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修改还了返回按钮的功能

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WIN-JHFT4D3SIVT\caoxiaozhu
2026-01-29 17:39:06 +08:00
parent d0675aede3
commit 0f98d67e41
3 changed files with 254 additions and 22 deletions
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122
src/api/model_chat.py
View File

@@ -6,6 +6,7 @@ import pymysql
import yaml
import requests
import concurrent.futures
import subprocess
from flask import Blueprint, request, jsonify
# 获取项目根目录
@@ -208,3 +209,124 @@ def model_chat_batch():
results.append(future.result())
return jsonify({'code': 0, 'data': results})
@model_chat_bp.route('/trained', methods=['POST'])
def chat_trained_model():
"""使用已训练模型进行对话推理"""
import pymysql
import yaml
data = request.json
model_name = data.get('model_name') # 模型名称
train_method = data.get('train_method', 'lora') # 训练方法: lora, full
system_prompt = data.get('system_prompt', '')
user_question = data.get('user_question')
temperature = data.get('temperature', 0.7)
max_tokens = data.get('max_tokens', 2048)
if not model_name:
return jsonify({'code': 1, 'message': '缺少模型名称'})
if not user_question:
return jsonify({'code': 1, 'message': '缺少用户提问'})
# 获取项目根目录
PROJECT_ROOT = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
CONFIG_PATH = os.path.join(PROJECT_ROOT, 'config.yaml')
with open(CONFIG_PATH, 'r', encoding='utf-8') as f:
CONFIG = yaml.safe_load(f)
# 获取基座模型路径 - 从数据库查询 model_name 对应的路径
try:
db_config = CONFIG['database']
conn = pymysql.connect(
host=db_config['host'],
port=db_config['port'],
user=db_config['username'],
password=db_config['password'],
database=db_config['name'],
charset=db_config.get('charset', 'utf8mb4'),
cursorclass=pymysql.cursors.DictCursor
)
cursor = conn.cursor()
cursor.execute("SELECT path FROM model_manage WHERE name = %s LIMIT 1", (model_name,))
model_result = cursor.fetchone()
conn.close()
if not model_result or not model_result.get('path'):
return jsonify({'code': 1, 'message': f'未找到模型 {model_name} 的配置'})
base_model_path = model_result['path']
except Exception as e:
return jsonify({'code': 1, 'message': f'查询模型配置失败: {str(e)}'})
# 训练后的模型路径
trained_model_path = f"/app/base/saves/{train_method}/{model_name}"
if not os.path.exists(trained_model_path):
return jsonify({'code': 1, 'message': f'训练模型不存在: {trained_model_path}'})
# 构建 llamafactory-cli chat 命令
work_dir = '/app/base'
llamafactory_dir = '/app/base'
# 准备消息
messages = []
if system_prompt:
messages.append({'role': 'system', 'content': system_prompt})
messages.append({'role': 'user', 'content': user_question})
# 将消息转换为 JSON 字符串
messages_json = json.dumps(messages, ensure_ascii=False)
# 构建 llamafactory-cli chat 命令
full_cmd = f'cd {llamafactory_dir} && export CUDA_VISIBLE_DEVICES=0 && echo \'{messages_json}\' | llamafactory-cli chat --model_name_or_path {base_model_path} --adapter_name_or_path {trained_model_path} --template llama3 --finetuning_type lora --temperature {temperature} --max_tokens {max_tokens}'
try:
# 执行命令
result = subprocess.run(
full_cmd,
shell=True,
capture_output=True,
text=True,
timeout=120,
cwd=work_dir
)
output = result.stdout
error = result.stderr
# 解析输出，提取assistant回复
# llamafactory-cli chat 输出格式通常是:
# <|im_start|>assistant
# xxx
# <|im_end|>
assistant_content = ''
if '<|im_start|>assistant' in output:
parts = output.split('<|im_start|>assistant')
if len(parts) > 1:
content_part = parts[1].split('<|im_end|>')[0].strip()
# 移除可能存在的换行前缀
content_part = content_part.lstrip('\n').strip()
assistant_content = content_part
elif result.returncode == 0:
# 如果没有特殊标记，尝试提取最后一部分作为回复
lines = output.strip().split('\n')
assistant_content = '\n'.join(lines).strip()
else:
return jsonify({'code': 1, 'message': f'推理失败: {error or output}'})
return jsonify({
'code': 0,
'data': {
'model_name': model_name,
'train_method': train_method,
'response': assistant_content
}
})
except subprocess.TimeoutExpired:
return jsonify({'code': 1, 'message': '推理超时，请稍后重试'})
except Exception as e:
return jsonify({'code': 1, 'message': f'推理异常: {str(e)}'})

153
src/api/model_manage.py
View File

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

1
web/pages/model-manage-create.html
View File

@@ -675,6 +675,7 @@
function goBack() {
window.location.href = backUrl;
}
window.goBack = goBack;
})();
</script>