init
This commit is contained in:
shadowcz007
+17
-3
@@ -1261,7 +1261,7 @@ logging.info('\033[91m ### Mixlab Nodes: \033[93mLoaded')
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# print('\033[91m ### Mixlab Nodes: \033[93mLoaded')
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try:
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from .nodes.ChatGPT import SimulateDevDesignDiscussions,SiliconflowTextToImageNode,JsonRepair,ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
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from .nodes.ChatGPT import AvatarGeneratorAgent,SimulateDevDesignDiscussions,SiliconflowTextToImageNode,JsonRepair,ChatGPTNode,ShowTextForGPT,CharacterInText,TextSplitByDelimiter,SiliconflowFreeNode
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logging.info('ChatGPT.available True')
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NODE_CLASS_MAPPINGS_V = {
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@@ -1273,7 +1273,8 @@ try:
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"TextSplitByDelimiter":TextSplitByDelimiter,
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"JsonRepair":JsonRepair,
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"SimulateDevDesignDiscussions":SimulateDevDesignDiscussions
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"SimulateDevDesignDiscussions":SimulateDevDesignDiscussions,
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"AvatarGeneratorAgent":AvatarGeneratorAgent
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}
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# 一个包含节点友好/可读的标题的字典
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@@ -1286,7 +1287,8 @@ try:
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"TextSplitByDelimiter":"Text Split By Delimiter",
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"JsonRepair":"Json Repair",
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"SimulateDevDesignDiscussions":"SimulateDevDesignDiscussions ♾️Mixlab Podcast"
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"SimulateDevDesignDiscussions":"SimulateDevDesignDiscussions ♾️Mixlab Podcast",
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"AvatarGeneratorAgent":"Avatar Generator Agent ♾️Mixlab"
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}
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@@ -1474,4 +1476,16 @@ try:
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except Exception as e:
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logging.info('FalVideo.available False' )
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try:
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from .nodes.SocialProfile import SocialProfileNode
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logging.info('SocialProfile.available')
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# Update Node class mappings
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NODE_CLASS_MAPPINGS['SocialProfileNode']=SocialProfileNode
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NODE_DISPLAY_NAME_MAPPINGS["SocialProfileNode"]= "Social Profile ♾️Mixlab"
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except Exception as e:
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logging.info('SocialProfile.available False' )
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logging.info('\033[93m -------------- \033[0m')
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@@ -15,6 +15,7 @@ from io import BytesIO
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import torch
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import numpy as np
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python = sys.executable
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# Convert PIL to Tensor
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@@ -44,6 +45,13 @@ def extract_json_strings(text):
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return json_strings[0] if len(json_strings)>0 else "{}"
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# text2json
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def text_to_json(text):
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text=extract_json_strings(text)
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good_json_string = repair_json(text)
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# 将 JSON 字符串解析为 Python 对象
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data = json.loads(good_json_string)
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return data
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def is_installed(package, package_overwrite=None,auto_install=True):
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is_has=False
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@@ -75,6 +83,7 @@ def is_installed(package, package_overwrite=None,auto_install=True):
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# def is_installed(package):
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# try:
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# spec = importlib.util.find_spec(package)
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@@ -1085,3 +1094,295 @@ class SimulateDevDesignDiscussions:
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return ("\n".join(result),)
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class AvatarGeneratorAgent:
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@classmethod
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def INPUT_TYPES(cls):
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model_list=[
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"gpt-4o",
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"gpt-4o-2024-05-13",
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"gpt-4",
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"gpt-4-0314",
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"gpt-4-0613",
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"qwen-turbo",
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"qwen-plus",
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"qwen-long",
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"qwen-max",
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"qwen-max-longcontext",
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"glm-4",
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"glm-3-turbo",
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"moonshot-v1-8k",
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"moonshot-v1-32k",
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"moonshot-v1-128k",
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"deepseek-chat",
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"Qwen/Qwen2-7B-Instruct",
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"THUDM/glm-4-9b-chat",
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"01-ai/Yi-1.5-9B-Chat-16K"
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]
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return {
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"required": {
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"subject": ("STRING", {"multiline": True,"dynamicPrompts": False}),
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"social_profile":("STRING", {"forceInput": True}),
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"model": ( model_list,
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{"default": model_list[0]}),
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"api_url":(list(llm_apis_dict.keys()),
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{"default": list(llm_apis_dict.keys())[0]}),
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},
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"optional":{
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"api_key":("STRING", {"forceInput": True,}),
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"custom_model_name":("STRING", {"forceInput": True,}), #适合自定义model
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"custom_api_url":("STRING", {"forceInput": True,}), #适合自定义model
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},
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}
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("text",)
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FUNCTION = "generate_contextual_text"
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CATEGORY = "♾️Mixlab/GPT"
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INPUT_IS_LIST = False
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OUTPUT_IS_LIST = (False,)
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def generate_contextual_text(self,
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subject,
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social_profile,
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model,
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api_url,
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api_key=None,
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custom_model_name=None,
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custom_api_url=None,
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):
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# 设置黄色文本的ANSI转义序列
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YELLOW = "\033[33m"
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# 重置文本颜色的ANSI转义序列
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RESET = "\033[0m"
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if custom_model_name!=None:
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model=custom_model_name
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api_url=llm_apis_dict[api_url] if api_url in llm_apis_dict else ""
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if custom_api_url!=None:
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api_url=custom_api_url
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if api_key==None:
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api_key="lm_studio"
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print("api_key,api_url",api_key,api_url)
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#
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if is_azure_url(api_url):
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client=azure_client(api_key,api_url)
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else:
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# 根据用户选择的模型，设置相应的接口和模型名称
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if model == "glm-4" :
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client = ZhipuAI_client(api_key) # 使用 Zhipuai 的接口
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print('using Zhipuai interface')
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else :
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client = openai_client(api_key,api_url) # 使用 ChatGPT 的接口
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# 以下为多智能体框架
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client = Swarm(client=client)
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# 定义1个代理：生成器
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# instructions
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def generator_instructions(context_variables):
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# user = context_variables.get("name", "User")
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user=json.dumps(context_variables)
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return f'''
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根据用户的背景信息 {user} 来欢迎用户，并根据具体的指令进行深度思考后回复。
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请确保以下内容：
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* 使用 `username` 来欢迎用户。
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* 考虑用户的背景信息，提供相关和定制化的回复。
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* 详细思考过程，并确保结论或结果在最后给出。
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Steps
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=====
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1. 使用 `username` 来欢迎用户。
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2. 分析和理解用户的背景信息 {user}。
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3. 根据具体的指令进行深度思考。
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4. 详细的思考过程应包括对用户背景的分析和指令的具体执行。
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5. 最后给出结论或结果。
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Output Format
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=============
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* 欢迎语应简洁明了。
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* 思考过程不需要显示。
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* 直接输出结论或结果。
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Examples
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========
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**Example 1:**
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**Input:**
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背景信息: "username: John, occupation: software engineer, interests: AI, hobbies: chess"
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指令: "推荐一本书"
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**Output:**
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欢迎你, John！我推荐你阅读《人工智能：现代方法》。这本书详细介绍了AI的基本原理和最新进展，非常适合你这样的软件工程师。此外，如果你对休闲阅读也有兴趣，我还推荐《棋类游戏中的人工智能》，这本书结合了你的AI兴趣和下棋的爱好。
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**Example 2:**
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**Input:**
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背景信息: "username: Alice, occupation: graphic designer, interests: UX/UI, hobbies: painting"
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指令: "推荐一个在线课程"
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**Output:**
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欢迎你, Alice！我推荐你参加Coursera上的《用户体验设计基础》课程。这个课程专为像你这样的平面设计师设计，涵盖了UX/UI设计的基本原则和实践。考虑到你对绘画的爱好，你可能也会喜欢《数字艺术创作》这个课程，它将帮助你将传统艺术技巧应用到数字平台上。
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Notes
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=====
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* 确保欢迎语与用户背景信息相关。
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* 思考过程应包括对用户背景的分析和指令的具体执行。
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* 结论应简洁明了，并与用户的背景信息和指令密切相关。
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'''.strip()
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# 用于更新用户信息
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def update_account_details(context_variables: dict):
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profile_id = context_variables.get("profile_id", None)
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username = context_variables.get("username", None)
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skills = context_variables.get("skills", None)
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if skills:
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skills=",".join(skills)
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print(f"Account Details: {username} {profile_id} {skills}")
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#
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return "Success"
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generator_agent = Agent(
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name="generator_agent",
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instructions=generator_instructions,
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functions=[update_account_details],
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)
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# dict
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context_variables = json.loads(social_profile)
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response = client.run(
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messages=[{"role": "user", "content":subject}],
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agent=generator_agent,
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context_variables=context_variables,
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model_override=model
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)
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result=response.messages[-1]["content"]
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print(f"{YELLOW}{result}{RESET}")
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# 答案生成
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def answer_instructions(context_variables):
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# user = context_variables.get("name", "User")
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user=json.dumps(context_variables)
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return '''
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根据提供的金句，先判断属于哪个领域的信息，然后选择最不相关的其他领域里的新概念，结合用户的'''+user+'''信息，生成一个新的职业标签。目的是激发新灵感的产生。
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Steps
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=====
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1. **判断领域**: 根据提供的金句，判断其所属领域。
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2. **选择不相关概念**: 从其他领域中选择一个最不相关的新概念。
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3. **结合用户信息**: 将上述步骤中的信息与用户的user信息结合。
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4. **生成职业标签**: 生成一个新的职业标签，激发新灵感。
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Output Format
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=============
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用JSON格式输出新的职业标签:
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```
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{ "result": "new_tag" }
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```
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Examples
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========
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**Example 1:**
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* Input:
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* 金句: "科技是第一生产力"
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* user信息: "对金融科技有浓厚兴趣"
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* Output:
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```
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{ "result": "金融科技园艺师" }
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```
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**Example 2:**
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* Input:
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* 金句: "艺术是心灵的镜子"
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* user信息: "喜欢编程和数据分析"
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* Output:
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```
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{ "result": "数据分析画家" }
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```
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Notes
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=====
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* 确保新概念与原领域尽量不相关。
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* 职业标签应有创意且能激发灵感。'''.strip()
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answer_agent = Agent(
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name="Answer",
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instructions=answer_instructions)
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response = client.run(
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messages=[{"role": "user", "content": result}],
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agent=answer_agent,
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context_variables=context_variables,
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model_override=model
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)
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content=response.messages[-1]["content"]
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answer_json=text_to_json(content)
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print(f"{YELLOW}{content}{RESET}")
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new_tag=None
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if "result" in answer_json:
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new_tag=answer_json['result']
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context_variables['skills'].append(new_tag)
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role_desc
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if new_tag!=None:
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response = client.run(
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messages=[{"role": "user", "content": f"请80%参考{new_tag},20%特征参考我的数字分身信息，描绘一张游戏人物的角色设计图，直接输出角色设计图的描述给我，不要多余的不相关信息"}],
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agent=generator_agent,
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context_variables=context_variables,
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model_override=model
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)
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role_desc=response.messages[-1]["content"]
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print(f"{YELLOW}{role_desc}{RESET}")
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response = client.run(
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messages=[{"role": "user", "content": "请更新我的数字分身信息"}],
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agent=generator_agent,
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context_variables=context_variables,
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model_override=model
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)
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content=response.messages[-1]["content"]
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print(f"{YELLOW}{content}{RESET}")
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return (subject,result,role_desc,)
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@@ -0,0 +1,89 @@
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# 用于定义社交名片
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import json,os
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def save_to_json(file_path, data):
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try:
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with open(file_path, 'w') as f:
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json.dump(data, f)
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except Exception as e:
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print(e)
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def read_from_json(file_path):
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data={}
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try:
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with open(file_path, 'r') as f:
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data = json.load(f)
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except Exception as e:
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print(e)
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return data
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# read_from_json()
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current_path = os.path.abspath(os.path.dirname(__file__))
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social_profile_json=os.path.join(current_path,'social_profile.json')
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# print('Watcher:',config_json)
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def read_social_profile_config():
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config={
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"username":"shadow",
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"profile_id":"ML000",
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"skills":"Design Hacker,Programmer,Architect,Experience Designer".split(",")
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}
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try:
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if os.path.exists(social_profile_json):
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config=read_from_json(social_profile_json)
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except Exception as e:
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print(e)
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return config
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def save_social_profile_config(data):
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save_to_json(social_profile_json,data)
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user=read_social_profile_config()
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class SocialProfileNode:
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def __init__(self):
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self.user =read_social_profile_config()
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"username": ("STRING", {"forceInput": False, "default": self.user['username']}),
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"profile_id": ("STRING", {"forceInput": False, "default": self.user['profile_id']}),
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"skills": ("STRING", {"forceInput": False, "multiline": True, "default": ",".join(self.user['skills'])}),
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},
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}
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# INPUT_IS_LIST = False
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RETURN_TYPES = ("STRING",)
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RETURN_NAMES = ("social_profile(json_string)",)
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FUNCTION = "run"
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# OUTPUT_IS_LIST = (False,)
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CATEGORY = "♾️Mixlab/Test"
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def run(self, username, profile_id, skills):
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# 将skills字符串分割为列表
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skills_list = [skill.strip() for skill in skills.split(",")]
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# 创建社交名片的JSON对象
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social_profile = {
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"username": username,
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"profile_id": profile_id,
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"skills": skills_list,
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}
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# 将社交名片转换为JSON字符串
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json_string = json.dumps(social_profile, ensure_ascii=False)
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save_social_profile_config(social_profile)
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# 返回JSON字符串和一个示例值
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return (json_string,)
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Reference in New Issue
Block a user