application.py

from flask import Flask,request,render_template
import pickle
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler

application = Flask(__name__)
app = application

## import ridge regressor and standard scaler pickle
ridge_model = pickle.load(open('./models/ridge.pkl','rb')) 
standard_scaler = pickle.load(open('./models/scaler.pkl','rb'))

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

@app.route("/predictdata",methods=['GET','POST'])
def predict_datapoint():
    if request.method=='POST':
        Temperature=float(request.form.get('Temperature'))
        RH=float(request.form.get('RH'))
        Ws=float(request.form.get('Ws'))
        Rain=float(request.form.get('Rain'))
        FFMC=float(request.form.get('FFMC'))
        DMC=float(request.form.get('DMC'))
        ISI=float(request.form.get('ISI'))
        Classes=float(request.form.get('Classes'))
        Region=float(request.form.get('Region'))

        new_data_scaled=standard_scaler.transform([[Temperature,RH,Ws,Rain,FFMC,DMC,ISI,Classes,Region]])
        result=ridge_model.predict(new_data_scaled)
        return render_template('home.html',results=result[0])

    else:
        return render_template('home.html')

if __name__ == '__main__':
    app.run(host="0.0.0.0")