app.py

import numpy as np
import os
from flask import Flask, request, jsonify, render_template
import pickle

app = Flask(__name__)
model = pickle.load(open('rfmodel.pkl', 'rb'))

@app.route('/')
def home():
    return render_template('mainpage.html')

@app.route('/predict',methods=['POST'])
def predict():
    '''
    For rendering results on HTML GUI
    '''
    sm=[6,7,8]
    wt=[9,10,11]
    sp=[12,1,2,3]
    fl=[4,5]
    farr= [int(x) for x in request.form.values()]
    if farr[1] in sm:
        farr.append(0)
    elif farr[1] in wt:
        farr.append(1)
    elif farr[1] in sp:
        farr.append(2)
    else:
        farr.append(3)
    final_features=np.array(farr,dtype='int64')
    print(final_features)
    prediction = model.predict([final_features])
    

    output = round(prediction[0])

    if output==0:
        return render_template('mainpage.html', prediction_text='No delay will happen {}'.format(output))
    elif output==1:
        return render_template('mainpage.html', prediction_text='There is a chance to departure delay will happen {}'.format(output))
    elif output==2:
        return render_template('mainpage.html', prediction_text='here is a chance to both departure and arrival delay will happen {}'.format(output))
    elif output==3:
        return render_template('mainpage.html', prediction_text='here is a chance to flight  will diverted {}'.format(output))
    elif output==4:
        return render_template('mainpage.html', prediction_text='here is a chance to cancel the flight {}'.format(output))
    else:
        return render_template('mainpage.html', prediction_text='output {}'.format(output))
'''@app.route('/predict_api',methods=['POST'])
def predict_api():
    
    For direct API calls trought request
    
    data = request.get_json(force=True)
    prediction = model.predict([np.array(list(data.values()))])
    output = prediction[0]
    return jsonify(output)'''

if __name__ == "__main__":
    os.environ.setdefault('FLASK_ENV', 'development')
    app.run(debug=False)