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
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: 6em !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"

    # 내일 아침 6시 예보 찾기
    tomorrow_morning_weather = "없음"
    forecast_data = data['FCST24HOURS']['FCST24HOURS']
    if not isinstance(forecast_data, list):
        forecast_data = [forecast_data]

    # 현재 시간 기준으로 내일 날짜 계산
    current_time = datetime.utcnow() + timedelta(hours=9)
    tomorrow = current_time + timedelta(days=1)
    tomorrow_date = tomorrow.strftime('%Y%m%d')

    for forecast in forecast_data:
        # 날짜와 시간이 모두 일치하는지 확인
        if forecast['FCST_DT'][:8] == tomorrow_date and forecast['FCST_DT'][8:10] == '06':
            tomorrow_temp = forecast['TEMP']
            precip_type = forecast['PRECPT_TYPE']
            
            # 강수 타입에 따른 표시
            precip_mark = ""
            if precip_type == "비":
                precip_mark = "(비)"
            elif precip_type == "눈":
                precip_mark = "(눈)"
            elif precip_type == "비/눈":
                precip_mark = "(비/눈)"
                
            tomorrow_morning_weather = f"{tomorrow_temp}°C{precip_mark}"
            break

    # 화면에 표시
    st.markdown(f'''
        <div class="time-container">
            <div style="text-align: center; margin-bottom: 0.5rem; font-size: 10em; font-weight: bold; color: black;">
                {temp} &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)
    current_time_str = current_time.strftime('%Y%m%d%H%M')
    
    # 현재 시간 이후의 예보 데이터만 필터링
    valid_forecast_data = [fcst for fcst in forecast_data if fcst['FCST_DT'] >= current_time_str]
    
    # 유효한 데이터가 없으면 전체 데이터 사용
    if not valid_forecast_data:
        valid_forecast_data = forecast_data
    
    times = []
    temps = []
    weather_icons = []
    weather_descriptions = []
    date_changes = []

    # Find the index closest to current time
    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]

    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 == "구름많음":
            icon = "⛅"
            description = "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"
            )

    # 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
    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),
                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)

    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))
    
    if '23시' in time_set:
        fig.add_annotation(x='23시', y=max(temps) + 4, text="오늘", showarrow=False, font=dict(size=24))
    
    if '01시' in time_set:
        fig.add_annotation(x='01시', 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='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)'
        )
    )

    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"
        )

        forecast_data_str = "\n".join([f"{time}: {temp}도, {description}" for time, temp, description in zip(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"다음 시간대별 날씨 데이터를 보고 실제 날씨 상황에 맞는 정확한 날씨 예보를 200자로 만들어주세요. 비나 눈 예보가 있는 경우에만 우산을 준비하도록 안내해주세요. 옷차림은 다음을 참고하세요. \n27°C이상:반팔티, 반바지, 민소매.\n23°C~26°C: 얇은 셔츠, 반팔티, 반바지, 면바지.\n20°C~22°C: 얇은 가디건, 긴팔티, 긴바지.\n17°C~19°C: 얇은 니트, 가디건, 맨투맨, 얇은 자켓, 긴바지.\n12°C~16°C: 자켓, 가디건, 야상, 맨투맨, 니트, 스타킹, 긴바지\n9°C~11°C: 트렌치코트, 야상, 가죽 자켓, 스타킹, 긴바지\n5°C~8°C: 코트, 히트텍, 니트, 긴바지\n4°C이하: 패딩, 두꺼운 코트, 목도리, 기모제품: {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)


    # 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, 1))  # 최대 1초씩 대기
            st.rerun()

if __name__ == "__main__":
    main()