import gradio as gr import os import sys import toml from loguru import logger import shutil import ast import json is_share = False debug = True config_data = toml.load("config/bot_config.toml") #============================================== #env环境配置文件读取部分 def parse_env_config(config_file): """ 解析配置文件并将配置项存储到相应的变量中（变量名以env_为前缀）。 """ env_variables = {} # 读取配置文件 with open(config_file, "r", encoding="utf-8") as f: lines = f.readlines() # 逐行处理配置 for line in lines: line = line.strip() # 忽略空行和注释 if not line or line.startswith("#"): continue # 拆分键值对 key, value = line.split("=", 1) # 去掉空格并去除两端引号（如果有的话） key = key.strip() value = value.strip().strip('"').strip("'") # 将配置项存入以env_为前缀的变量 env_variable = f"env_{key}" env_variables[env_variable] = value # 动态创建环境变量 os.environ[env_variable] = value return env_variables #env环境配置文件保存函数 def save_to_env_file(env_variables, filename=".env.prod"): """ 将修改后的变量保存到指定的.env文件中，并在第一次保存前备份文件（如果备份文件不存在）。 """ backup_filename = f"{filename}.bak" # 如果备份文件不存在，则备份原文件 if not os.path.exists(backup_filename): if os.path.exists(filename): logger.info(f"{filename} 已存在，正在备份到 {backup_filename}...") shutil.copy(filename, backup_filename) # 备份文件 logger.success(f"文件已备份到 {backup_filename}") else: logger.warning(f"{filename} 不存在，无法进行备份。") # 保存新配置 with open(filename, "w",encoding="utf-8") as f: for var, value in env_variables.items(): f.write(f"{var[4:]}={value}\n") # 移除env_前缀 logger.info(f"配置已保存到 {filename}") env_config_file = ".env.prod" # 配置文件路径 env_config_data = parse_env_config(env_config_file) #env读取保存结束 #============================================== #============================================== #env环境文件中插件修改更新函数 def add_item(new_item, current_list): updated_list = current_list.copy() if new_item.strip(): updated_list.append(new_item.strip()) return [ updated_list, # 更新State "\n".join(updated_list), # 更新TextArea gr.update(choices=updated_list), # 更新Dropdown ", ".join(updated_list) # 更新最终结果 ] def delete_item(selected_item, current_list): updated_list = current_list.copy() if selected_item in updated_list: updated_list.remove(selected_item) return [ updated_list, "\n".join(updated_list), gr.update(choices=updated_list), ", ".join(updated_list) ] #env文件中插件值处理函数 def parse_list_str(input_str): """ 将形如["src2.plugins.chat"]的字符串解析为Python列表 parse_list_str('["src2.plugins.chat"]') ['src2.plugins.chat'] parse_list_str("['plugin1', 'plugin2']") ['plugin1', 'plugin2'] """ try: return ast.literal_eval(input_str.strip()) except (ValueError, SyntaxError): # 处理不符合Python列表格式的字符串 cleaned = input_str.strip(" []") # 去除方括号 return [item.strip(" '\"") for item in cleaned.split(",") if item.strip()] def format_list_to_str(lst): """ 将Python列表转换为形如["src2.plugins.chat"]的字符串格式 format_list_to_str(['src2.plugins.chat']) '["src2.plugins.chat"]' format_list_to_str([1, "two", 3.0]) '[1, "two", 3.0]' """ resarr = lst.split(", ") res = "" for items in resarr: temp = '"' + str(items) + '"' res += temp + "," res = res[:-1] return "[" + res + "]" def format_list_to_str_alias(lst): """ 将Python列表转换为形如["src2.plugins.chat"]的字符串格式 format_list_to_str(['src2.plugins.chat']) '["src2.plugins.chat"]' format_list_to_str([1, "two", 3.0]) '[1, "two", 3.0]' """ resarr = lst.split(", ") return resarr #env保存函数 def save_trigger(server_address, server_port, final_result_list,t_mongodb_host,t_mongodb_port,t_mongodb_database_name,t_chatanywhere_base_url,t_chatanywhere_key,t_siliconflow_base_url,t_siliconflow_key,t_deepseek_base_url,t_deepseek_key): final_result_lists = format_list_to_str(final_result_list) env_config_data["env_HOST"] = server_address env_config_data["env_PORT"] = server_port env_config_data["env_PLUGINS"] = final_result_lists env_config_data["env_MONGODB_HOST"] = t_mongodb_host env_config_data["env_MONGODB_PORT"] = t_mongodb_port env_config_data["env_DATABASE_NAME"] = t_mongodb_database_name env_config_data["env_CHAT_ANY_WHERE_BASE_URL"] = t_chatanywhere_base_url env_config_data["env_CHAT_ANY_WHERE_KEY"] = t_chatanywhere_key env_config_data["env_SILICONFLOW_BASE_URL"] = t_siliconflow_base_url env_config_data["env_SILICONFLOW_KEY"] = t_siliconflow_key env_config_data["env_DEEP_SEEK_BASE_URL"] = t_deepseek_base_url env_config_data["env_DEEP_SEEK_KEY"] = t_deepseek_key save_to_env_file(env_config_data) logger.success("配置已保存到 .env.prod 文件中") return "配置已保存" #============================================== #============================================== #主要配置文件保存函数 def save_config_to_file(t_config_data): with open("config/bot_config.toml", "w", encoding="utf-8") as f: toml.dump(t_config_data, f) logger.success("配置已保存到 bot_config.toml 文件中") def save_bot_config(t_qqbot_qq, t_nickname,t_nickname_final_result): config_data["bot"]["qq"] = int(t_qqbot_qq) config_data["bot"]["nickname"] = t_nickname config_data["bot"]["alias_names"] = format_list_to_str_alias(t_nickname_final_result) save_config_to_file(config_data) logger.info("Bot配置已保存") return "Bot配置已保存" # 监听滑块的值变化，确保总和不超过 1，并显示警告 def adjust_greater_probabilities(t_personality_1, t_personality_2, t_personality_3): total = t_personality_1 + t_personality_2 + t_personality_3 if total > 1.0: warning_message = f"警告: 人格1、人格2和人格3的概率总和为 {total:.2f}，超过了 1.0！请调整滑块使总和等于 1.0。" return warning_message else: return "" # 没有警告时返回空字符串 def adjust_less_probabilities(t_personality_1, t_personality_2, t_personality_3): total = t_personality_1 + t_personality_2 + t_personality_3 if total < 1.0: warning_message = f"警告: 人格1、人格2和人格3的概率总和为 {total:.2f}，小于 1.0！请调整滑块使总和等于 1.0。" return warning_message else: return "" # 没有警告时返回空字符串 def adjust_model_greater_probabilities(t_personality_1, t_personality_2, t_personality_3): total = t_personality_1 + t_personality_2 + t_personality_3 if total > 1.0: warning_message = f"警告: 选择模型1、模型2和模型3的概率总和为 {total:.2f}，超过了 1.0！请调整滑块使总和等于 1.0。" return warning_message else: return "" # 没有警告时返回空字符串 def adjust_model_less_probabilities(t_personality_1, t_personality_2, t_personality_3): total = t_personality_1 + t_personality_2 + t_personality_3 if total > 1.0: warning_message = f"警告: 选择模型1、模型2和模型3的概率总和为 {total:.2f}，小于了 1.0！请调整滑块使总和等于 1.0。" return warning_message else: return "" # 没有警告时返回空字符串 #============================================== #人格保存函数 def save_personality_config(t_personality_1, t_personality_2, t_personality_3, t_prompt_schedule): config_data["personality"]["personality_1_probability"] = t_personality_1 config_data["personality"]["personality_2_probability"] = t_personality_2 config_data["personality"]["personality_3_probability"] = t_personality_3 config_data["personality"]["prompt_schedule"] = t_prompt_schedule save_config_to_file(config_data) logger.info("人格配置已保存到 bot_config.toml 文件中") return "人格配置已保存" def save_message_and_emoji_config(t_min_text_length, t_max_context_size, t_emoji_chance, t_thinking_timeout, t_response_willing_amplifier, t_response_interested_rate_amplifier, t_down_frequency_rate, t_ban_words_final_result, t_ban_msgs_regex_final_result, t_check_interval, t_register_interval, t_auto_save, t_enable_check, t_check_prompt): config_data["message"]["min_text_length"] = t_min_text_length config_data["message"]["max_context_size"] = t_max_context_size config_data["message"]["emoji_chance"] = t_emoji_chance config_data["message"]["thinking_timeout"] = t_thinking_timeout config_data["message"]["response_willing_amplifier"] = t_response_willing_amplifier config_data["message"]["response_interested_rate_amplifier"] = t_response_interested_rate_amplifier config_data["message"]["down_frequency_rate"] = t_down_frequency_rate config_data["message"]["ban_words"] = format_list_to_str_alias(t_ban_words_final_result) config_data["message"]["ban_msgs_regex"] = format_list_to_str_alias(t_ban_msgs_regex_final_result) config_data["emoji"]["check_interval"] = t_check_interval config_data["emoji"]["register_interval"] = t_register_interval config_data["emoji"]["auto_save"] = t_auto_save config_data["emoji"]["enable_check"] = t_enable_check config_data["emoji"]["check_prompt"] = t_check_prompt save_config_to_file(config_data) logger.info("消息和表情配置已保存到 bot_config.toml 文件中") return "消息和表情配置已保存" def save_response_model_config(t_model_r1_probability, t_model_r2_probability, t_model_r3_probability, t_max_response_length, t_model1_name, t_model1_provider, t_model1_pri_in, t_model1_pri_out, t_model2_name, t_model2_provider, t_model3_name, t_model3_provider, t_emotion_model_name, t_emotion_model_provider, t_topic_judge_model_name, t_topic_judge_model_provider, t_summary_by_topic_model_name, t_summary_by_topic_model_provider): config_data["response"]["model_r1_probability"] = t_model_r1_probability config_data["response"]["model_r2_probability"] = t_model_r2_probability config_data["response"]["model_r3_probability"] = t_model_r3_probability config_data["response"]["max_response_length"] = t_max_response_length config_data['model']['llm_reasoning']['name'] = t_model1_name config_data['model']['llm_reasoning']['provider'] = t_model1_provider config_data['model']['llm_reasoning']['pri_in'] = t_model1_pri_in config_data['model']['llm_reasoning']['pri_out'] = t_model1_pri_out config_data['model']['llm_normal']['name'] = t_model2_name config_data['model']['llm_normal']['provider'] = t_model2_provider config_data['model']['llm_reasoning_minor']['name'] = t_model3_name config_data['model']['llm_normal']['provider'] = t_model3_provider config_data['model']['llm_emotion_judge']['name'] = t_emotion_model_name config_data['model']['llm_emotion_judge']['provider'] = t_emotion_model_provider config_data['model']['llm_topic_judge']['name'] = t_topic_judge_model_name config_data['model']['llm_topic_judge']['provider'] = t_topic_judge_model_provider config_data['model']['llm_summary_by_topic']['name'] = t_summary_by_topic_model_name config_data['model']['llm_summary_by_topic']['provider'] = t_summary_by_topic_model_provider save_config_to_file(config_data) logger.info("回复&模型设置已保存到 bot_config.toml 文件中") return "回复&模型设置已保存" with (gr.Blocks(title="MaimBot配置文件编辑") as app): gr.Markdown( value=""" 欢迎使用由墨梓柒MotricSeven编写的MaimBot配置文件编辑器\n """ ) gr.Markdown( value="配置文件版本：" + config_data["inner"]["version"] ) with gr.Tabs(): with gr.TabItem("0-环境设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): gr.Markdown( value=""" MaimBot服务器地址，默认127.0.0.1\n 不熟悉配置的不要轻易改动此项！！\n """ ) with gr.Row(): server_address = gr.Textbox( label="服务器地址", value=env_config_data["env_HOST"], interactive=True ) with gr.Row(): server_port = gr.Textbox( label="服务器端口", value=env_config_data["env_PORT"], interactive=True ) with gr.Row(): plugin_list = parse_list_str(env_config_data['env_PLUGINS']) with gr.Blocks(): list_state = gr.State(value=plugin_list.copy()) with gr.Row(): list_display = gr.TextArea( value="\n".join(plugin_list), label="插件列表", interactive=False, lines=5 ) with gr.Row(): with gr.Column(scale=3): new_item_input = gr.Textbox(label="添加新插件") add_btn = gr.Button("添加", scale=1) with gr.Row(): with gr.Column(scale=3): item_to_delete = gr.Dropdown( choices=plugin_list, label="选择要删除的插件" ) delete_btn = gr.Button("删除", scale=1) final_result = gr.Text(label="修改后的列表") add_btn.click( add_item, inputs=[new_item_input, list_state], outputs=[list_state, list_display, item_to_delete, final_result] ) delete_btn.click( delete_item, inputs=[item_to_delete, list_state], outputs=[list_state, list_display, item_to_delete, final_result] ) with gr.Row(): gr.Markdown( '''MongoDB设置项\n 保持默认即可，如果你有能力承担修改过后的后果（简称能改回来（笑））\n 可以对以下配置项进行修改\n ''' ) with gr.Row(): mongodb_host = gr.Textbox( label="MongoDB服务器地址", value=env_config_data["env_MONGODB_HOST"], interactive=True ) with gr.Row(): mongodb_port = gr.Textbox( label="MongoDB服务器端口", value=env_config_data["env_MONGODB_PORT"], interactive=True ) with gr.Row(): mongodb_database_name = gr.Textbox( label="MongoDB数据库名称", value=env_config_data["env_DATABASE_NAME"], interactive=True ) with gr.Row(): gr.Markdown( '''ChatAntWhere的baseURL和APIkey\n 改完了记得保存！！！ ''' ) with gr.Row(): chatanywhere_base_url = gr.Textbox( label="ChatAntWhere的BaseURL", value=env_config_data["env_CHAT_ANY_WHERE_BASE_URL"], interactive=True ) with gr.Row(): chatanywhere_key = gr.Textbox( label="ChatAntWhere的key", value=env_config_data["env_CHAT_ANY_WHERE_KEY"], interactive=True ) with gr.Row(): gr.Markdown( '''SiliconFlow的baseURL和APIkey\n 改完了记得保存！！！ ''' ) with gr.Row(): siliconflow_base_url = gr.Textbox( label="SiliconFlow的BaseURL", value=env_config_data["env_SILICONFLOW_BASE_URL"], interactive=True ) with gr.Row(): siliconflow_key = gr.Textbox( label="SiliconFlow的key", value=env_config_data["env_SILICONFLOW_KEY"], interactive=True ) with gr.Row(): gr.Markdown( '''DeepSeek的baseURL和APIkey\n 改完了记得保存！！！ ''' ) with gr.Row(): deepseek_base_url = gr.Textbox( label="DeepSeek的BaseURL", value=env_config_data["env_DEEP_SEEK_BASE_URL"], interactive=True ) with gr.Row(): deepseek_key = gr.Textbox( label="DeepSeek的key", value=env_config_data["env_DEEP_SEEK_KEY"], interactive=True ) with gr.Row(): save_env_btn = gr.Button("保存环境配置") with gr.Row(): save_env_btn.click( save_trigger, inputs=[server_address,server_port,final_result,mongodb_host,mongodb_port,mongodb_database_name,chatanywhere_base_url,chatanywhere_key,siliconflow_base_url,siliconflow_key,deepseek_base_url,deepseek_key], outputs=[gr.Textbox( label="保存结果", interactive=False )] ) with gr.TabItem("1-Bot基础设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): qqbot_qq = gr.Textbox( label="QQ机器人QQ号", value=config_data["bot"]["qq"], interactive=True ) with gr.Row(): nickname = gr.Textbox( label="昵称", value=config_data["bot"]["nickname"], interactive=True ) with gr.Row(): nickname_list = config_data['bot']['alias_names'] with gr.Blocks(): nickname_list_state = gr.State(value=nickname_list.copy()) with gr.Row(): nickname_list_display = gr.TextArea( value="\n".join(nickname_list), label="别名列表", interactive=False, lines=5 ) with gr.Row(): with gr.Column(scale=3): nickname_new_item_input = gr.Textbox(label="添加新别名") nickname_add_btn = gr.Button("添加", scale=1) with gr.Row(): with gr.Column(scale=3): nickname_item_to_delete = gr.Dropdown( choices=nickname_list, label="选择要删除的别名" ) nickname_delete_btn = gr.Button("删除", scale=1) nickname_final_result = gr.Text(label="修改后的列表") nickname_add_btn.click( add_item, inputs=[nickname_new_item_input, nickname_list_state], outputs=[nickname_list_state, nickname_list_display, nickname_item_to_delete, nickname_final_result] ) nickname_delete_btn.click( delete_item, inputs=[nickname_item_to_delete, nickname_list_state], outputs=[nickname_list_state, nickname_list_display, nickname_item_to_delete, nickname_final_result] ) gr.Button( "保存Bot配置", variant="primary", elem_id="save_bot_btn", elem_classes="save_bot_btn" ).click( save_bot_config, inputs=[qqbot_qq, nickname,nickname_final_result], outputs=[gr.Textbox( label="保存Bot结果" )] ) with gr.TabItem("2-人格设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): prompt_personality_1 = gr.Textbox( label="人格1提示词", value=config_data['personality']['prompt_personality'][0], interactive=True ) with gr.Row(): prompt_personality_2 = gr.Textbox( label="人格2提示词", value=config_data['personality']['prompt_personality'][1], interactive=True ) with gr.Row(): prompt_personality_3 = gr.Textbox( label="人格3提示词", value=config_data['personality']['prompt_personality'][2], interactive=True ) with gr.Column(scale=3): # 创建三个滑块 personality_1 = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data["personality"]["personality_1_probability"], label="人格1概率") personality_2 = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data["personality"]["personality_2_probability"], label="人格2概率") personality_3 = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data["personality"]["personality_3_probability"], label="人格3概率") # 用于显示警告消息 warning_greater_text = gr.Markdown() warning_less_text = gr.Markdown() # 绑定滑块的值变化事件，确保总和必须等于 1.0 personality_1.change(adjust_greater_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_greater_text]) personality_2.change(adjust_greater_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_greater_text]) personality_3.change(adjust_greater_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_greater_text]) personality_1.change(adjust_less_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_less_text]) personality_2.change(adjust_less_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_less_text]) personality_3.change(adjust_less_probabilities, inputs=[personality_1, personality_2, personality_3], outputs=[warning_less_text]) with gr.Row(): prompt_schedule = gr.Textbox( label="日程生成提示词", value=config_data["personality"]["prompt_schedule"], interactive=True ) with gr.Row(): gr.Button( "保存人格配置", variant="primary", elem_id="save_personality_btn", elem_classes="save_personality_btn" ).click( save_personality_config, inputs=[personality_1, personality_2, personality_3, prompt_schedule], outputs=[gr.Textbox( label="保存人格结果" )] ) with gr.TabItem("3-消息&表情包设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): min_text_length = gr.Number(value=config_data['message']['min_text_length'], label="与麦麦聊天时麦麦只会回答文本大于等于此数的消息") with gr.Row(): max_context_size = gr.Number(value=config_data['message']['max_context_size'], label="麦麦获得的上文数量") with gr.Row(): emoji_chance = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data['message']['emoji_chance'], label="麦麦使用表情包的概率") with gr.Row(): thinking_timeout = gr.Number(value=config_data['message']['thinking_timeout'], label="麦麦正在思考时，如果超过此秒数，则停止思考") with gr.Row(): response_willing_amplifier = gr.Number(value=config_data['message']['response_willing_amplifier'], label="麦麦回复意愿放大系数，一般为1") with gr.Row(): response_interested_rate_amplifier = gr.Number(value=config_data['message']['response_interested_rate_amplifier'], label="麦麦回复兴趣度放大系数,听到记忆里的内容时放大系数") with gr.Row(): down_frequency_rate = gr.Number(value=config_data['message']['down_frequency_rate'], label="降低回复频率的群组回复意愿降低系数") with gr.Row(): gr.Markdown("### 违禁词列表") with gr.Row(): ban_words_list = config_data['message']['ban_words'] with gr.Blocks(): ban_words_list_state = gr.State(value=ban_words_list.copy()) with gr.Row(): ban_words_list_display = gr.TextArea( value="\n".join(ban_words_list), label="违禁词列表", interactive=False, lines=5 ) with gr.Row(): with gr.Column(scale=3): ban_words_new_item_input = gr.Textbox(label="添加新违禁词") ban_words_add_btn = gr.Button("添加", scale=1) with gr.Row(): with gr.Column(scale=3): ban_words_item_to_delete = gr.Dropdown( choices=ban_words_list, label="选择要删除的违禁词" ) ban_words_delete_btn = gr.Button("删除", scale=1) ban_words_final_result = gr.Text(label="修改后的违禁词") ban_words_add_btn.click( add_item, inputs=[ban_words_new_item_input, ban_words_list_state], outputs=[ban_words_list_state, ban_words_list_display, ban_words_item_to_delete, ban_words_final_result] ) ban_words_delete_btn.click( delete_item, inputs=[ban_words_item_to_delete, ban_words_list_state], outputs=[ban_words_list_state, ban_words_list_display, ban_words_item_to_delete, ban_words_final_result] ) with gr.Row(): gr.Markdown("### 检测违禁消息正则表达式列表") with gr.Row(): gr.Markdown( """ 需要过滤的消息（原始消息）匹配的正则表达式，匹配到的消息将被过滤（支持CQ码），若不了解正则表达式请勿修改\n "https?://[^\\s]+", # 匹配https链接\n "\\d{4}-\\d{2}-\\d{2}", # 匹配日期\n "\\[CQ:at,qq=\\d+\\]" # 匹配@\n """ ) with gr.Row(): ban_msgs_regex_list = config_data['message']['ban_msgs_regex'] with gr.Blocks(): ban_msgs_regex_list_state = gr.State(value=ban_msgs_regex_list.copy()) with gr.Row(): ban_msgs_regex_list_display = gr.TextArea( value="\n".join(ban_msgs_regex_list), label="违禁词列表", interactive=False, lines=5 ) with gr.Row(): with gr.Column(scale=3): ban_msgs_regex_new_item_input = gr.Textbox(label="添加新违禁词") ban_msgs_regex_add_btn = gr.Button("添加", scale=1) with gr.Row(): with gr.Column(scale=3): ban_msgs_regex_item_to_delete = gr.Dropdown( choices=ban_msgs_regex_list, label="选择要删除的违禁词" ) ban_msgs_regex_delete_btn = gr.Button("删除", scale=1) ban_msgs_regex_final_result = gr.Text(label="修改后的违禁词") ban_msgs_regex_add_btn.click( add_item, inputs=[ban_msgs_regex_new_item_input, ban_msgs_regex_list_state], outputs=[ban_msgs_regex_list_state, ban_msgs_regex_list_display, ban_msgs_regex_item_to_delete, ban_msgs_regex_final_result] ) ban_msgs_regex_delete_btn.click( delete_item, inputs=[ban_msgs_regex_item_to_delete, ban_msgs_regex_list_state], outputs=[ban_msgs_regex_list_state, ban_msgs_regex_list_display, ban_msgs_regex_item_to_delete, ban_msgs_regex_final_result] ) with gr.Row(): check_interval = gr.Number(value=config_data['emoji']['check_interval'], label="检查表情包的时间间隔") with gr.Row(): register_interval = gr.Number(value=config_data['emoji']['register_interval'], label="注册表情包的时间间隔") with gr.Row(): auto_save = gr.Checkbox(value=config_data['emoji']['auto_save'], label="自动保存表情包") with gr.Row(): enable_check = gr.Checkbox(value=config_data['emoji']['enable_check'], label="启用表情包检查") with gr.Row(): check_prompt = gr.Textbox(value=config_data['emoji']['check_prompt'], label="表情包过滤要求") with gr.Row(): gr.Button( "保存消息&表情包设置", variant="primary", elem_id="save_personality_btn", elem_classes="save_personality_btn" ).click( save_message_and_emoji_config, inputs=[ min_text_length, max_context_size, emoji_chance, thinking_timeout, response_willing_amplifier, response_interested_rate_amplifier, down_frequency_rate, ban_words_final_result, ban_msgs_regex_final_result, check_interval, register_interval, auto_save, enable_check, check_prompt ], outputs=[gr.Textbox( label="消息&表情包设置保存结果" )] ) with gr.TabItem("4-回复&模型设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): gr.Markdown( """### 回复设置""" ) with gr.Row(): model_r1_probability = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data['response']['model_r1_probability'], label="麦麦回答时选择主要回复模型1 模型的概率") with gr.Row(): model_r2_probability = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data['response']['model_r1_probability'], label="麦麦回答时选择主要回复模型2 模型的概率") with gr.Row(): model_r3_probability = gr.Slider(minimum=0, maximum=1, step=0.01, value=config_data['response']['model_r1_probability'], label="麦麦回答时选择主要回复模型3 模型的概率") # 用于显示警告消息 with gr.Row(): model_warning_greater_text = gr.Markdown() model_warning_less_text = gr.Markdown() # 绑定滑块的值变化事件，确保总和必须等于 1.0 model_r1_probability.change(adjust_model_greater_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_greater_text]) model_r2_probability.change(adjust_model_greater_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_greater_text]) model_r3_probability.change(adjust_model_greater_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_greater_text]) model_r1_probability.change(adjust_model_less_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_less_text]) model_r2_probability.change(adjust_model_less_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_less_text]) model_r3_probability.change(adjust_model_less_probabilities, inputs=[model_r1_probability, model_r2_probability, model_r3_probability], outputs=[model_warning_less_text]) with gr.Row(): max_response_length = gr.Number(value=config_data['response']['max_response_length'], label="麦麦回答的最大token数") with gr.Row(): gr.Markdown( """### 模型设置""" ) with gr.Tabs(): with gr.TabItem("1-主要回复模型"): with gr.Row(): model1_name = gr.Textbox(value=config_data['model']['llm_reasoning']['name'], label="模型1的名称") with gr.Row(): model1_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_reasoning']['provider'], label="模型1（主要回复模型）提供商") with gr.Row(): model1_pri_in = gr.Textbox(value=config_data['model']['llm_reasoning']['pri_in'], label="模型1（主要回复模型）的输入价格（非必填，可以记录消耗）") with gr.Row(): model1_pri_out = gr.Textbox(value=config_data['model']['llm_reasoning']['pri_out'], label="模型1（主要回复模型）的输出价格（非必填，可以记录消耗）") with gr.TabItem("2-次要回复模型"): with gr.Row(): model2_name = gr.Textbox(value=config_data['model']['llm_normal']['name'], label="模型2的名称") with gr.Row(): model2_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_normal']['provider'], label="模型2提供商") with gr.TabItem("3-次要模型"): with gr.Row(): model3_name = gr.Textbox(value=config_data['model']['llm_reasoning_minor']['name'], label="模型3的名称") with gr.Row(): model3_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_reasoning_minor']['provider'], label="模型3提供商") with gr.TabItem("4-情感&主题模型"): with gr.Row(): gr.Markdown( """### 情感模型设置""" ) with gr.Row(): emotion_model_name = gr.Textbox(value=config_data['model']['llm_emotion_judge']['name'], label="情感模型名称") with gr.Row(): emotion_model_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_emotion_judge']['provider'], label="情感模型提供商") with gr.Row(): gr.Markdown( """### 主题模型设置""" ) with gr.Row(): topic_judge_model_name = gr.Textbox(value=config_data['model']['llm_topic_judge']['name'], label="主题判断模型名称") with gr.Row(): topic_judge_model_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_topic_judge']['provider'], label="主题判断模型提供商") with gr.Row(): summary_by_topic_model_name = gr.Textbox(value=config_data['model']['llm_summary_by_topic']['name'], label="主题总结模型名称") with gr.Row(): summary_by_topic_model_provider = gr.Dropdown(choices=["SILICONFLOW","DEEP_SEEK", "CHAT_ANY_WHERE"], value=config_data['model']['llm_summary_by_topic']['provider'], label="主题总结模型提供商") with gr.Row(): save_model_btn = gr.Button("保存 [模型] 配置") with gr.Row(): save_btn_message = gr.Textbox() save_model_btn.click( save_response_model_config, inputs=[model_r1_probability,model_r2_probability,model_r3_probability,max_response_length,model1_name, model1_provider, model1_pri_in, model1_pri_out, model2_name, model2_provider, model3_name, model3_provider, emotion_model_name, emotion_model_provider, topic_judge_model_name, topic_judge_model_provider, summary_by_topic_model_name,summary_by_topic_model_provider], outputs=[save_btn_message] ) with gr.TabItem("5-记忆&心情设置"): with gr.Row(): with gr.Column(scale=3): with gr.Row(): gr.Markdown( """### 记忆设置""" ) with gr.Row(): build_memory_interval = gr.Number(value=config_data['memory']['build_memory_interval'], label="记忆构建间隔 单位秒,间隔越低，麦麦学习越多，但是冗余信息也会增多") app.queue().launch(#concurrency_count=511, max_size=1022 server_name="0.0.0.0", inbrowser=True, share=is_share, server_port=7000, debug=debug, quiet=True, )