new_contents: import model_1 as m
  import numpy as np
  import sys
  
  ACTION_TO_IND_MAP = {
      'check': 1,
      'call': 1,
      'bet': 2,
      'raise': 2,
      'fold': 0
  }
  
  MAX_ACTIONS = m.INPUT_LENGTH
  PLAYER_RANGE = (4, 7)
  
  def new_settings():
      num_players = int(raw_input('Number of players? '))
      print "Options: reset (reset settings), nh (new hand), check, call, bet, raise, fold."
  
      return num_players
  
  def new_hand(num_players):
      print "New Hand"
      return [False for _ in range(num_players)], 0, 0, [(None, None)]
  
  def get_input_vec(num_players, pos, action):
      input_vec = [0] * 14
      if num_players is not None:
          ind = num_players - PLAYER_RANGE[0]
          input_vec[ind] = 1
      if pos is not None:
          ind = pos + 4
          input_vec[ind] = 1
      if action is not None:
          ind = action + 11
          input_vec[ind] = 1
  
      return input_vec
  
  def predict(model, num_players, moves, turn=None):
      arr = [get_input_vec(num_players, pos, action) for pos, action in moves]
      arr.extend([get_input_vec(num_players, None, None) for _ in range(MAX_ACTIONS - len(arr))])
      arr = np.array([arr])
      output = model.predict(arr)[0]
      return output[0] if turn is None else output[turn + 1]
  
  
  def interact(model):
      num_players = new_settings()
      folded, turn, pos, moves = new_hand(num_players)
      print predict(model, num_players, moves)
  
      while True:
          inp = raw_input('--> ').lower().strip()
          if inp == 'reset':
              num_players = new_settings()
              folded, turn, pos, moves = new_hand(num_players)
              print predict(model, num_players, moves)
          elif inp == 'nh':
              folded, turn, pos, moves = new_hand(num_players)
              print predict(model, num_players, moves)
          else:
              if inp not in ACTION_TO_IND_MAP:
                  continue
              action = ACTION_TO_IND_MAP[inp]
              if action == 0:
                  folded[pos] = True
  
              if min(folded):
                  folded, turn, pos, moves = new_hand(num_players)
                  print predict(model, num_players, moves)
              else:
                  moves.append((pos, action))
                  print predict(model, num_players, moves, turn)
                  turn += 1
                  while True:
                      pos = (pos + 1) % num_players
                      if not folded[pos]:
                          break
  
  if __name__ == '__main__':
      model = m.build_model('cpu')
      model.load_weights(sys.argv[1])
      interact(model)
subject: Add model tester.