logging -> loguru: final stage

2025-12-06 06:26:47 +00:00 · 2024-09-15 15:51:51 +00:00
--- a/request_llms/bridge_all.py
+++ b/request_llms/bridge_all.py
@@ -1022,7 +1022,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
        # 如果是已知模型，则尝试获取其信息
        original_model_info = model_info.get(origin_model_name.replace("one-api-", "", 1), None)
    except:
-        print(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
        continue
    this_model_info = {
        "fn_with_ui": chatgpt_ui,
@@ -1053,7 +1053,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("vllm-")]:
    try:
        _, max_token_tmp = read_one_api_model_name(model)
    except:
-        print(f"vllm模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"vllm模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
        continue
    model_info.update({
        model: {
@@ -1080,7 +1080,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
    try:
        _, max_token_tmp = read_one_api_model_name(model)
    except:
-        print(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
        continue
    model_info.update({
        model: {
--- a/request_llms/bridge_chatglmft.py
+++ b/request_llms/bridge_chatglmft.py
@@ -1,12 +1,13 @@

 from transformers import AutoModel, AutoTokenizer
+from loguru import logger
+from toolbox import update_ui, get_conf
+from multiprocessing import Process, Pipe
 import time
 import os
 import json
 import threading
 import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe

 load_message = "ChatGLMFT尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLMFT消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"

@@ -78,7 +79,7 @@ class GetGLMFTHandle(Process):
                    config.pre_seq_len = model_args['pre_seq_len']
                    config.prefix_projection = model_args['prefix_projection']

-                    print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
+                    logger.info(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
                    model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
                    prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
                    new_prefix_state_dict = {}
@@ -88,7 +89,7 @@ class GetGLMFTHandle(Process):
                    model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)

                    if model_args['quantization_bit'] is not None and model_args['quantization_bit'] != 0:
-                        print(f"Quantized to {model_args['quantization_bit']} bit")
+                        logger.info(f"Quantized to {model_args['quantization_bit']} bit")
                        model = model.quantize(model_args['quantization_bit'])
                    model = model.cuda()
                    if model_args['pre_seq_len'] is not None:
--- a/request_llms/bridge_chatgpt.py
+++ b/request_llms/bridge_chatgpt.py
@@ -16,6 +16,8 @@ import traceback
 import requests
 import random

+from loguru import logger
+
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
@@ -146,7 +148,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')

    stream_response = response.iter_lines()
    result = ''
@@ -179,7 +181,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
        if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
        if has_content: # has_role = True/False
            result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
+            if not console_slience: logger.info(delta["content"], end='')
            if observe_window is not None:
                # 观测窗，把已经获取的数据显示出去
                if len(observe_window) >= 1:
@@ -342,7 +344,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                    error_msg = chunk_decoded
                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                    return

 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
@@ -493,10 +495,7 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
        "n": 1,
        "stream": stream,
    }
-    # try:
-    #     print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    # except:
-    #     print('输入中可能存在乱码。')
+
    return headers,payload


--- a/request_llms/bridge_chatgpt_vision.py
+++ b/request_llms/bridge_chatgpt_vision.py
@@ -14,6 +14,7 @@ import time
 import requests
 import base64
 import glob
+from loguru import logger
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, \
    update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files, log_chat

@@ -208,7 +209,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                    error_msg = chunk_decoded
                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                    return

 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key=""):
@@ -299,10 +300,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
        "presence_penalty": 0,
        "frequency_penalty": 0,
    }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
+
    return headers, payload, api_key


--- a/request_llms/bridge_chatgpt_website.py
+++ b/request_llms/bridge_chatgpt_website.py
@@ -1,281 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-"""
-    该文件中主要包含三个函数
-
-    不具备多线程能力的函数：
-    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
-
-    具备多线程调用能力的函数
-    2. predict_no_ui_long_connection：支持多线程
-"""
-
-import json
-import time
-import gradio as gr
-import traceback
-import requests
-import importlib
-from loguru import logger as logging
-
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
-proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
-    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
-
-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 get_full_error(chunk, stream_response):
-    """
-        获取完整的从Openai返回的报错
-    """
-    while True:
-        try:
-            chunk += next(stream_response)
-        except:
-            break
-    return chunk
-
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
-    """
-    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
-    inputs：
-        是本次问询的输入
-    sys_prompt:
-        系统静默prompt
-    llm_kwargs：
-        chatGPT的内部调优参数
-    history：
-        是之前的对话列表
-    observe_window = None：
-        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
-    """
-    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=False
-            from .bridge_all import model_info
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
-        except requests.exceptions.ReadTimeout as e:
-            retry += 1
-            traceback.print_exc()
-            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-
-    stream_response =  response.iter_lines()
-    result = ''
-    while True:
-        try: chunk = next(stream_response).decode()
-        except StopIteration:
-            break
-        except requests.exceptions.ConnectionError:
-            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
-        if len(chunk)==0: continue
-        if not chunk.startswith('data:'):
-            error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
-            if "reduce the length" in error_msg:
-                raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
-            else:
-                raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        if ('data: [DONE]' in chunk): break # api2d 正常完成
-        json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
-        delta = json_data["delta"]
-        if len(delta) == 0: break
-        if "role" in delta: continue
-        if "content" in delta:
-            result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
-            if observe_window is not None:
-                # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += delta["content"]
-                # 看门狗，如果超过期限没有喂狗，则终止
-                if len(observe_window) >= 2:
-                    if (time.time()-observe_window[1]) > watch_dog_patience:
-                        raise RuntimeError("用户取消了程序。")
-        else: raise RuntimeError("意外Json结构："+delta)
-    if json_data['finish_reason'] == 'content_filter':
-        raise RuntimeError("由于提问含不合规内容被Azure过滤。")
-    if json_data['finish_reason'] == 'length':
-        raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
-    return result
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-    发送至chatGPT，流式获取输出。
-    用于基础的对话功能。
-    inputs 是本次问询的输入
-    top_p, temperature是chatGPT的内部调优参数
-    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
-    additional_fn代表点击的哪个按钮，按钮见functional.py
-    """
-    if additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
-    chatbot.append((inputs, ""))
-    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
-
-    try:
-        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
-    except RuntimeError as e:
-        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
-        yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
-        return
-
-    history.append(inputs); history.append("")
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=True
-            from .bridge_all import model_info
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-        except:
-            retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
-            if retry > MAX_RETRY: raise TimeoutError
-
-    gpt_replying_buffer = ""
-
-    is_head_of_the_stream = True
-    if stream:
-        stream_response =  response.iter_lines()
-        while True:
-            try:
-                chunk = next(stream_response)
-            except StopIteration:
-                # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
-                chunk_decoded = chunk.decode()
-                error_msg = chunk_decoded
-                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
-                return
-
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
-                # 数据流的第一帧不携带content
-                is_head_of_the_stream = False; continue
-
-            if chunk:
-                try:
-                    chunk_decoded = chunk.decode()
-                    # 前者是API2D的结束条件，后者是OPENAI的结束条件
-                    if 'data: [DONE]' in chunk_decoded:
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
-                        break
-                    # 处理数据流的主体
-                    chunkjson = json.loads(chunk_decoded[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
-                    delta = chunkjson['choices'][0]["delta"]
-                    if "content" in delta:
-                        gpt_replying_buffer = gpt_replying_buffer + delta["content"]
-                    history[-1] = gpt_replying_buffer
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
-                except Exception as e:
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
-                    chunk = get_full_error(chunk, stream_response)
-                    chunk_decoded = chunk.decode()
-                    error_msg = chunk_decoded
-                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
-                    return
-
-def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
-    from .bridge_all import model_info
-    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
-    if "reduce the length" in error_msg:
-        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
-        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'],
-                                               max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
-                        # history = []    # 清除历史
-    elif "does not exist" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
-    elif "Incorrect API key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
-    elif "exceeded your current quota" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
-    elif "account is not active" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "associated with a deactivated account" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "bad forward key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
-    elif "Not enough point" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
-    else:
-        from toolbox import regular_txt_to_markdown
-        tb_str = '```\n' + trimmed_format_exc() + '```'
-        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
-    return chatbot, history
-
-def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
-    """
-    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
-    """
-    if not is_any_api_key(llm_kwargs['api_key']):
-        raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
-
-    headers = {
-        "Content-Type": "application/json",
-    }
-
-    conversation_cnt = len(history) // 2
-
-    messages = [{"role": "system", "content": system_prompt}]
-    if conversation_cnt:
-        for index in range(0, 2*conversation_cnt, 2):
-            what_i_have_asked = {}
-            what_i_have_asked["role"] = "user"
-            what_i_have_asked["content"] = history[index]
-            what_gpt_answer = {}
-            what_gpt_answer["role"] = "assistant"
-            what_gpt_answer["content"] = history[index+1]
-            if what_i_have_asked["content"] != "":
-                if what_gpt_answer["content"] == "": continue
-                if what_gpt_answer["content"] == timeout_bot_msg: continue
-                messages.append(what_i_have_asked)
-                messages.append(what_gpt_answer)
-            else:
-                messages[-1]['content'] = what_gpt_answer['content']
-
-    what_i_ask_now = {}
-    what_i_ask_now["role"] = "user"
-    what_i_ask_now["content"] = inputs
-    messages.append(what_i_ask_now)
-
-    payload = {
-        "model": llm_kwargs['llm_model'].strip('api2d-'),
-        "messages": messages,
-        "temperature": llm_kwargs['temperature'],  # 1.0,
-        "top_p": llm_kwargs['top_p'],  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
-    return headers,payload
-
-
--- a/request_llms/bridge_claude.py
+++ b/request_llms/bridge_claude.py
@@ -14,7 +14,7 @@ import time
 import traceback
 import json
 import requests
-from loguru import logger as logging
+from loguru import logger
 from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path, log_chat

 picture_system_prompt = "\n当回复图像时,必须说明正在回复哪张图像。所有图像仅在最后一个问题中提供,即使它们在历史记录中被提及。请使用'这是第X张图像:'的格式来指明您正在描述的是哪张图像。"
@@ -102,7 +102,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
    stream_response = response.iter_lines()
    result = ''
    while True:
@@ -117,12 +117,11 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                if need_to_pass:
                    pass
                elif is_last_chunk:
-                    # logging.info(f'[response] {result}')
+                    # logger.info(f'[response] {result}')
                    break
                else:
                    if chunkjson and chunkjson['type'] == 'content_block_delta':
                        result += chunkjson['delta']['text']
-                        print(chunkjson['delta']['text'], end='')
                        if observe_window is not None:
                            # 观测窗，把已经获取的数据显示出去
                            if len(observe_window) >= 1:
@@ -135,7 +134,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                chunk = get_full_error(chunk, stream_response)
                chunk_decoded = chunk.decode()
                error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                raise RuntimeError("Json解析不合常规")

    return result
@@ -201,7 +200,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
    stream_response = response.iter_lines()
    gpt_replying_buffer = ""

@@ -218,7 +217,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                    pass
                elif is_last_chunk:
                    log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
-                    # logging.info(f'[response] {gpt_replying_buffer}')
+                    # logger.info(f'[response] {gpt_replying_buffer}')
                    break
                else:
                    if chunkjson and chunkjson['type'] == 'content_block_delta':
@@ -231,7 +230,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                chunk = get_full_error(chunk, stream_response)
                chunk_decoded = chunk.decode()
                error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                raise RuntimeError("Json解析不合常规")

 def multiple_picture_types(image_paths):
--- a/request_llms/bridge_cohere.py
+++ b/request_llms/bridge_cohere.py
@@ -15,7 +15,7 @@ import time
 import gradio as gr
 import traceback
 import requests
-from loguru import logger as logging
+from loguru import logger

 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -96,7 +96,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')

    stream_response = response.iter_lines()
    result = ''
@@ -111,7 +111,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
        if chunkjson['event_type'] == 'stream-start': continue
        if chunkjson['event_type'] == 'text-generation':
            result += chunkjson["text"]
-            if not console_slience: print(chunkjson["text"], end='')
+            if not console_slience: logger.info(chunkjson["text"], end='')
            if observe_window is not None:
                # 观测窗，把已经获取的数据显示出去
                if len(observe_window) >= 1:
@@ -151,7 +151,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)

    raw_input = inputs
-    # logging.info(f'[raw_input] {raw_input}')
+    # logger.info(f'[raw_input] {raw_input}')
    chatbot.append((inputs, ""))
    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面

@@ -235,7 +235,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                    error_msg = chunk_decoded
                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                    return

 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
--- a/request_llms/bridge_deepseekcoder.py
+++ b/request_llms/bridge_deepseekcoder.py
@@ -1,12 +1,13 @@
 model_name = "deepseek-coder-6.7b-instruct"
 cmd_to_install = "未知" # "`pip install -r request_llms/requirements_qwen.txt`"

-import os
 from toolbox import ProxyNetworkActivate
 from toolbox import get_conf
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+from request_llms.local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 from threading import Thread
+from loguru import logger
 import torch
+import os

 def download_huggingface_model(model_name, max_retry, local_dir):
    from huggingface_hub import snapshot_download
@@ -15,7 +16,7 @@ def download_huggingface_model(model_name, max_retry, local_dir):
            snapshot_download(repo_id=model_name, local_dir=local_dir, resume_download=True)
            break
        except Exception as e:
-            print(f'\n\n下载失败，重试第{i}次中...\n\n')
+            logger.error(f'\n\n下载失败，重试第{i}次中...\n\n')
    return local_dir
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
@@ -112,7 +113,6 @@ class GetCoderLMHandle(LocalLLMHandle):
        generated_text = ""
        for new_text in self._streamer:
            generated_text += new_text
-            # print(generated_text)
            yield generated_text


--- a/request_llms/bridge_ollama.py
+++ b/request_llms/bridge_ollama.py
@@ -17,7 +17,7 @@ import traceback
 import requests
 import importlib
 import random
-from loguru import logger as logging 
+from loguru import logger

 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -81,7 +81,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
            retry += 1
            traceback.print_exc()
            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')

    stream_response = response.iter_lines()
    result = ''
@@ -96,10 +96,10 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
            try:
                if is_last_chunk:
                    # 判定为数据流的结束，gpt_replying_buffer也写完了
-                    logging.info(f'[response] {result}')
+                    logger.info(f'[response] {result}')
                    break
                result += chunkjson['message']["content"]
-                if not console_slience: print(chunkjson['message']["content"], end='')
+                if not console_slience: logger.info(chunkjson['message']["content"], end='')
                if observe_window is not None:
                    # 观测窗，把已经获取的数据显示出去
                    if len(observe_window) >= 1:
@@ -112,7 +112,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                chunk = get_full_error(chunk, stream_response)
                chunk_decoded = chunk.decode()
                error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                raise RuntimeError("Json解析不合常规")
    return result

@@ -134,7 +134,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)

    raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
+    logger.info(f'[raw_input] {raw_input}')
    chatbot.append((inputs, ""))
    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面

@@ -183,7 +183,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                try:
                    if is_last_chunk:
                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
+                        logger.info(f'[response] {gpt_replying_buffer}')
                        break
                    # 处理数据流的主体
                    try:
@@ -202,7 +202,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                    error_msg = chunk_decoded
                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                    return

 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
@@ -265,8 +265,5 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
        "messages": messages,
        "options": options,
    }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
+
    return headers,payload
--- a/request_llms/com_google.py
+++ b/request_llms/com_google.py
@@ -218,5 +218,3 @@ class GoogleChatInit:

 if __name__ == "__main__":
    google = GoogleChatInit()
-    # print(gootle.generate_message_payload('你好呀', {},  ['123123', '3123123'], ''))
-    # gootle.input_encode_handle('123123[123123](./123123), ![53425](./asfafa/fff.jpg)')
--- a/request_llms/com_sparkapi.py
+++ b/request_llms/com_sparkapi.py
@@ -1,17 +1,18 @@
-from toolbox import get_conf, get_pictures_list, encode_image
 import base64
 import datetime
 import hashlib
 import hmac
 import json
-from urllib.parse import urlparse
 import ssl
+import websocket
+import threading
+from toolbox import get_conf, get_pictures_list, encode_image
+from loguru import logger
+from urllib.parse import urlparse
 from datetime import datetime
 from time import mktime
 from urllib.parse import urlencode
 from wsgiref.handlers import format_date_time
-import websocket
-import threading, time

 timeout_bot_msg = '[Local Message] Request timeout. Network error.'

@@ -104,7 +105,7 @@ class SparkRequestInstance():
            if llm_kwargs['most_recent_uploaded'].get('path'):
                file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
                if len(file_manifest) > 0:
-                    print('正在使用讯飞图片理解API')
+                    logger.info('正在使用讯飞图片理解API')
                    gpt_url = self.gpt_url_img
        wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
        websocket.enableTrace(False)
@@ -123,7 +124,7 @@ class SparkRequestInstance():
            data = json.loads(message)
            code = data['header']['code']
            if code != 0:
-                print(f'请求错误: {code}, {data}')
+                logger.error(f'请求错误: {code}, {data}')
                self.result_buf += str(data)
                ws.close()
                self.time_to_exit_event.set()
@@ -140,7 +141,7 @@ class SparkRequestInstance():

        # 收到websocket错误的处理
        def on_error(ws, error):
-            print("error:", error)
+            logger.error("error:", error)
            self.time_to_exit_event.set()

        # 收到websocket关闭的处理
--- a/request_llms/com_zhipuglm.py
+++ b/request_llms/com_zhipuglm.py
@@ -5,7 +5,8 @@
 from toolbox import get_conf
 from zhipuai import ZhipuAI
 from toolbox import get_conf, encode_image, get_pictures_list
-import logging, os
+from loguru import logger
+import os


 def input_encode_handler(inputs:str, llm_kwargs:dict):
@@ -24,7 +25,7 @@ class ZhipuChatInit:
    def __init__(self):
        ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
        if len(ZHIPUAI_MODEL) > 0:
-            logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
+            logger.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
        self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
        self.model = ''

@@ -37,8 +38,7 @@ class ZhipuChatInit:
            what_i_have_asked['content'].append({"type": 'text', "text": user_input})
            if encode_img:
                if len(encode_img) > 1:
-                    logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
-                    print("glm-4v只支持一张图片,将只取第一张图片进行处理")
+                    logger.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
                img_d = {"type": "image_url",
                            "image_url": {
                                "url": encode_img[0]['data']
--- a/request_llms/local_llm_class.py
+++ b/request_llms/local_llm_class.py
@@ -5,6 +5,7 @@ from toolbox import ChatBotWithCookies
 from multiprocessing import Process, Pipe
 from contextlib import redirect_stdout
 from request_llms.queued_pipe import create_queue_pipe
+from loguru import logger

 class ThreadLock(object):
    def __init__(self):
@@ -51,7 +52,7 @@ def reset_tqdm_output():
            getattr(sys.stdout, 'flush', lambda: None)()

        def fp_write(s):
-            print(s)
+            logger.info(s)
        last_len = [0]

        def print_status(s):
@@ -199,7 +200,7 @@ class LocalLLMHandle(Process):
                if res.startswith(self.std_tag):
                    new_output = res[len(self.std_tag):]
                    std_out = std_out[:std_out_clip_len]
-                    print(new_output, end='')
+                    logger.info(new_output, end='')
                    std_out = new_output + std_out
                    yield self.std_tag + '\n```\n' + std_out + '\n```\n'
                elif res == '[Finish]':
--- a/request_llms/oai_std_model_template.py
+++ b/request_llms/oai_std_model_template.py
@@ -2,7 +2,7 @@ import json
 import time
 import traceback
 import requests
-from loguru import logger as logging
+from loguru import logger

 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -106,10 +106,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
        "stream": True,
        "max_tokens": max_output_token,
    }
-    try:
-        print(f" {model} : {conversation_cnt} : {input[:100]} ..........")
-    except:
-        print("输入中可能存在乱码。")
+
    return headers, playload


@@ -196,7 +193,7 @@ def get_predict_function(
                if retry > MAX_RETRY:
                    raise TimeoutError
                if MAX_RETRY != 0:
-                    print(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
+                    logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")

        stream_response = response.iter_lines()
        result = ""
@@ -219,18 +216,17 @@ def get_predict_function(
            ):
                chunk = get_full_error(chunk, stream_response)
                chunk_decoded = chunk.decode()
-                print(chunk_decoded)
+                logger.error(chunk_decoded)
                raise RuntimeError(
                    f"API异常,请检测终端输出。可能的原因是:{finish_reason}"
                )
            if chunk:
                try:
                    if finish_reason == "stop":
-                        logging.info(f"[response] {result}")
+                        if not console_slience:
+                            logger.info(f"[response] {result}")
                        break
                    result += response_text
-                    if not console_slience:
-                        print(response_text, end="")
                    if observe_window is not None:
                        # 观测窗，把已经获取的数据显示出去
                        if len(observe_window) >= 1:
@@ -243,7 +239,7 @@ def get_predict_function(
                    chunk = get_full_error(chunk, stream_response)
                    chunk_decoded = chunk.decode()
                    error_msg = chunk_decoded
-                    print(error_msg)
+                    logger.error(error_msg)
                    raise RuntimeError("Json解析不合常规")
        return result

@@ -276,7 +272,7 @@ def get_predict_function(
            inputs, history = handle_core_functionality(
                additional_fn, inputs, history, chatbot
            )
-        logging.info(f"[raw_input] {inputs}")
+        logger.info(f"[raw_input] {inputs}")
        chatbot.append((inputs, ""))
        yield from update_ui(
            chatbot=chatbot, history=history, msg="等待响应"
@@ -376,11 +372,11 @@ def get_predict_function(
                            history=history,
                            msg="API异常:" + chunk_decoded,
                        )  # 刷新界面
-                        print(chunk_decoded)
+                        logger.error(chunk_decoded)
                        return

                    if finish_reason == "stop":
-                        logging.info(f"[response] {gpt_replying_buffer}")
+                        logger.info(f"[response] {gpt_replying_buffer}")
                        break
                    status_text = f"finish_reason: {finish_reason}"
                    gpt_replying_buffer += response_text
@@ -403,7 +399,7 @@ def get_predict_function(
                    yield from update_ui(
                        chatbot=chatbot, history=history, msg="Json异常" + chunk_decoded
                    )  # 刷新界面
-                    print(chunk_decoded)
+                    logger.error(chunk_decoded)
                    return

    return predict_no_ui_long_connection, predict