Update app.py

app.py

@@ -1,11 +1,17 @@
 import gradio as gr
 import numpy as np
-import random
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from game2048 import Game2048
 
+# 检测可用设备
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+elif torch.xpu.is_available():
+    device = torch.device("xpu")
+else:
+    device = torch.device("cpu")
 # 创建游戏实例
 game = Game2048(size=4)
 
@@ -23,7 +29,9 @@ TILE_COLORS = {
     512: "#edc850",   # 512
     1024: "#edc53f",  # 1024
     2048: "#edc22e",  # 2048
-    4096: "#3c3a32",  # 4096+
+    4096: "#3c3a32",  # 4096+,
+    8192: "#3c3a32",  # 8192+
+    16384: "#3c3a32", # 16384+
 }
 
 # 文本颜色映射（根据背景深浅）
@@ -41,6 +49,8 @@ TEXT_COLORS = {
     1024: "#f9f6f2",  # 1024+
     2048: "#f9f6f2",  # 2048+
     4096: "#f9f6f2",  # 4096+
+    8192: "#f9f6f2",  # 8192+
+    16384: "#f9f6f2", # 16384+
 }
 
 # 定义DQN网络结构（与训练时相同）
@@ -86,22 +96,56 @@ class DQN(nn.Module):
         q_values = value + advantage - advantage.mean(dim=1, keepdim=True)
         return q_values
 
-# 加载模型
-def load_model(model_path):
-    model = DQN(4, 4)  # 输入通道4，输出动作4个
+# 加载模型（支持CUDA）
+def load_model(model_path, device):
+    model = DQN(4, 4).to(device)  # 将模型移动到指定设备
     try:
-        checkpoint = torch.load(model_path, map_location=torch.device('cpu'))
-        model.load_state_dict(checkpoint['policy_net_state_dict'])
+        # 尝试加载模型
+        checkpoint = torch.load(model_path, map_location=device)
+        
+        # 检查检查点是否包含完整的模型状态
+        if 'policy_net_state_dict' in checkpoint:
+            model.load_state_dict(checkpoint['policy_net_state_dict'])
+        else:
+            # 如果检查点不包含policy_net_state_dict，尝试直接加载
+            model.load_state_dict(checkpoint)
+            
         model.eval()
-        print("模型加载成功")
+        print(f"模型成功加载到: {device}")
         return model
     except Exception as e:
         print(f"模型加载失败: {e}")
-        return None
+        # 尝试备选模型路径
+        alt_path = model_path.replace('_best_tile', '')
+        try:
+            checkpoint = torch.load(alt_path, map_location=device)
+            if 'policy_net_state_dict' in checkpoint:
+                model.load_state_dict(checkpoint['policy_net_state_dict'])
+            else:
+                model.load_state_dict(checkpoint)
+            model.eval()
+            print(f"备选模型成功加载: {alt_path}")
+            return model
+        except Exception as e2:
+            print(f"备选模型加载失败: {e2}")
+            return None
 
 # 尝试加载模型
-model_path = "dqn_2048_best_tile.pth"
-model = load_model(model_path)
+model_paths = [
+    "models/dqn_2048_best_tile.pth",
+    "models/dqn_2048.pth",
+    "dqn_2048_best_tile.pth",
+    "dqn_2048.pth"
+]
+
+model = None
+for path in model_paths:
+    model = load_model(path, device)
+    if model:
+        break
+
+if not model:
+    print("警告: 未加载任何模型，AI功能将不可用")
 
 def render_board(board):
     html = "<div style='background-color:#bbada0; padding:10px; border-radius:6px;'>"
@@ -181,82 +225,134 @@ def ai_move():
     if not valid_moves:
         return render_board(game.board), "<b>游戏结束!</b> 没有有效移动"
     
-    # 转换状态为模型输入
-    state_tensor = torch.tensor(state, dtype=torch.float).unsqueeze(0)
-    
-    # 模型预测
-    with torch.no_grad():
-        q_values = model(state_tensor).numpy().flatten()
-    
-    # 只考虑有效动作
-    valid_q_values = np.full(4, -np.inf)
-    for move in valid_moves:
-        valid_q_values[move] = q_values[move]
-    
-    # 选择最佳动作
-    action = np.argmax(valid_q_values)
-    
-    # 执行移动
-    direction_names = ["上", "右", "下", "左"]
-    new_board, game_over = game.move(action)
-    
-    # 渲染棋盘
-    board_html = render_board(new_board)
-    
-    # 更新状态信息
-    status = f"<b>AI移动方向:</b> {direction_names[action]}"
-    status += f"<br><b>当前分数:</b> {game.score}"
-    status += f"<br><b>最大方块:</b> {np.max(game.board)}"
-    
-    if game.game_over:
-        status += "<br><br><div style='color:#ff0000; font-weight:bold;'>游戏结束!</div>"
-        status += f"<br><b>最终分数:</b> {game.score}"
+    try:
+        # 转换状态为模型输入并移动到设备
+        state_tensor = torch.tensor(state, dtype=torch.float).unsqueeze(0).to(device)
+        
+        # 模型预测
+        with torch.no_grad():
+            q_values = model(state_tensor).cpu().numpy().flatten()
+        
+        # 只考虑有效动作
+        valid_q_values = np.full(4, -np.inf)
+        for move in valid_moves:
+            valid_q_values[move] = q_values[move]
+        
+        # 选择最佳动作
+        action = np.argmax(valid_q_values)
+        
+        # 执行移动
+        direction_names = ["上", "右", "下", "左"]
+        new_board, game_over = game.move(action)
+        
+        # 渲染棋盘
+        board_html = render_board(new_board)
+        
+        # 更新状态信息
+        status = f"<b>AI移动方向:</b> {direction_names[action]}"
+        status += f"<br><b>当前分数:</b> {game.score}"
+        status += f"<br><b>最大方块:</b> {np.max(game.board)}"
+        status += f"<br><b>设备:</b> {'GPU' if device.type == 'cuda' else 'CPU'}"
+        
+        if game.game_over:
+            status += "<br><br><div style='color:#ff0000; font-weight:bold;'>游戏结束!</div>"
+            status += f"<br><b>最终分数:</b> {game.score}"
+        
+        return board_html, status
     
-    return board_html, status
+    except Exception as e:
+        print(f"AI移动出错: {e}")
+        return render_board(game.board), f"<b>错误:</b> AI移动失败 - {str(e)}"
 
 # 创建Gradio界面
 with gr.Blocks(title="2048游戏", theme="soft") as demo:
     gr.Markdown("# 🎮 2048游戏")
+    gr.Markdown(f"当前运行设备: **{'GPU' if device.type == 'cuda' else 'CPU'}**")
     gr.Markdown("使用方向键或下方的按钮移动方块，相同数字的方块相撞时会合并!")
+    
     with gr.Row():
         with gr.Column(scale=2):
             board_html = gr.HTML(render_board(game.board))
-            with gr.Row(visible=False):
-                status_display = gr.HTML("<b>当前分数:</b> 0<br><b>最大方块:</b> 2")
+            status_display = gr.HTML("<b>当前分数:</b> 0<br><b>最大方块:</b> 2")
+            
         with gr.Column():
             gr.Markdown("## 手动操作")
             with gr.Row():
-                gr.Button("上 ↑", elem_id="up-btn").click(
-                    fn=lambda: make_move(0), 
-                    outputs=[board_html, status_display]
-                )
-                gr.Button("左 ←", elem_id="left-btn").click(
-                    fn=lambda: make_move(3), 
-                    outputs=[board_html, status_display]
-                )
-            with gr.Row():
-                gr.Button("下 ↓", elem_id="down-btn").click(
-                    fn=lambda: make_move(2), 
-                    outputs=[board_html, status_display]
-                )
-                gr.Button("右 →", elem_id="right-btn").click(
-                    fn=lambda: make_move(1), 
-                    outputs=[board_html, status_display]
-                )
+                up_btn = gr.Button("上 ↑", elem_id="up-btn")
+                left_btn = gr.Button("左 ←", elem_id="left-btn")
             with gr.Row():
-                gr.Button("🔄 重置游戏", elem_id="reset-btn").click(
-                fn=reset_game, 
-                    outputs=[board_html, status_display]
-                )
+                down_btn = gr.Button("下 ↓", elem_id="down-btn")
+                right_btn = gr.Button("右 →", elem_id="right-btn")
             with gr.Row():
-                status_display = gr.HTML("<b>当前分数:</b> 0<br><b>最大方块:</b> 2")
-        with gr.Column():
+                reset_btn = gr.Button("🔄 重置游戏", elem_id="reset-btn")
+                
             gr.Markdown("## AI操作")
-            gr.Button("🤖 AI移动一步", elem_id="ai-btn").click(
-                fn=ai_move, 
-                outputs=[board_html, status_display]
-            )
-            gr.Markdown("基于DQN神经网络提供支持")
+            with gr.Row():
+                ai_btn = gr.Button("🤖 AI移动一步", elem_id="ai-btn")
+                auto_btn = gr.Button("🚀 连续AI模式", elem_id="auto-btn")
+    
+    # 连接按钮事件
+    up_btn.click(lambda: make_move(0), outputs=[board_html, status_display])
+    right_btn.click(lambda: make_move(1), outputs=[board_html, status_display])
+    down_btn.click(lambda: make_move(2), outputs=[board_html, status_display])
+    left_btn.click(lambda: make_move(3), outputs=[board_html, status_display])
+    reset_btn.click(reset_game, outputs=[board_html, status_display])
+    ai_btn.click(ai_move, outputs=[board_html, status_display])
+    
+    # 连续AI模式
+    def auto_play():
+        """连续AI移动直到游戏结束"""
+        if model is None:
+            return render_board(game.board), "<b>错误:</b> 未加载AI模型"
+        
+        moves = 0
+        max_moves = 200  # 防止无限循环
+        
+        while not game.game_over and moves < max_moves:
+            # 获取当前状态
+            state = game.get_state()
+            
+            # 获取有效移动
+            valid_moves = game.get_valid_moves()
+            if not valid_moves:
+                break
+                
+            # 转换状态为模型输入并移动到设备
+            state_tensor = torch.tensor(state, dtype=torch.float).unsqueeze(0).to(device)
+            
+            # 模型预测
+            with torch.no_grad():
+                q_values = model(state_tensor).cpu().numpy().flatten()
+            
+            # 只考虑有效动作
+            valid_q_values = np.full(4, -np.inf)
+            for move in valid_moves:
+                valid_q_values[move] = q_values[move]
+            
+            # 选择最佳动作
+            action = np.argmax(valid_q_values)
+            
+            # 执行移动
+            game.move(action)
+            moves += 1
+        
+        # 渲染棋盘
+        board_html = render_board(game.board)
+        
+        # 更新状态信息
+        status = f"<b>连续AI完成!</b>"
+        status += f"<br><b>移动次数:</b> {moves}"
+        status += f"<br><b>当前分数:</b> {game.score}"
+        status += f"<br><b>最大方块:</b> {np.max(game.board)}"
+        status += f"<br><b>设备:</b> {'GPU' if device.type == 'cuda' else 'CPU'}"
+        
+        if game.game_over:
+            status += "<br><br><div style='color:#ff0000; font-weight:bold;'>游戏结束!</div>"
+            status += f"<br><b>最终分数:</b> {game.score}"
+        
+        return board_html, status
+    
+    auto_btn.click(auto_play, outputs=[board_html, status_display])
     
     # 添加键盘快捷键支持
     demo.load(
@@ -277,17 +373,22 @@ with gr.Blocks(title="2048游戏", theme="soft") as demo:
                     document.getElementById('reset-btn').click();
                 } else if (e.key === 'a' || e.key === 'A') {
                     document.getElementById('ai-btn').click();
+                } else if (e.key === 's' || e.key === 'S') {
+                    document.getElementById('auto-btn').click();
                 }
             });
         }"""
     )
+    
     gr.Markdown("### 📚 使用说明")
-    gr.Markdown("1. 使用方向键或下方的按钮移动方块。")
-    gr.Markdown("2. 相同数字的方块相撞时会合并。")
-    gr.Markdown("3. 快捷键说明：上/下/左/右键移动方块，R键重置游戏，A键AI移动一步。")
-    gr.Markdown("4. 点击 '🤖 AI移动一步' 按钮可以使用AI模型进行一步移动。")
-    gr.Markdown("5. 游戏结束后，会显示最终分数和最大方块。")
+    gr.Markdown("1. 使用方向键或下方的按钮移动方块")
+    gr.Markdown("2. 相同数字的方块相撞时会合并")
+    gr.Markdown("3. **快捷键说明**:")
+    gr.Markdown("   - ↑/↓/←/→: 移动方块")
+    gr.Markdown("   - R: 重置游戏")
+    gr.Markdown("   - A: AI移动一步")
+    gr.Markdown("   - S: 连续AI模式(自动玩到游戏结束)")
 
 # 启动界面
 if __name__ == "__main__":
-    demo.launch(share=True)
+    demo.launch()