btc_pred_webapp.py

from sklearn.externals import joblib
from flask import Flask, request, jsonify, render_template
import pickle

# create an instance (our app)
app = Flask(__name__)

model = joblib.load('SARIMA_btc_pred.pkl')

@app.route('/', methods=['GET', 'POST'])
@app.route('/predicted-price')
def predict(name = None):
    return render_template('start.html', name=name)
# name is parameter in the template: {{name}}

@app.route('/predict')
def predict():
    return render_template('prediction.html')

@app.route('/predicted', methods=['GET', 'POST'])
def predicted():
    if request.method == 'POST':
        x1 = request.form['x1']
        x2 = request.form['x2']
        X = [[x1, x2]]
        predicted = model.predict(X)
          
        return render_template("predicted.html", content=X, prediction=predicted)
    
@app.route('/bye')
def bye():
    return render_template('bye.html')

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