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(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=model_name, 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 model.system_prompt = original_model.system_prompt if dont_change_lora_selector: return model, msg, chatbot, gr.update(), access_key, presudo_key else: return model, msg, chatbot, gr.Dropdown(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}")