gpt-academic111 / predict.py
3v324v23's picture
增加读latex文章的功能,添加测试样例
f238a34
raw
history blame
6.6 kB
# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
import json
import gradio as gr
import logging
import traceback
import requests
import importlib
from colorful import *
# config_private.py放自己的秘密如API和代理网址
# 读取时首先看是否存在私密的config_private配置文件(不受git管控),如果有,则覆盖原config文件
try: from config_private import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
except: from config import proxies, API_URL, API_KEY, TIMEOUT_SECONDS, MAX_RETRY, LLM_MODEL
timeout_bot_msg = '[local] Request timeout, network error. please check proxy settings in config.py.'
def get_full_error(chunk, stream_response):
while True:
try:
chunk += next(stream_response)
except:
break
return chunk
def predict_no_ui(inputs, top_p, temperature, history=[]):
headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt="", stream=False)
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=False
response = requests.post(API_URL, headers=headers, proxies=proxies,
json=payload, stream=False, timeout=TIMEOUT_SECONDS*2); break
except TimeoutError as e:
retry += 1
traceback.print_exc()
if MAX_RETRY!=0: print(f'请求超时,正在重试 ({retry}/{MAX_RETRY}) ……')
if retry > MAX_RETRY: raise TimeoutError
try:
result = json.loads(response.text)["choices"][0]["message"]["content"]
return result
except Exception as e:
if "choices" not in response.text: print(response.text)
raise ConnectionAbortedError("Json解析不合常规,可能是文本过长" + response.text)
def predict(inputs, top_p, temperature, chatbot=[], history=[], system_prompt='',
stream = True, additional_fn=None):
if additional_fn is not None:
import functional
importlib.reload(functional)
functional = functional.get_functionals()
inputs = functional[additional_fn]["Prefix"] + inputs + functional[additional_fn]["Suffix"]
if stream:
raw_input = inputs
logging.info(f'[raw_input] {raw_input}')
chatbot.append((inputs, ""))
yield chatbot, history, "等待响应"
headers, payload = generate_payload(inputs, top_p, temperature, history, system_prompt, stream)
history.append(inputs); history.append(" ")
retry = 0
while True:
try:
# make a POST request to the API endpoint, stream=True
response = requests.post(API_URL, headers=headers, proxies=proxies,
json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
except:
retry += 1
chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
retry_msg = f",正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
yield chatbot, history, "请求超时"+retry_msg
if retry > MAX_RETRY: raise TimeoutError
gpt_replying_buffer = ""
is_head_of_the_stream = True
if stream:
stream_response = response.iter_lines()
while True:
chunk = next(stream_response)
# print(chunk.decode()[6:])
if is_head_of_the_stream:
# 数据流的第一帧不携带content
is_head_of_the_stream = False; continue
if chunk:
try:
if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
# 判定为数据流的结束,gpt_replying_buffer也写完了
logging.info(f'[response] {gpt_replying_buffer}')
break
# 处理数据流的主体
chunkjson = json.loads(chunk.decode()[6:])
status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
# 如果这里抛出异常,一般是文本过长,详情见get_full_error的输出
gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"]
history[-1] = gpt_replying_buffer
chatbot[-1] = (history[-2], history[-1])
yield chatbot, history, status_text
except Exception as e:
traceback.print_exc()
yield chatbot, history, "Json解析不合常规,很可能是文本过长"
chunk = get_full_error(chunk, stream_response)
error_msg = chunk.decode()
if "reduce the length" in error_msg:
chatbot[-1] = (history[-1], "[local] input is too long, reduce input or clear history.")
yield chatbot, history, "Json解析不合常规,很可能是文本过长" + error_msg
return
def generate_payload(inputs, top_p, temperature, history, system_prompt, stream):
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}
conversation_cnt = len(history) // 2
messages = [{"role": "system", "content": system_prompt}]
if conversation_cnt:
for index in range(0, 2*conversation_cnt, 2):
what_i_have_asked = {}
what_i_have_asked["role"] = "user"
what_i_have_asked["content"] = history[index]
what_gpt_answer = {}
what_gpt_answer["role"] = "assistant"
what_gpt_answer["content"] = history[index+1]
if what_i_have_asked["content"] != "":
if what_gpt_answer["content"] == "": continue
if what_gpt_answer["content"] == timeout_bot_msg: continue
messages.append(what_i_have_asked)
messages.append(what_gpt_answer)
else:
messages[-1]['content'] = what_gpt_answer['content']
what_i_ask_now = {}
what_i_ask_now["role"] = "user"
what_i_ask_now["content"] = inputs
messages.append(what_i_ask_now)
payload = {
"model": LLM_MODEL,
"messages": messages,
"temperature": temperature, # 1.0,
"top_p": top_p, # 1.0,
"n": 1,
"stream": stream,
"presence_penalty": 0,
"frequency_penalty": 0,
}
print(f" {LLM_MODEL} : {conversation_cnt} : {inputs}")
return headers,payload