Update app.py

app.py

@@ -2,9 +2,15 @@ import streamlit as st
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
+import matplotlib.font_manager as fm
 from sklearn.cluster import KMeans
 
-# 데이터 생성
+# Set the font to NanumGothic for Korean support
+font_path = '/NanumGothic-Regular.ttf'
+font = fm.FontProperties(fname=font_path).get_name()
+plt.rc('font', family=font)
+
+# Data Generation
 np.random.seed(42)
 num_samples = 30
 traffic_centers = [(20, 20), (80, 80)]
@@ -25,14 +31,13 @@ def apply_kmeans(data, k):
     labels = kmeans.labels_
     return centroids, labels
 
-
 def main():
-    st.title("K-means Clustering Simulator")
+    st.title("K-means 클러스터링 시뮬레이터")
     
     # Global variables declaration
     global traffic_df, nature_df, population_df
 
-    if st.button("Initialize Datasets"):
+    if st.button("데이터셋 초기화"):
         traffic_data = np.random.uniform(0, 100, (num_samples * len(traffic_centers), 2))
         nature_data = np.random.uniform(0, 100, (num_samples * len(nature_centers), 2))
         population_data = np.random.uniform(0, 100, (num_samples * len(population_centers), 2))
@@ -41,16 +46,15 @@ def main():
         nature_df = pd.DataFrame(nature_data, columns=["x", "y"])
         population_df = pd.DataFrame(population_data, columns=["x", "y"])
     
-    datasets = st.multiselect("Choose datasets:", ["Traffic Accessibility", "Natural Environment", "Population Density"])
-    k_value = st.slider("Select k value:", 1, 10)
+    datasets = st.multiselect("데이터셋 선택:", ["교통접근성", "자연환경", "인구밀집도"])
+    k_value = st.slider("k 값 선택:", 1, 10)
     
     dataset_mapping = {
-        "Traffic Accessibility": (traffic_df, 'o'),
-        "Natural Environment": (nature_df, 'x'),
-        "Population Density": (population_df, '^')
+        "교통접근성": (traffic_df, 'o'),
+        "자연환경": (nature_df, 'x'),
+        "인구밀집도": (population_df, '^')
     }
 
-    # Check if any dataset is selected
     if datasets:
         combined_data = pd.concat([dataset_mapping[dataset_name][0] for dataset_name in datasets])
         
@@ -66,9 +70,9 @@ def main():
         ax.scatter(centroids[:, 0], centroids[:, 1], s=200, c='red', marker='X')
         ax.set_xlim(0, 100)
         ax.set_ylim(0, 100)
-        ax.set_title(f"K-means clustering result (k={k_value})")
+        ax.set_title(f"K-means 클러스터링 결과 (k={k_value})")
         ax.legend()
         st.pyplot(fig)
 
 if __name__ == "__main__":
-    main()
+    main()