Update app.py

app.py

@@ -27,13 +27,15 @@ def apply_kmeans(data, k):
 
 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]
+    
+    # 랜덤데이터 생성
+    traffic_data = np.random.uniform(0, 100, (num_samples, 2))
+    nature_data = np.random.uniform(0, 100, (num_samples, 2))
+    population_data = np.random.uniform(0, 100, (num_samples, 2))
 
-    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"])
+    traffic_df = pd.DataFrame(traffic_data, columns=["x", "y"])
+    nature_df = pd.DataFrame(nature_data, columns=["x", "y"])
+    population_df = pd.DataFrame(population_data, columns=["x", "y"])
 
 def main():
     st.title("K-means Clustering Simulator")
@@ -49,7 +51,7 @@ def main():
         "인구밀집도": population_df
     }
     
-    # Check if any dataset is selected
+    # 아무 값도 없을때
     if datasets:
         combined_data = pd.concat([dataset_mapping[dataset_name] for dataset_name in datasets])