app.py

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

app = Flask(__name__)

## Load the model
rgmodel = pickle.load(open('regmodel.pkl' , 'rb'))
scalar=pickle.load(open('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())).reshape(1,-1))
    new_data = scalar.transform(np.array(list(data.values())).reshape(1,-1))
    output = rgmodel.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 = rgmodel.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)

#with heroku use Gunicorn(Green) because it is a pure python http server with wsgi app it allows run py concurrently