application.py

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

application=Flask(__name__)
app=application

standard_scaler=pickle.load(open('models/scaler.pkl','rb'))
ridge_model=pickle.load(open('models/Ridge_Regression.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=[[Temperature,RH,WS,Rain,FFMC,DMC,ISI,Classes,Region]]
        new_data_scaled=standard_scaler.transform(new_data)
        result=ridge_model.predict(new_data_scaled)
        return render_template('home.html',result=result[0])
    else:
        return render_template('home.html')

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