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更新WebUI配置可视化编辑器内容（虽然还是没做完......）

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DrSmoothl
2025-03-14 19:00:51 +08:00
parent a5dde7fd3f
commit 9018201dc7
1 changed files with 154 additions and 4 deletions
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158
webui.py
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@@ -181,7 +181,7 @@ def save_bot_config(t_qqbot_qq, t_nickname,t_nickname_final_result):
def adjust_greater_probabilities(t_personality_1, t_personality_2, t_personality_3): def adjust_greater_probabilities(t_personality_1, t_personality_2, t_personality_3):
total = t_personality_1 + t_personality_2 + t_personality_3 total = t_personality_1 + t_personality_2 + t_personality_3
if total > 1.0: 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！请调整滑块使总和不超过 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 return warning_message
else: else:
return "" # 没有警告时返回空字符串 return "" # 没有警告时返回空字符串
@@ -189,7 +189,23 @@ def adjust_greater_probabilities(t_personality_1, t_personality_2, t_personality
def adjust_less_probabilities(t_personality_1, t_personality_2, t_personality_3): def adjust_less_probabilities(t_personality_1, t_personality_2, t_personality_3):
total = t_personality_1 + t_personality_2 + t_personality_3 total = t_personality_1 + t_personality_2 + t_personality_3
if total < 1.0: 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！请调整滑块使总和不超过 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 return warning_message
else: else:
return "" # 没有警告时返回空字符串 return "" # 没有警告时返回空字符串
@@ -237,6 +253,47 @@ def save_message_and_emoji_config(t_min_text_length,
logger.info("消息和表情配置已保存到 bot_config.toml 文件中") 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" 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): with (gr.Blocks(title="MaimBot配置文件编辑") as app):
gr.Markdown( gr.Markdown(
value=""" value="""
@@ -297,7 +354,7 @@ with (gr.Blocks(title="MaimBot配置文件编辑") as app):
final_result = gr.Text(label="修改后的列表") final_result = gr.Text(label="修改后的列表")
add_btn.click( add_btn.click(
add_item, add_item,
inputs=[new_item_input, list_state], inputs=[new_item_input, list_state],
outputs=[list_state, list_display, item_to_delete, final_result] outputs=[list_state, list_display, item_to_delete, final_result]
) )
@@ -305,7 +362,7 @@ with (gr.Blocks(title="MaimBot配置文件编辑") as app):
delete_item, delete_item,
inputs=[item_to_delete, list_state], inputs=[item_to_delete, list_state],
outputs=[list_state, list_display, item_to_delete, final_result] outputs=[list_state, list_display, item_to_delete, final_result]
) )
with gr.Row(): with gr.Row():
gr.Markdown( gr.Markdown(
'''MongoDB设置项\n '''MongoDB设置项\n
@@ -657,6 +714,99 @@ with (gr.Blocks(title="MaimBot配置文件编辑") as app):
label="消息&表情包设置保存结果" 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")