import asyncio
from typing import Sequence
from autogen_agentchat.agents import AssistantAgent, SocietyOfMindAgent, UserProxyAgent
from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination, HandoffTermination
from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage, ToolCallExecutionEvent
from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_agentchat.base import Handoff
from autogen_ext.models.openai import OpenAIChatCompletionClient
from constant import MODEL, OPENAI_API_KEY, OPENAI_BASE_URL
from scientist_team import create_scientist_team
from engineer_team import create_engineer_team
from robot_platform import create_robot_team
from analyst_team import create_analyst_team

model_client = OpenAIChatCompletionClient(
    model=MODEL,
    base_url=OPENAI_BASE_URL,
    api_key=OPENAI_API_KEY,
    model_info={
        "vision": True,
        "function_calling": True,
        "json_output": True,
        "family": "unknown",
    },
)

user = UserProxyAgent("User", input_func=input)

scientist_team = create_scientist_team(model_client=model_client)
engineer_team = create_engineer_team()
# await code_executor.start()
robot_platform = create_robot_team()
analyst_team = create_analyst_team()

result = {}
planning_agent = AssistantAgent(
    "PlanningAgent",
    description="An agent for planning tasks, this agent should be the first to engage when given a new task.",
    model_client=model_client,
    system_message="""
    You are a planning agent.
    Your job is to break down complex Materials science research tasks into smaller, manageable subtasks.
    Assign these subtasks to the appropriate sub-teams; not all sub-teams are required to participate in every task.
    Your sub-teams are:
        1. User: A human agent to whom you transfer information whenever you need to confirm your execution steps to a human.
        2. Scientist: A professional team of material scientists who are mainly responsible for consulting on material synthesis, structure, application and properties.
            - The scientist team has the following members: 
            2.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
            2.2 Structure Scientist: focusing on agents of structural topics in materials science.
            2.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
            2.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.
        3. Engineer: A team of professional engineers who are responsible for writing code, visualizing experimental schemes, converting experimental schemes to JSON, and more.
            - The engineer team has the following members: 
            3.1 Structural engineer: A professional structural engineer who focus on converting natural language synthesis schemes to JSON or XML formated scheme, and then upload this JSON to S3 Storage.
            3.2 Software engineer: A professional software engineers will coding with Python.
            3.3 Code reviewer: A professional code reviewer will review the code written by software engineers and execute it.
            3.4 Scheme Plotter: An agent responsible for converting a expriment scheme into a Mermaid flowchart.
        4. Executor: A robotic platform is responsible for performing automated synthesis experiments, automated characterization experiments, and collecting experimental datas.
            - The Executor team has the following members: 
            4.1 MobileRobot_Agent: This agent controls the mobile robot by calling the funciton sendScheme2MobileRobot to place the experimental container into the robot workstation. This agent called before RobotWorkstation_Agent.
            4.2 RobotWorkstation_Agent: This agent is called by the mobile robot agent, do not plan it alone.
            4.3 DataCollector_Agent: This agent collects experimental data and experimental logs from the characterization device in the robot platform and stores them.
        5. Analyst: A team of data analysts who are responsible for analyzing and visualizing experimental data and logs.
            - The Data Analysis team has the following members:
            5.1 Expriment_Analyst: The agent of data analysts who are responsible for analyzing experimental data and logs.
            5.2 Expriment_Optimizer: The agent optimizes the experimental scheme by means of component regulation and so on to make the experimental result close to the desired goal of the user.
            5.3 Data_Visulizer: The agent of data visulizers who are responsible for visualizing experimental data and logs.

    You only plan and delegate tasks - you do not execute them yourself.
    
    回答时你需要初始化/更新如下任务分配表和Mermaid流程图，并按顺序执行，使用如下格式并利用：
    | Team_name   | Member_name   | sub-task                             |
    | ----------- | ------------- | ------------------------------------ |
    | <team_name> | <member_name> | <status: brief sub-task description> |
    
    ```mermaid
    graph TD
    User[User]
    subgraph <team_name>
        A1[<member_name>]
    end
    style xxx # 推荐多样的风格
    ...
    User --> A1
    ...
    ```

    每次回答时，你需要清晰明确的指出已经完成的子任务下一步子任务，使用如下格式：
    **已完成子任务：**
    1. <team> : <subtask>
    **Next sub-task:**
    n. <team> : <subtask>
    
    You can end with "HUMAN" if you need to, which means you need human approval or other advice or instructions;
    After plan and delegate tasks are complete, end with "START";
    Determine if all sub-teams have completed their tasks, and if so, summarize the findings and end with "TERMINATE".
    """,
    reflect_on_tool_use=False
)

# The termination condition is a combination of text mention termination and max message termination.
text_mention_termination = TextMentionTermination("TERMINATE")
max_messages_termination = MaxMessageTermination(max_messages=200)
termination = text_mention_termination | max_messages_termination

# The selector function is a function that takes the current message thread of the group chat
# and returns the next speaker's name. If None is returned, the LLM-based selection method will be used.
def selector_func(messages: Sequence[AgentEvent | ChatMessage]) -> str | None:
    if messages[-1].source != planning_agent.name:
        return planning_agent.name # Always return to the planning agent after the other agents have spoken.
    elif "HUMAN" in messages[-1].content:
        return user.name
    return None

team = SelectorGroupChat(
    [planning_agent, user, scientist_team, engineer_team, robot_platform, analyst_team],
    model_client=model_client, # Use a smaller model for the selector.
    termination_condition=termination,
    selector_func=selector_func,
)



async def main(task: str = "") -> dict:
    await Console(team.run_stream(task=task))
    # await code_executor.stop() 
    # async for message in team.run_stream(task=task):
    #     if isinstance(message, TextMessage):
    #         print(f"----------------{message.source}----------------\n {message.content}")
    #         result[message.source] = message.content
    #     elif isinstance(message, ToolCallExecutionEvent):
    #         print(f"----------------{message.source}----------------\n {message.content}")
    #         result[message.source] = [content.content for content in message.content]
    return result


# Example usage in another function
async def main_1():
    # result = await main(input("Enter your instructions below: \n"))
    result = await main("Let the robot synthesize CsPbBr3 nanocubes at room temperature")
    # result = await main("查一下CsPbBr3的晶体结构")

    print(result)

if __name__ == "__main__":
    asyncio.run(main_1())