app.py

import pickle
from flask import Flask, request, app, jsonify, url_for, render_template
import numpy as np
import pandas as pd

app = Flask(__name__)

## loading the model
regmodel = pickle.load(open('Model/regressionmodel.pkl', 'rb'))
scalar = pickle.load(open('Model/scaling.pkl', 'rb'))

@app.route('/')
def home():
    return render_template('home.html')

# @app.route('/predict_api', methods=['POST'])
# def predict_api():
#     data = request.json['data']
#     print(data)
#     print(np.array(list(data.values())[0])).reshape(1, -1)
#     new_data = scalar.transform(np.array(list(data.values())).reshape(1, -1))
#     output = regmodel.predict(new_data)
#     print(output[0])
#     return jsonify(output[0])

@app.route('/predict', methods=['POST'])
def predict():
    data = [ float(x) for x in request.form.values()]
    final_input = scalar.transform(np.array(data).reshape(1, -1))
    print(final_input)
    output = regmodel.predict(final_input)[0]
    return render_template("home.html", prediction_text = "The house price prediction is {}".format(output))

if __name__ == "__main__":
    app.run(debug=True)