chore: 提取 models.py 中的类

modules/models/ChatGLM.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import logging
+import os
+import platform
+
+import colorama
+
+from ..index_func import *
+from ..presets import *
+from ..utils import *
+from .base_model import BaseLLMModel
+
+
+class ChatGLM_Client(BaseLLMModel):
+    def __init__(self, model_name, user_name="") -> None:
+        super().__init__(model_name=model_name, user=user_name)
+        import torch
+        from transformers import AutoModel, AutoTokenizer
+        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

modules/models/{Google_PaLM.py → GooglePaLM.py}

@@ -1,6 +1,7 @@
 from .base_model import BaseLLMModel
 import google.generativeai as palm
 
+
 class Google_PaLM_Client(BaseLLMModel):
     def __init__(self, model_name, api_key, user_name="") -> None:
         super().__init__(model_name=model_name, user=user_name)
@@ -18,9 +19,11 @@ class Google_PaLM_Client(BaseLLMModel):
     def get_answer_at_once(self):
         palm.configure(api_key=self.api_key)
         messages = self._get_palm_style_input()
-        response = palm.chat(context=self.system_prompt, messages=messages, temperature=self.temperature, top_p=self.top_p)
+        response = palm.chat(context=self.system_prompt, messages=messages,
+                             temperature=self.temperature, top_p=self.top_p)
         if response.last is not None:
             return response.last, len(response.last)
         else:
-            reasons = '\n\n'.join(reason['reason'].name for reason in response.filters)
-            return "由于下面的原因，Google 拒绝返回 PaLM 的回答：\n\n" + reasons, 0
+            reasons = '\n\n'.join(
+                reason['reason'].name for reason in response.filters)
+            return "由于下面的原因，Google 拒绝返回 PaLM 的回答：\n\n" + reasons, 0

modules/models/LLaMA.py

@@ -0,0 +1,116 @@
+from __future__ import annotations
+
+import json
+import os
+
+from ..index_func import *
+from ..presets import *
+from ..utils import *
+from .base_model import BaseLLMModel
+
+
+class LLaMA_Client(BaseLLMModel):
+    def __init__(
+        self,
+        model_name,
+        lora_path=None,
+        user_name=""
+    ) -> None:
+        super().__init__(model_name=model_name, user=user_name)
+        from lmflow.args import (DatasetArguments, InferencerArguments,
+                                 ModelArguments)
+        from lmflow.datasets.dataset import Dataset
+        from lmflow.models.auto_model import AutoModel
+        from lmflow.pipeline.auto_pipeline import AutoPipeline
+
+        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", encoding="utf-8") 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

modules/models/OpenAI.py

@@ -0,0 +1,270 @@
+from __future__ import annotations
+
+import json
+import logging
+
+import colorama
+import requests
+
+from .. import shared
+from ..config import retrieve_proxy, sensitive_id, usage_limit
+from ..index_func import *
+from ..presets import *
+from ..utils import *
+from .base_model import BaseLLMModel
+
+
+class OpenAIClient(BaseLLMModel):
+    def __init__(
+        self,
+        model_name,
+        api_key,
+        system_prompt=INITIAL_SYSTEM_PROMPT,
+        temperature=1.0,
+        top_p=1.0,
+        user_name=""
+    ) -> None:
+        super().__init__(
+            model_name=model_name,
+            temperature=temperature,
+            top_p=top_p,
+            system_prompt=system_prompt,
+            user=user_name
+        )
+        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))
+                if "Invalid authorization header" in str(e):
+                    return i18n("**获取API使用情况失败**，需在填写`config.json`中正确填写sensitive_id")
+                elif "Incorrect API key provided: sess" in str(e):
+                    return i18n("**获取API使用情况失败**，sensitive_id错误或已过期")
+                return i18n("**获取API使用情况失败**")
+            # rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
+            rounded_usage = round(usage_data["total_usage"] / 100, 5)
+            usage_percent = round(usage_data["total_usage"] / usage_limit, 2)
+            from ..webui import get_html
+
+            # return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
+            return get_html("billing_info.html").format(
+                label=i18n("本月使用金额"),
+                usage_percent=usage_percent,
+                rounded_usage=rounded_usage,
+                usage_limit=usage_limit
+            )
+        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:
+            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.debug(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 {sensitive_id}",
+        }
+
+    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:
+                    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
+
+    def _single_query_at_once(self, history, temperature=1.0):
+        timeout = TIMEOUT_ALL
+        headers = {
+            "Content-Type": "application/json",
+            "Authorization": f"Bearer {self.api_key}",
+            "temperature": f"{temperature}",
+        }
+        payload = {
+            "model": self.model_name,
+            "messages": history,
+        }
+        # 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.debug(f"使用自定义API URL: {shared.state.completion_url}")
+
+        with retrieve_proxy():
+            response = requests.post(
+                shared.state.completion_url,
+                headers=headers,
+                json=payload,
+                stream=False,
+                timeout=timeout,
+            )
+
+        return response
+
+    def auto_name_chat_history(self, name_chat_method, user_question, chatbot, user_name, single_turn_checkbox):
+        if len(self.history) == 2 and not single_turn_checkbox:
+            user_question = self.history[0]["content"]
+            if name_chat_method == i18n("模型自动总结（消耗tokens）"):
+                ai_answer = self.history[1]["content"]
+                try:
+                    history = [
+                        {"role": "system", "content": SUMMARY_CHAT_SYSTEM_PROMPT},
+                        {"role": "user", "content": f"Please write a title based on the following conversation:\n---\nUser: {user_question}\nAssistant: {ai_answer}"}
+                    ]
+                    response = self._single_query_at_once(
+                        history, temperature=0.0)
+                    response = json.loads(response.text)
+                    content = response["choices"][0]["message"]["content"]
+                    filename = replace_special_symbols(content) + ".json"
+                except Exception as e:
+                    logging.info(f"自动命名失败。{e}")
+                    filename = replace_special_symbols(user_question)[
+                        :16] + ".json"
+                return self.rename_chat_history(filename, chatbot, user_name)
+            elif name_chat_method == i18n("第一条提问"):
+                filename = replace_special_symbols(user_question)[
+                    :16] + ".json"
+                return self.rename_chat_history(filename, chatbot, user_name)
+            else:
+                return gr.update()
+        else:
+            return gr.update()

modules/models/XMChat.py

@@ -0,0 +1,149 @@
+from __future__ import annotations
+
+import base64
+import json
+import logging
+import os
+import uuid
+from io import BytesIO
+
+import requests
+from PIL import Image
+
+from ..index_func import *
+from ..presets import *
+from ..utils import *
+from .base_model import BaseLLMModel
+
+
+class XMChatClient(BaseLLMModel):
+    def __init__(self, api_key, user_name=""):
+        super().__init__(model_name="xmchat", user=user_name)
+        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"
+        }
+        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"
+        }
+        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, language):
+        """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)

modules/models/models.py

@@ -1,599 +1,19 @@
 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
-import asyncio
-import aiohttp
-from enum import Enum
-import uuid
+import commentjson as cjson
+
+from modules import config
 
-from ..presets import *
 from ..index_func import *
+from ..presets import *
 from ..utils import *
-from .. import shared
-from ..config import retrieve_proxy, usage_limit, sensitive_id
-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,
-        user_name=""
-    ) -> None:
-        super().__init__(
-            model_name=model_name,
-            temperature=temperature,
-            top_p=top_p,
-            system_prompt=system_prompt,
-            user=user_name
-        )
-        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))
-                if "Invalid authorization header" in str(e):
-                    return i18n("**获取API使用情况失败**，需在填写`config.json`中正确填写sensitive_id")
-                elif "Incorrect API key provided: sess" in str(e):
-                    return i18n("**获取API使用情况失败**，sensitive_id错误或已过期")
-                return i18n("**获取API使用情况失败**")
-            # rounded_usage = "{:.5f}".format(usage_data["total_usage"] / 100)
-            rounded_usage = round(usage_data["total_usage"] / 100, 5)
-            usage_percent = round(usage_data["total_usage"] / usage_limit, 2)
-            from ..webui import get_html
-            # return i18n("**本月使用金额** ") + f"\u3000 ${rounded_usage}"
-            return get_html("billing_info.html").format(
-                    label = i18n("本月使用金额"),
-                    usage_percent = usage_percent,
-                    rounded_usage = rounded_usage,
-                    usage_limit = usage_limit
-                )
-        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:
-            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.debug(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 {sensitive_id}",
-        }
-
-
-    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:
-                    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
-    
-    def _single_query_at_once(self, history, temperature=1.0):
-        timeout = TIMEOUT_ALL
-        headers = {
-            "Content-Type": "application/json",
-            "Authorization": f"Bearer {self.api_key}",
-            "temperature": f"{temperature}",
-        }
-        payload = {
-            "model": self.model_name,
-            "messages": history,
-        }
-        # 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.debug(f"使用自定义API URL: {shared.state.completion_url}")
-
-        with retrieve_proxy():
-            response = requests.post(
-                shared.state.completion_url,
-                headers=headers,
-                json=payload,
-                stream=False,
-                timeout=timeout,
-            )
-
-        return response
-
-    
-    def auto_name_chat_history(self, name_chat_method, user_question, chatbot, user_name, single_turn_checkbox):
-        if len(self.history) == 2 and not single_turn_checkbox:
-            user_question = self.history[0]["content"]
-            if name_chat_method == i18n("模型自动总结（消耗tokens）"):
-                ai_answer = self.history[1]["content"]
-                try:
-                    history = [
-                        { "role": "system", "content": SUMMARY_CHAT_SYSTEM_PROMPT},
-                        { "role": "user", "content": f"Please write a title based on the following conversation:\n---\nUser: {user_question}\nAssistant: {ai_answer}"}
-                    ]
-                    response = self._single_query_at_once(history, temperature=0.0)
-                    response = json.loads(response.text)
-                    content = response["choices"][0]["message"]["content"]
-                    filename = replace_special_symbols(content) + ".json"
-                except Exception as e:
-                    logging.info(f"自动命名失败。{e}")
-                    filename = replace_special_symbols(user_question)[:16] + ".json"
-                return self.rename_chat_history(filename, chatbot, user_name)
-            elif name_chat_method == i18n("第一条提问"):
-                filename = replace_special_symbols(user_question)[:16] + ".json"
-                return self.rename_chat_history(filename, chatbot, user_name)
-            else:
-                return gr.update()
-        else:
-            return gr.update()
-
-
-class ChatGLM_Client(BaseLLMModel):
-    def __init__(self, model_name, user_name="") -> None:
-        super().__init__(model_name=model_name, user=user_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,
-        user_name=""
-    ) -> None:
-        super().__init__(model_name=model_name, user=user_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", encoding="utf-8") 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, user_name=""):
-        super().__init__(model_name="xmchat", user=user_name)
-        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"
-        }
-        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"
-        }
-        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, language):
-        """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,
@@ -616,6 +36,7 @@ def get_model(
     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,
@@ -627,6 +48,7 @@ def get_model(
             )
         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"
@@ -637,6 +59,7 @@ def get_model(
             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}")
+            from .LLaMA import LLaMA_Client
             dont_change_lora_selector = True
             if lora_model_path == "No LoRA":
                 lora_model_path = None
@@ -646,9 +69,10 @@ def get_model(
             model = LLaMA_Client(
                 model_name, lora_model_path, user_name=user_name)
         elif model_type == ModelType.XMChat:
+            from .XMChat import XMChatClient
             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)
+            model = XMChatClient(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)
@@ -657,29 +81,35 @@ def get_model(
             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)
+            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)
+            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)
+            model = ChuanhuAgent_Client(
+                model_name, access_key, user_name=user_name)
         elif model_type == ModelType.GooglePaLM:
-            from .Google_PaLM import Google_PaLM_Client
+            from .GooglePaLM import Google_PaLM_Client
             access_key = os.environ.get("GOOGLE_PALM_API_KEY", access_key)
-            model = Google_PaLM_Client(model_name, access_key, user_name=user_name)
+            model = Google_PaLM_Client(
+                model_name, access_key, user_name=user_name)
         elif model_type == ModelType.LangchainChat:
-            from .azure import Azure_OpenAI_Client
+            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)
+            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)
+            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.Unknown:
             raise ValueError(f"未知模型: {model_name}")
         logging.info(msg)