feat(train_page): 添加 TensorBoard 可视化

- 在训练页面添加 TensorBoard iframe 显示框
- 实现动态生成 TensorBoard iframe 功能
- 更新训练按钮点击事件，同时更新 TensorBoard iframe

frontend/train_page.py

@@ -25,18 +25,23 @@ def train_page():
             interactive=True
         )
         
-        # 新增超参数输入组件
+        with gr.Row():
-        learning_rate_input = gr.Number(value=2e-4, label="学习率")
+            with gr.Column(scale=1):
-        per_device_train_batch_size_input = gr.Number(value=1, label="batch size", precision=0)
+                # 新增超参数输入组件
-        epoch_input = gr.Number(value=1, label="epoch", precision=0)
+                learning_rate_input = gr.Number(value=2e-4, label="学习率")
-        save_steps_input = gr.Number(value=20, label="保存步数", precision=0)  # 新增保存步数输入框
+                per_device_train_batch_size_input = gr.Number(value=1, label="batch size", precision=0)
-        lora_rank_input = gr.Number(value=16, label="LoRA秩", precision=0)  # 新增LoRA秩输入框
+                epoch_input = gr.Number(value=1, label="epoch", precision=0)
-        
+                save_steps_input = gr.Number(value=20, label="保存步数", precision=0)  # 新增保存步数输入框
-        train_button = gr.Button("开始微调")
+                lora_rank_input = gr.Number(value=16, label="LoRA秩", precision=0)  # 新增LoRA秩输入框
-        
+                
-        # 训练状态输出
+                train_button = gr.Button("开始微调")
-        output = gr.Textbox(label="训练日志", interactive=False)
+                
-        
+                # 训练状态输出
+                output = gr.Textbox(label="训练日志", interactive=False)
+            with gr.Column(scale=3):
+                # 新增 TensorBoard iframe 显示框
+                tensorboard_iframe = gr.HTML(label="TensorBoard 可视化")
+
         def start_training(dataset_name, learning_rate, per_device_train_batch_size, epoch, save_steps, lora_rank):
             # 使用动态传入的超参数
             learning_rate = float(learning_rate)
@@ -68,6 +73,11 @@ def train_page():
             )
             print("TensorBoard 已启动，日志目录:", tensorboard_logdir)
 
+            # 动态生成 TensorBoard iframe
+            tensorboard_url = f"http://localhost:{tensorboard_port}"
+            tensorboard_iframe_value = f'<iframe src="{tensorboard_url}" width="100%" height="500px"></iframe>'
+            yield "训练开始...", tensorboard_iframe_value  # 返回两个值，分别对应 textbox 和 html
+
             try:
                 train_model(get_model(), get_tokenizer(),
                             dataset, new_training_dir, 
@@ -88,7 +98,7 @@ def train_page():
                 save_steps_input,
                 lora_rank_input
             ],
-            outputs=output
+            outputs=[output, tensorboard_iframe]  # 更新输出以包含 iframe
         )
 
     return demo