Merge remote-tracking branch 'github/master'

# Conflicts:
#	config.py

config.py

@@ -14,7 +14,7 @@ API_KEY = "在此处填写APIKEY"    # 可同时填写多个API-KEY，用英文
 DASHSCOPE_API_KEY = "" # 阿里灵积云API_KEY
 
 # [step 1-3]>> ( 接入 deepseek-reasoner, 即 deepseek-r1 ) 深度求索(DeepSeek) API KEY，默认请求地址为"https://api.deepseek.com/v1/chat/completions"
-DEEPSEEK_API_KEY = "sk-d99b8cc6b7414cc88a5d950a3ff7585e"
+DEEPSEEK_API_KEY = ""
 
 # [step 2]>> 改为True应用代理。如果使用本地或无地域限制的大模型时，此处不修改；如果直接在海外服务器部署，此处不修改
 USE_PROXY = False
@@ -81,7 +81,7 @@ API_URL_REDIRECT = {}
 
 # 多线程函数插件中，默认允许多少路线程同时访问OpenAI。Free trial users的限制是每分钟3次，Pay-as-you-go users的限制是每分钟3500次
 # 一言以蔽之：免费（5刀）用户填3，OpenAI绑了信用卡的用户可以填 16 或者更高。提高限制请查询：https://platform.openai.com/docs/guides/rate-limits/overview
-DEFAULT_WORKER_NUM = 3
+DEFAULT_WORKER_NUM = 8
 
 
 # 色彩主题, 可选 ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast"]
@@ -103,6 +103,7 @@ AVAIL_FONTS = [
     "华文中宋(STZhongsong)",  
     "华文新魏(STXinwei)",  
     "华文隶书(STLiti)", 
+    # 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)" 
     "思源宋体(Source Han Serif CN VF@https://chinese-fonts-cdn.deno.dev/packages/syst/dist/SourceHanSerifCN/result.css)",
     "月星楷(Moon Stars Kai HW@https://chinese-fonts-cdn.deno.dev/packages/moon-stars-kai/dist/MoonStarsKaiHW-Regular/result.css)",
     "珠圆体(MaokenZhuyuanTi@https://chinese-fonts-cdn.deno.dev/packages/mkzyt/dist/猫啃珠圆体/result.css)",

main.py

@@ -54,7 +54,7 @@ def main():
     # 如果WEB_PORT是-1, 则随机选取WEB端口
     PORT = find_free_port() if WEB_PORT <= 0 else WEB_PORT
     from check_proxy import get_current_version
-    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_show_or_hide, js_code_show_or_hide_group2
+    from themes.theme import adjust_theme, advanced_css, theme_declaration, js_code_clear, js_code_show_or_hide
     from themes.theme import js_code_for_toggle_darkmode
     from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
@@ -207,7 +207,7 @@ def main():
             ret.update({area_customize: gr.update(visible=("自定义菜单" in a))})
             return ret
         checkboxes_2.select(fn_area_visibility_2, [checkboxes_2], [area_customize] )
-        checkboxes_2.select(None, [checkboxes_2], None, _js=js_code_show_or_hide_group2)
+        checkboxes_2.select(None, [checkboxes_2], None, _js="""apply_checkbox_change_for_group2""")
 
         # 整理反复出现的控件句柄组合
         input_combo = [cookies, max_length_sl, md_dropdown, txt, txt2, top_p, temperature, chatbot, history, system_prompt, plugin_advanced_arg]

request_llms/bridge_all.py

@@ -1072,7 +1072,7 @@ if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容
         })
     except:
         logger.error(trimmed_format_exc())
-# -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
+# -=-=-=-=-=-=- 幻方-深度求索本地大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:
         from .bridge_deepseekcoder import predict_no_ui_long_connection as deepseekcoder_noui

request_llms/bridge_deepseekcoder.py

@@ -6,7 +6,6 @@ from toolbox import get_conf
 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):
@@ -29,6 +28,7 @@ class GetCoderLMHandle(LocalLLMHandle):
         self.cmd_to_install = cmd_to_install
 
     def load_model_and_tokenizer(self):
+        import torch
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
         with ProxyNetworkActivate('Download_LLM'):
             from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer

request_llms/bridge_openrouter.py

@@ -512,7 +512,7 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         model, _ = read_one_api_model_name(model)
     if llm_kwargs['llm_model'].startswith('openrouter-'):
         model = llm_kwargs['llm_model'][len('openrouter-'):]
-        model= read_one_api_model_name(model)
+        model, _= read_one_api_model_name(model)
     if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
         model = random.choice([
             "gpt-3.5-turbo",

request_llms/oai_std_model_template.py

@@ -2,15 +2,9 @@ import json
 import time
 import traceback
 import requests
-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_the_upload_folder,
-)
+from loguru import logger
+from toolbox import get_conf, is_the_upload_folder, update_ui, update_ui_lastest_msg
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf(
     "proxies", "TIMEOUT_SECONDS", "MAX_RETRY"
@@ -39,27 +33,35 @@ def decode_chunk(chunk):
     用于解读"content"和"finish_reason"的内容（如果支持思维链也会返回"reasoning_content"内容）
     """
     chunk = chunk.decode()
-    respose = ""
+    response = ""
     reasoning_content = ""
     finish_reason = "False"
+
+    # 考虑返回类型是 text/json 和 text/event-stream 两种
+    if chunk.startswith("data: "):
+        chunk = chunk[6:]
+    else:
+        chunk = chunk
+    
     try:
-        chunk = json.loads(chunk[6:])
+        chunk = json.loads(chunk)
     except:
-        respose = ""
+        response = ""
         finish_reason = chunk
+
     # 错误处理部分
     if "error" in chunk:
-        respose = "API_ERROR"
+        response = "API_ERROR"
         try:
             chunk = json.loads(chunk)
             finish_reason = chunk["error"]["code"]
         except:
             finish_reason = "API_ERROR"
-        return respose, finish_reason
+        return response, reasoning_content, finish_reason
 
     try:
         if chunk["choices"][0]["delta"]["content"] is not None:
-            respose = chunk["choices"][0]["delta"]["content"]
+            response = chunk["choices"][0]["delta"]["content"]
     except:
         pass
     try:
@@ -71,7 +73,7 @@ def decode_chunk(chunk):
         finish_reason = chunk["choices"][0]["finish_reason"]
     except:
         pass
-    return respose, reasoning_content, finish_reason
+    return response, reasoning_content, finish_reason, str(chunk)
 
 
 def generate_message(input, model, key, history, max_output_token, system_prompt, temperature):
@@ -106,7 +108,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
     what_i_ask_now["role"] = "user"
     what_i_ask_now["content"] = input
     messages.append(what_i_ask_now)
-    playload = {
+    payload = {
         "model": model,
         "messages": messages,
         "temperature": temperature,
@@ -114,7 +116,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
         "max_tokens": max_output_token,
     }
 
-    return headers, playload
+    return headers, payload
 
 
 def get_predict_function(
@@ -141,7 +143,7 @@ def get_predict_function(
         history=[],
         sys_prompt="",
         observe_window=None,
-        console_slience=False,
+        console_silence=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。
@@ -157,12 +159,12 @@ def get_predict_function(
             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
         """
         from .bridge_all import model_info
-        watch_dog_patience = 5  # 看门狗的耐心，设置5秒不准咬人(咬的也不是人
+        watch_dog_patience = 5  # 看门狗的耐心，设置5秒不准咬人 (咬的也不是人)
         if len(APIKEY) == 0:
             raise RuntimeError(f"APIKEY为空,请检查配置文件的{APIKEY}")
         if inputs == "":
             inputs = "你好👋"
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -182,7 +184,7 @@ def get_predict_function(
                     endpoint,
                     headers=headers,
                     proxies=None if disable_proxy else proxies,
-                    json=playload,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -198,7 +200,7 @@ def get_predict_function(
         result = ""
         finish_reason = ""
         if reasoning:
-            resoning_buffer = ""
+            reasoning_buffer = ""
         
         stream_response = response.iter_lines()
         while True:
@@ -210,7 +212,7 @@ def get_predict_function(
                 break
             except requests.exceptions.ConnectionError:
                 chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
-            response_text, reasoning_content, finish_reason = decode_chunk(chunk)
+            response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
             # 返回的数据流第一次为空，继续等待
             if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
                 continue
@@ -226,12 +228,12 @@ def get_predict_function(
             if chunk:
                 try:
                     if finish_reason == "stop":
-                        if not console_slience:
+                        if not console_silence:
                             print(f"[response] {result}")
                         break
                     result += response_text
                     if reasoning:
-                        resoning_buffer += reasoning_content
+                        reasoning_buffer += reasoning_content
                     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:
@@ -247,9 +249,8 @@ def get_predict_function(
                     logger.error(error_msg)
                     raise RuntimeError("Json解析不合常规")
         if reasoning:
-            # reasoning 的部分加上框 (>)
-            return '\n'.join(map(lambda x: '> ' + x, resoning_buffer.split('\n'))) + \
-                   '\n\n' + result
+            paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in reasoning_buffer.split('\n')])
+            return f'''<div class="reasoning_process" >{paragraphs}</div>\n\n''' + result
         return result
 
     def predict(
@@ -268,7 +269,7 @@ def get_predict_function(
         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
+        chatbot 为WebUI中显示的对话列表，修改它，然后yield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
         """
         from .bridge_all import model_info
@@ -299,7 +300,7 @@ def get_predict_function(
             )  # 刷新界面
             time.sleep(2)
 
-        headers, playload = generate_message(
+        headers, payload = generate_message(
             input=inputs,
             model=llm_kwargs["llm_model"],
             key=APIKEY,
@@ -321,7 +322,7 @@ def get_predict_function(
                     endpoint,
                     headers=headers,
                     proxies=None if disable_proxy else proxies,
-                    json=playload,
+                    json=payload,
                     stream=True,
                     timeout=TIMEOUT_SECONDS,
                 )
@@ -343,14 +344,21 @@ def get_predict_function(
             gpt_reasoning_buffer = ""
 
         stream_response = response.iter_lines()
+        wait_counter = 0
         while True:
             try:
                 chunk = next(stream_response)
             except StopIteration:
+                if wait_counter != 0 and gpt_replying_buffer == "":
+                    yield from update_ui_lastest_msg(lastmsg="模型调用失败 ...", chatbot=chatbot, history=history, msg="failed")
                 break
             except requests.exceptions.ConnectionError:
                 chunk = next(stream_response)  # 失败了，重试一次？再失败就没办法了。
-            response_text, reasoning_content, finish_reason = decode_chunk(chunk)
+            response_text, reasoning_content, finish_reason, decoded_chunk = decode_chunk(chunk)
+            if decoded_chunk == ': keep-alive':
+                wait_counter += 1
+                yield from update_ui_lastest_msg(lastmsg="等待中 " + "".join(["."] * (wait_counter%10)), chatbot=chatbot, history=history, msg="waiting ...")
+                continue
             # 返回的数据流第一次为空，继续等待
             if response_text == "" and (reasoning == False or reasoning_content == "") and finish_reason != "False":
                 status_text = f"finish_reason: {finish_reason}"
@@ -367,7 +375,7 @@ def get_predict_function(
                         chunk_decoded = chunk.decode()
                         chatbot[-1] = (
                             chatbot[-1][0],
-                            "[Local Message] {finish_reason},获得以下报错信息：\n"
+                            f"[Local Message] {finish_reason}, 获得以下报错信息：\n"
                             + chunk_decoded,
                         )
                         yield from update_ui(
@@ -385,7 +393,8 @@ def get_predict_function(
                     if reasoning:
                         gpt_replying_buffer += response_text
                         gpt_reasoning_buffer += reasoning_content
-                        history[-1] = '\n'.join(map(lambda x: '> ' + x, gpt_reasoning_buffer.split('\n'))) + '\n\n' + gpt_replying_buffer
+                        paragraphs = ''.join([f'<p style="margin: 1.25em 0;">{line}</p>' for line in gpt_reasoning_buffer.split('\n')])
+                        history[-1] = f'<div class="reasoning_process">{paragraphs}</div>\n\n---\n\n' + gpt_replying_buffer
                     else:
                         gpt_replying_buffer += response_text
                         # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出

shared_utils/handle_upload.py

@@ -111,6 +111,8 @@ def extract_archive(file_path, dest_dir):
                     member_path = os.path.normpath(member.name)
                     full_path = os.path.join(dest_dir, member_path)
                     full_path = os.path.abspath(full_path)
+                    if member.islnk() or member.issym():
+                        raise Exception(f"Attempted Symlink in {member.name}")
                     if not full_path.startswith(os.path.abspath(dest_dir) + os.sep):
                         raise Exception(f"Attempted Path Traversal in {member.name}")
 

themes/common.css

@@ -311,3 +311,15 @@
     backdrop-filter: blur(10px);
     background-color: rgba(var(--block-background-fill), 0.5);
 }
+
+
+.reasoning_process {
+    font-size: smaller;
+    font-style: italic;
+    margin: 0px;
+    padding: 1em;
+    line-height: 1.5;
+    text-wrap: wrap;
+    opacity: 0.8;
+}
+

themes/gui_toolbar.py

@@ -26,8 +26,8 @@ def define_gui_toolbar(AVAIL_LLM_MODELS, LLM_MODEL, INIT_SYS_PROMPT, THEME, AVAI
                 fontfamily_dropdown = gr.Dropdown(AVAIL_FONTS, value=get_conf("FONT"), elem_id="elem_fontfamily", label="更换字体类型").style(container=False)
                 fontsize_slider = gr.Slider(minimum=5, maximum=25, value=15, step=1, interactive=True, label="字体大小(默认15)", elem_id="elem_fontsize")
                 checkboxes = gr.CheckboxGroup(["基础功能区", "函数插件区", "浮动输入区", "输入清除键", "插件参数区"], value=["基础功能区", "函数插件区"], label="显示/隐藏功能区", elem_id='cbs').style(container=False)
-                opt = ["自定义菜单"]
-                value=[]
+                opt = ["自定义菜单", "主标题", "副标题", "显示logo"]
+                value=["主标题", "副标题", "显示logo"]
                 if ADD_WAIFU: opt += ["添加Live2D形象"]; value += ["添加Live2D形象"]
                 checkboxes_2 = gr.CheckboxGroup(opt, value=value, label="显示/隐藏自定义菜单", elem_id='cbsc').style(container=False)
                 dark_mode_btn = gr.Button("切换界面明暗 ☀", variant="secondary").style(size="sm")

themes/init.js

@@ -128,6 +128,14 @@ function gpt_academic_change_chatbot_font(fontfamily, fontsize, fontcolor) {
     }
 }
 
+function footer_show_hide(show) {
+    if (show) {
+        document.querySelector('footer').style.display = '';
+    } else {
+        document.querySelector('footer').style.display = 'none';
+    }
+}
+
 async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
     // 第一部分，布局初始化
     remove_legacy_cookie();
@@ -179,6 +187,7 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
             }
         }
     }
+
     // 字体
     gpt_academic_gradio_saveload("load", "elem_fontfamily", "js_fontfamily", null, "str");
     gpt_academic_change_chatbot_font(getCookie("js_fontfamily"), null, null);
@@ -205,7 +214,93 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
     }
 
 
-
+    if (getCookie("js_show_title")) {
+        // have cookie
+        bool_value = getCookie("js_show_title")
+        bool_value = bool_value == "True";
+        searchString = "主标题";
+        tool_bar_group = "cbsc";
+        const true_function = function () {
+            document.querySelector('.prose.svelte-1ybaih5 h1').style.display = '';
+        }
+        const false_function = function () {
+            document.querySelector('.prose.svelte-1ybaih5 h1').style.display = 'none';
+        }
+        if (bool_value) {
+            // make btns appear
+            true_function();
+            // deal with checkboxes
+            let arr_with_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "add"
+            )
+            push_data_to_gradio_component(arr_with_clear_btn, tool_bar_group, "no_conversion");
+        } else {
+            false_function();
+            // deal with checkboxes
+            let arr_without_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "remove"
+            )
+            push_data_to_gradio_component(arr_without_clear_btn, tool_bar_group, "no_conversion");
+        }
+    }
+    if (getCookie("js_show_subtitle")) {
+        // have cookie
+        bool_value = getCookie("js_show_subtitle")
+        bool_value = bool_value == "True";
+        searchString = "副标题";
+        tool_bar_group = "cbsc";
+        const true_function = function () {
+            element = document.querySelector('.prose.svelte-1ybaih5 h2');
+            if (element) element.style.display = '';
+            element = document.querySelector('.prose.svelte-1ybaih5 h6');
+            if (element) element.style.display = '';
+        }
+        const false_function = function () {
+            element = document.querySelector('.prose.svelte-1ybaih5 h2');
+            if (element) element.style.display = 'none';
+            element = document.querySelector('.prose.svelte-1ybaih5 h6');
+            if (element) element.style.display = 'none';
+        }
+        if (bool_value) {
+            // make btns appear
+            true_function();
+            // deal with checkboxes
+            let arr_with_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "add"
+            )
+            push_data_to_gradio_component(arr_with_clear_btn, tool_bar_group, "no_conversion");
+        } else {
+            false_function();
+            // deal with checkboxes
+            let arr_without_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "remove"
+            )
+            push_data_to_gradio_component(arr_without_clear_btn, tool_bar_group, "no_conversion");
+        }
+    }
+    if (getCookie("js_show_footer")) {
+        // have cookie
+        bool_value = getCookie("js_show_footer")
+        searchString = "显示logo";
+        tool_bar_group = "cbsc";
+        bool_value = bool_value == "True";
+        if (bool_value) {
+            // make btns appear
+            footer_show_hide(true);
+            // deal with checkboxes
+            let arr_with_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "add"
+            )
+            push_data_to_gradio_component(arr_with_clear_btn, tool_bar_group, "no_conversion");
+        } else {
+            footer_show_hide(false);
+            // deal with checkboxes
+            let arr_without_clear_btn = update_array(
+                await get_data_from_gradio_component(tool_bar_group), searchString, "remove"
+            )
+            push_data_to_gradio_component(arr_without_clear_btn, tool_bar_group, "no_conversion");
+        }
+    }
     // clearButton 自动清除按钮
     if (getCookie("js_clearbtn_show_cookie")) {
         // have cookie
@@ -219,7 +314,7 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
             let clearButton2 = document.getElementById("elem_clear2"); clearButton2.style.display = "block";
             // deal with checkboxes
             let arr_with_clear_btn = update_array(
-                await get_data_from_gradio_component('cbs'), "输入清除键", "add"
+                await get_data_from_gradio_component("cbs"), "输入清除键", "add"
             )
             push_data_to_gradio_component(arr_with_clear_btn, "cbs", "no_conversion");
         } else {
@@ -228,7 +323,7 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
             let clearButton2 = document.getElementById("elem_clear2"); clearButton2.style.display = "none";
             // deal with checkboxes
             let arr_without_clear_btn = update_array(
-                await get_data_from_gradio_component('cbs'), "输入清除键", "remove"
+                await get_data_from_gradio_component("cbs"), "输入清除键", "remove"
             )
             push_data_to_gradio_component(arr_without_clear_btn, "cbs", "no_conversion");
         }
@@ -268,3 +363,47 @@ async function GptAcademicJavaScriptInit(dark, prompt, live2d, layout, tts) {
     change_theme("", "")
 
 }
+
+
+
+function apply_checkbox_change_for_group2(display_panel_arr) {
+    setTimeout(() => {
+        display_panel_arr = get_checkbox_selected_items("cbsc");
+
+        let searchString = "添加Live2D形象";
+        if (display_panel_arr.includes(searchString)) {
+            setCookie("js_live2d_show_cookie", "True", 365);
+            loadLive2D();
+        } else {
+            try {
+                setCookie("js_live2d_show_cookie", "False", 365);
+                $('.waifu').hide();
+            } catch (e) {
+            }
+        }
+
+
+        function handleDisplay(searchString, key, displayElement, showFn, hideFn) {
+            if (display_panel_arr.includes(searchString)) {
+                setCookie(key, "True", 365);
+                if (showFn) showFn();
+                if (displayElement) displayElement.style.display = '';
+            } else {
+                setCookie(key, "False", 365);
+                if (hideFn) hideFn();
+                if (displayElement) displayElement.style.display = 'none';
+            }
+        }
+
+        // 主标题
+        const mainTitle = document.querySelector('.prose.svelte-1ybaih5 h1');
+        handleDisplay("主标题", "js_show_title", mainTitle, null, null);
+
+        // 副标题
+        const subTitle = document.querySelector('.prose.svelte-1ybaih5 h2');
+        handleDisplay("副标题", "js_show_subtitle", subTitle, null, null);
+
+        // 显示logo
+        handleDisplay("显示logo", "js_show_footer", null, () => footer_show_hide(true), () => footer_show_hide(false));
+    }, 50);
+}

themes/theme.py

@@ -141,23 +141,3 @@ setTimeout(() => {
 }
 """
 
-
-
-js_code_show_or_hide_group2 = """
-(display_panel_arr)=>{
-setTimeout(() => {
-    display_panel_arr = get_checkbox_selected_items("cbsc");
-
-    let searchString = "添加Live2D形象";
-    let ele = "none";
-    if (display_panel_arr.includes(searchString)) {
-        setCookie("js_live2d_show_cookie", "True", 365);
-        loadLive2D();
-    } else {
-        setCookie("js_live2d_show_cookie", "False", 365);
-        $('.waifu').hide();
-    }
-
-}, 50);
-}
-"""