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# encoding: utf-8
# @Time : 2023/12/21
# @Author : Spike
# @Descr :
import json
import re
import os
import time
from request_llms.com_google import GoogleChatInit
from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc
proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
'网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
console_slience=False):
# 检查API_KEY
if get_conf("GEMINI_API_KEY") == "":
raise ValueError(f"请配置 GEMINI_API_KEY。")
genai = GoogleChatInit()
watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
gpt_replying_buffer = ''
stream_response = genai.generate_chat(inputs, llm_kwargs, history, sys_prompt)
for response in stream_response:
results = response.decode()
match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
error_match = re.search(r'\"message\":\s*\"(.*?)\"', results, flags=re.DOTALL)
if match:
try:
paraphrase = json.loads('{"text": "%s"}' % match.group(1))
except:
raise ValueError(f"解析GEMINI消息出错。")
buffer = paraphrase['text']
gpt_replying_buffer += buffer
if len(observe_window) >= 1:
observe_window[0] = gpt_replying_buffer
if len(observe_window) >= 2:
if (time.time() - observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
if error_match:
raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
return gpt_replying_buffer
def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream=True, additional_fn=None):
# 检查API_KEY
if get_conf("GEMINI_API_KEY") == "":
yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
return
# 适配润色区域
if additional_fn is not None:
from core_functional import handle_core_functionality
inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
if "vision" in llm_kwargs["llm_model"]:
have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
if not have_recent_file:
chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
return
def make_media_input(inputs, image_paths):
for image_path in image_paths:
inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
return inputs
if have_recent_file:
inputs = make_media_input(inputs, image_paths)
chatbot.append((inputs, ""))
yield from update_ui(chatbot=chatbot, history=history)
genai = GoogleChatInit()
retry = 0
while True:
try:
stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
break
except Exception as e:
retry += 1
chatbot[-1] = ((chatbot[-1][0], trimmed_format_exc()))
yield from update_ui(chatbot=chatbot, history=history, msg="请求失败") # 刷新界面
return
gpt_replying_buffer = ""
gpt_security_policy = ""
history.extend([inputs, ''])
for response in stream_response:
results = response.decode("utf-8") # 被这个解码给耍了。。
gpt_security_policy += results
match = re.search(r'"text":\s*"((?:[^"\\]|\\.)*)"', results, flags=re.DOTALL)
error_match = re.search(r'\"message\":\s*\"(.*)\"', results, flags=re.DOTALL)
if match:
try:
paraphrase = json.loads('{"text": "%s"}' % match.group(1))
except:
raise ValueError(f"解析GEMINI消息出错。")
gpt_replying_buffer += paraphrase['text'] # 使用 json 解析库进行处理
chatbot[-1] = (inputs, gpt_replying_buffer)
history[-1] = gpt_replying_buffer
yield from update_ui(chatbot=chatbot, history=history)
if error_match:
history = history[-2] # 错误的不纳入对话
chatbot[-1] = (inputs, gpt_replying_buffer + f"对话错误，请查看message\n\n```\n{error_match.group(1)}\n```")
yield from update_ui(chatbot=chatbot, history=history)
raise RuntimeError('对话错误')
if not gpt_replying_buffer:
history = history[-2] # 错误的不纳入对话
chatbot[-1] = (inputs, gpt_replying_buffer + f"触发了Google的安全访问策略，没有回答\n\n```\n{gpt_security_policy}\n```")
yield from update_ui(chatbot=chatbot, history=history)
if __name__ == '__main__':
import sys
llm_kwargs = {'llm_model': 'gemini-pro'}
result = predict('Write long a story about a magic backpack.', llm_kwargs, llm_kwargs, [])
for i in result:
print(i)
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