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from flask import Flask, request, jsonify
import joblib
import pandas as pd
import numpy as np
app = Flask(__name__)
# Load the pre-trained model and scaler
try:
kmeans_model = joblib.load("kmeans_model.joblib")
scaler_model = joblib.load("scaler_model.joblib")
print("Models loaded successfully!")
except Exception as e:
print(f"Error loading models: {e}")
kmeans_model = None
scaler_model = None
@app.route("/predict", methods=["POST"])
def predict():
if kmeans_model is None or scaler_model is None:
return jsonify({"error": "Model not loaded. Please check deployment logs."}), 500
data = request.get_json(force=True)
try:
# Extract features and ensure order
age = data.get("age")
annual_income = data.get("annual_income")
spending_score = data.get("spending_score")
if age is None or annual_income is None or spending_score is None:
return jsonify({"error": "Missing required input features (age, annual_income, spending_score)."}), 400
# Create a DataFrame for scaling
features_df = pd.DataFrame([[age, annual_income, spending_score]],
columns=['Age', 'Annual Income (k$)', 'Spending Score (1-100)'])
# Scale the input features using the loaded scaler
scaled_features = scaler_model.transform(features_df)
# Predict the cluster
prediction = kmeans_model.predict(scaled_features)
cluster_id = int(prediction[0]) # Convert numpy int to Python int
return jsonify({"cluster_id": cluster_id})
except Exception as e:
return jsonify({"error": str(e)}), 500
if __name__ == "__main__":
app.run(debug=True) # debug=True for local testing, set to False for deployment |