append(fcst)
    
    # 유효한 데이터가 없으면 전체 데이터 사용
    if not valid_forecast_data:
        valid_forecast_data = forecast_data

    # 현재 시각과 가장 가까운 예보 시간 찾기
    current_time = datetime.utcnow() + timedelta(hours=9)
    
    # 녹색 세로선 추가 및 "현재" 텍스트 표시 - 이제 항상 첫 번째 데이터 포인트에 표시
    time_differences = []
    for fcst in valid_forecast_data:
        forecast_time = datetime.strptime(fcst['FCST_DT'], '%Y%m%d%H%M')
        time_diff = abs((forecast_time - current_time).total_seconds())
        time_differences.append(time_diff)
    
    current_index = time_differences.index(min(time_differences))
    
    # Reorder forecast data to start from current time
    valid_forecast_data = valid_forecast_data[current_index:] + valid_forecast_data[:current_index]

    times = []
    temps = []
    weather_icons = []
    weather_descriptions = []
    date_changes = []

    for i, forecast in enumerate(valid_forecast_data):
        time_str = forecast['FCST_DT']
        date = time_str[6:8]
        hour = time_str[8:10]

        if i > 0 and valid_forecast_data[i-1]['FCST_DT'][6:8] != date:
            date_changes.append(i)
        times.append(f"{hour}시")
        temps.append(float(forecast['TEMP']))

        sky_status = forecast['SKY_STTS']
        precip_type = forecast['PRECPT_TYPE']

        if precip_type == "비":
            icon = "☔"
            description = "비"
        elif precip_type == "눈":
            icon = "❄"
            description = "눈"
        elif precip_type == "비/눈":
            icon = "☔❄"
            description = "비/눈"
        elif sky_status == "맑음":
            icon = "🌞"
            description = "맑음"
        elif sky_status in ["구름", "구름많음"]:
            icon = "⛅"
            description = "구름" if sky_status == "구름" else "구름<br>많음"
        elif sky_status == "흐림":
            icon = "☁️"
            description = "흐림"
        else:
            icon = "☀️"
            description = "정보없음"

        weather_icons.append(icon)
        weather_descriptions.append(description)

    df = pd.DataFrame({
        '시간': times,
        '기온': temps,
        '날씨': weather_icons,
        '설명': weather_descriptions,
        'FCST_DT': [f['FCST_DT'] for f in valid_forecast_data]
    })

    fig = px.line(df, x='시간', y='기온', markers=True)

    # Add nighttime overlay (18:00-06:00)
    for i in range(len(times)):
        hour = int(times[i].replace('시', ''))
        if hour >= 18 or hour < 6:
            fig.add_vrect(
                x0=times[i],
                x1=times[i+1] if i < len(times)-1 else times[-1],
                fillcolor='rgba(0, 0, 0, 0.1)',
                layer='below',
                line_width=0,
                annotation_text="",
                annotation_position="top left"
            )

    # 녹색 세로선 추가 및 "현재" 텍스트 표시
    fig.add_vline(x=times[0], line_width=2, line_dash="dash", line_color="green")
    fig.add_annotation(
        x=times[0],
        y=max(temps) + 4,
        text="<b>현재</b>",
        showarrow=True,
        arrowhead=2,
    )

    bold_times = ["00시", "06시", "12시", "18시", "24시"]
    for time in bold_times:
        if time in times:
            index = times.index(time)
            fig.add_annotation(
                x=time,
                y=min(temps) - 3,
                text=time,
                showarrow=False,
                font=dict(size=30, color="black", family="Arial")
            )

    fig.add_vline(x='12시', line_width=2, line_dash="dash", line_color="rgba(0,0,0,0.5)")
    
    # 오늘과 내일, 오전과 오후 텍스트는 해당 시간대의 데이터가 있을 때만 표시
    time_set = set(times)
    current_date = datetime.utcnow() + timedelta(hours=9)  # KST
    current_hour = current_date.hour

    if '11시' in time_set:
        fig.add_annotation(x='11시', y=max(temps) + 4, text="오전", showarrow=False, font=dict(size=24))
    
    if '13시' in time_set:
        fig.add_annotation(x='13시', y=max(temps) + 4, text="오후", showarrow=False, font=dict(size=24))
    
    # 시간 순서대로 정렬된 데이터라고 가정
    for i, time in enumerate(times):
        hour = int(time.replace('시', ''))
        
        # 현재 시각이 23시이고, times[0]이 00시라면 첫 번째 23시가 오늘 23시
        if hour == 23 and times[0] == '00시':
            if i == 0:  # 첫 번째 23시 (오늘 23시)
                fig.add_annotation(x=time, y=max(temps) + 4, text="오늘", showarrow=False, font=dict(size=24))
        
        # 01시는 다음 날이므로 "내일" 표시 (00시 다음에 오는 01시)
        if hour == 1 and i > 0 and times[i-1] == '00시':
            fig.add_annotation(x=time, y=max(temps) + 4, text="내일", showarrow=False, font=dict(size=24))

    fig.update_traces(
        line_color='#FF6B6B',
        marker=dict(size=10, color='#FF6B6B'),
        textposition="top center",
        mode='lines+markers+text',
        text=[f"<b>{int(round(temp))}°</b>" for temp in df['기온']],
        textfont=dict(size=24)
    )

    for i, (icon, description) in enumerate(zip(weather_icons, weather_descriptions)):
        fig.add_annotation(
            x=times[i],
            y=max(temps) + 3,
            text=f"{icon}",
            showarrow=False,
            font=dict(size=30)
        )
        fig.add_annotation(
            x=times[i],
            y=max(temps) + 2,
            text=f"{description}",
            showarrow=False,
            font=dict(size=16),
            textangle=0
        )

    for date_change in date_changes:
        fig.add_vline(
            x=times[date_change],
            line_width=2,
            line_dash="dash",
            line_color="rgba(255, 0, 0, 0.7)"
        )

    fig.update_layout(
        title=None,
        xaxis_title='',
        yaxis_title=None, #'기온 (°C)',
        height=600,
        width=7200,
        showlegend=False,
        plot_bgcolor='rgba(255,255,255,0.9)',
        paper_bgcolor='rgba(0,0,0,0)',
        margin=dict(l=50, r=50, t=0, b=0),
        xaxis=dict(
            tickangle=0,
            tickfont=dict(size=14),
            gridcolor='rgba(0,0,0,0.1)',
            dtick=1,
            tickmode='array',
            ticktext=[f"{i:02d}시" for i in range(24)],
            tickvals=[f"{i:02d}시" for i in range(24)]
        ),
        yaxis=dict(
            tickfont=dict(size=14),
            gridcolor='rgba(0,0,0,0.1)',
            showticklabels=True,  
            tickformat='d',  
            ticksuffix='°C',  
            automargin=True,  
            rangemode='tozero'  
        )
    )

    st.plotly_chart(fig, use_container_width=True)

    # 날씨 예보 생성 및 표시 부분을 세션 상태로 관리
    if 'weather_forecast' not in st.session_state:
        client = OpenAI(
            api_key="glhf_9ea0e0babe1e45353dd03b44cb979e22",
            base_url="https://glhf.chat/api/openai/v1",
            http_client=httpx.Client(
                follow_redirects=True,
                timeout=30.0
            )
        )


        # 현재 시간과 예보 시간 정보 추가
        current_time = datetime.utcnow() + timedelta(hours=9)
        current_date_str = current_time.strftime('%Y년 %m월 %d일')
        tomorrow_date_str = (current_time + timedelta(days=1)).strftime('%Y년 %m월 %d일')

        forecast_data_str = "\n".join([
            f"[{f['FCST_DT'][:4]}년 {f['FCST_DT'][4:6]}월 {f['FCST_DT'][6:8]}일 {f['FCST_DT'][8:10]}시] {temp}도, {description}" 
            for f, time, temp, description in zip(valid_forecast_data, times, temps, weather_descriptions)
        ])

        response = client.chat.completions.create(
            model="hf:Nexusflow/Athene-V2-Chat",
            messages=[
                {"role": "system", "content": "당신은 날씨 예보관입니다. 주어진 시간대별 날씨 데이터를 바탕으로 정확한 날씨 예보를 생성해주세요."},
                {"role": "user", "content": f"""현재 시각은 {current_time.strftime('%H시 %M분')}입니다.

            다음 FCST_DT의 시간대별 날씨 데이터를 보고 실제 날씨 상황에 맞는 정확한 날씨 예보를 200자의 자연스러운 문장으로 만들어주세요. 비나 눈 예보가 있는 경우에만 우산을 준비하도록 안내해주세요. 옷차림은 다음을 참고하세요.
            27°C이상: 반팔티, 반바지, 민소매
            23°C~26°C: 얇은 셔츠, 반팔티, 반바지, 면바지
            20°C~22°C: 얇은 가디건, 긴팔티, 긴바지
            17°C~19°C: 얇은 니트, 가디건, 맨투맨, 얇은 자켓, 긴바지
            12°C~16°C: 자켓, 가디건, 야상, 맨투맨, 니트, 스타킹, 긴바지
            9°C~11°C: 트렌치코트, 야상, 가죽 자켓, 스타킹, 긴바지
            5°C~8°C: 코트, 히트텍, 니트, 긴바지
            4°C이하: 패딩, 두꺼운 코트, 목도리, 기모제품

            시간대별 날씨 데이터:
            {forecast_data_str}"""}
            ]
        )

        st.session_state.weather_forecast = response.choices[0].message.content

    # 저장된 날씨 예보 표시
    st.markdown(f'''
        <div class="scroll-container">
            <div class="scroll-text">{st.session_state.weather_forecast}</div>
        </div>
    ''', unsafe_allow_html=True)

    # 스크롤 텍스트 위에 버튼이 오도록 마진 추가
    st.markdown('''
        <div style="margin-bottom: 10px;">  
    ''', unsafe_allow_html=True)

    # 우리집 날씨 정보로 돌아가기 버튼 추가
    st.button("우리집 날씨 정보로 돌아가기", on_click=lambda: st.session_state.update({'current_section': 'weather'}))

    st.markdown('</div>', unsafe_allow_html=True)

def main():
    if 'current_section' not in st.session_state:
        st.session_state.current_section = 'weather'
        st.session_state.last_api_call = 0
        st.session_state.weather_data = None

    # 현재 시간을 서울 시간으로 가져옵니다.
    current_time = datetime.utcnow() + timedelta(hours=9)
    current_timestamp = current_time.timestamp()

    # 데이터 새로고침 체크
    if 'last_api_call' not in st.session_state:
        st.session_state.last_api_call = 0
    
    time_since_last_call = current_timestamp - st.session_state.last_api_call

    # 자동 새로고침을 위한 placeholder
    refresh_placeholder = st.empty()

    # 데이터 갱신이 필요한 경우
    if not st.session_state.weather_data or time_since_last_call >= 300:
        try:
            new_data = get_weather_data()
            if new_data:  # 새 데이터를 성공적으로 받아왔을 때만 업데이트
                st.session_state.weather_data = new_data
                st.session_state.last_api_call = current_timestamp
                st.rerun()  # 페이지 새로고침
        except Exception as e:
            st.error(f"Failed to refresh data: {str(e)}")

    data = st.session_state.weather_data
    if data:
        pm10_value = data['PM10']
        background_color = get_background_color(pm10_value)

        st.markdown(f"""
            <style>
                .stApp {{
                    background-color: {background_color};
                }}
            </style>
        """, unsafe_allow_html=True)

        if st.session_state.current_section == 'weather':
            show_weather_info(data)
        else:
            show_temperature_graph(data)

    # 자동 새로고침을 위한 타이머
    with refresh_placeholder:
        if time_since_last_call < 300:
            remaining_time = 300 - time_since_last_call
            time.sleep(min(remaining_time, 300))  # 그래프 5분마다 렌더링
            st.rerun()

if __name__ == "__main__":
    main()