import numpy as np import pandas as pd import gradio as gr from sklearn.cluster import KMeans import matplotlib.pyplot as plt from PIL import Image from io import BytesIO # Load your dataset dataset = pd.read_csv('Flipcart.com Clusturing Model.csv') X = dataset.iloc[:, [2, 4]].values # Create a K-Means clustering model kmeans = KMeans(n_clusters=4, init='k-means++', random_state=42) y_means = kmeans.fit_predict(X) # Function to perform clustering and return cluster labels and the cluster visualization image def cluster_data(age, purchase_rating): features = np.array([age, purchase_rating]).reshape(1, -1) cluster = kmeans.predict(features)[0] # Scatter plot to visualize clusters plt.figure(figsize=(8, 6)) plt.scatter(X[y_means == 0, 0], X[y_means == 0, 1], s=100, c='magenta', label='Cluster 1') plt.scatter(X[y_means == 1, 0], X[y_means == 1, 1], s=100, c='blue', label='Cluster 2') plt.scatter(X[y_means == 2, 0], X[y_means == 2, 1], s=100, c='red', label='Cluster 3') plt.scatter(X[y_means == 3, 0], X[y_means == 3, 1], s=100, c='cyan', label='Cluster 4') plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='black', label='Centroids') plt.title('Cluster of Amazon users') plt.xlabel('Age') plt.ylabel('Purchase Rating') plt.legend() plt.grid(True) # Save the plot as an image image_buffer = BytesIO() plt.savefig(image_buffer, format='png') image_buffer.seek(0) # Create a PIL image from the buffer pil_image = Image.open(image_buffer) return f'Data point belongs to Cluster {cluster}', pil_image # Create a Gradio interface for the clustering model iface = gr.Interface( fn=cluster_data, inputs=[ gr.Number(label="Age"), gr.Number(label="Purchase Rating") ], outputs=[ gr.Textbox(label="Cluster"), gr.Image(label="Cluster Visualization") ], examples=[[23, 44], [26, 91], [72, 5]], live=True, description="Press flag if any erroneous output comes", theme=gr.themes.Soft(), title="FlipKart User Segmentation" ) # Launch the Gradio app iface.launch(inline=False)