text: import threading
  import time
  import numpy as np
  from brainflow.board_shim import BoardShim, BrainFlowInputParams, BoardIds
  import pandas as pd
  import tkinter as tk
  from tkinter import filedialog
  from queue import Queue
  from threading import Thread
  import streamlit as st
  from streamlit.scriptrunner import add_script_run_ctx
  
  
  
  class Client():
      def __init__(self, datatype):
          self.params = BrainFlowInputParams()
          self.params.serial_port = 'com3'
          self.params.board_id = 0
          self.board = BoardShim(0, self.params)
          self.datatype = datatype
          self.file_path = None
          self.fake_matrix = None
          self.df = None
          self.fake_df = None
          self.times_to_go_over = 0
  
      def collect_data(self, datatype):
          if datatype == 'real':
              start_real = Real(self)
              start_real.collect_data_live()
  
          else:
              start_fake = Fake(self)
              self.file_path = start_fake.choose_file()
              self.fake_matrix = start_fake.read_file()
              self.times_to_go_over = start_fake.passes_calc()
              return self.fake_matrix, self.times_to_go_over
  
      def real_data_collection(self):
          start_real = Real(self)
          m = start_real.read_data()
          for i in range(10):
              time.sleep(1)
              d = start_real.read_data()
              m = np.append(m, d, axis=1)
          return m
  
  
  class Real(Client):
      pass
  
      def start_stream(self):
          self.board.prepare_session()
          self.board.start_stream()
  
      def read_data(self):
          data = self.board.get_board_data()
          return data
  
      def stop_stream(self):
          self.board.stop_stream()
          self.board.release_session()
  
  
  class Fake(Client):
      def choose_file(self):
          root = tk.Tk()
          root.withdraw()
          self.file_path = filedialog.askopenfilename()
          return self.file_path
  
      def read_file(self):
          self.df = pd.read_csv(self.file_path, sep=" ", header=None,
                                names=["samples", "channel 1", "channel 2", "channel 3",
                                       "channel 4", "channel 5"])
          return self.df
  
      def passes_calc(self):
          rows = len(self.df.index)
          self.times_to_go_over = int(np.floor(rows / 256))
          return self.times_to_go_over
  
  
  def the_data(datatype, out_q):
      if datatype == 'real':
          start_real = Real(datatype)
          start_real.start_stream()
          counter = 0
          time.sleep(1)
          while counter < 600:
              d = start_real.read_data()
              A = pd.DataFrame(d)
              A = A.transpose()
              out_q.put(A)
              counter += 1
  
      if datatype == 'fake':
          fake_matrix = Client(datatype)
          the_fake_matrix, passes = fake_matrix.collect_data(datatype)
          time.sleep(1)
          for i in range(passes):
              temp_df = the_fake_matrix[i * 256:i * 256 + 256]
              out_q.put(temp_df)
  
  
  def get_all_queue_result(queue):
      result_list = []
      while not queue.empty():
          result_list.append(queue.get())
      return result_list
  
  
  def testing_queue(in_q, all_data):
      while True:
          time.sleep(5)
          temporary_df = pd.DataFrame()
          for i in range(in_q.qsize()):
              temporary_df = pd.concat([temporary_df, in_q.get()])
          all_data = pd.concat([all_data, temporary_df], axis=0)
          in_q.task_done()
  
  
  def streamlitapp(q):
      header = st.container()
      dataset = st.container()
      features = st.container()
      modelTraining = st.container()
  
      with header:
          st.title('welcome to my project')
          st.text('description')
      with features:
          st.header('features')
          st.text('info about features')
      with dataset:
          st.header('Dataset')
          st.text('info about dataset')
          # data =
          data = abs(data) / 1000
          placeholder = st.empty()
          placeholder.line_chart(data.iloc[1:50, 1:5])
          with placeholder.container():
              for i in range(1, len(data), 2):
                  time.sleep(0.058)
                  placeholder.line_chart(data.iloc[i:i + 50, 1:5])
      with modelTraining:
          st.header('time to train the model')
          st.text('info about training the model')
  
  
  def main():
      datatype = 'real'
      q = Queue()
      all_data = pd.DataFrame()
      t1 = Thread(target=the_data, args=(datatype, q))
      t2 = Thread(target=testing_queue, args=(q, all_data))
      t1.start()
      t2.start()
      t3 = Thread(target=streamlitapp, args=(q,))
      add_script_run_ctx(t3)
      t3.start()
      q.join()
  
  
  if __name__ == '__main__':
      main()
  
  