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

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",
    },
)


async def main(task: str = "") -> dict:
    scientist_team = create_scientist_team()
    engineer_team = create_engineer_team()
    
    result = {}
    user = UserProxyAgent("user", input_func=input)

    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. Engineer team: 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: 
                2.1 Software engineer: Good at writing code to turn structured schemes in JSON format into Python code that can be executed by robots.
                2.2 Structural engineer: Focus on converting natural language synthesis schemes to formatted synthesis schemes in JSON or XML format.
            3. Scientist team: 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: 
                3.1 Synthesis Scientist: who is good at giving perfect and correct synthesis solutions.
                3.2 Structure Scientist: focusing on agents of structural topics in materials science.
                3.3 Property Scientist: focuses on physical and chemistry property topics in materials science.
                3.4 Application Scientist: Focus on practical applications of materials, such as devices, chips, etc.

        You only plan and delegate tasks - you do not execute them yourself.
        
        第一次回答时你需要初始化任务分配并按顺序执行，在后续的回答中重申你的任务分配，使用如下格式并利用Mermaid绘制流程图：
        | Team_name   | Member_name   | sub-task                     |
        | ----------- | ------------- | ---------------------------- |
        | <team_name> | <member_name> | <brief sub-task description> |
        
        ```mermaid
        graph
        ...
        ```

        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 "USER" 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=100)
    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 "USER" in messages[-1].content:
            return user.name
        return None

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