Create app.py

app.py

@@ -0,0 +1,40 @@
+import gradio as gr
+import pickle
+import pandas as pd
+from sklearn.preprocessing import StandardScaler
+from sklearn.cluster import KMeans
+
+with open('kmeans_model.pkl', 'rb') as file:
+    kmeans = pickle.load(file)
+with open('scaler.pkl', 'rb') as file:
+    scaler = pickle.load(file)
+
+def predict_spending_score(annual_income, family_size, work_experience):
+    df = pd.DataFrame({
+        'Annual Income ($)': [annual_income],
+        'Family Size': [family_size],
+        'Work Experience': [work_experience]
+    })
+    
+    df['Family_Income_Product'] = df['Family Size'] * df['Annual Income ($)']
+    df['Family_Income_Ratio'] = df['Family Size'] / (df['Annual Income ($)'] + 1e-5)
+    
+    features = df[['Annual Income ($)', 'Family Size', 'Family_Income_Product', 'Family_Income_Ratio']]
+    features_scaled = scaler.transform(features)
+    
+ 
+    cluster = kmeans.predict(features_scaled)
+    return f'Cluster: {int(cluster[0])}'
+
+iface = gr.Interface(
+    fn=predict_spending_score,
+    inputs=[
+        gr.Number(label="Annual Income ($)", default=50000),
+        gr.Number(label="Family Size", default=2),
+        gr.Number(label="Work Experience (years)", default=5)
+    ],
+    outputs="text",
+    live=True
+)
+
+iface.launch()