Spaces:
Build error
Build error
File size: 2,554 Bytes
17d0a32 d0703ef 17d0a32 8dd4d48 17d0a32 d0703ef 17d0a32 d0703ef 8dd4d48 17d0a32 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
import time
import threading
import importlib
from toolbox import update_ui, get_conf, update_ui_lastest_msg
from multiprocessing import Process, Pipe
model_name = '星火认知大模型'
def validate_key():
XFYUN_APPID = get_conf('XFYUN_APPID')
if XFYUN_APPID == '00000000' or XFYUN_APPID == '':
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('请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET')
from .com_sparkapi import SparkRequestInstance
sri = SparkRequestInstance()
for response in sri.generate(inputs, llm_kwargs, history, sys_prompt, use_image_api=False):
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)
if validate_key() is False:
yield from update_ui_lastest_msg(lastmsg="[Local Message] 请配置讯飞星火大模型的XFYUN_APPID, XFYUN_API_KEY, XFYUN_API_SECRET", 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)
# 开始接收回复
from .com_sparkapi import SparkRequestInstance
sri = SparkRequestInstance()
response = f"[Local Message] 等待{model_name}响应中 ..."
for response in sri.generate(inputs, llm_kwargs, history, system_prompt, use_image_api=True):
chatbot[-1] = (inputs, response)
yield from update_ui(chatbot=chatbot, history=history)
# 总结输出
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) |