application.py

from flask import Flask, render_template, request, redirect, url_for, session,flash
import os, json, boto3
import subprocess
import atexit
from bokeh.embed import server_document
from werkzeug.utils import secure_filename

app = Flask(__name__)
app.secret_key = 'AWEDSDOLASJ'

bokeh_process = subprocess.Popen(
    ['bokeh', 'serve','--allow-websocket-origin=127.0.0.1:5000','rf.py','svm.py','nb.py','sgd.py','mlp.py','gbc.py','ll.py','dt.py','knn.py','lr.py','rdg.py'])

@atexit.register
def kill_server():
    bokeh_process.kill()
 
# Listen for GET requests to yourdomain.com/account/
@app.route("/")
def account():
  return render_template('index.html')

@app.route('/classification', methods=['GET', 'POST'])
def classification():
    if request.method == 'POST':
        return redirect(url_for('index'))
    return render_template('first.html')

@app.route('/regression', methods=['GET', 'POST'])
def regression():
    if request.method == 'POST':
        return redirect(url_for('index'))
    return render_template('third.html') 

@app.route("/letsplay_classification", methods=['POST'])
def play():
  if request.method == 'POST':
    f = request.files['file-input']
    selected_algo = request.form.get('algo')
    print(selected_algo)
    fname = f.filename
    print(fname)
    dict = {'dsname': fname}
    session['dsname'] = fname
    bokeh_script=server_document(url="http://127.0.0.1:5006/"+selected_algo,arguments=dict)
    return render_template('second.html',bokeh_script=bokeh_script,dsname=fname)

@app.route("/nextpage_classification", methods=['POST'])
def nxtpage():
  if request.method == 'POST':
    selected_algo = request.form.get('algo')
    print(selected_algo)
    dsname = session.get('dsname', None)
    dict = {'dsname': dsname}
    bokeh_script=server_document(url="http://127.0.0.1:5006/"+selected_algo,arguments=dict)
    return render_template('second.html',bokeh_script=bokeh_script)

@app.route("/letsplay_regression", methods=['POST'])
def play_reg():
  if request.method == 'POST':
    f = request.files['file-input']
    selected_algo = request.form.get('algo')
    print(selected_algo)
    fname = f.filename
    print(fname)
    dict = {'dsname': fname}
    session['dsname'] = fname
    bokeh_script=server_document(url="http://127.0.0.1:5006/"+selected_algo,arguments=dict)
    return render_template('fourth.html',bokeh_script=bokeh_script,dsname=fname)

@app.route("/nextpage_regression", methods=['POST'])
def nxtpage_reg():
  if request.method == 'POST':
    selected_algo = request.form.get('algo')
    print(selected_algo)
    dsname = session.get('dsname', None)
    dict = {'dsname': dsname}
    bokeh_script=server_document(url="http://127.0.0.1:5006/"+selected_algo,arguments=dict)
    return render_template('fourth.html',bokeh_script=bokeh_script)




@app.route('/sign-s3/')
def sign_s3():
  # Load necessary information into the application
  #S3_BUCKET = os.environ.get('S3_BUCKET')
  S3_BUCKET = "cs297-mlplayground"
  # Load required data from the request
  file_name = request.args.get('file-name')
  file_type = request.args.get('file-type')

  # Initialise the S3 client
  s3 = boto3.client('s3')

  # Generate and return the presigned URL
  presigned_post = s3.generate_presigned_post(
    Bucket = S3_BUCKET,
    Key = file_name,
    Fields = {"acl": "public-read", "Content-Type": file_type},
    Conditions = [
      {"acl": "public-read"},
      {"Content-Type": file_type}
    ],
    ExpiresIn = 3600
  )

  # Return the data to the client
  return json.dumps({
    'data': presigned_post,
    'url': 'https://%s.s3.amazonaws.com/%s' % (S3_BUCKET, file_name)
  })


# Main code
if __name__ == '__main__':
  port = int(os.environ.get('PORT', 5000))
  app.run(host='127.0.0.1', debug=True)