algerian-fire-prediction/application.py at main · the-bipu/algerian-fire-prediction · GitHub

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from flask import Flask, request, jsonify, 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('/predict', methods=['GET','POST'])
def predict():
    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 = pd.DataFrame([[Temperature, RH, Ws, Rain, FFMC, DMC, ISI, Classes, Region]],
                        columns=['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', results=result[0])
    else:
        return render_template('home.html')

if __name__ == '__main__':
    app.run()