Update app.py

app.py

@@ -26,6 +26,7 @@ def apply_kmeans(data, k):
     return centroids, labels
 
 def generate_data():
+    global traffic_df, nature_df, population_df
     traffic_data = [np.random.normal(center, 10, (num_samples, 2)) for center in traffic_centers]
     nature_data = [np.random.normal(center, 10, (num_samples, 2)) for center in nature_centers]
     population_data = [np.random.normal(center, 10, (num_samples, 2)) for center in population_centers]
@@ -33,17 +34,12 @@ def generate_data():
     traffic_df = pd.DataFrame(np.vstack(traffic_data), columns=["x", "y"])
     nature_df = pd.DataFrame(np.vstack(nature_data), columns=["x", "y"])
     population_df = pd.DataFrame(np.vstack(population_data), columns=["x", "y"])
-    
-    return traffic_df, nature_df, population_df
-
-traffic_df, nature_df, population_df = generate_data()
 
 def main():
     st.title("K-means Clustering Simulator")
     
     if st.button("Initialize Datasets"):
-        traffic_df, nature_df, population_df = generate_data()
-    
+        generate_data()    
     datasets = st.multiselect("Choose datasets:", ["교통접근성", "자연환경", "인구밀집도"])
     k_value = st.slider("Select k value:", 1, 10)