Random 데이터셋 추가

data.py

@@ -6,15 +6,15 @@ import os
 token = os.getenv('HF_TOKEN')
 
 data_files = {
-    "train": "ELG_Busan_PoC_per_CA_site_0226_0407.csv",
-    "test": "ELG_Busan_PoC_per_CA_site_0408_0519.csv"
+    "Part_A": "ELG_Busan_PoC_per_CA_site_0226_0407.csv",
+    "Part_B": "ELG_Busan_PoC_per_CA_site_0408_0519.csv"
 }
 
 @st.cache_data(show_spinner=False)
 def load_data():
 
     dataset = load_dataset('skt-asap/busan-poc-dataset', data_files=data_files, token=token)
-    df = dataset['train'].to_pandas()
+    df = dataset['Part_A'].to_pandas()
 
     df.fillna(0, inplace=True)
     df.drop(columns=['Unnamed: 0'], inplace=True)

pages/Recommendation System.py

@@ -1,9 +1,13 @@
 import streamlit as st
+from datasets import load_dataset
 import pandas as pd
 from datetime import datetime, time
 from streamlit_folium import folium_static as st_folium
 import map_recommend
 import data
+import os
+
+token = os.getenv('HF_TOKEN')
 
 st.set_page_config(layout="centered")
 
@@ -42,7 +46,7 @@ def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_
     if progress_callback:
         progress_callback(10)
 
-    if model_name == 'CSV 업로드' and st.session_state.uploaded_csv is not None:
+    if model_name == 'CSV File' and st.session_state.uploaded_csv is not None:
         df = st.session_state.uploaded_csv
         df['timestamp'] = pd.to_datetime(df['timestamp'])
         timestamp_input = pd.to_datetime(timestamp_input)
@@ -52,6 +56,18 @@ def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_
             st.stop()
         else:
             df = df[df['timestamp'] == timestamp_input]
+    elif model_name == 'Random':
+        with st.spinner('Random 데이터셋 로딩 중...'):
+            dataset = load_dataset('skt-asap/examples', data_files='Random.csv', token=token)
+        df = dataset['train'].to_pandas()
+        df['timestamp'] = pd.to_datetime(df['timestamp'])
+        timestamp_input = pd.to_datetime(timestamp_input)
+        st.info(f"**{df['timestamp'].min().strftime('%Y/%m/%d')} ~ {df['timestamp'].max().strftime('%Y/%m/%d')}** 사이의 날짜를 입력해주세요.")
+        if timestamp_input not in df['timestamp'].values:
+            st.error('CSV 파일에 해당 타임스탬프가 없습니다.')
+            st.stop()
+        else:
+            df = df[df['timestamp'] == timestamp_input]
 
     for freq in frequencies:
         if model_name == 'Time-based':
@@ -62,7 +78,7 @@ def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_
                     recommended_cell_states[freq][row['enbid_pci']] = 'ON'
                 if progress_callback:
                     progress_callback(10 + 80 * (idx + 1) // len(df_map))
-        elif model_name == 'CSV 업로드':
+        elif model_name == 'CSV File':
             if st.session_state.uploaded_csv is not None:
                 for idx, row in df.iterrows():
                     if row[f'Status_{freq}'] == 0:
@@ -74,19 +90,28 @@ def load_and_predict(model_name, df_map, timestamp_input, frequencies, progress_
             else:
                 st.error('CSV 파일을 업로드해주세요.')
                 st.stop()
+        elif model_name == 'Random':
+            for idx, row in df.iterrows():
+                if row[f'Status_{freq}'] == 0:
+                    recommended_cell_states[freq][row['enbid_pci']] = 'OFF'
+                elif row[f'Status_{freq}'] == 1:
+                    recommended_cell_states[freq][row['enbid_pci']] = 'ON'
+                else:
+                    recommended_cell_states[freq][row['enbid_pci']] = 'UNKNOWN'
 
-    return recommended_cell_states
-
+        else:
+            st.error('올바른 모델을 선택해주세요.')
+            st.stop()
 
     return recommended_cell_states
 
 frequencies_to_check = ['2100', '2600_10', '2600_20']
 
-model_options = ['Time-based', 'CSV 업로드']
+model_options = ['CSV File', 'Random', 'Time-based']
 selected_model = st.selectbox('모델:', model_options, index=model_options.index(st.session_state.selected_model) if st.session_state.selected_model else 0)
 
-if selected_model == 'CSV 업로드':
-    uploaded_file = st.file_uploader("CSV 파일 업로드", type="csv", label_visibility='collapsed')
+if selected_model == 'CSV File':
+    uploaded_file = st.file_uploader("CSV File Upload", type="csv", label_visibility='collapsed')
     if uploaded_file is not None:
         st.session_state.uploaded_csv = pd.read_csv(uploaded_file)
 
@@ -142,7 +167,7 @@ if st.session_state.map_data:
     iframe {
         max-width: 2000px;
         width: 100%;
-        height: 600px;
+        height: 300px;
         border-radius: 10px;
         border: 1px solid #d3d3d3;
     }