from prompts.react_2shot import REACT_SYS_PROMPT
from prompts.assigner_experts_v2 import (
    ASSIGNER_PROMPT,
    WHOLEBODY_EXPERT_PROMPT_NO_EXPERIENCE,
    HEAD_EXPERT_PROMPT_NO_EXPERIENCE,
    CARDIAC_EXPERT_PROMPT_NO_EXPERIENCE,
    THORACIC_EXPERT_PROMPT_NO_EXPERIENCE,
    ABDOMINAL_EXPERT_PROMPT_NO_EXPERIENCE,
)
from autogen import config_list_from_json, UserProxyAgent, ConversableAgent
from utils.reply_utils import extract_llm_answer


def chat_with_llm_react_multiexpert_selfverify(query,
                                               model,
                                               autogen_config_path,
                                               sys_prompt_dict=REACT_SYS_PROMPT):
    autogen_config = config_list_from_json(env_or_file=autogen_config_path,
                                           filter_dict={"model": [model]})[0]
    general_chat_history = []
    user_proxy = UserProxyAgent("user_proxy", code_execution_config=False)

    # Assigner agent
    assigner_agent = ConversableAgent(
        name="assigner",
        system_message=ASSIGNER_PROMPT,
        llm_config=autogen_config,
        human_input_mode="NEVER",
    )

    # Mapping of expert names to their corresponding agents and no-experience prompts
    expert_prompts_no_experience = {
        "WHOLEBODY": WHOLEBODY_EXPERT_PROMPT_NO_EXPERIENCE,
        "ABDOMINAL": ABDOMINAL_EXPERT_PROMPT_NO_EXPERIENCE,
        "HEAD": HEAD_EXPERT_PROMPT_NO_EXPERIENCE,
        "CARDIAC": CARDIAC_EXPERT_PROMPT_NO_EXPERIENCE,
        "THORACIC": THORACIC_EXPERT_PROMPT_NO_EXPERIENCE,
    }

    # Create expert agents dynamically using no-experience prompts
    experts = {
        name:
        ConversableAgent(
            name=f"{name.lower()}_expert",
            system_message=prompt,
            llm_config=autogen_config,
            human_input_mode="NEVER",
        )
        for name, prompt in expert_prompts_no_experience.items()
    }

    # User -> Assigner
    assigner_result = user_proxy.initiate_chat(assigner_agent, message=query, max_turns=1)
    general_chat_history.extend(assigner_result.chat_history)
    expert_names = extract_llm_answer(assigner_result.summary)

    # Assigner -> Expert(s) -> Self-Verify
    expert_answers = {}
    for expert_name in expert_names:
        expert_agent = experts.get(expert_name)
        if expert_agent:
            # Expert provides initial answer
            expert_result = user_proxy.initiate_chat(expert_agent, message=query, max_turns=1)
            llm_answer = extract_llm_answer(expert_result.summary)
            general_chat_history.extend(expert_result.chat_history)

            # Expert self-verifies their answer
            self_verify_query = (
                f"The question was: {query}\n"
                f"Your previous answer was: {llm_answer}\n"
                "Please review your previous answer. Does it align with your knowledge and expertise?\n"
                "If your answer is correct, please confirm it. If there are any errors or omissions, please correct them in your reply."
            )
            self_verify_result = user_proxy.initiate_chat(expert_agent,
                                                          message=self_verify_query,
                                                          max_turns=1)
            refined_llm_answer = extract_llm_answer(self_verify_result.summary)
            general_chat_history.extend(self_verify_result.chat_history)

            expert_answers[expert_name] = refined_llm_answer
        else:
            print(f"Expert '{expert_name}' not found.")

    # Combine refined answers
    final_llm_answer = {}
    for ans in expert_answers.values():
        final_llm_answer.update(ans)

    return {
        "reply": final_llm_answer,
        "chat_history": general_chat_history,
    }