Create app.py

app.py

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+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
+from elevenlabs import ElevenLabs
+
+# 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()