import asyncio
from typing import Sequence
from autogen_agentchat.agents import AssistantAgent
from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermination
from autogen_agentchat.messages import AgentEvent, ChatMessage, TextMessage, ToolCallExecutionEvent
from autogen_agentchat.teams import SelectorGroupChat, RoundRobinGroupChat
from autogen_agentchat.ui import Console
from autogen_ext.models.openai import OpenAIChatCompletionClient
from constant import MODEL, OPENAI_API_KEY, OPENAI_BASE_URL
from tools import retrieval_from_knowledge_base, search_from_oqmd_by_composition

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",
    },
)


def create_team() -> SelectorGroupChat:
    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 search tasks into smaller, manageable subtasks.
        Assign these subtasks to the appropriate team members; not all team members are required to participate in every task.
        Your team members are:
            Vector search agent: Searches for paper information in Vector database of knowledge base.
            OQMD search agent: Searches for crystal structure and property information in OQMD database by composition.

        You only plan and delegate tasks - you do not execute them yourself.

        When assigning tasks, use this format:
        1. <agent> : <task>

        After all search tasks are complete, summarize the findings and end with "TERMINATE".
        """,
    )

    vector_search_agent = AssistantAgent(
        "VectorSearcher",
        description="A vector search agent.",
        tools=[retrieval_from_knowledge_base],
        model_client=model_client,
        system_message="""
        You are a vector search agent.
        Your only tool is retrieval_from_knowledge_base - use it to find information.
        You make only one search call at a time.
        Once you have the results, you never do calculations based on them.
        """,
        reflect_on_tool_use=False, # Set to True to have the model reflect on the tool use, set to False to return the tool call result directly.
    )

    oqmd_database_search_agent = AssistantAgent(
        "OQMDDatabaseSearcher",
        description="A database search agent.",
        tools=[search_from_oqmd_by_composition],
        model_client=model_client,
        system_message="""
        You are a database search agent of OQMD.
        Your only tool is search_from_oqmd_by_composition - use it to find information.
        You make only one search call at a time.
        Once you have the results, you never do calculations based on them.
        """,
        reflect_on_tool_use=False, # Set to True to have the model reflect on the tool use, set to False to return the tool call result directly.
    )

    # 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=25)
    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.
        return None

    team = SelectorGroupChat(
        [planning_agent, vector_search_agent, oqmd_database_search_agent],
        model_client=model_client, # Use a smaller model for the selector.
        termination_condition=termination,
        selector_func=selector_func,
    )
    return team

async def main(task: str = "") -> dict:
    team = create_team()
    
    result = {}
    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("How to synthesis CsPbBr3 nanocubes at room temperature?")
    # Now you can use result in main_1
    print(result)

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