feat(train_page): 实现训练目录自动递增功能

- 在 training 文件夹下创建递增的目录结构
- 确保 training 文件夹存在
- 扫描现有目录，生成下一个可用的目录编号
- 更新训练模型函数，使用新的训练目录

frontend/train_page.py

@@ -1,3 +1,4 @@
+import os
 import gradio as gr
 import sys
 from tinydb import Query
@@ -36,21 +37,28 @@ def train_page():
         output = gr.Textbox(label="训练日志", interactive=False)
         
         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
+            # 使用动态传入的超参数
             learning_rate = float(learning_rate)
             per_device_train_batch_size = int(per_device_train_batch_size)
             epoch = int(epoch)
             save_steps = int(save_steps)  # 新增保存步数参数
             lora_rank = int(lora_rank)  # 新增LoRA秩参数
+            
             # 加载数据集
             dataset = get_datasets().get(Query().name == dataset_name)
             dataset = [ds["message"][0] for ds in dataset["dataset_items"]] 
 
-            train_model(get_model(), get_tokenizer(),
-                         dataset, get_workdir() + "/training" + "/1", 
-                         learning_rate, per_device_train_batch_size, epoch,
-                           save_steps, lora_rank)
+            # 扫描 training 文件夹并生成递增目录
+            training_dir = get_workdir() + "/training"
+            os.makedirs(training_dir, exist_ok=True)  # 确保 training 文件夹存在
+            existing_dirs = [d for d in os.listdir(training_dir) if d.isdigit()]
+            next_dir_number = max([int(d) for d in existing_dirs], default=0) + 1
+            new_training_dir = os.path.join(training_dir, str(next_dir_number))
 
+            train_model(get_model(), get_tokenizer(),
+                        dataset, new_training_dir, 
+                        learning_rate, per_device_train_batch_size, epoch,
+                        save_steps, lora_rank)
 
         train_button.click(
             fn=start_training,