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import os
from dotenv import load_dotenv
import gradio as gr
from langchain_huggingface import HuggingFaceEndpoint
# Load environment variables
load_dotenv()
HF_TOKEN = os.getenv("HF_TOKEN")
# Initialize the HuggingFace inference endpoint
llm = HuggingFaceEndpoint(
repo_id="mistralai/Mistral-7B-Instruct-v0.3",
huggingfacehub_api_token=HF_TOKEN.strip(),
temperature=0.7,
)
# Initialize the game board and state
def initialize_game():
board = [["" for _ in range(3)] for _ in range(3)]
current_player = "X"
status = "Player 1's turn (X)"
buttons = [gr.Button(value="", elem_classes=["cell-btn"], interactive=True) for _ in range(9)]
return board, current_player, status, *buttons
# Check for a winner
def check_winner(board):
for i in range(3):
if board[i][0] == board[i][1] == board[i][2] and board[i][0] != "":
return board[i][0]
if board[0][i] == board[1][i] == board[2][i] and board[0][i] != "":
return board[0][i]
if board[0][0] == board[1][1] == board[2][2] and board[0][0] != "":
return board[0][0]
if board[0][2] == board[1][1] == board[2][0] and board[0][2] != "":
return board[0][2]
return None
# Check for a draw
def check_draw(board):
return all(cell != "" for row in board for cell in row)
# Minimax algorithm for AI's move
def minimax(board, depth, is_maximizing):
winner = check_winner(board)
if winner == "X":
return -10 + depth
elif winner == "O":
return 10 - depth
elif check_draw(board):
return 0
if is_maximizing:
best = -float('inf')
for i in range(3):
for j in range(3):
if board[i][j] == "":
board[i][j] = "O"
best = max(best, minimax(board, depth + 1, False))
board[i][j] = ""
return best
else:
best = float('inf')
for i in range(3):
for j in range(3):
if board[i][j] == "":
board[i][j] = "X"
best = min(best, minimax(board, depth + 1, True))
board[i][j] = ""
return best
# Find the best move for AI
def get_best_move(board):
best_val = -float('inf')
best_move = (-1, -1)
for i in range(3):
for j in range(3):
if board[i][j] == "":
board[i][j] = "O"
move_val = minimax(board, 0, False)
board[i][j] = ""
if move_val > best_val:
best_move = (i, j)
best_val = move_val
return best_move
# Handle a move
def handle_move(board, current_player, button_idx, game_status):
if "wins" in game_status or "draw" in game_status:
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"], interactive=False) for i in range(9)]
return board, current_player, game_status, *buttons
row, col = divmod(button_idx, 3)
if board[row][col] != "":
status = f"Invalid move! Player {1 if current_player == 'X' else 2}'s turn ({current_player})"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"]) for i in range(9)]
return board, current_player, status, *buttons
board[row][col] = current_player
winner = check_winner(board)
if winner:
status = f"Player {1 if winner == 'X' else 2} ({winner}) wins! 🎉"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"], interactive=False) for i in range(9)]
return board, current_player, status, *buttons
if check_draw(board):
status = "It's a draw! 🤝"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"], interactive=False) for i in range(9)]
return board, current_player, status, *buttons
# AI's turn
if current_player == "X":
current_player = "O"
ai_row, ai_col = get_best_move(board)
board[ai_row][ai_col] = "O"
winner = check_winner(board)
if winner:
status = f"AI ({winner}) wins! 🎉"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"], interactive=False) for i in range(9)]
return board, current_player, status, *buttons
if check_draw(board):
status = "It's a draw! 🤝"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"], interactive=False) for i in range(9)]
return board, current_player, status, *buttons
current_player = "X"
status = f"Player 1's turn (X)"
buttons = [gr.Button(value=board[i//3][i%3], elem_classes=["cell-btn"]) for i in range(9)]
return board, current_player, status, *buttons
# Generate a hint using LLM
def get_hint_from_llm(board):
prompt = f"The current Tic-Tac-Toe board state is {board}. Suggest the best move for Player X with reasoning."
hint = llm(prompt)
return hint
# Build the Gradio UI
with gr.Blocks(css=".cell-btn {height: 100px; width: 100px; font-size: 2em; text-align: center;}") as tic_tac_toe:
gr.Markdown("## Tic-Tac-Toe with AI 🎮")
# Initialize states
board_state = gr.State([["" for _ in range(3)] for _ in range(3)])
current_player = gr.State("X")
game_status = gr.Textbox(value="Player 1's turn (X)", label="Game Status", interactive=False)
# Create grid buttons
buttons = []
for i in range(3):
with gr.Row():
for j in range(3):
btn = gr.Button(value="", elem_classes=["cell-btn"])
buttons.append(btn)
# Hint button
hint_button = gr.Button("Get Hint")
hint_display = gr.Textbox(value="", label="Hint", interactive=False)
hint_button.click(get_hint_from_llm, inputs=[board_state], outputs=[hint_display])
# Update buttons dynamically on click
for idx, btn in enumerate(buttons):
btn.click(
handle_move,
inputs=[board_state, current_player, gr.Number(idx, visible=False), game_status],
outputs=[board_state, current_player, game_status, *buttons],
)
# Reset game button
reset_button = gr.Button("Reset Game")
reset_button.click(
initialize_game,
inputs=[],
outputs=[board_state, current_player, game_status, *buttons],
)
tic_tac_toe.launch()
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