Update app.py

app.py

@@ -49,26 +49,26 @@ def main():
         "Natural Environment": (nature_df, 'x'),
         "Population Density": (population_df, '^')
     }
-    
-    combined_data = pd.concat([dataset_mapping[dataset_name][0] for dataset_name in datasets])
 
-    centroids, labels = apply_kmeans(combined_data.values, k_value)
-    
-    fig, ax = plt.subplots(figsize=(8, 8))
-    
-    for dataset_name in datasets:
-        data, marker = dataset_mapping[dataset_name]
-        subset_labels = labels[:len(data)]
-        ax.scatter(data['x'], data['y'], c=subset_labels, cmap='viridis', marker=marker, label=dataset_name)
-        labels = labels[len(data):]
+    # Check if any dataset is selected
+    if datasets:
+        combined_data = pd.concat([dataset_mapping[dataset_name][0] for dataset_name in datasets])
         
-    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.legend()
-    st.pyplot(fig)
+        centroids, labels = apply_kmeans(combined_data.values, k_value)
+        fig, ax = plt.subplots(figsize=(8, 8))
+        
+        for dataset_name in datasets:
+            data, marker = dataset_mapping[dataset_name]
+            subset_labels = labels[:len(data)]
+            ax.scatter(data['x'], data['y'], c=subset_labels, cmap='viridis', marker=marker, label=dataset_name)
+            labels = labels[len(data):]
+            
+        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.legend()
+        st.pyplot(fig)
 
 if __name__ == "__main__":
     main()