application.py

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

application = Flask(__name__)
app = application

ridge_model = pickle.load(open('C:\\Users\\shrey\\Documents\\Coding\\Machine learning algorithm\\ML Project 1\\models\\ridge.pkl','rb'))
standard_scalar = pickle.load(open('C:\\Users\\shrey\\Documents\\Coding\\Machine learning algorithm\\ML Project 1\\models\\scaler.pkl','rb'))

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

@app.route("/predictdata",methods = ['POST','GET'])
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_scalar.transform([[Temperature,RH,Ws,Rain,FFMC,DMC,ISI,Classes,Region]])
        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(host='0.0.0.0')