from __future__ import annotations from typing import TYPE_CHECKING, List import logging import json import commentjson as cjson import os import sys import requests import urllib3 import platform import base64 from io import BytesIO from PIL import Image from tqdm import tqdm import colorama from duckduckgo_search import ddg import asyncio import aiohttp from enum import Enum import uuid from .presets import * from .llama_func import * from .utils import * from . import shared from .config import retrieve_proxy from modules import config from .base_model import BaseLLMModel, ModelType class OpenAIClient(BaseLLMModel): def __init__( self, model_name, api_key, system_prompt=INITIAL_SYSTEM_PROMPT, temperature=1.0, top_p=1.0, ) -> None: super().__init__( model_name=model_name, temperature=temperature, top_p=top_p, system_prompt=system_prompt, ) self.api_key = api_key self.need_api_key = True self._refresh_header() def get_answer_stream_iter(self): response = self._get_response(stream=True) if response is not None: iter = self._decode_chat_response(response) partial_text = "" for i in iter: partial_text += i yield partial_text else: yield STANDARD_ERROR_MSG + GENERAL_ERROR_MSG def get_answer_at_once(self): response = self._get_response() response = json.loads(response.text) content = response["choices"][0]["message"]["content"] total_token_count = response["usage"]["total_tokens"] return content, total_token_count def count_token(self, user_input): input_token_count = count_token(construct_user(user_input)) if self.system_prompt is not None and len(self.all_token_counts) == 0: system_prompt_token_count = count_token( construct_system(self.system_prompt) ) return input_token_count + system_prompt_token_count return input_token_count def billing_info(self): try: curr_time = datetime.datetime.now() last_day_of_month = get_last_day_of_month( curr_time).strftime("%Y-%m-%d") first_day_of_month = curr_time.replace(day=1).strftime("%Y-%m-%d") usage_url = f"{shared.state.usage_api_url}?start_date={first_day_of_month}&end_date={last_day_of_month}" try: usage_data = self._get_billing_data(usage_url) except Exception as e: logging.error(f"获取API使用情况失败:" + str(e)) return i18n("**获取API使用情况失败**") rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100) return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}" except requests.exceptions.ConnectTimeout: status_text = ( STANDARD_ERROR_MSG + CONNECTION_TIMEOUT_MSG + ERROR_RETRIEVE_MSG ) return status_text except requests.exceptions.ReadTimeout: status_text = STANDARD_ERROR_MSG + READ_TIMEOUT_MSG + ERROR_RETRIEVE_MSG return status_text except Exception as e: import traceback traceback.print_exc() logging.error(i18n("获取API使用情况失败:") + str(e)) return STANDARD_ERROR_MSG + ERROR_RETRIEVE_MSG def set_token_upper_limit(self, new_upper_limit): pass @shared.state.switching_api_key # 在不开启多账号模式的时候，这个装饰器不会起作用 def _get_response(self, stream=False): openai_api_key = self.api_key system_prompt = self.system_prompt history = self.history logging.debug(colorama.Fore.YELLOW + f"{history}" + colorama.Fore.RESET) headers = { "Content-Type": "application/json", "Authorization": f"Bearer {openai_api_key}", } if system_prompt is not None: history = [construct_system(system_prompt), *history] payload = { "model": self.model_name, "messages": history, "temperature": self.temperature, "top_p": self.top_p, "n": self.n_choices, "stream": stream, "presence_penalty": self.presence_penalty, "frequency_penalty": self.frequency_penalty, } if self.max_generation_token is not None: payload["max_tokens"] = self.max_generation_token if self.stop_sequence is not None: payload["stop"] = self.stop_sequence if self.logit_bias is not None: payload["logit_bias"] = self.logit_bias if self.user_identifier is not None: payload["user"] = self.user_identifier if stream: timeout = TIMEOUT_STREAMING else: timeout = TIMEOUT_ALL # 如果有自定义的api-host，使用自定义host发送请求，否则使用默认设置发送请求 if shared.state.completion_url != COMPLETION_URL: logging.info(f"使用自定义API URL: {shared.state.completion_url}") with retrieve_proxy(): try: response = requests.post( shared.state.completion_url, headers=headers, json=payload, stream=stream, timeout=timeout, ) except: return None return response def _refresh_header(self): self.headers = { "Content-Type": "application/json", "Authorization": f"Bearer {self.api_key}", } def _get_billing_data(self, billing_url): with retrieve_proxy(): response = requests.get( billing_url, headers=self.headers, timeout=TIMEOUT_ALL, ) if response.status_code == 200: data = response.json() return data else: raise Exception( f"API request failed with status code {response.status_code}: {response.text}" ) def _decode_chat_response(self, response): error_msg = "" for chunk in response.iter_lines(): if chunk: chunk = chunk.decode() chunk_length = len(chunk) try: chunk = json.loads(chunk[6:]) except json.JSONDecodeError: print(i18n("JSON解析错误,收到的内容: ") + f"{chunk}") error_msg += chunk continue if chunk_length > 6 and "delta" in chunk["choices"][0]: if chunk["choices"][0]["finish_reason"] == "stop": break try: yield chunk["choices"][0]["delta"]["content"] except Exception as e: # logging.error(f"Error: {e}") continue if error_msg: raise Exception(error_msg) def set_key(self, new_access_key): ret = super().set_key(new_access_key) self._refresh_header() return ret class ChatGLM_Client(BaseLLMModel): def __init__(self, model_name) -> None: super().__init__(model_name=model_name) from transformers import AutoTokenizer, AutoModel import torch global CHATGLM_TOKENIZER, CHATGLM_MODEL if CHATGLM_TOKENIZER is None or CHATGLM_MODEL is None: system_name = platform.system() model_path = None if os.path.exists("models"): model_dirs = os.listdir("models") if model_name in model_dirs: model_path = f"models/{model_name}" if model_path is not None: model_source = model_path else: model_source = f"THUDM/{model_name}" CHATGLM_TOKENIZER = AutoTokenizer.from_pretrained( model_source, trust_remote_code=True ) quantified = False if "int4" in model_name: quantified = True model = AutoModel.from_pretrained( model_source, trust_remote_code=True ) if torch.cuda.is_available(): # run on CUDA logging.info("CUDA is available, using CUDA") model = model.half().cuda() # mps加速还存在一些问题，暂时不使用 elif system_name == "Darwin" and model_path is not None and not quantified: logging.info("Running on macOS, using MPS") # running on macOS and model already downloaded model = model.half().to("mps") else: logging.info("GPU is not available, using CPU") model = model.float() model = model.eval() CHATGLM_MODEL = model def _get_glm_style_input(self): history = [x["content"] for x in self.history] query = history.pop() logging.debug(colorama.Fore.YELLOW + f"{history}" + colorama.Fore.RESET) assert ( len(history) % 2 == 0 ), f"History should be even length. current history is: {history}" history = [[history[i], history[i + 1]] for i in range(0, len(history), 2)] return history, query def get_answer_at_once(self): history, query = self._get_glm_style_input() response, _ = CHATGLM_MODEL.chat( CHATGLM_TOKENIZER, query, history=history) return response, len(response) def get_answer_stream_iter(self): history, query = self._get_glm_style_input() for response, history in CHATGLM_MODEL.stream_chat( CHATGLM_TOKENIZER, query, history, max_length=self.token_upper_limit, top_p=self.top_p, temperature=self.temperature, ): yield response class LLaMA_Client(BaseLLMModel): def __init__( self, model_name, lora_path=None, ) -> None: super().__init__(model_name=model_name) from lmflow.datasets.dataset import Dataset from lmflow.pipeline.auto_pipeline import AutoPipeline from lmflow.models.auto_model import AutoModel from lmflow.args import ModelArguments, DatasetArguments, InferencerArguments self.max_generation_token = 1000 self.end_string = "\n\n" # We don't need input data data_args = DatasetArguments(dataset_path=None) self.dataset = Dataset(data_args) self.system_prompt = "" global LLAMA_MODEL, LLAMA_INFERENCER if LLAMA_MODEL is None or LLAMA_INFERENCER is None: model_path = None if os.path.exists("models"): model_dirs = os.listdir("models") if model_name in model_dirs: model_path = f"models/{model_name}" if model_path is not None: model_source = model_path else: model_source = f"decapoda-research/{model_name}" # raise Exception(f"models目录下没有这个模型: {model_name}") if lora_path is not None: lora_path = f"lora/{lora_path}" model_args = ModelArguments(model_name_or_path=model_source, lora_model_path=lora_path, model_type=None, config_overrides=None, config_name=None, tokenizer_name=None, cache_dir=None, use_fast_tokenizer=True, model_revision='main', use_auth_token=False, torch_dtype=None, use_lora=False, lora_r=8, lora_alpha=32, lora_dropout=0.1, use_ram_optimized_load=True) pipeline_args = InferencerArguments( local_rank=0, random_seed=1, deepspeed='configs/ds_config_chatbot.json', mixed_precision='bf16') with open(pipeline_args.deepspeed, "r") as f: ds_config = json.load(f) LLAMA_MODEL = AutoModel.get_model( model_args, tune_strategy="none", ds_config=ds_config, ) LLAMA_INFERENCER = AutoPipeline.get_pipeline( pipeline_name="inferencer", model_args=model_args, data_args=data_args, pipeline_args=pipeline_args, ) def _get_llama_style_input(self): history = [] instruction = "" if self.system_prompt: instruction = (f"Instruction: {self.system_prompt}\n") for x in self.history: if x["role"] == "user": history.append(f"{instruction}Input: {x['content']}") else: history.append(f"Output: {x['content']}") context = "\n\n".join(history) context += "\n\nOutput: " return context def get_answer_at_once(self): context = self._get_llama_style_input() input_dataset = self.dataset.from_dict( {"type": "text_only", "instances": [{"text": context}]} ) output_dataset = LLAMA_INFERENCER.inference( model=LLAMA_MODEL, dataset=input_dataset, max_new_tokens=self.max_generation_token, temperature=self.temperature, ) response = output_dataset.to_dict()["instances"][0]["text"] return response, len(response) def get_answer_stream_iter(self): context = self._get_llama_style_input() partial_text = "" step = 1 for _ in range(0, self.max_generation_token, step): input_dataset = self.dataset.from_dict( {"type": "text_only", "instances": [ {"text": context + partial_text}]} ) output_dataset = LLAMA_INFERENCER.inference( model=LLAMA_MODEL, dataset=input_dataset, max_new_tokens=step, temperature=self.temperature, ) response = output_dataset.to_dict()["instances"][0]["text"] if response == "" or response == self.end_string: break partial_text += response yield partial_text class XMChat(BaseLLMModel): def __init__(self, api_key): super().__init__(model_name="xmchat") self.api_key = api_key self.session_id = None self.reset() self.image_bytes = None self.image_path = None self.xm_history = [] self.url = "https://xmbot.net/web" self.last_conv_id = None def reset(self): self.session_id = str(uuid.uuid4()) self.last_conv_id = None return [], "已重置" def image_to_base64(self, image_path): # 打开并加载图片 img = Image.open(image_path) # 获取图片的宽度和高度 width, height = img.size # 计算压缩比例，以确保最长边小于4096像素 max_dimension = 2048 scale_ratio = min(max_dimension / width, max_dimension / height) if scale_ratio < 1: # 按压缩比例调整图片大小 new_width = int(width * scale_ratio) new_height = int(height * scale_ratio) img = img.resize((new_width, new_height), Image.ANTIALIAS) # 将图片转换为jpg格式的二进制数据 buffer = BytesIO() if img.mode == "RGBA": img = img.convert("RGB") img.save(buffer, format='JPEG') binary_image = buffer.getvalue() # 对二进制数据进行Base64编码 base64_image = base64.b64encode(binary_image).decode('utf-8') return base64_image def try_read_image(self, filepath): def is_image_file(filepath): # 判断文件是否为图片 valid_image_extensions = [".jpg", ".jpeg", ".png", ".bmp", ".gif", ".tiff"] file_extension = os.path.splitext(filepath)[1].lower() return file_extension in valid_image_extensions if is_image_file(filepath): logging.info(f"读取图片文件: {filepath}") self.image_bytes = self.image_to_base64(filepath) self.image_path = filepath else: self.image_bytes = None self.image_path = None def like(self): if self.last_conv_id is None: return "点赞失败，你还没发送过消息" data = { "uuid": self.last_conv_id, "appraise": "good" } response = requests.post(self.url, json=data) return "👍点赞成功，，感谢反馈～" def dislike(self): if self.last_conv_id is None: return "点踩失败，你还没发送过消息" data = { "uuid": self.last_conv_id, "appraise": "bad" } response = requests.post(self.url, json=data) return "👎点踩成功，感谢反馈～" def prepare_inputs(self, real_inputs, use_websearch, files, reply_language, chatbot): fake_inputs = real_inputs display_append = "" limited_context = False return limited_context, fake_inputs, display_append, real_inputs, chatbot def handle_file_upload(self, files, chatbot): """if the model accepts multi modal input, implement this function""" if files: for file in files: if file.name: logging.info(f"尝试读取图像: {file.name}") self.try_read_image(file.name) if self.image_path is not None: chatbot = chatbot + [((self.image_path,), None)] if self.image_bytes is not None: logging.info("使用图片作为输入") # XMChat的一轮对话中实际上只能处理一张图片 self.reset() conv_id = str(uuid.uuid4()) data = { "user_id": self.api_key, "session_id": self.session_id, "uuid": conv_id, "data_type": "imgbase64", "data": self.image_bytes } response = requests.post(self.url, json=data) response = json.loads(response.text) logging.info(f"图片回复: {response['data']}") return None, chatbot, None def get_answer_at_once(self): question = self.history[-1]["content"] conv_id = str(uuid.uuid4()) self.last_conv_id = conv_id data = { "user_id": self.api_key, "session_id": self.session_id, "uuid": conv_id, "data_type": "text", "data": question } response = requests.post(self.url, json=data) try: response = json.loads(response.text) return response["data"], len(response["data"]) except Exception as e: return response.text, len(response.text) def get_model( model_name, lora_model_path=None, access_key=None, temperature=None, top_p=None, system_prompt=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 = [] dont_change_lora_selector = False if model_type != ModelType.OpenAI: config.local_embedding = True # del current_model.model model = None try: if model_type == ModelType.OpenAI: logging.info(f"正在加载OpenAI模型: {model_name}") model = OpenAIClient( model_name=model_name, api_key=access_key, system_prompt=system_prompt, temperature=temperature, top_p=top_p, ) elif model_type == ModelType.ChatGLM: logging.info(f"正在加载ChatGLM模型: {model_name}") model = ChatGLM_Client(model_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 = get_file_names( "lora", plain=True, filetypes=[""]) lora_choices = ["No LoRA"] + lora_choices elif model_type == ModelType.LLaMA and lora_model_path != "": logging.info(f"正在加载LLaMA模型: {model_name} + {lora_model_path}") 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) elif model_type == ModelType.XMChat: if os.environ.get("XMCHAT_API_KEY") != "": access_key = os.environ.get("XMCHAT_API_KEY") model = XMChat(api_key=access_key) elif model_type == ModelType.Unknown: raise ValueError(f"未知模型: {model_name}") logging.info(msg) except Exception as e: logging.error(e) msg = f"{STANDARD_ERROR_MSG}: {e}" if dont_change_lora_selector: return model, msg else: return model, msg, gr.Dropdown.update(choices=lora_choices, visible=lora_selector_visibility) if __name__ == "__main__": with open("config.json", "r") 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}")