PavithraNanjundan/Insurance_Predictor

1from flask import Flask, request, jsonify, render_template2import numpy as np3import pandas as pd4import statsmodels.api as sm5import pickle6import waitress7 8app = Flask(__name__)9 10# Load the trained model (Assuming model is saved as 'model.pkl')11with open("model.pkl", "rb") as model_file:12    model = pickle.load(model_file)13 14@app.route("/")15def home():16    return render_template("index.html")17 18@app.route('/predict', methods=['POST'])19def predict():20    try:21        age = request.form.get("age", type=int)22        bmi = request.form.get("bmi", type=float)23        children = request.form.get("children", type=int)24        smoker_yes = request.form.get("smoker_yes", type=int)25        26        df = pd.DataFrame([[age, bmi, children, smoker_yes]], columns=['age', 'bmi', 'children', 'smoker_yes'])27 28        29        # Make prediction30        prediction = model.predict(df)31        32        return render_template("index.html", result=round(float(prediction[0][0]), 2), original_input=request.form)33    34    except Exception as e:35        return jsonify({'error': str(e)}), 50036 37if __name__ == '__main__':38    waitress.serve(app, host='0.0.0.0', port=8080, debug=True)39