페이지 구조 개선

.streamlit/config.toml

@@ -0,0 +1,3 @@
+[server]
+maxUploadSize = 1000
+maxMessageSize = 1000

app.py

@@ -5,6 +5,7 @@ if "logged_in" not in st.session_state:
     st.session_state.logged_in = False
 
 def login():
+    st.write("# SKT AI Fellowship Team ASAP 👋")
     with st.form("Login"):
         st.write("### Login")
         username = st.text_input("Username", type="default")
@@ -31,6 +32,10 @@ Home = st.Page(
 About = st.Page(
     "pages/About.py", title="About", icon=":material/info:"
 )
+
+# Data = st.Page(
+#     "pages/Data.py", title="Data", icon=":material/data_usage:"
+# )
 Dashboard = st.Page(
     "pages/Dashboard.py", title="Dashboard", icon=":material/dashboard:"
 )
@@ -42,8 +47,9 @@ Recommendation_System = st.Page(
 if st.session_state.logged_in:
     pg = st.navigation(
         {
-            "": [Home, logout_page, About],
-            "System" : [Dashboard, Recommendation_System]
+            "": [Home, About],
+            "System" : [Data, Dashboard, Recommendation_System],
+            "Account": [logout_page]
         }
     )
 else:

data.py

@@ -16,6 +16,9 @@ def load_data():
     dataset = load_dataset('skt-asap/busan-poc-dataset', data_files=data_files, token=token)
     df = dataset['train'].to_pandas()
 
+    df.fillna(0, inplace=True)
+    df.drop(columns=['Unnamed: 0'], inplace=True)
+
     df['ru_svc_lat_val'] = df['ru_svc_lat_val'].astype(float)
     df['ru_svc_lng_val'] = df['ru_svc_lng_val'].astype(float)
 

pages/About.py

@@ -3,10 +3,6 @@ from streamlit.logger import get_logger
 
 LOGGER = get_logger(__name__)
 
-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.*

pages/Dashboard.py

@@ -6,9 +6,7 @@ import map
 import js
 import chart
 
-st.set_page_config(page_title="Dashboard",
-                   layout="wide",
-                   page_icon="🗺️")
+st.set_page_config(layout="wide")
 
 with st.sidebar:
     with st.spinner("데이터 로딩 중..."):

pages/Data.py

@@ -0,0 +1,20 @@
+import streamlit as st
+import pandas as pd
+from datasets import load_dataset
+import data
+import seaborn as sns
+
+st.set_page_config(layout="wide")
+
+with st.spinner("데이터 로딩 중..."):
+    df, df_map = data.load_data()
+    st.dataframe(df)
+
+st.title("Data Exploration")
+
+st.write("### 데이터 요약")
+st.write(df.describe())
+
+st.write("### 데이터 시각화")
+sns.pairplot(df)
+st.pyplot()

pages/Home.py

@@ -4,10 +4,5 @@ import yaml
 
 LOGGER = get_logger(__name__)
 
-st.set_page_config(
-    page_title="SKT AI Fellowship Team ASAP",
-    page_icon="📡",
-    layout="centered"
-)
-
 st.write("# SKT AI Fellowship Team ASAP 👋")
+

pages/Recommendation System.py

@@ -5,12 +5,8 @@ from streamlit_folium import st_folium
 import map_recommend
 import data
 
-# Streamlit 페이지 설정
-st.set_page_config(page_title="Frequency Off Recommendation System",
-                   layout="centered",
-                   page_icon="📡")
+st.title('Recommendation System')
 
-# 세션 상태 초기화
 if 'run_button_clicked' not in st.session_state:
     st.session_state.run_button_clicked = False
 
@@ -31,19 +27,14 @@ with st.sidebar:
         df, df_map = data.load_data()
         st.sidebar.success('데이터 로드 완료')
 
-st.title('Cell Off Recommendation System')
-
-# 사용자 입력
 date_input = st.date_input('날짜 선택:', st.session_state.date_input)
 time_input = st.time_input('시간 선택:', st.session_state.time_input)
 model_options = ['Rule-based', 'Kernel Model Based Reinforcement Learning']
 selected_model = st.selectbox('모델 선택:', model_options, index=model_options.index(st.session_state.selected_model) if st.session_state.selected_model else 0)
 
-# 주파수 선택
 frequency_options = ['2100MHz', '2600MHz - 10', '2600MHz - 20']
 frequency_input = st.selectbox('주파수 선택:', frequency_options, index=frequency_options.index(st.session_state.frequency_input))
 
-# 입력이 변경되면 상태 초기화
 if st.session_state.date_input != date_input:
     st.session_state.run_button_clicked = False
     st.session_state.date_input = date_input
@@ -60,11 +51,9 @@ if st.session_state.frequency_input != frequency_input:
     st.session_state.run_button_clicked = False
     st.session_state.frequency_input = frequency_input
 
-# Run 버튼
 run_button = st.button('Run')
 
 def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_callback=None):
-    # 주파수 별 추천 셀 상태 초기화
     recommended_cell_states = {freq: {} for freq in frequencies}
 
     if progress_callback:
@@ -82,18 +71,15 @@ def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_
 
     return recommended_cell_states
 
-# 버튼이 클릭되면 세션 상태를 True로 설정
 if run_button:
     st.session_state.run_button_clicked = True
 
-# 세션 상태를 활용하여 run_button_clicked가 True일 때 실행
 if st.session_state.run_button_clicked:
 
     progress_bar = st.sidebar.progress(0)
 
-    # 입력된 타임스탬프 처리
     timestamp_input = datetime.combine(date_input, time_input)
-    frequencies_to_check = ['2100MHz', '2600MHz - 10', '2600MHz - 20']  # 특정 주파수 목록
+    frequencies_to_check = ['2100MHz', '2600MHz - 10', '2600MHz - 20']
     recommended_cell_states = load_and_predict(
         selected_model,
         df_map,
@@ -120,7 +106,6 @@ if st.session_state.run_button_clicked:
 
         st.markdown(f"##### **Cell**: {matching_enbid_pci}")
 
-        # 선택된 주파수의 상태 표시
         for freq in frequencies_to_check:
             status = recommended_cell_states[freq].get(matching_enbid_pci, 'Unknown')
             color = '#D32F2F' if status == '0' else '#1976D2'  # 빨간색 (OFF) / 파란색 (ON)