Create app.py

app.py

@@ -0,0 +1,54 @@
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.cluster import KMeans
+
+def cluster_plot(data_text, n_clusters):
+    try:
+        # Parse user input
+        # Expect input like: [[1,30],[2,32],[1.5,31],[5,60]]
+        X = np.array(eval(data_text))
+        if X.ndim != 2 or X.shape[1] != 2:
+            return " Please enter a 2D list with two columns (e.g. [[1,30],[2,32],[3,35]])", None
+
+        # KMeans clustering
+        kmeans = KMeans(n_clusters=n_clusters, random_state=0)
+        labels = kmeans.fit_predict(X)
+        centroids = kmeans.cluster_centers_
+
+        # Plot scatter
+        plt.figure(figsize=(6,4))
+        plt.scatter(X[:,0], X[:,1], c=labels, s=80, cmap='rainbow', edgecolors='k', alpha=0.8)
+        plt.scatter(centroids[:,0], centroids[:,1], c='black', s=150, marker='X', label='Centroids')
+        plt.xlabel("Years of Experience")
+        plt.ylabel("Salary")
+        plt.title("KMeans Clustering")
+        plt.legend()
+        plt.grid(True)
+
+        # Return plot
+        return f" Cluster labels: {labels.tolist()}", plt.gcf()
+
+    except Exception as e:
+        return f" Error: {e}", None
+
+# Gradio interface
+demo = gr.Interface(
+    fn=cluster_plot,
+    inputs=[
+        gr.Textbox(
+            label="Enter 2D Array (Experience vs Salary)",
+            placeholder="Example: [[1,30],[2,32],[1.5,31],[5,60],[10,100]]"
+        ),
+        gr.Slider(2, 10, value=3, step=1, label="Number of Clusters")
+    ],
+    outputs=[
+        gr.Textbox(label="Result"),
+        gr.Plot(label="Cluster Plot")
+    ],
+    title="KMeans Clustering Visualizer",
+    description="Enter a 2D array of points and choose the number of clusters to see the scatter plot."
+)
+
+if __name__ == "__main__":
+    demo.launch()