application.py

import pickle
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from flask import Flask,request, jsonify , render_template
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['Temperature'])
        RH = float(request.form['RH'])
        Ws = float(request.form['Ws'])
        Rain = float(request.form['Rain'])
        FFMC = float(request.form['FFMC'])
        DMC = float(request.form['DMC'])
        ISI = float(request.form['ISI'])
        Classes = float(request.form['Classes'])
        Region = 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', result = result[0])
    else:
        return render_template('home.html')

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