修改还了返回按钮的功能
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@@ -45,6 +45,49 @@ def generic_get_all(table_name, order_by='create_time DESC'):
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return result
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def get_model_path_by_name(model_name):
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"""根据模型名称查询模型路径（用于获取基座模型路径）"""
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try:
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conn = get_db_connection()
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cursor = conn.cursor()
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# 优先从训练任务表查询基座模型
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cursor.execute("""
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SELECT base_model FROM fine_tune
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WHERE output_model_name LIKE %s OR output_model_name LIKE %s
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LIMIT 1
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""", (f'%/{model_name}', f'%{model_name}%'))
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ft_result = cursor.fetchone()
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if ft_result and ft_result.get('base_model'):
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base_model_val = ft_result['base_model']
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# 如果是数字ID，查询模型管理表获取路径
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if str(base_model_val).isdigit():
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cursor.execute("SELECT path FROM model_manage WHERE id = %s LIMIT 1", (base_model_val,))
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model_result = cursor.fetchone()
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if model_result:
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cursor.close()
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conn.close()
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return model_result.get('path')
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else:
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# 直接是路径
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cursor.close()
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conn.close()
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return base_model_val
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# 如果训练任务表没找到，尝试从模型管理表按名称查询
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cursor.execute("SELECT path FROM model_manage WHERE name = %s LIMIT 1", (model_name,))
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result = cursor.fetchone()
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cursor.close()
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conn.close()
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if result:
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return result.get('path')
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return None
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except Exception as e:
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logger.error(f"[ERROR] 查询模型路径失败: {e}")
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return None
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def generic_create(table_name, data):
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"""通用创建"""
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conn = get_db_connection()
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@@ -226,42 +269,108 @@ def get_trained_models():
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try:
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# 路径结构: /app/base/saves/{train_method}/{model_name}/
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# train_method: lora, full, qlora, dpo, cpt 等
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# 同时兼容老结构: /app/base/saves/{model_name}/
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for train_method in os.listdir(base_path):
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train_method_path = os.path.join(base_path, train_method)
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if not os.path.isdir(train_method_path):
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train_methods = ['lora', 'full', 'qlora', 'dpo', 'cpt', 'prefix', 'adapter', 'peft']
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for item in os.listdir(base_path):
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item_path = os.path.join(base_path, item)
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if not os.path.isdir(item_path):
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continue
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logger.info(f"[DEBUG] 检查训练方法目录: {train_method}")
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model_count = 0
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# 情况1: 新结构 {train_method}/{model_name}
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if item in train_methods:
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logger.info(f"[DEBUG] 检查训练方法目录: {item}")
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model_count = 0
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# 遍历模型文件夹
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for model_name in os.listdir(train_method_path):
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model_path = os.path.join(train_method_path, model_name)
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if not os.path.isdir(model_path):
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continue
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for model_name in os.listdir(item_path):
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model_path = os.path.join(item_path, model_name)
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if not os.path.isdir(model_path):
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continue
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# 检查是否有模型文件
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try:
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files = os.listdir(model_path)
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has_model = any(f.endswith('.bin') or f.endswith('.safetensors') for f in files)
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if has_model:
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logger.info(f"[DEBUG] 找到模型: {item}/{model_name}")
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# 获取文件创建时间
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try:
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import time
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stat = os.stat(model_path)
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create_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(stat.st_mtime))
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except:
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create_time = None
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# 查询基座模型路径
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base_model_path = get_model_path_by_name(model_name)
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models.append({
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'name': model_name,
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'path': model_path,
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'base_model_path': base_model_path,
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'create_time': create_time,
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'train_methods': [{
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'name': item,
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'path': model_path
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}]
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})
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model_count += 1
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except Exception as file_err:
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logger.error(f"[DEBUG] 读取 {model_path} 失败: {file_err}")
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logger.info(f"[DEBUG] {item} 找到 {model_count} 个模型")
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# 情况2: 老结构 {model_name} 直接在 saves 下
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else:
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logger.info(f"[DEBUG] 检查老结构模型目录: {item}")
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try:
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files = os.listdir(model_path)
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logger.info(f"[DEBUG] {train_method}/{model_name} 文件: {files[:5]}...")
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files = os.listdir(item_path)
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has_model = any(f.endswith('.bin') or f.endswith('.safetensors') for f in files)
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if has_model:
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logger.info(f"[DEBUG] 找到模型: {train_method}/{model_name}")
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logger.info(f"[DEBUG] 找到模型: {item}")
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# 尝试从 adapter_config.json 推断 train_method
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inferred_method = 'lora' # 默认
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config_file = os.path.join(item_path, 'adapter_config.json')
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if os.path.exists(config_file):
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try:
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import json
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with open(config_file, 'r', encoding='utf-8') as f:
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config = json.load(f)
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if 'peft_type' in config:
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peft_type = config['peft_type'].lower()
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if 'lora' in peft_type:
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inferred_method = 'lora'
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elif 'full' in peft_type or 'pt' in peft_type:
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inferred_method = 'full'
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except:
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pass
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# 获取文件创建时间
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try:
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import time
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stat = os.stat(item_path)
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create_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(stat.st_mtime))
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except:
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create_time = None
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# 查询基座模型路径
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base_model_path = get_model_path_by_name(item)
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models.append({
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'name': model_name,
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'path': model_path,
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'name': item,
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'path': item_path,
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'base_model_path': base_model_path,
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'create_time': create_time,
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'train_methods': [{
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'name': train_method,
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'path': model_path
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'name': inferred_method,
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'path': item_path
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}]
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})
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model_count += 1
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except Exception as file_err:
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logger.error(f"[DEBUG] 读取 {model_path} 失败: {file_err}")
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logger.info(f"[DEBUG] {train_method} 找到 {model_count} 个模型")
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logger.error(f"[DEBUG] 读取 {item_path} 失败: {file_err}")
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except Exception as list_err:
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logger.error(f"[DEBUG] 遍历目录失败: {list_err}")
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