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import agentscope
model_configs = [
    {
        "model_type": "openai",
        "config_name": "gpt-3.5-turbo",
        "model_name": "gpt-3.5-turbo",
        # "api_key": "xxx",  # Load from env if not provided
        # "organization": "xxx",  # Load from env if not provided
        "generate_args": {
            "temperature": 0.5,
        },
    },
]
agentscope.init(model_configs=model_configs)

# init the self-organizing conversation
agent_builder = DialogAgent(
    name="agent_builder",
    sys_prompt="You're a helpful assistant.",
    model_config_name="gpt-3.5-turbo",
)

Act as a group discussion organizer. Please provide the suitable scenario for discussing this question, and list the roles of the people who need to participate in the discussion in order to answer this question, along with their system prompt to describe their characteristics.
The response must in the format of:
#scenario#: <discussion scenario>
#participants#:
* <participant1 type>: <characteristic description>
* <participant2 type>: <characteristic description>
Here are some examples.
Question: Joy can read 8 pages of a book in 20 minutes. How many hours will it take her to read 120 pages?
Answer:
#scenario#: grade school class discussion
#participants#:
* Instructor: Act as an instructor who is in a class group discussion to guide the student group discussion. Please encourage critical thinking. Encourage participants to think critically and challenge assumptions by asking thought-provoking questions or presenting different perspectives.
* broad-minded-student: Act as a student who is broad-minded and is open to trying new or different ways to solve problems. You are in a group discussion with other student under the guidance of the instructor.
* knowledgeable-student: Act as a knowledgeable student and discuss with others to retrieve more information about the topic. If you do not know the answer to a question, please do not share false information
Please give the discussion scenario and the corresponding participants for the following question:
Question: {question}
Answer:

query = "¼ÙÉèÄãÑÛ¾¦µÄÍ«¿×Ö±¾¶Îª5ºÁÃ×£¬ÄãÓÐһ̨¿×¾¶Îª50ÀåÃ×µÄÍûÔ¶¾µ¡£ÍûÔ¶¾µÄܱÈÄãµÄÑÛ¾¦¶àÊÕ¼¯¶àÉٹ⣿"
x = load_txt(
"D:\\GitHub\\LEARN_LLM\\agentscope\\start_0\\conversation_self_organizing\\agent_builder_instruct.txt",
).format(
    question=query,
)
x = Msg("user", x, role="user")
settings = agent_builder(x)

tools:extract_scenario_and_participants:82 - {'Scenario': 'Physics class discussion on optics', 'Participants': {'PhysicsTeacher': 'Act as a physics teacher who is leading the discussion on optics. Your role is to facilitate the conversation, provide explanations, and ensure that the discussion stays focused on the topic of light collection and optics.', 'curious-student': 'Act as a student who is curious and eager to learn more about optics and light collection. You ask insightful questions and actively participate in the discussion to deepen your understanding.', 'analytical-student': 'Act as a student who is analytical and enjoys solving problems related to optics. You approach the question methodically and use logical reasoning to arrive at solutions.'}}

def extract_scenario_and_participants(content: str) -> dict:
    result = {}
    # define regular expression
    scenario_pattern = r"#scenario#:\s*(.*)"
    participants_pattern = r"\*\s*([^:\n]+):\s*([^\n]+)"
    # search and extract scenario
    scenario_match = re.search(scenario_pattern, content)
    if scenario_match:
        result["Scenario"] = scenario_match.group(1).strip()
    # search and extract participants
    participants_matches = re.finditer(participants_pattern, content)
    participants_dict = {}
    for match in participants_matches:
        participant_type, characteristic = match.groups()
        participants_dict[
            participant_type.strip().replace(" ", "_")
        ] = characteristic.strip()
    result["Participants"] = participants_dict
    
    logger.info(result)
    return result
scenario_participants = extract_scenario_and_participants(settings["content"])

# set the agents that participant the discussion
agents = [
    DialogAgent(
        name=key,
        sys_prompt=val,
        model_config_name="gpt-3.5-turbo",
    )
    for key, val in scenario_participants["Participants"].items()
]

max_round = 2
msg = Msg("user", f"let's discuss to solve the question with chinese: {query}", role="user")
for i in range(max_round):
    msg = sequentialpipeline(agents, msg)