bexgboost BexTuychiev

I'll help you build a 4x4 tic-tac-toe game in Python with a clean terminal interface. Let me create a comprehensive plan for this project.

Project Plan: 4x4 Tic-Tac-Toe Game

	import streamlit as st
	import requests
	import json
	import uuid
	from typing import Iterator, Dict, Any


	def setup_streamlit_page():
	st.set_page_config(
	page_title="LangFlow Chat Interface", page_icon="🤖", layout="wide"

	from langflow.custom import Component
	from langflow.io import SecretStrInput, MessageTextInput, IntInput, BoolInput, DropdownInput, StrInput, Output
	from langflow.schema import Data
	import httpx
	from loguru import logger

	class FirecrawlSearchComponent(Component):
	display_name = "Firecrawl Search"
	description = "Search the web and get full content using Firecrawl"
	icon = "flame"

	class TicTacToe:
	def __init__(self):
	# Initialize empty board (using ' ' for empty squares)
	self.board = [" " for _ in range(9)]
	self.human_player = "O"
	self.ai_player = "X"

	def print_board(self):
	"""Print the current state of the board"""
	for i in range(0, 9, 3):

	# 1. Import statements and environment setup
	import os
	import random
	import warnings
	from typing import Callable, Tuple

	import matplotlib.pyplot as plt
	import neptune
	import numpy as np
	import tensorflow as tf

	import os
	import time
	import warnings
	import neptune

	from dotenv import load_dotenv
	from lightgbm import LGBMClassifier
	from xgboost import XGBClassifier
	from sklearn.datasets import make_classification
	from sklearn.model_selection import train_test_split

	import gymnasium as gym
	import numpy as np
	import matplotlib.pyplot as plt
	from moviepy.editor import ImageSequenceClip

	def create_environment(env_name='Taxi-v3', render_mode='rgb_array'):
	"""Create and return a Gymnasium environment."""
	return gym.make(env_name, render_mode=render_mode)

	def initialize_q_table(env):

	# Please, run the following GitHub gist first: https://gist.github.com/BexTuychiev/00b665c256351a63cca0512b1cd6d220

	import pandas as pd
	import matplotlib.pyplot as plt
	import numpy as np
	from matplotlib.ticker import ScalarFormatter

	# Read the CSV file
	data = pd.read_csv("optimization_results.csv")

	import seaborn as sns
	import numpy as np
	import pandas as pd
	import time
	from tabulate import tabulate # pip install tabulate

	np.random.seed(42)

	# Load and prepare data
	dataset_size = 20_000