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__)

#load model
model = pickle.load(open("regression_pickle", "rb"))
scaler = pickle.load(open("scalling.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 = scaler.transform(np.array(list(data.values())).reshape(1, -1))
    output = model.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 = scaler.transform(np.array(data).reshape(1,-1))
    print(final_input)
    output = model.predict(final_input)[0]
    return render_template("home.html",prediction_text="Prediction House Price : {}".format(output))

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