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from __future__ import annotations
import logging
import os
import colorama
import commentjson as cjson
from modules import config
from ..index_func import *
from ..presets import *
from ..utils import *
from .base_model import BaseLLMModel, ModelType
def get_model(
model_name,
lora_model_path=None,
access_key=None,
temperature=None,
top_p=None,
system_prompt=None,
user_name="",
original_model = None
) -> BaseLLMModel:
msg = i18n("模型设置为了:") + f" {model_name}"
model_type = ModelType.get_type(model_name)
lora_selector_visibility = False
lora_choices = ["No LoRA"]
dont_change_lora_selector = False
if model_type != ModelType.OpenAI:
config.local_embedding = True
# del current_model.model
model = original_model
chatbot = gr.Chatbot.update(label=model_name)
try:
if model_type == ModelType.OpenAI:
logging.info(f"正在加载OpenAI模型: {model_name}")
from .OpenAI import OpenAIClient
access_key = os.environ.get("OPENAI_API_KEY", access_key)
model = OpenAIClient(
model_name=model_name,
api_key=access_key,
system_prompt=system_prompt,
user_name=user_name,
)
elif model_type == ModelType.OpenAIInstruct:
logging.info(f"正在加载OpenAI Instruct模型: {model_name}")
from .OpenAIInstruct import OpenAI_Instruct_Client
access_key = os.environ.get("OPENAI_API_KEY", access_key)
model = OpenAI_Instruct_Client(
model_name, api_key=access_key, user_name=user_name)
elif model_type == ModelType.OpenAIVision:
logging.info(f"正在加载OpenAI Vision模型: {model_name}")
from .OpenAIVision import OpenAIVisionClient
access_key = os.environ.get("OPENAI_API_KEY", access_key)
model = OpenAIVisionClient(
model_name, api_key=access_key, user_name=user_name)
elif model_type == ModelType.ChatGLM:
logging.info(f"正在加载ChatGLM模型: {model_name}")
from .ChatGLM import ChatGLM_Client
model = ChatGLM_Client(model_name, user_name=user_name)
elif model_type == ModelType.LLaMA and lora_model_path == "":
msg = f"现在请为 {model_name} 选择LoRA模型"
logging.info(msg)
lora_selector_visibility = True
if os.path.isdir("lora"):
lora_choices = ["No LoRA"] + get_file_names_by_pinyin("lora", filetypes=[""])
elif model_type == ModelType.LLaMA and lora_model_path != "":
logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}")
from .LLaMA import LLaMA_Client
dont_change_lora_selector = True
if lora_model_path == "No LoRA":
lora_model_path = None
msg += " + No LoRA"
else:
msg += f" + {lora_model_path}"
model = LLaMA_Client(
model_name, lora_model_path, user_name=user_name)
elif model_type == ModelType.XMChat:
from .XMChat import XMChat
if os.environ.get("XMCHAT_API_KEY") != "":
access_key = os.environ.get("XMCHAT_API_KEY")
model = XMChat(api_key=access_key, user_name=user_name)
elif model_type == ModelType.StableLM:
from .StableLM import StableLM_Client
model = StableLM_Client(model_name, user_name=user_name)
elif model_type == ModelType.MOSS:
from .MOSS import MOSS_Client
model = MOSS_Client(model_name, user_name=user_name)
elif model_type == ModelType.YuanAI:
from .inspurai import Yuan_Client
model = Yuan_Client(model_name, api_key=access_key,
user_name=user_name, system_prompt=system_prompt)
elif model_type == ModelType.Minimax:
from .minimax import MiniMax_Client
if os.environ.get("MINIMAX_API_KEY") != "":
access_key = os.environ.get("MINIMAX_API_KEY")
model = MiniMax_Client(
model_name, api_key=access_key, user_name=user_name, system_prompt=system_prompt)
elif model_type == ModelType.ChuanhuAgent:
from .ChuanhuAgent import ChuanhuAgent_Client
model = ChuanhuAgent_Client(model_name, access_key, user_name=user_name)
msg = i18n("启用的工具:") + ", ".join([i.name for i in model.tools])
elif model_type == ModelType.GooglePaLM:
from .GooglePaLM import Google_PaLM_Client
access_key = os.environ.get("GOOGLE_GENAI_API_KEY", access_key)
model = Google_PaLM_Client(
model_name, access_key, user_name=user_name)
elif model_type == ModelType.GoogleGemini:
from .GoogleGemini import GoogleGeminiClient
access_key = os.environ.get("GOOGLE_GENAI_API_KEY", access_key)
model = GoogleGeminiClient(
model_name, access_key, user_name=user_name)
elif model_type == ModelType.LangchainChat:
from .Azure import Azure_OpenAI_Client
model = Azure_OpenAI_Client(model_name, user_name=user_name)
elif model_type == ModelType.Midjourney:
from .midjourney import Midjourney_Client
mj_proxy_api_secret = os.getenv("MIDJOURNEY_PROXY_API_SECRET")
model = Midjourney_Client(
model_name, mj_proxy_api_secret, user_name=user_name)
elif model_type == ModelType.Spark:
from .spark import Spark_Client
model = Spark_Client(model_name, os.getenv("SPARK_APPID"), os.getenv(
"SPARK_API_KEY"), os.getenv("SPARK_API_SECRET"), user_name=user_name)
elif model_type == ModelType.Claude:
from .Claude import Claude_Client
model = Claude_Client(model_name="claude-2", api_secret=os.getenv("CLAUDE_API_SECRET"))
elif model_type == ModelType.Qwen:
from .Qwen import Qwen_Client
model = Qwen_Client(model_name, user_name=user_name)
elif model_type == ModelType.ERNIE:
from .ERNIE import ERNIE_Client
model = ERNIE_Client(model_name, api_key=os.getenv("ERNIE_APIKEY"),secret_key=os.getenv("ERNIE_SECRETKEY"))
elif model_type == ModelType.DALLE3:
from .DALLE3 import OpenAI_DALLE3_Client
access_key = os.environ.get("OPENAI_API_KEY", access_key)
model = OpenAI_DALLE3_Client(model_name, api_key=access_key, user_name=user_name)
elif model_type == ModelType.Ollama:
from .Ollama import OllamaClient
ollama_host = os.environ.get("OLLAMA_HOST", access_key)
model = OllamaClient(model_name, user_name=user_name, backend_model=lora_model_path)
model_list = model.get_model_list()
lora_selector_visibility = True
lora_choices = [i["name"] for i in model_list["models"]]
elif model_type == ModelType.GoogleGemma:
from .GoogleGemma import GoogleGemmaClient
model = GoogleGemmaClient(
model_name, access_key, user_name=user_name)
elif model_type == ModelType.Unknown:
raise ValueError(f"Unknown model: {model_name}")
else:
raise ValueError(f"Unimplemented model type: {model_type}")
logging.info(msg)
except Exception as e:
import traceback
traceback.print_exc()
msg = f"{STANDARD_ERROR_MSG}: {e}"
presudo_key = hide_middle_chars(access_key)
if original_model is not None and model is not None:
model.history = original_model.history
model.history_file_path = original_model.history_file_path
if dont_change_lora_selector:
return model, msg, chatbot, gr.update(), access_key, presudo_key
else:
return model, msg, chatbot, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility), access_key, presudo_key
if __name__ == "__main__":
with open("config.json", "r", encoding="utf-8") as f:
openai_api_key = cjson.load(f)["openai_api_key"]
# set logging level to debug
logging.basicConfig(level=logging.DEBUG)
# client = ModelManager(model_name="gpt-3.5-turbo", access_key=openai_api_key)
client = get_model(model_name="chatglm-6b-int4")
chatbot = []
stream = False
# 测试账单功能
logging.info(colorama.Back.GREEN + "测试账单功能" + colorama.Back.RESET)
logging.info(client.billing_info())
# 测试问答
logging.info(colorama.Back.GREEN + "测试问答" + colorama.Back.RESET)
question = "巴黎是中国的首都吗?"
for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
logging.info(i)
logging.info(f"测试问答后history : {client.history}")
# 测试记忆力
logging.info(colorama.Back.GREEN + "测试记忆力" + colorama.Back.RESET)
question = "我刚刚问了你什么问题?"
for i in client.predict(inputs=question, chatbot=chatbot, stream=stream):
logging.info(i)
logging.info(f"测试记忆力后history : {client.history}")
# 测试重试功能
logging.info(colorama.Back.GREEN + "测试重试功能" + colorama.Back.RESET)
for i in client.retry(chatbot=chatbot, stream=stream):
logging.info(i)
logging.info(f"重试后history : {client.history}")
# # 测试总结功能
# print(colorama.Back.GREEN + "测试总结功能" + colorama.Back.RESET)
# chatbot, msg = client.reduce_token_size(chatbot=chatbot)
# print(chatbot, msg)
# print(f"总结后history: {client.history}")