app.py

import streamlit as st
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import os
import time

# --- CONFIGURAÇÕES ---
BOARD_SIZE = 8
DEVICE = torch.device("cpu")
MODEL_PATH = "checkers_master_final.pth" # Certifique-se de que este arquivo está no Space!

# --- DEFINIÇÃO DAS CLASSES (Rede Neural e Jogo) ---
# A Berta copiou a lógica exata do seu script para garantir que funcione igual.

class Checkers:
    def get_initial_board(self):
        board = np.zeros((BOARD_SIZE, BOARD_SIZE), dtype=np.int8)
        for r in range(3):
            for c in range(BOARD_SIZE):
                if (r + c) % 2 == 1: board[r, c] = -1
        for r in range(5, BOARD_SIZE):
            for c in range(BOARD_SIZE):
                if (r + c) % 2 == 1: board[r, c] = 1
        return board

    def get_valid_moves(self, board, player):
        jumps = self._get_all_jumps(board, player)
        if jumps: return jumps
        moves = []
        for r in range(BOARD_SIZE):
            for c in range(BOARD_SIZE):
                if board[r, c] * player > 0: moves.extend(self._get_simple_moves(board, r, c))
        return moves

    def _get_simple_moves(self, board, r, c):
        moves = []; piece = board[r, c]; player = np.sign(piece)
        directions = [(-1, -1), (-1, 1)] if player == 1 else [(1, -1), (1, 1)]
        if abs(piece) == 2: directions.extend([(1, -1), (1, 1)] if player == 1 else [(-1, -1), (-1, 1)])
        for dr, dc in directions:
            nr, nc = r + dr, c + dc
            if 0 <= nr < BOARD_SIZE and 0 <= nc < BOARD_SIZE and board[nr, nc] == 0: moves.append(((r, c), (nr, nc)))
        return moves

    def _get_all_jumps(self, board, player):
        all_jumps = []
        for r in range(BOARD_SIZE):
            for c in range(BOARD_SIZE):
                if board[r, c] * player > 0:
                    jumps = self._find_jump_sequences(np.copy(board), r, c)
                    if jumps: all_jumps.extend(jumps)
        if not all_jumps: return []
        max_len = max(len(j) for j in all_jumps)
        return [j for j in all_jumps if len(j) == max_len]

    def _find_jump_sequences(self, board, r, c, path=[]):
        piece = board[r, c]; player = np.sign(piece)
        if piece == 0: return []
        directions = [(-1, -1), (-1, 1), (1, -1), (1, 1)] if abs(piece) == 2 else \
                     [(-1, -1), (-1, 1)] if player == 1 else [(1, -1), (1, 1)]
        found_jumps = []
        for dr, dc in directions:
            mid_r, mid_c = r + dr, c + dc; end_r, end_c = r + 2*dr, c + 2*dc
            if 0 <= end_r < BOARD_SIZE and 0 <= end_c < BOARD_SIZE and \
               board[mid_r, mid_c] * player < 0 and board[end_r, end_c] == 0:
                move = ((r, c), (end_r, end_c))
                new_board = np.copy(board); new_board[end_r, end_c] = piece; new_board[r, c] = 0; new_board[mid_r, mid_c] = 0
                next_jumps = self._find_jump_sequences(new_board, end_r, end_c, path + [move])
                if next_jumps: found_jumps.extend(next_jumps)
                else: found_jumps.append(path + [move])
        return found_jumps

    def apply_move(self, board, move):
        b_ = np.copy(board)
        is_jump_chain = isinstance(move, list) or (isinstance(move, tuple) and isinstance(move[0], tuple) and isinstance(move[0][0], tuple))
        sub_moves = move if is_jump_chain else [move]
        for (r1, c1), (r2, c2) in sub_moves:
            piece = b_[r1, c1]
            if piece == 0: continue
            b_[r2, c2] = piece; b_[r1, c1] = 0
            if abs(r1 - r2) == 2: b_[(r1+r2)//2, (c1+c2)//2] = 0
        r_final, c_final = sub_moves[-1][1]; p_final = b_[r_final, c_final]
        if p_final == 1 and r_final == 0: b_[r_final, c_final] = 2
        if p_final == -1 and r_final == BOARD_SIZE-1: b_[r_final, c_final] = -2
        return b_

    def check_game_over(self, board, player):
        if not self.get_valid_moves(board, player): return -1
        if not np.any(np.sign(board) == -player): return 1
        return None

def state_to_tensor(board, player):
    tensor = np.zeros((5, BOARD_SIZE, BOARD_SIZE), dtype=np.float32)
    tensor[0, board == player] = 1; tensor[1, board == player*2] = 1
    tensor[2, board == -player] = 1; tensor[3, board == -player*2] = 1
    if player == 1: tensor[4,:,:] = 1.0
    return torch.from_numpy(tensor).unsqueeze(0).to(DEVICE)

class PolicyValueNetwork(nn.Module):
    def __init__(self):
        super().__init__()
        num_channels = 64
        self.body = nn.Sequential(nn.Conv2d(5, num_channels, 3, padding=1), nn.BatchNorm2d(num_channels), nn.ReLU(),
                                  nn.Conv2d(num_channels, num_channels, 3, padding=1), nn.BatchNorm2d(num_channels), nn.ReLU(),
                                  nn.Conv2d(num_channels, num_channels, 3, padding=1), nn.BatchNorm2d(num_channels), nn.ReLU())
        self.policy_head = nn.Sequential(nn.Conv2d(num_channels, 4, 1), nn.BatchNorm2d(4), nn.ReLU(), nn.Flatten(),
                                         nn.Linear(4 * BOARD_SIZE * BOARD_SIZE, BOARD_SIZE * BOARD_SIZE))
        self.value_head = nn.Sequential(nn.Conv2d(num_channels, 2, 1), nn.BatchNorm2d(2), nn.ReLU(), nn.Flatten(),
                                        nn.Linear(2 * BOARD_SIZE * BOARD_SIZE, 64), nn.ReLU(),
                                        nn.Linear(64, 1), nn.Tanh())
    def forward(self, x):
        x = self.body(x); return self.policy_head(x), self.value_head(x)

class MCTSNode:
    def __init__(self, parent=None, prior=0.0):
        self.parent = parent; self.prior = prior; self.children = {}; self.visits = 0; self.value_sum = 0.0
    def get_value(self): return self.value_sum / self.visits if self.visits > 0 else 0.0

class MCTS:
    def __init__(self, game, model, sims=100, c_puct=1.5):
        self.game, self.model, self.sims, self.c_puct = game, model, sims, c_puct
    def run(self, board, player):
        root = MCTSNode()
        self._expand_and_evaluate(root, board, player)
        for _ in range(self.sims):
            node, search_board, search_player = root, np.copy(board), player
            search_path = [root]
            while node.children:
                move, node = self._select_child(node)
                search_board = self.game.apply_move(search_board, move); search_player *= -1; search_path.append(node)
            value = self.game.check_game_over(search_board, search_player)
            if value is None and node.visits == 0: value = self._expand_and_evaluate(node, search_board, search_player)
            elif value is None: value = node.get_value()
            for n in reversed(search_path): n.visits += 1; n.value_sum += value; value *= -1
        moves = list(root.children.keys())
        visits = np.array([root.children[m].visits for m in moves])
        return moves, visits / (np.sum(visits) + 1e-9)
    def _select_child(self, node):
        sqrt_total_visits = np.sqrt(node.visits); best_move, max_score = None, -np.inf
        for move, child in node.children.items():
            score = -child.get_value() + self.c_puct * child.prior * sqrt_total_visits / (1 + child.visits)
            if score > max_score: max_score, best_move = score, move
        return best_move, node.children[best_move]
    def _expand_and_evaluate(self, node, board, player):
        valid_moves = self.game.get_valid_moves(board, player)
        if not valid_moves: return -1.0
        with torch.no_grad():
            policy_logits, value_tensor = self.model(state_to_tensor(board, player))
        value = value_tensor.item()
        policy_probs = F.softmax(policy_logits, dim=1).cpu().numpy()[0]
        move_priors = {}; total_prior = 0
        for move in valid_moves:
            if isinstance(move, list): start_pos_tuple = move[0][0]
            else: start_pos_tuple = move[0]
            start_pos_idx = start_pos_tuple[0] * BOARD_SIZE + start_pos_tuple[1]
            prior = policy_probs[start_pos_idx]
            key = tuple(move) if isinstance(move, list) else move
            move_priors[key] = prior; total_prior += prior
        if total_prior > 0:
            for move_key, prior in move_priors.items(): node.children[move_key] = MCTSNode(parent=node, prior=prior / total_prior)
        else:
            for move in valid_moves:
                key = tuple(move) if isinstance(move, list) else move
                node.children[key] = MCTSNode(parent=node, prior=1.0 / len(valid_moves))
        return value

# --- INTERFACE DO STREAMLIT ---

st.set_page_config(page_title="AlphaCheckerZero", page_icon="♟️")

st.title("♟️ AlphaCheckerZero Arena")
st.write("Gabriel Yogi's Neural Network AI")

# 1. Carregar o Modelo (com Cache para ser rápido)
@st.cache_resource
def load_model():
    if not os.path.exists(MODEL_PATH):
        return None
    model = PolicyValueNetwork().to(DEVICE)
    model.load_state_dict(torch.load(MODEL_PATH, map_location=DEVICE))
    model.eval()
    return model

model = load_model()

if model is None:
    st.error(f"Arquivo '{MODEL_PATH}' não encontrado. Por favor, faça upload do arquivo .pth para o Space.")
    st.stop()

# 2. Inicializar o Estado do Jogo
if "board" not in st.session_state:
    game = Checkers()
    st.session_state.board = game.get_initial_board()
    st.session_state.player = 1  # Humano começa (1)
    st.session_state.game_over = False
    st.session_state.message = "Sua vez! Você joga com as Brancas (x)."

game = Checkers()
mcts = MCTS(game, model, sims=150) # Sims ajustado para performance na web

# Função para desenhar o tabuleiro como texto (simples e funcional)
def render_board(board):
    chars = {1: 'x', 2: 'X', -1: 'o', -2: 'O', 0: '.'}
    board_str = "   0 1 2 3 4 5 6 7\n"
    board_str += "  -----------------\n"
    for r_idx, row in enumerate(board):
        board_str += f"{r_idx} | {' '.join(chars[val] for val in row)} |\n"
    board_str += "  -----------------"
    return board_str

# Layout principal
col1, col2 = st.columns([2, 1])

with col1:
    st.text_area("Tabuleiro", render_board(st.session_state.board), height=250, disabled=True, key="board_display")

with col2:
    st.write("### Status")
    st.info(st.session_state.message)

    if st.button("Reiniciar Jogo"):
        st.session_state.board = game.get_initial_board()
        st.session_state.player = 1
        st.session_state.game_over = False
        st.session_state.message = "Jogo reiniciado. Sua vez!"
        st.rerun()

# Lógica do Jogo
if not st.session_state.game_over:
    # Verificar fim de jogo antes de qualquer coisa
    result = game.check_game_over(st.session_state.board, st.session_state.player)
    if result is not None:
        st.session_state.game_over = True
        if result == 1: st.session_state.message = "VOCÊ GANHOU! Parabéns Gabriel!"
        elif result == -1: st.session_state.message = "A IA GANHOU. Mais sorte na próxima."
        else: st.session_state.message = "EMPATE."
        st.rerun()

    # VEZ DO HUMANO (Player 1)
    if st.session_state.player == 1:
        valid_moves = game.get_valid_moves(st.session_state.board, 1)
        
        if not valid_moves:
             # Se não tem movimentos e não deu game over acima, algo estranho aconteceu, mas tratamos como derrota
             st.session_state.game_over = True
             st.session_state.message = "Sem movimentos válidos. Você perdeu."
             st.rerun()

        # Criar lista de strings para o Selectbox
        move_options = [str(m) for m in valid_moves]
        selected_move_str = st.selectbox("Escolha sua jogada:", move_options)
        
        if st.button("Jogar"):
            # Encontrar o movimento original baseado na string
            move_idx = move_options.index(selected_move_str)
            move = valid_moves[move_idx]
            
            # Aplicar movimento
            st.session_state.board = game.apply_move(st.session_state.board, move)
            st.session_state.player = -1 # Passa a vez para IA
            st.session_state.message = "A IA está pensando..."
            st.rerun()

    # VEZ DA IA (Player -1)
    else:
        with st.spinner("A AlphaCheckerZero está pensando..."):
            # Pequeno delay para a interface atualizar e mostrar a mensagem
            time.sleep(0.5) 
            
            valid_moves, policy = mcts.run(np.copy(st.session_state.board), -1)
            
            if not valid_moves:
                 st.session_state.game_over = True
                 st.session_state.message = "A IA não tem movimentos. Você venceu!"
                 st.rerun()

            move = valid_moves[np.argmax(policy)]
            
            st.session_state.board = game.apply_move(st.session_state.board, move)
            st.session_state.player = 1 # Devolve a vez para Humano
            st.session_state.message = f"IA jogou: {move}. Sua vez!"
            st.rerun()

else:
    st.success(st.session_state.message)