app.py

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

app = Flask(__name__)


#Generate a secure random key
secret_key = secrets.token_hex(16)
app.secret_key = secret_key



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

## Route for home page
@app.route('/')
def index():
    return render_template('index.html')

@app.route('/predict_datapoint',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('index.html',result=result[0])

    else:
        return render_template("index.html")




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