title: When I run my python script it shows a blank screen and disappears quickly
body: <p>When I run my .py file, it quickly shows a blank black screen then disappears and does nothing. The script works fine in the editor though! The full code is down below, I know it could use some improvement but I'm just looking for answers to the blank screen for now. :) (The script is a simple genetic algorithm btw)</p>
  
  <pre><code>#!/usr/bin/python3
  from fuzzywuzzy import fuzz
  import random
  import string
  
  def error_msg():
      print('\nSomethine went wrong!\nMake sure you typed the information correctly!')
      mainF()
  
  def mainF():
      try:
          while 7 == 7:
              print()
              stri = input('Enter string here: ')
              gene = input('Enter number of generations here: ')
              agen = input('Enter number of agents here: ')
              muta = input('Enter chance of mutation here (0 - 1): ')
              thre = input('Enter threshold here: ')
              if stri == '' or gene == '' or agen == '' or thre == '' or muta == '' or stri.isdigit() or gene.isalpha() or agen.isalpha() or thre.isalpha() or muta.isalpha():
                  print('\nSomethine went wrong!\nMake sure you typed the information correctly!')
              else:
                  ga(stri, len(stri), agen, gene, thre, muta)
      except:
          error_msg()
  
  class Agent:
      def __init__(self, length):
          self.string = ''.join(random.choice(string.ascii_letters) for _ in range(length))
          self.fitness = -1
      def __str__(self):
          return 'String: ' + str(self.string) + ', Fitness: ' + str(self.fitness) + '.'
  
  def init_agents(population_p, length):
      try:
          population = int(population_p)
          return [Agent(length) for _ in range(population)]
      except:
          error_msg()
  
  def fitness(agents, in_str_p):
      try:
          in_str = in_str_p
          for agent in agents:
              agent.fitness = fuzz.ratio(agent.string, in_str)
          return agents
      except:
          error_msg()
  
  def selection(agents):
      try:
          agents = sorted(agents, key=lambda agent: agent.fitness, reverse=True)
          print('\n'.join(map(str, agents)))
          agents = agents[:int(0.2 * len(agents))]
          return agents
      except:
          error_msg()
  
  def crossover(agents, in_str_len_p, population_p):
      try:
          population = int(population_p)
          in_str_len = in_str_len_p
          offspring = []
          for _ in range((population - len(agents)) // 2):
              parent1 = random.choice(agents)
              parent2 = random.choice(agents)
              child1 = Agent(in_str_len)
              child2 = Agent(in_str_len)
              split = random.randint(0, in_str_len)
              child1.string = parent1.string[0:split] + parent2.string[split:in_str_len]
              child2.string = parent2.string[0:split] + parent1.string[split:in_str_len]
              offspring.append(child1)
              offspring.append(child2)
          agents.extend(offspring)
          return agents
      except:
          error_msg()
  
  def mutation(agents, in_str_len_p, mutation_chance_p):
      try:
          mutation_chance = float(mutation_chance_p)
          in_str_len = in_str_len_p
          for agent in agents:
              for idx, param in enumerate(agent.string):
                  if random.uniform(0.0, 1.0) &lt;= mutation_chance:
                      agent.string = agent.string[0:idx] + random.choice(string.ascii_letters) + agent.string[idx+1:in_str_len]
          return agents
      except:
          error_msg()
  
  def ga(in_str_p, in_str_len_p, population_p, generations_p, threshold_p, mutation_chance_p):
      mutation_chance = mutation_chance_p
      threshold = int(threshold_p)
      population = population_p
      generations = int(generations_p)
      in_str = in_str_p
      in_str_len = in_str_len_p
      agents = init_agents(population, in_str_len)
      for generation in range(generations):
          print('Generation: ' + str(generation))
          agents = fitness(agents, in_str)
          agents = selection(agents)
          agents = crossover(agents, in_str_len, population)
          agents = mutation(agents, in_str_len, mutation_chance)
          if any(agent.fitness &gt;= threshold for agent in agents):
              print('Threshold met!')
              mainF()
  
  if __name__ == '__main__':
      mainF()
  </code></pre>