| import time
|
| from toolbox import update_ui, get_conf, update_ui_lastest_msg
|
| from toolbox import check_packages, report_exception
|
|
|
| model_name = '云雀大模型'
|
|
|
| def validate_key():
|
| YUNQUE_SECRET_KEY = get_conf("YUNQUE_SECRET_KEY")
|
| if YUNQUE_SECRET_KEY == '': return False
|
| return True
|
|
|
| def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
|
| """
|
| ⭐ 多线程方法
|
| 函数的说明请见 request_llms/bridge_all.py
|
| """
|
| watch_dog_patience = 5
|
| response = ""
|
|
|
| if validate_key() is False:
|
| raise RuntimeError('请配置YUNQUE_SECRET_KEY')
|
|
|
| from .com_skylark2api import YUNQUERequestInstance
|
| sri = YUNQUERequestInstance()
|
| for response in sri.generate(inputs, llm_kwargs, history, sys_prompt):
|
| if len(observe_window) >= 1:
|
| observe_window[0] = response
|
| if len(observe_window) >= 2:
|
| if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
|
| return response
|
|
|
| def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
|
| """
|
| ⭐ 单线程方法
|
| 函数的说明请见 request_llms/bridge_all.py
|
| """
|
| chatbot.append((inputs, ""))
|
| yield from update_ui(chatbot=chatbot, history=history)
|
|
|
|
|
| try:
|
| check_packages(["zhipuai"])
|
| except:
|
| yield from update_ui_lastest_msg(f"导入软件依赖失败。使用该模型需要额外依赖，安装方法```pip install --upgrade zhipuai```。",
|
| chatbot=chatbot, history=history, delay=0)
|
| return
|
|
|
| if validate_key() is False:
|
| yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置HUOSHAN_API_KEY", chatbot=chatbot, history=history, delay=0)
|
| return
|
|
|
| if additional_fn is not None:
|
| from core_functional import handle_core_functionality
|
| inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
|
|
|
|
|
| from .com_skylark2api import YUNQUERequestInstance
|
| sri = YUNQUERequestInstance()
|
| response = f"[Local Message] 等待{model_name}响应中 ..."
|
| for response in sri.generate(inputs, llm_kwargs, history, system_prompt):
|
| chatbot[-1] = (inputs, response)
|
| yield from update_ui(chatbot=chatbot, history=history)
|
|
|
|
|
| if response == f"[Local Message] 等待{model_name}响应中 ...":
|
| response = f"[Local Message] {model_name}响应异常 ..."
|
| history.extend([inputs, response])
|
| yield from update_ui(chatbot=chatbot, history=history) |
