Update app.py

app.py

@@ -1,9 +1,28 @@
 import gradio as gr
 import matplotlib
 matplotlib.use('Agg')  # Non-interactive backend for Gradio
-import matplotlib.pyplot as plt
+
+import pandas as pd
 import numpy as np
-import joblib, json
+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')
@@ -98,6 +117,8 @@ def predict_segment(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]
@@ -196,7 +217,7 @@ with gr.Blocks(css=css, theme=gr.themes.Base(primary_hue='blue'), title='Custome
 
     gr.Markdown("""
     ---
-    **Segments:** ⚠️ Cautious Savers · 🚀 High Potential · 🧑‍💼 Standard Customers · 💰 Budget Shoppers · 👑 Premium Loyalists
+    **Segments:** ⚠️ Cautious Savers · 🚀 High Potential · 🧑‍💼 Standard Customers · 💰 Budget Shoppers · 👑 Premium Loyalists  
     **Model:** K-Means (K=5, k-means++ init) · Scaler: StandardScaler · Dataset: Mall Customers
     """)