app.py

import pickle, bz2
from flask import Flask, request, render_template
import numpy as np
import pandas as pd
from app_logger import log
import warnings
warnings.filterwarnings("ignore")


app = Flask(__name__)

# Import Classification and Regression model file
C_pickle = bz2.BZ2File('Classification.pkl', 'rb')
R_pickle = bz2.BZ2File('Regression.pkl', 'rb')
model_C = pickle.load(C_pickle)
model_R = pickle.load(R_pickle)



# Route for homepage
@app.route('/')
def home():
    log.info('Home page loaded successfully')
    return render_template('index.html')



# Route for Classification Model

@app.route('/predictC', methods=['POST', 'GET'])
def predictC():
    if request.method == 'POST':
        try:
            #  reading the inputs given by the user
            Temperature=float(request.form['Temperature'])
            Wind_Speed =int(request.form['Ws'])
            FFMC=float(request.form['FFMC'])
            DMC=float(request.form['DMC'])
            ISI=float(request.form['ISI'])

            features = [Temperature, Wind_Speed,FFMC, DMC, ISI]

            Float_features = [float(x) for x in features]
            final_features = [np.array(Float_features)]
            prediction = model_C.predict(final_features)[0]

            log.info('Prediction done for Classification model')

            if prediction == 0:
                text = 'Forest is Safe!'
            else:
                text = 'Forest is in Danger!'
            return render_template('index.html', prediction_text1="{} --- Chance of Fire is {}".format(text, prediction))
        except Exception as e:
            log.error('Input error, check input', e)
        return render_template('index.html', prediction_text1="Check the Input again!!!")


# Route for Regression Model


@app.route('/predictR', methods=['POST'])
def predictR():
    if request.method == 'POST':
        try:
            #  reading the inputs given by the user
            Temperature=float(request.form['Temperature'])
            Wind_Speed =int(request.form['Ws'])
            FFMC=float(request.form['FFMC'])
            DMC=float(request.form['DMC'])
            ISI=float(request.form['ISI'])

            features = [Temperature, Wind_Speed,FFMC, DMC, ISI]

            Float_features = [float(x) for x in features]
            final_features = [np.array(Float_features)]
            prediction = model_R.predict(final_features)[0]

            log.info('Prediction done for Regression model')

            if prediction > 15:
                return render_template('index.html', prediction_text2="Fuel Moisture Code index is {:.4f} ---- Warning!!! High hazard rating".format(prediction))
            else:
                return render_template('index.html', prediction_text2="Fuel Moisture Code index is {:.4f} ---- Safe.. Low hazard rating".format(prediction))
        except Exception as e:
            log.error('Input error, check input', e)
        return render_template('index.html', prediction_text2="Check the Input again!!!")
            


# Run APP in Debug mode

if __name__ == "__main__":
    app.run(debug=True, port= 5000)