Create app.py

app.py

@@ -0,0 +1,54 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+# Generate the datasets
+np.random.seed(42)
+num_samples = 100
+traffic_centers = [(20, 20), (80, 20)]
+nature_centers = [(20, 80), (80, 80)]
+population_centers = [(50, 50), (30, 30), (70, 70)]
+
+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]
+
+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"])
+
+def apply_kmeans(data, k):
+    kmeans = KMeans(n_clusters=k, random_state=42).fit(data)
+    centroids = kmeans.cluster_centers_
+    labels = kmeans.labels_
+    return centroids, labels
+
+def main():
+    st.title("K-means Clustering Simulator")
+    
+    dataset = st.selectbox("Choose a dataset:", ["", "교통접근성", "자연환경", "인구밀집도"])
+    k_value = st.slider("Select k value:", 1, 10)
+    
+    data = None
+    if dataset == "교통접근성":
+        data = traffic_df
+    elif dataset == "자연환경":
+        data = nature_df
+    elif dataset == "인구밀집도":
+        data = population_df
+
+    if data is not None:
+        centroids, labels = apply_kmeans(data.values, k_value)
+        
+        plt.figure(figsize=(8, 8))
+        plt.scatter(data['x'], data['y'], c=labels, cmap='viridis')
+        plt.scatter(centroids[:, 0], centroids[:, 1], s=200, c='red', marker='X')
+        plt.xlim(0, 100)
+        plt.ylim(0, 100)
+        plt.title(f"K-means clustering result for {dataset} Dataset (k={k_value})")
+        st.pyplot()
+
+if __name__ == "__main__":
+    main()