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'''
Contributed by SagsMug. Modified by binary-husky
https://github.com/oobabooga/text-generation-webui/pull/175
'''
import asyncio
import json
import random
import string
import websockets
import logging
import time
import threading
import importlib
from toolbox import get_conf
LLM_MODEL, = get_conf('LLM_MODEL')
model_name, addr, port = LLM_MODEL.split('@')
def random_hash():
letters = string.ascii_lowercase + string.digits
return ''.join(random.choice(letters) for i in range(9))
async def run(context):
params = {
'max_new_tokens': 1024,
'do_sample': True,
'temperature': 0.5,
'top_p': 0.9,
'typical_p': 1,
'repetition_penalty': 1.05,
'encoder_repetition_penalty': 1.0,
'top_k': 0,
'min_length': 0,
'no_repeat_ngram_size': 0,
'num_beams': 1,
'penalty_alpha': 0,
'length_penalty': 1,
'early_stopping': False,
'seed': -1,
}
session = random_hash()
async with websockets.connect(f"ws://{addr}:{port}/queue/join") as websocket:
while content := json.loads(await websocket.recv()):
#Python3.10 syntax, replace with if elif on older
if content["msg"] == "send_hash":
await websocket.send(json.dumps({
"session_hash": session,
"fn_index": 12
}))
elif content["msg"] == "estimation":
pass
elif content["msg"] == "send_data":
await websocket.send(json.dumps({
"session_hash": session,
"fn_index": 12,
"data": [
context,
params['max_new_tokens'],
params['do_sample'],
params['temperature'],
params['top_p'],
params['typical_p'],
params['repetition_penalty'],
params['encoder_repetition_penalty'],
params['top_k'],
params['min_length'],
params['no_repeat_ngram_size'],
params['num_beams'],
params['penalty_alpha'],
params['length_penalty'],
params['early_stopping'],
params['seed'],
]
}))
elif content["msg"] == "process_starts":
pass
elif content["msg"] in ["process_generating", "process_completed"]:
yield content["output"]["data"][0]
# You can search for your desired end indicator and
# stop generation by closing the websocket here
if (content["msg"] == "process_completed"):
break
def predict_tgui(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='', stream = True, additional_fn=None):
"""
发送至chatGPT，流式获取输出。
用于基础的对话功能。
inputs 是本次问询的输入
top_p, temperature是chatGPT的内部调优参数
history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
additional_fn代表点击的哪个按钮，按钮见functional.py
"""
if additional_fn is not None:
import functional
importlib.reload(functional) # 热更新prompt
functional = functional.get_functionals()
if "PreProcess" in functional[additional_fn]: inputs = functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数（如果有的话）
inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
raw_input = "What I would like to say is the following: " + inputs
logging.info(f'[raw_input] {raw_input}')
history.extend([inputs, ""])
chatbot.append([inputs, ""])
yield chatbot, history, "等待响应"
prompt = inputs
tgui_say = ""
mutable = [""]
def run_coorotine(mutable):
async def get_result(mutable):
async for response in run(prompt):
# Print intermediate steps
mutable[0] = response
asyncio.run(get_result(mutable))
thread_listen = threading.Thread(target=run_coorotine, args=(mutable,))
thread_listen.start()
while thread_listen.is_alive():
time.sleep(1)
# Print intermediate steps
if tgui_say != mutable[0]:
tgui_say = mutable[0]
history[-1] = tgui_say
chatbot[-1] = (history[-2], history[-1])
yield chatbot, history, "status_text"
logging.info(f'[response] {tgui_say}')
def predict_tgui_no_ui(inputs, top_p, temperature, history=[], sys_prompt=""):
raw_input = "What I would like to say is the following: " + inputs
prompt = inputs
tgui_say = ""
mutable = [""]
def run_coorotine(mutable):
async def get_result(mutable):
async for response in run(prompt):
# Print intermediate steps
mutable[0] = response
asyncio.run(get_result(mutable))
thread_listen = threading.Thread(target=run_coorotine, args=(mutable,))
thread_listen.start()
thread_listen.join()
tgui_say = mutable[0]
return tgui_say
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