import gradio as gr import matplotlib matplotlib.use('Agg') # Non-interactive backend for Gradio import pandas as pd import numpy as np import matplotlib.pyplot as plt import matplotlib.cm as cm import seaborn as sns import plotly.express as px import plotly.graph_objects as go from plotly.subplots import make_subplots import warnings warnings.filterwarnings('ignore') from sklearn.preprocessing import StandardScaler from sklearn.cluster import KMeans from sklearn.decomposition import PCA from sklearn.manifold import TSNE from sklearn.metrics import silhouette_score, silhouette_samples import joblib import io, base64 import json K_OPTIMAL = 5 # Load saved artifacts kmeans_loaded = joblib.load('kmeans_model.pkl') scaler_loaded = joblib.load('scaler.pkl') with open('cluster_names.json') as f: cluster_names_loaded = {int(k): v for k, v in json.load(f).items()} with open('cluster_insights.json') as f: insights_loaded = {int(k): v for k, v in json.load(f).items()} SEGMENT_COLORS = { 0: '#FF6B6B', 1: '#4ECDC4', 2: '#45B7D1', 3: '#96CEB4', 4: '#FFEAA7' } SEGMENT_EMOJIS = {0: 'โš ๏ธ', 1: '๐Ÿš€', 2: '๐Ÿง‘โ€๐Ÿ’ผ', 3: '๐Ÿ’ฐ', 4: '๐Ÿ‘‘'} def make_radar_chart(cluster_id): """Generate a radar chart for the predicted cluster.""" centers = (kmeans_loaded.cluster_centers_ - kmeans_loaded.cluster_centers_.min(axis=0)) / \ (kmeans_loaded.cluster_centers_.max(axis=0) - kmeans_loaded.cluster_centers_.min(axis=0)) categories = ['Age', 'Annual Income', 'Spending Score'] angles = np.linspace(0, 2 * np.pi, len(categories), endpoint=False).tolist() angles += angles[:1] fig, ax = plt.subplots(figsize=(4, 4), subplot_kw=dict(polar=True)) fig.patch.set_facecolor('#1a1a2e') ax.set_facecolor('#16213e') for i in range(len(centers)): vals = centers[i].tolist() + [centers[i][0]] color = SEGMENT_COLORS[i] lw = 3 if i == cluster_id else 1 alpha_fill = 0.4 if i == cluster_id else 0.05 ax.plot(angles, vals, 'o-', linewidth=lw, color=color, label=cluster_names_loaded[i], alpha=1.0 if i == cluster_id else 0.4) ax.fill(angles, vals, alpha=alpha_fill, color=color) ax.set_thetagrids(np.degrees(angles[:-1]), categories, color='white', fontsize=9) ax.set_ylim(0, 1) ax.set_title(f'{SEGMENT_EMOJIS[cluster_id]} {cluster_names_loaded[cluster_id]}', color='white', fontsize=11, fontweight='bold', pad=20) ax.tick_params(colors='white') ax.spines['polar'].set_color('#333') ax.yaxis.set_tick_params(colors='#555') ax.set_yticklabels([]) ax.grid(color='#333', linestyle='--', alpha=0.5) plt.tight_layout() return fig def make_comparison_bar(user_vals, cluster_id): """Bar chart: user values vs cluster centroid.""" centroid = scaler_loaded.inverse_transform( kmeans_loaded.cluster_centers_[cluster_id].reshape(1, -1) )[0] features = ['Age', 'Annual Income (k$)', 'Spending Score'] x = np.arange(len(features)) width = 0.35 fig, ax = plt.subplots(figsize=(6, 3.5)) fig.patch.set_facecolor('#1a1a2e') ax.set_facecolor('#16213e') bars1 = ax.bar(x - width/2, user_vals, width, label='You', color=SEGMENT_COLORS[cluster_id], alpha=0.9, edgecolor='white') bars2 = ax.bar(x + width/2, centroid, width, label='Cluster Avg', color='#aaaaaa', alpha=0.6, edgecolor='white') ax.set_xticks(x) ax.set_xticklabels(features, color='white', fontsize=9) ax.set_ylabel('Value', color='white') ax.set_title('You vs Cluster Average', color='white', fontweight='bold') ax.tick_params(colors='white') ax.spines[['top','right','left','bottom']].set_color('#333') ax.yaxis.set_tick_params(colors='white') ax.legend(facecolor='#222', labelcolor='white', fontsize=9) for bar in bars1: ax.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.5, f'{bar.get_height():.0f}', ha='center', va='bottom', color='white', fontsize=8) plt.tight_layout() return fig def predict_segment(age, annual_income, spending_score): """Core prediction function called by Gradio.""" user_input = np.array([[age, annual_income, spending_score]]) user_scaled = scaler_loaded.transform(user_input) cluster_id = int(kmeans_loaded.predict(user_scaled)[0]) K_OPTIMAL = 5 info = insights_loaded[cluster_id] color = SEGMENT_COLORS[cluster_id] emoji = SEGMENT_EMOJIS[cluster_id] # Distance to all centroids dists = kmeans_loaded.transform(user_scaled)[0] confidence = 1 - (dists[cluster_id] / dists.sum()) result_html = f"""
{emoji}
Cluster {cluster_id}: {cluster_names_loaded[cluster_id]}
Confidence: {confidence:.1%}
Profile
{info['desc']}
๐ŸŽฏ Recommended Strategy
{info['strategy']}
๐Ÿ“ Distance to All Centroids (lower = closer)
{''.join([ f'
' + f'{cluster_names_loaded[i][:12]}' + f'
' + f'
' + f'
{dists[i]:.2f}
' for i in range(K_OPTIMAL) ])}
""" radar = make_radar_chart(cluster_id) bar = make_comparison_bar([age, annual_income, spending_score], cluster_id) return result_html, radar, bar # โ”€โ”€โ”€ Gradio UI โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€ css = """ .gradio-container { max-width: 960px !important; margin: auto; font-family: 'Segoe UI', sans-serif; } #title { text-align: center; margin-bottom: 20px; } .input-panel { background: #16213e; border-radius: 12px; padding: 16px; } """ EXAMPLES = [ [25, 80, 90], [45, 30, 20], [35, 60, 55], [22, 15, 85], [55, 90, 15], ] with gr.Blocks(css=css, theme=gr.themes.Base(primary_hue='blue'), title='Customer Segmentation') as demo: gr.HTML("""

๐Ÿ›๏ธ Customer Segmentation

K-Means Clustering ยท 5 Customer Segments ยท Real-time Prediction

""") with gr.Row(): with gr.Column(scale=1, elem_classes='input-panel'): gr.Markdown('### ๐Ÿ“ Enter Customer Details') age_inp = gr.Slider(18, 70, value=30, step=1, label='Age') income_inp = gr.Slider(10, 140, value=60, step=1, label='Annual Income (k$)') spend_inp = gr.Slider(1, 100, value=50, step=1, label='Spending Score (1โ€“100)') predict_btn = gr.Button('๐Ÿ” Predict Segment', variant='primary', size='lg') gr.Markdown('### ๐Ÿ’ก Try Examples') gr.Examples( examples=EXAMPLES, inputs=[age_inp, income_inp, spend_inp], label='Quick Examples' ) with gr.Column(scale=2): result_html = gr.HTML(label='Segment Result') with gr.Row(): radar_plot = gr.Plot(label='Cluster Radar Profile') bar_plot = gr.Plot(label='You vs Cluster Average') gr.Markdown(""" --- **Segments:** โš ๏ธ Cautious Savers ยท ๐Ÿš€ High Potential ยท ๐Ÿง‘โ€๐Ÿ’ผ Standard Customers ยท ๐Ÿ’ฐ Budget Shoppers ยท ๐Ÿ‘‘ Premium Loyalists **Model:** K-Means (K=5, k-means++ init) ยท Scaler: StandardScaler ยท Dataset: Mall Customers """) predict_btn.click( fn=predict_segment, inputs=[age_inp, income_inp, spend_inp], outputs=[result_html, radar_plot, bar_plot] ) # Launch demo.launch(share=True, debug=False)