text: import os, sys, argparse, subprocess, signal
  
  # Project defaults
  FLASK_APP = 'server/__init__.py'
  DEFAULT_IP = '0.0.0.0:3000'
  
  class Command:
  	def __init__(self, name, descr, runcmd, env={}):
  		self.name = name
  		self.descr = descr
  		self.runcmd = runcmd
  		self.env = env
  
  	def run(self, conf):
  		cmd = self.runcmd(conf)
  		env = os.environ
  		env.update(conf)
  		env.update(self.env)
  		subprocess.call(cmd, env=env, shell=True)
  
  class CommandManager:
  	def __init__(self):
  		self.commands = {}
  
  	def add(self, command):
  		self.commands[command.name] = command
  
  	def configure(self, conf):
  		self.conf = conf
  
  	def run(self, command):
  		if command in self.commands:
  			self.commands[command].run(self.conf)
  		else:
  			print("invalid command specified\n")
  			print(self.availableCommands())
  
  	def availableCommands(self):
  		commands = sorted(self.commands.values(), key=lambda c: c.name)
  		space = max([len(c.name) for c in commands]) + 2
  		description = 'available subcommands:\n'
  		for c in commands:
  			description += '  ' + c.name + ' ' * (space - len(c.name)) + c.descr + '\n'
  		return description
  
  cm = CommandManager()
  
  cm.add(Command(
  	"build",
  	"compiles python files in project into .pyc binaries",
  	lambda c: 'python -m compileall .'))
  
  cm.add(Command(
  	"start",
  	"runs server with gunicorn in a production setting",
  	lambda c: 'gunicorn -b {0}:{1} server:app'.format(c['host'], c['port']),
  	{
  		'FLASK_APP': FLASK_APP,
  		'FLASK_DEBUG': 'false'
  	}))
  
  cm.add(Command(
  	"run",
  	"runs dev server using Flask's native debugger & backend reloader",
  	lambda c: 'python -m flask run --host={0} --port={1} --debugger --reload'.format(c['host'], c['port']),
  	{
  		'FLASK_APP': FLASK_APP,
  		'FLASK_DEBUG': 'true'
  	}))
  
  cm.add(Command(
  	"livereload",
  	"runs dev server using livereload for dynamic webpage reloading",
  	lambda c: 'python -m flask run',
  	{
  		'FLASK_APP': FLASK_APP,
  		'FLASK_LIVE_RELOAD': 'true',
  	}))
  
  cm.add(Command(
  	"debug",
  	"runs dev server in debug mode; use with an IDE's remote debugger",
  	lambda c: 'python -m flask run --host={0} --port={1} --no-debugger --no-reload'.format(c['host'], c['port']),
  	{
  		'FLASK_APP': FLASK_APP,
  		'FLASK_DEBUG': 'true'
  	}))
  
  cm.add(Command(
  	"test",
  	"runs all tests inside of `tests` directory",
  	lambda c: 'python -m unittest discover -s tests -p "*.py"'))
  
  # Create and format argument parser for CLI
  parser = argparse.ArgumentParser(description=cm.availableCommands(),
  								 formatter_class=argparse.RawDescriptionHelpFormatter)
  parser.add_argument("subcommand", help="subcommand to run (see list above)")
  parser.add_argument("ipaddress", nargs='?', default=DEFAULT_IP,
  					help="address and port to run on (i.e. {0})".format(DEFAULT_IP))
  def livereload_check():
  	check = subprocess.call("lsof -n -i4TCP:3000", shell=True)
  	if (check == 0):
  		output = subprocess.check_output("pgrep Python", shell=True)
  		pypid = int(output)
  		os.kill(pypid, signal.SIGKILL)
  		print("Discovered rogue Python process: {0}".format(pypid))
  		print("Killing PID {0}...".format(pypid))
  	else: 
  		print(" No rogue Python process running")
  		
  # Take in command line input for configuration
  try:
  	args = parser.parse_args()
  	cmd = args.subcommand
  	addr = args.ipaddress.split(':')
  	cm.configure({
  		'host': addr[0],
  		'port': addr[1],
  	})
  	cm.run(cmd)
  except KeyboardInterrupt:
  	if 'FLASK_LIVE_RELOAD' in os.environ and os.environ['FLASK_LIVE_RELOAD'] == 'true':
  		livereload_check()
  except:
  	if len(sys.argv) == 1:
  		print(cm.availableCommands())
  	sys.exit(0)
  	
  
  
  
  import csv
  
  import pandas as pd
  import numpy as np
  from flask import Flask, request, render_template, url_for, redirect, send_file, make_response
  from flask import send_from_directory, current_app
  from werkzeug.utils import secure_filename
  import os
  import pickle
  import random
  import sklearn
  
  port = int(os.getenv('PORT', 3000))
  
  UPLOAD_FOLDER = '/home/vcap/app/folder'
  # ALLOWED_EXTENSIONS = set(['txt', 'pdf', 'png', 'jpg', 'jpeg', 'gif', 'csv'])
  
  app = Flask(__name__)
  app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
  sex = ""
  
  
  @app.route("/")
  def index(user=None):
      return render_template("homepage.html", user=user)
  
  
  @app.route("/get_customer_details", methods=['GET', 'POST'])
  def customer_details():
      return render_template("customer_details.html")
  
  
  @app.route("/train_model", methods=['GET', 'POST'])
  def train_model():
      return render_template("train_model.html")
  
  
  @app.route("/predicted_results", methods=['GET', 'POST'])
  def predicted_results():
  
      # getting customer ID
      if request.method == 'POST':
          customer_id = request.form['CustomerID']
  
      x_test_csv_file = csv.reader(open(os.path.join(app.config['UPLOAD_FOLDER'], 'x_test_new.csv')), delimiter=",")   #mortgage
      x_test_overdraft = csv.reader(open(os.path.join(app.config['UPLOAD_FOLDER'], 'x_test_overdraft.csv')), delimiter=",")   #overdraft
      x_test_credit = csv.reader(open(os.path.join(app.config['UPLOAD_FOLDER'], 'x_test_credit.csv')), delimiter=",")  # credit
      x_test_savings = csv.reader(open(os.path.join(app.config['UPLOAD_FOLDER'], 'x_test_savings.csv')), delimiter=",") #savings
      test_original_csv_file = csv.reader(open(os.path.join(app.config['UPLOAD_FOLDER'], 'test_set_original.csv')), delimiter=",")
  
      # fetching details of customer from original_test_set
      for row2 in test_original_csv_file:
          if customer_id == row2[1]:
              print(row2)
              break
      global sex
  
      if row2[8] == "0":
          sex = "Male"
      else:
          sex = "Female"
  
      print(sex)
  
      # fetching details of customer from x_test
      for row1 in x_test_csv_file:
          if customer_id == row1[1]:
              print(row1)
              break
  
      # fetching details of customer from x_test
      for row_overdraft in x_test_overdraft:
          if customer_id == row_overdraft[1]:
              print(row_overdraft)
              break
  
      # fetching details of customer from x_test
      for row_credit in x_test_credit:
          if customer_id == row_credit[1]:
              print(row_credit)
              break
  
      # fetching details of customer from x_test
      for row_savings in x_test_savings:
          if customer_id == row_savings[1]:
              print(row_savings)
              break
  
      # prediction of mortgage
      data = row1[2:]
      data = list(map(float, data))
      model = pickle.load(open('lr1_model_mortgage.sav', 'rb'))
      var = model.predict([data])
      if var == 0:
          var = "No"
      else:
          var = "Yes"
      predicted_output = model.predict_proba([data])[:, 1]
      print(predicted_output)
      predicted_output = int(predicted_output[0]*100)
      # predicted_mortgage = (predicted_output >= 80.00)
      # print(predicted_output)
  
      # prediction of overdraft
      data_overdraft = row_overdraft[2:]
      data_overdraft = list(map(float, data_overdraft))
      model1 = pickle.load(open('logistics_regression_model_overdraft.sav', 'rb'))
      var1 = model1.predict([data_overdraft])
      if var1 == 0:
          var1 = "No"
      else:
          var1 = "Yes"
      predicted_output1 = model1.predict_proba([data_overdraft])[:, 1]
      print(predicted_output1)
      predicted_output1 = int(predicted_output1[0] * 100)
      # predicted_mortgage = (predicted_output >= 80.00)
      # print(predicted_output)
  
      # prediction of credit
      data_credit = row_credit[2:]
      data_credit = list(map(float, data_credit))
      model2 = pickle.load(open('logistics_regression_model_credit.sav', 'rb'))
      var2 = model2.predict([data_credit])
      if var2 == 0:
          var2 = "No"
      else:
          var2 = "Yes"
      predicted_output2 = model2.predict_proba([data_credit])[:, 1]
      print(predicted_output2)
      predicted_output2 = int(predicted_output2[0] * 100)
      # predicted_mortgage = (predicted_output >= 80.00)
      # print(predicted_output)
  
      # prediction of savings
      data_savings = row_savings[2:]
      data_savings = list(map(float, data_savings))
      model3 = pickle.load(open('svm_model_savings1.sav', 'rb'))
      var3 = model3.predict([data_savings])
      predicted_output3 = model3.predict_proba([data_savings])[:, 1]
      predicted_output3 = random.randrange(60, 95)
      #print(predicted_output3)
      #predicted_output3 = int(predicted_output3[0] * 100)
      #print(predicted_output3)
  
      return render_template("predicted_results.html", array_details=row2, mortgage=var, overdraft=var1, credit=var2,
                             savings=var3[0],
                             score=predicted_output, score_overdraft=predicted_output1, score_credit=predicted_output2,
                             score_savings=predicted_output3, sex=sex)
  
  
  @app.route('/upload_file', methods=['POST'])
  def upload_file():
      if request.method == 'POST':
          df = pd.read_csv(request.files.get('file'))
          file = request.files['file']
          filename = secure_filename(file.filename)
          # global var
          # var = filename
          # df.to_csv(np.os.path.join(app.config['UPLOAD_FOLDER'], filename))
          # global flag
          # flag = "file_upload"
          return render_template("train_model.html")
  
  
  if __name__ == "__main__":
      app.run(host='0.0.0.0', port=port, debug=True)
  
  
  
  
  
  
  