Create app.py

app.py

@@ -0,0 +1,44 @@
+import streamlit as st
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.datasets import make_blobs
+from sklearn.cluster import KMeans
+
+def generate_data(n_samples=300):
+    data, _ = make_blobs(n_samples=n_samples, centers=4, cluster_std=1.0, random_state=42)
+    return data
+
+def plot_clusters(data, k):
+    kmeans = KMeans(n_clusters=k)
+    y_kmeans = kmeans.fit_predict(data)
+
+    plt.scatter(data[:, 0], data[:, 1], c=y_kmeans, s=50, cmap='viridis')
+    centers = kmeans.cluster_centers_
+    plt.scatter(centers[:, 0], centers[:, 1], c='red', s=200, alpha=0.75, marker='X')
+    plt.title(f'K-means Clustering with k={k}')
+    plt.xlabel('Feature 1')
+    plt.ylabel('Feature 2')
+    return plt
+
+def main():
+    st.title("K-means Clustering Simulator")
+
+    st.write("This is a simple simulator to visualize how k-means clustering works.")
+
+    data = generate_data()
+
+    st.write("Here is the sample data without clustering:")
+    plt.scatter(data[:, 0], data[:, 1], s=50, cmap='viridis')
+    plt.title('Sample Data')
+    plt.xlabel('Feature 1')
+    plt.ylabel('Feature 2')
+    st.pyplot()
+
+    k = st.slider("Select the number of clusters (k)", 1, 10, 4)
+    st.write(f"You selected k={k}")
+
+    plt = plot_clusters(data, k)
+    st.pyplot()
+
+if __name__ == '__main__':
+    main()