Initial commit

.devcontainer/devcontainer.json

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+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "Home.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run Home.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.gitignore

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+generate_keys.py
+__pycache__/
+.streamlit/

app.py

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+import streamlit as st
+from streamlit.logger import get_logger
+import streamlit_authenticator as stauth
+import yaml
+
+LOGGER = get_logger(__name__)
+
+def run():
+    st.set_page_config(
+        page_title="SKT AI Fellowship Team ASAP",
+        page_icon="📡",
+        layout="centered"
+    )
+
+    st.write("# SKT AI Fellowship Team ASAP 👋")
+
+def st_authenticator():
+    with open('config.yaml') as file:
+        config = yaml.load(file, Loader=stauth.SafeLoader)
+
+    authenticator = stauth.Authenticate(
+        config['credentials'],
+        config['cookie']['name'],
+        config['cookie']['key'],
+        config['cookie']['expiry_days'],
+        config['preauthorized']
+    )
+
+    return authenticator
+
+if __name__ == "__main__":
+    run()
+    authenticator = st_authenticator()
+
+    if 'authentication_status' not in st.session_state:
+        st.session_state['authentication_status'] = None
+        st.session_state['username'] = None
+
+    name, authentication_status, username = authenticator.login("main", "Login")
+
+    if authentication_status:
+        st.session_state['authentication_status'] = True
+        st.session_state['username'] = username
+        st.success(f"{name}님, 환영합니다!")
+        authenticator.logout('Logout', 'main', key='unique_key')
+
+    elif authentication_status is False:
+        st.session_state['authentication_status'] = False
+        st.error('아이디/비밀번호가 잘못되었습니다.')
+
+    elif authentication_status is None:
+        st.session_state['authentication_status'] = None
+        st.warning('아이디와 비밀번호를 입력해주세요.')
+

chart.py

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+import pandas as pd
+import altair as alt
+
+def create_area_chart(filtered_data_long, selected_cell):
+    chart = alt.Chart(filtered_data_long).mark_area().encode(
+        x=alt.X('timestamp:T', title='Timestamp'),
+        y=alt.Y('Value:Q', title='RB Used'),
+        color=alt.Color('RB:N', title='RB Type'),
+        tooltip=['timestamp:T', 'RB:N', 'Value:Q']
+    ).properties(
+        width=800,
+        height=400,
+        title=f'RB Usage for Cell ID: {selected_cell}'
+    ).interactive()
+
+    return chart

config.yaml

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+cookie:
+  expiry_days: 1
+  key: some_signature_key
+  name: streamlit-cookie
+credentials:
+  usernames:
+    asap:
+      name: asap
+      password: $2b$12$T657ZXBqAyJuartbrwlUMOP9G.n94O8qsdsuyDZRxUKQ2Yj.TsJHu
+preauthorized:
+  emails:
+  - [email protected]

data.py

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+import pandas as pd
+import streamlit as st
+from google.oauth2.service_account import Credentials
+from googleapiclient.discovery import build
+from googleapiclient.http import MediaIoBaseDownload
+import io
+
+# Google Drive 및 Sheets API 인증
+scope = [
+    "https://www.googleapis.com/auth/drive.readonly"
+]
+credentials = Credentials.from_service_account_info(st.secrets["google"], scopes=scope)
+file_id = st.secrets["drive"]["file_id"]
+
+drive_service = build('drive', 'v3', credentials=credentials)
+
+@st.cache_data(show_spinner=False)
+def load_data():
+    request = drive_service.files().get_media(fileId=file_id)
+
+    file_buffer = io.BytesIO()
+
+    downloader = MediaIoBaseDownload(file_buffer, request)
+    done = False
+
+    progress_bar = st.sidebar.progress(0)
+    progress_text = st.sidebar.empty()
+    progress_text.text("데이터 불러오는 중...")
+
+    while not done:
+        status, done = downloader.next_chunk()
+        progress = int(status.progress() * 100)
+        progress_bar.progress(progress)
+        progress_text.text(f"데이터 불러오는 중...({progress}%)")
+
+    file_buffer.seek(0)
+    df = pd.read_csv(file_buffer)
+
+    df['ru_svc_lat_val'] = df['ru_svc_lat_val'].astype(float)
+    df['ru_svc_lng_val'] = df['ru_svc_lng_val'].astype(float)
+
+    df_map = df.drop_duplicates(subset=['ru_svc_lat_val', 'ru_svc_lng_val'])
+
+    progress_bar.empty()
+    progress_text.empty()
+    st.sidebar.success("데이터 로드 완료!")
+
+    return df, df_map

js.py

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+from folium.map import Marker
+from jinja2 import Template
+
+def set_marker_click_template():
+    click_marker = """
+    {% macro script(this, kwargs) %}
+
+    function copyToClipboard(text) {
+      navigator.clipboard.writeText(text).then(function() {
+        console.log("클립보드에 복사되었습니다: ", text);
+      }, function(err) {
+        console.error("클립보드 복사에 실패했습니다: ", err);
+      });
+    }
+
+    function showToast() {
+      var toast = document.getElementById("toast");
+      toast.style.visibility = "visible";
+      setTimeout(function() {
+        toast.style.visibility = "hidden";
+      }, 3000);
+    }
+
+    function onClick(e) {
+        var marker = e.target;
+        if (marker.getTooltip()) {
+            var cellID = marker.getTooltip().getElement().innerText.trim();
+            copyToClipboard(cellID);
+        }
+    }
+
+    var {{ this.get_name() }} = L.marker(
+        {{ this.location|tojson }},
+        {{ this.options|tojson }}
+    ).addTo({{ this._parent.get_name() }}).on('click', onClick);
+
+    {% endmacro %}
+    """
+
+    Marker._template = Template(click_marker)
+

map.py

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+import folium
+from folium import CustomIcon, DivIcon
+
+def create_map(dataframe):
+    m = folium.Map(
+        location=[35.1796, 129.0756],
+        zoom_start=12,
+        control_scale=True,
+        tiles='CartoDB positron'
+    )
+
+    for idx, row in dataframe.iterrows():
+        cell_id = row['enbid_pci']
+
+        color = 'green'
+        custom_icon = DivIcon(
+            html=f"""
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" width="20" height="20">
+              <circle cx="10" cy="10" r="5.625" fill="{color}" stroke="#000000" stroke-width="1.25"/>
+            </svg>
+            """,
+            icon_size=(20, 20),
+            icon_anchor=(10, 10)
+        )
+
+        marker = folium.Marker(
+            # popup=f'Cell ID: {cell_id}',
+            location=(row['ru_svc_lat_val'], row['ru_svc_lng_val']),
+            icon=custom_icon,
+            tooltip=f'{cell_id}'
+        )
+
+        marker.add_to(m)
+
+    return m

map_recommend.py

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+import folium
+from folium import CustomIcon, DivIcon
+
+def create_map(dataframe, recommendations):
+    m = folium.Map(
+        location=[35.1796, 129.0756],
+        zoom_start=12,
+        control_scale=True,
+        tiles='CartoDB positron'
+    )
+
+    for idx, row in dataframe.iterrows():
+        cell_id = row['enbid_pci']
+
+        recommended_cell_state = recommendations.get(cell_id, 'OFF')
+
+        if recommended_cell_state == 'ON':
+            color = 'blue'
+        else:
+            color = 'red'
+
+        custom_icon = DivIcon(
+            html=f"""
+            <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 20 20" width="20" height="20">
+              <circle cx="10" cy="10" r="5.625" fill="{color}" stroke="#000000" stroke-width="1.25"/>
+            </svg>
+            """,
+            icon_size=(20, 20),
+            icon_anchor=(10, 10)
+        )
+
+        marker = folium.Marker(
+            location=(row['ru_svc_lat_val'], row['ru_svc_lng_val']),
+            icon=custom_icon,
+            tooltip=f'{cell_id} - {recommended_cell_state}'
+        )
+
+        marker.add_to(m)
+
+    return m

pages/About.py

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+import streamlit as st
+from streamlit.logger import get_logger
+
+LOGGER = get_logger(__name__)
+
+def run():
+    st.set_page_config(
+        page_title="About"
+    )
+
+    st.markdown(
+        """
+        ##### *Team ASAP은 네트워크의 에너지 소비를 줄이고 탄소 배출을 감소시키기 위한 AI 알고리즘을 개발하고 있습니다. 우리의 목표는 모바일 네트워크에서 에너지 효율성을 극대화하여 지속 가능한 발전을 이루는 것입니다.*
+
+        ##### Green AI의 필요성
+        - **효율성**: AI 기반의 알고리즘은 네트워크의 에너지 소비 패턴을 분석하고 최적화하여 불필요한 소비를 줄입니다.
+        - **고객 경험**: 고객 경험에 영향을 주지 않으면서 에너지를 절약할 수 있는 솔루션을 개발합니다.
+        - **지속 가능성**: 탄소 배출을 줄이고 운영비용(OPEX)을 절감하여 환경과 경제적 이익을 동시에 추구합니다.
+
+        ##### 주요 기술
+        - **AI 기반 에너지 절약 알고리즘**: 네트워크 트래픽 데이터를 실시간으로 분석하여, 에너지 소비를 최소화하는 방법을 추천합니다.
+        - **MLOps**: 자동화된 모델 학습 및 배포 파이프라인을 통해 AI 모델의 효율적인 운영을 지원합니다.
+
+        ##### 프로젝트의 가치
+        - **에너지 절감**: 에너지 절약 기능을 통해 전기 요금을 절감하고, 고객에게 최상의 서비스를 제공합니다.
+    """
+    )
+
+if __name__ == "__main__":
+    run()

pages/Dashboard.py

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+import streamlit as st
+from streamlit_folium import folium_static
+import pandas as pd
+import data
+import map
+import js
+import chart
+
+st.set_page_config(page_title="Dashboard",
+                   layout="wide",
+                   page_icon="🗺️")
+
+if not st.session_state.get('authentication_status', False):
+    st.write("### 🚨 **접근 불가** 🚨")
+    st.write("이 페이지를 볼 수 있는 권한이 없습니다.<br>로그인해 주세요.", unsafe_allow_html=True)
+    st.stop()
+
+df, df_map = data.load_data()
+
+cell_map = map.create_map(df_map)
+
+if 'selected_cell' not in st.session_state:
+    st.session_state['selected_cell'] = ""
+
+if 'selected_rbs' not in st.session_state:
+    st.session_state['selected_rbs'] = ['RB_800', 'RB_1800', 'RB_2100', 'RB_2600_10', 'RB_2600_20']
+
+def main():
+    st.markdown("# Dashboard")
+
+    show_map = st.sidebar.checkbox("지도 보기", True)
+    if show_map:
+        st.markdown("### 🗺️ 부산 PoC 셀 사이트")
+        
+        folium_static(cell_map)
+
+        st.write("""
+        <style>
+        iframe {
+            max-width: 2000px;
+            width: 100%;
+            height: 600px;
+            border-radius: 10px;
+            border: 1px solid #d3d3d3;
+        }
+        </style>
+        """, unsafe_allow_html=True)
+
+        js.set_marker_click_template()
+
+    unique_cells = df['enbid_pci'].unique().tolist()
+
+    show_chart = st.sidebar.checkbox("차트 보기", True)
+    if show_chart:
+        selected_cell = st.selectbox("조회할 셀 ID:", unique_cells, index=unique_cells.index(st.session_state['selected_cell']) if st.session_state['selected_cell'] in unique_cells else 0)
+        st.session_state['selected_cell'] = selected_cell
+
+        cell_data = df[df['enbid_pci'] == st.session_state['selected_cell']].copy()
+
+        if not cell_data.empty:
+            rb_options = []
+            if cell_data['Equip_800'].eq(1).any():
+                rb_options.append('RB_800')
+            if cell_data['Equip_1800'].eq(1).any():
+                rb_options.append('RB_1800')
+            if cell_data['Equip_2100'].eq(1).any():
+                rb_options.append('RB_2100')
+            if cell_data['Equip_2600_10'].eq(1).any():
+                rb_options.append('RB_2600_10')
+            if cell_data['Equip_2600_20'].eq(1).any():
+                rb_options.append('RB_2600_20')
+
+            selected_rbs = st.multiselect("RB 컬럼:", rb_options, default=[rb for rb in st.session_state['selected_rbs'] if rb in rb_options])
+            st.session_state['selected_rbs'] = selected_rbs
+
+            cell_data['timestamp'] = pd.to_datetime(cell_data['timestamp'])
+
+            min_date = cell_data['timestamp'].min().to_pydatetime()
+            max_date = cell_data['timestamp'].max().to_pydatetime()
+
+            start_date, end_date = st.slider(
+                "날짜 범위:",
+                min_value=min_date,
+                max_value=max_date,
+                value=(min_date, max_date),
+                format="MM/DD/YY"
+            )
+
+            filtered_data = cell_data[(cell_data['timestamp'] >= start_date) & (cell_data['timestamp'] <= end_date)]
+
+            filtered_data_long = pd.melt(
+                filtered_data,
+                id_vars=['timestamp'],
+                value_vars=st.session_state['selected_rbs'],
+                var_name='RB',
+                value_name='Value'
+            )
+
+            chart_obj = chart.create_area_chart(filtered_data_long, st.session_state['selected_cell'])
+
+            # 차트 표시
+            st.altair_chart(chart_obj, use_container_width=True)
+        else:
+            st.write("선택한 셀 ID에 대한 데이터가 없습니다.")
+
+if __name__ == "__main__":
+    main()

pages/Recommendation System.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from datetime import datetime, time
+import tensorflow as tf
+from streamlit_folium import folium_static
+import map_recommend
+import data
+
+st.set_page_config(page_title="Frequency Off Recommendation System",
+                   layout="wide",
+                   page_icon="📡")
+
+if not st.session_state.get('authentication_status', False):
+    st.write("### 🚨 **접근 불가** 🚨")
+    st.write("이 페이지를 볼 수 있는 권한이 없습니다.<br>로그인해 주세요.", unsafe_allow_html=True)
+    st.stop()
+
+st.title('Cell Off Recommendation System')
+
+date_input = st.date_input('날짜 선택:', datetime(2024, 8, 6))
+time_input = st.time_input('시간 선택:', time(0, 0))
+
+timestamp_input = datetime.combine(date_input, time_input)
+
+model_options = ['모델 1: Rule-based']
+selected_model = st.selectbox('모델 선택:', model_options)
+
+run_button = st.button('Run')
+
+input_data = pd.DataFrame({
+    'hour': [timestamp_input.hour],
+    'is_weekend': [timestamp_input.weekday() >= 5]
+})
+
+def load_and_predict(model_name, input_data, df_map, progress_callback=None):
+    recommended_cell_states = {}
+
+    if progress_callback:
+        progress_callback(10)
+
+    if model_name == '모델 1: Rule-based':
+        for idx, row in df_map.iterrows():
+            if timestamp_input.hour < 6 or timestamp_input.hour > 22:
+                recommended_cell_states[row['enbid_pci']] = 'OFF'
+            else:
+                recommended_cell_states[row['enbid_pci']] = 'ON'
+            if progress_callback:
+                progress_callback(10 + 80 * (idx + 1) // len(df_map))
+
+    return recommended_cell_states
+
+if run_button:
+
+    # with st.sidebar:
+    #     with st.spinner('데이터 로딩 중...'):
+    #         df, df_map = data.load_data()
+    #         st.sidebar.success('데이터 로드 완료')
+    df, df_map = data.load_data()
+
+    progress_bar = st.sidebar.progress(0)
+
+    timestamp_input = pd.to_datetime(timestamp_input)
+
+    recommended_cell_states = load_and_predict(
+        selected_model,
+        input_data,
+        df_map,
+        progress_callback=progress_bar.progress
+    )
+
+    m = map_recommend.create_map(df_map, recommended_cell_states)
+
+    progress_bar.progress(100)
+    st.sidebar.success('분석 완료')
+
+    progress_bar.empty()
+
+    folium_static(m)
+
+    st.write("""
+    <style>
+    iframe {
+        max-width: 2000px;
+        width: 100%;
+        height: 600px;
+        border-radius: 10px;
+        border: 1px solid #d3d3d3;
+    }
+    </style>
+    """, unsafe_allow_html=True)

requirements.txt

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+extra-streamlit-components==0.1.71
+Flask==3.0.3
+Flask-Cors==4.0.1
+Flask-SocketIO==5.3.6
+flatbuffers==24.3.25
+folium==0.17.0
+google-api-core==2.19.1
+google-api-python-client==2.139.0
+google-auth==2.32.0
+google-auth-httplib2==0.2.0
+google-auth-oauthlib==1.2.1
+google-pasta==0.2.0
+googleapis-common-protos==1.63.2
+gspread==5.12.4
+gspread-dataframe==4.0.0
+gspread-formatting==1.2.0
+gspread-pandas==3.3.0
+keras==3.4.1
+matplotlib==3.9.1
+matplotlib-inline==0.1.7
+numpy==1.26.4
+pandas==1.5.3
+pip==24.2
+scikit-learn==1.5.1
+scipy==1.14.0
+seaborn==0.13.2
+streamlit==1.37.1
+streamlit-authenticator==0.3.3
+streamlit-cookies-controller==0.0.4
+streamlit-cookies-manager==0.2.0
+streamlit_folium==0.22.0
+streamlit-javascript==0.1.5
+tensorboard==2.17.0
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+tensorflow-io-gcs-filesystem==0.37.1
+tqdm==4.66.5