mirror of
https://github.com/OpenBMB/ChatDev.git
synced 2024-11-07 18:40:13 +03:00
652 lines
30 KiB
Python
652 lines
30 KiB
Python
import os
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import re
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from abc import ABC, abstractmethod
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from camel.agents import RolePlaying
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from camel.messages import ChatMessage
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from camel.typing import TaskType, ModelType
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from chatdev.chat_env import ChatEnv
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from chatdev.statistics import get_info
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from chatdev.utils import log_visualize, log_arguments
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class Phase(ABC):
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def __init__(self,
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assistant_role_name,
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user_role_name,
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phase_prompt,
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role_prompts,
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phase_name,
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model_type,
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log_filepath):
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"""
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Args:
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assistant_role_name: who receives chat in a phase
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user_role_name: who starts the chat in a phase
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phase_prompt: prompt of this phase
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role_prompts: prompts of all roles
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phase_name: name of this phase
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"""
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self.seminar_conclusion = None
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self.assistant_role_name = assistant_role_name
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self.user_role_name = user_role_name
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self.phase_prompt = phase_prompt
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self.phase_env = dict()
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self.phase_name = phase_name
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self.assistant_role_prompt = role_prompts[assistant_role_name]
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self.user_role_prompt = role_prompts[user_role_name]
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self.ceo_prompt = role_prompts["Chief Executive Officer"]
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self.counselor_prompt = role_prompts["Counselor"]
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self.max_retries = 3
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self.reflection_prompt = """Here is a conversation between two roles: {conversations} {question}"""
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self.model_type = model_type
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self.log_filepath = log_filepath
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@log_arguments
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def chatting(
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self,
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chat_env,
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task_prompt: str,
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assistant_role_name: str,
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user_role_name: str,
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phase_prompt: str,
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phase_name: str,
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assistant_role_prompt: str,
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user_role_prompt: str,
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task_type=TaskType.CHATDEV,
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need_reflect=False,
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with_task_specify=False,
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model_type=ModelType.GPT_3_5_TURBO,
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memory=None,
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placeholders=None,
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chat_turn_limit=10
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) -> str:
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"""
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Args:
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chat_env: global chatchain environment
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task_prompt: user query prompt for building the software
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assistant_role_name: who receives the chat
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user_role_name: who starts the chat
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phase_prompt: prompt of the phase
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phase_name: name of the phase
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assistant_role_prompt: prompt of assistant role
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user_role_prompt: prompt of user role
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task_type: task type
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need_reflect: flag for checking reflection
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with_task_specify: with task specify
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model_type: model type
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placeholders: placeholders for phase environment to generate phase prompt
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chat_turn_limit: turn limits in each chat
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Returns:
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"""
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if placeholders is None:
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placeholders = {}
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assert 1 <= chat_turn_limit <= 100
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if not chat_env.exist_employee(assistant_role_name):
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raise ValueError(f"{assistant_role_name} not recruited in ChatEnv.")
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if not chat_env.exist_employee(user_role_name):
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raise ValueError(f"{user_role_name} not recruited in ChatEnv.")
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# init role play
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role_play_session = RolePlaying(
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assistant_role_name=assistant_role_name,
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user_role_name=user_role_name,
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assistant_role_prompt=assistant_role_prompt,
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user_role_prompt=user_role_prompt,
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task_prompt=task_prompt,
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task_type=task_type,
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with_task_specify=with_task_specify,
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memory=memory,
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model_type=model_type,
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background_prompt=chat_env.config.background_prompt
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)
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# log_visualize("System", role_play_session.assistant_sys_msg)
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# log_visualize("System", role_play_session.user_sys_msg)
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# start the chat
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_, input_user_msg = role_play_session.init_chat(None, placeholders, phase_prompt)
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seminar_conclusion = None
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# handle chats
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# the purpose of the chatting in one phase is to get a seminar conclusion
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# there are two types of conclusion
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# 1. with "<INFO>" mark
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# 1.1 get seminar conclusion flag (ChatAgent.info) from assistant or user role, which means there exist special "<INFO>" mark in the conversation
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# 1.2 add "<INFO>" to the reflected content of the chat (which may be terminated chat without "<INFO>" mark)
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# 2. without "<INFO>" mark, which means the chat is terminated or normally ended without generating a marked conclusion, and there is no need to reflect
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for i in range(chat_turn_limit):
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# start the chat, we represent the user and send msg to assistant
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# 1. so the input_user_msg should be assistant_role_prompt + phase_prompt
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# 2. then input_user_msg send to LLM and get assistant_response
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# 3. now we represent the assistant and send msg to user, so the input_assistant_msg is user_role_prompt + assistant_response
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# 4. then input_assistant_msg send to LLM and get user_response
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# all above are done in role_play_session.step, which contains two interactions with LLM
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# the first interaction is logged in role_play_session.init_chat
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assistant_response, user_response = role_play_session.step(input_user_msg, chat_turn_limit == 1)
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conversation_meta = "**" + assistant_role_name + "<->" + user_role_name + " on : " + str(
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phase_name) + ", turn " + str(i) + "**\n\n"
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# TODO: max_tokens_exceeded errors here
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if isinstance(assistant_response.msg, ChatMessage):
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# we log the second interaction here
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log_visualize(role_play_session.assistant_agent.role_name,
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conversation_meta + "[" + role_play_session.user_agent.system_message.content + "]\n\n" + assistant_response.msg.content)
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if role_play_session.assistant_agent.info:
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seminar_conclusion = assistant_response.msg.content
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break
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if assistant_response.terminated:
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break
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if isinstance(user_response.msg, ChatMessage):
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# here is the result of the second interaction, which may be used to start the next chat turn
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log_visualize(role_play_session.user_agent.role_name,
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conversation_meta + "[" + role_play_session.assistant_agent.system_message.content + "]\n\n" + user_response.msg.content)
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if role_play_session.user_agent.info:
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seminar_conclusion = user_response.msg.content
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break
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if user_response.terminated:
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break
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# continue the chat
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if chat_turn_limit > 1 and isinstance(user_response.msg, ChatMessage):
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input_user_msg = user_response.msg
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else:
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break
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# conduct self reflection
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if need_reflect:
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if seminar_conclusion in [None, ""]:
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seminar_conclusion = "<INFO> " + self.self_reflection(task_prompt, role_play_session, phase_name,
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chat_env)
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if "recruiting" in phase_name:
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if "Yes".lower() not in seminar_conclusion.lower() and "No".lower() not in seminar_conclusion.lower():
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seminar_conclusion = "<INFO> " + self.self_reflection(task_prompt, role_play_session,
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phase_name,
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chat_env)
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elif seminar_conclusion in [None, ""]:
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seminar_conclusion = "<INFO> " + self.self_reflection(task_prompt, role_play_session, phase_name,
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chat_env)
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else:
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seminar_conclusion = assistant_response.msg.content
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log_visualize("**[Seminar Conclusion]**:\n\n {}".format(seminar_conclusion))
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seminar_conclusion = seminar_conclusion.split("<INFO>")[-1]
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return seminar_conclusion
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def self_reflection(self,
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task_prompt: str,
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role_play_session: RolePlaying,
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phase_name: str,
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chat_env: ChatEnv) -> str:
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"""
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Args:
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task_prompt: user query prompt for building the software
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role_play_session: role play session from the chat phase which needs reflection
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phase_name: name of the chat phase which needs reflection
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chat_env: global chatchain environment
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Returns:
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reflected_content: str, reflected results
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"""
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messages = role_play_session.assistant_agent.stored_messages if len(
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role_play_session.assistant_agent.stored_messages) >= len(
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role_play_session.user_agent.stored_messages) else role_play_session.user_agent.stored_messages
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messages = ["{}: {}".format(message.role_name, message.content.replace("\n\n", "\n")) for message in messages]
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messages = "\n\n".join(messages)
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if "recruiting" in phase_name:
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question = """Answer their final discussed conclusion (Yes or No) in the discussion without any other words, e.g., "Yes" """
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elif phase_name == "DemandAnalysis":
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question = """Answer their final product modality in the discussion without any other words, e.g., "PowerPoint" """
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elif phase_name == "LanguageChoose":
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question = """Conclude the programming language being discussed for software development, in the format: "*" where '*' represents a programming language." """
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elif phase_name == "EnvironmentDoc":
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question = """According to the codes and file format listed above, write a requirements.txt file to specify the dependencies or packages required for the project to run properly." """
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else:
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raise ValueError(f"Reflection of phase {phase_name}: Not Assigned.")
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# Reflections actually is a special phase between CEO and counselor
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# They read the whole chatting history of this phase and give refined conclusion of this phase
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reflected_content = \
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self.chatting(chat_env=chat_env,
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task_prompt=task_prompt,
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assistant_role_name="Chief Executive Officer",
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user_role_name="Counselor",
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phase_prompt=self.reflection_prompt,
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phase_name="Reflection",
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assistant_role_prompt=self.ceo_prompt,
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user_role_prompt=self.counselor_prompt,
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placeholders={"conversations": messages, "question": question},
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need_reflect=False,
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memory=chat_env.memory,
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chat_turn_limit=1,
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model_type=self.model_type)
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if "recruiting" in phase_name:
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if "Yes".lower() in reflected_content.lower():
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return "Yes"
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return "No"
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else:
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return reflected_content
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@abstractmethod
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def update_phase_env(self, chat_env):
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"""
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update self.phase_env (if needed) using chat_env, then the chatting will use self.phase_env to follow the context and fill placeholders in phase prompt
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must be implemented in customized phase
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the usual format is just like:
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```
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self.phase_env.update({key:chat_env[key]})
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```
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Args:
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chat_env: global chat chain environment
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Returns: None
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"""
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pass
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@abstractmethod
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def update_chat_env(self, chat_env) -> ChatEnv:
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"""
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update chan_env based on the results of self.execute, which is self.seminar_conclusion
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must be implemented in customized phase
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the usual format is just like:
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```
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chat_env.xxx = some_func_for_postprocess(self.seminar_conclusion)
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```
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Args:
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chat_env:global chat chain environment
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Returns:
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chat_env: updated global chat chain environment
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"""
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pass
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def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv:
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"""
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execute the chatting in this phase
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1. receive information from environment: update the phase environment from global environment
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2. execute the chatting
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3. change the environment: update the global environment using the conclusion
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Args:
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chat_env: global chat chain environment
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chat_turn_limit: turn limit in each chat
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need_reflect: flag for reflection
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Returns:
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chat_env: updated global chat chain environment using the conclusion from this phase execution
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"""
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self.update_phase_env(chat_env)
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self.seminar_conclusion = \
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self.chatting(chat_env=chat_env,
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task_prompt=chat_env.env_dict['task_prompt'],
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need_reflect=need_reflect,
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assistant_role_name=self.assistant_role_name,
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user_role_name=self.user_role_name,
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phase_prompt=self.phase_prompt,
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phase_name=self.phase_name,
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assistant_role_prompt=self.assistant_role_prompt,
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user_role_prompt=self.user_role_prompt,
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chat_turn_limit=chat_turn_limit,
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placeholders=self.phase_env,
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memory=chat_env.memory,
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model_type=self.model_type)
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chat_env = self.update_chat_env(chat_env)
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return chat_env
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class DemandAnalysis(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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pass
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def update_chat_env(self, chat_env) -> ChatEnv:
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if len(self.seminar_conclusion) > 0:
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chat_env.env_dict['modality'] = self.seminar_conclusion.split("<INFO>")[-1].lower().replace(".", "").strip()
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return chat_env
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class LanguageChoose(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
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"description": chat_env.env_dict['task_description'],
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"modality": chat_env.env_dict['modality'],
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"ideas": chat_env.env_dict['ideas']})
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def update_chat_env(self, chat_env) -> ChatEnv:
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if len(self.seminar_conclusion) > 0 and "<INFO>" in self.seminar_conclusion:
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chat_env.env_dict['language'] = self.seminar_conclusion.split("<INFO>")[-1].lower().replace(".", "").strip()
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elif len(self.seminar_conclusion) > 0:
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chat_env.env_dict['language'] = self.seminar_conclusion
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else:
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chat_env.env_dict['language'] = "Python"
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return chat_env
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class Coding(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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gui = "" if not chat_env.config.gui_design \
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else "The software should be equipped with graphical user interface (GUI) so that user can visually and graphically use it; so you must choose a GUI framework (e.g., in Python, you can implement GUI via tkinter, Pygame, Flexx, PyGUI, etc,)."
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self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
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"description": chat_env.env_dict['task_description'],
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"modality": chat_env.env_dict['modality'],
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"ideas": chat_env.env_dict['ideas'],
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"language": chat_env.env_dict['language'],
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"gui": gui})
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def update_chat_env(self, chat_env) -> ChatEnv:
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chat_env.update_codes(self.seminar_conclusion)
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if len(chat_env.codes.codebooks.keys()) == 0:
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raise ValueError("No Valid Codes.")
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chat_env.rewrite_codes("Finish Coding")
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log_visualize(
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"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
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return chat_env
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class ArtDesign(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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self.phase_env = {"task": chat_env.env_dict['task_prompt'],
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"description": chat_env.env_dict['task_description'],
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"language": chat_env.env_dict['language'],
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"codes": chat_env.get_codes()}
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def update_chat_env(self, chat_env) -> ChatEnv:
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chat_env.proposed_images = chat_env.get_proposed_images_from_message(self.seminar_conclusion)
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log_visualize(
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"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
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return chat_env
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class ArtIntegration(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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self.phase_env = {"task": chat_env.env_dict['task_prompt'],
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"language": chat_env.env_dict['language'],
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"codes": chat_env.get_codes(),
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"images": "\n".join(
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["{}: {}".format(filename, chat_env.proposed_images[filename]) for
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filename in sorted(list(chat_env.proposed_images.keys()))])}
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def update_chat_env(self, chat_env) -> ChatEnv:
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chat_env.update_codes(self.seminar_conclusion)
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chat_env.rewrite_codes("Finish Art Integration")
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# chat_env.generate_images_from_codes()
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log_visualize(
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"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
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return chat_env
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class CodeComplete(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
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"modality": chat_env.env_dict['modality'],
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"ideas": chat_env.env_dict['ideas'],
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"language": chat_env.env_dict['language'],
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"codes": chat_env.get_codes(),
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"unimplemented_file": ""})
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unimplemented_file = ""
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for filename in self.phase_env['pyfiles']:
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code_content = open(os.path.join(chat_env.env_dict['directory'], filename)).read()
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lines = [line.strip() for line in code_content.split("\n") if line.strip() == "pass"]
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if len(lines) > 0 and self.phase_env['num_tried'][filename] < self.phase_env['max_num_implement']:
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unimplemented_file = filename
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break
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self.phase_env['num_tried'][unimplemented_file] += 1
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self.phase_env['unimplemented_file'] = unimplemented_file
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def update_chat_env(self, chat_env) -> ChatEnv:
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chat_env.update_codes(self.seminar_conclusion)
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if len(chat_env.codes.codebooks.keys()) == 0:
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raise ValueError("No Valid Codes.")
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chat_env.rewrite_codes("Code Complete #" + str(self.phase_env["cycle_index"]) + " Finished")
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log_visualize(
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"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
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return chat_env
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class CodeReviewComment(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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def update_phase_env(self, chat_env):
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self.phase_env.update(
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{"task": chat_env.env_dict['task_prompt'],
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"modality": chat_env.env_dict['modality'],
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"ideas": chat_env.env_dict['ideas'],
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"language": chat_env.env_dict['language'],
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"codes": chat_env.get_codes(),
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"images": ", ".join(chat_env.incorporated_images)})
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def update_chat_env(self, chat_env) -> ChatEnv:
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chat_env.env_dict['review_comments'] = self.seminar_conclusion
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return chat_env
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class CodeReviewModification(Phase):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"codes": chat_env.get_codes(),
|
|
"comments": chat_env.env_dict['review_comments']})
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
if "```".lower() in self.seminar_conclusion.lower():
|
|
chat_env.update_codes(self.seminar_conclusion)
|
|
chat_env.rewrite_codes("Review #" + str(self.phase_env["cycle_index"]) + " Finished")
|
|
log_visualize(
|
|
"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
|
|
self.phase_env['modification_conclusion'] = self.seminar_conclusion
|
|
return chat_env
|
|
|
|
|
|
class CodeReviewHuman(Phase):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"codes": chat_env.get_codes()})
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
if "```".lower() in self.seminar_conclusion.lower():
|
|
chat_env.update_codes(self.seminar_conclusion)
|
|
chat_env.rewrite_codes("Human Review #" + str(self.phase_env["cycle_index"]) + " Finished")
|
|
log_visualize(
|
|
"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
|
|
return chat_env
|
|
|
|
def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv:
|
|
self.update_phase_env(chat_env)
|
|
log_visualize(
|
|
f"**[Human-Agent-Interaction]**\n\n"
|
|
f"Now you can participate in the development of the software!\n"
|
|
f"The task is: {chat_env.env_dict['task_prompt']}\n"
|
|
f"Please input your feedback (in multiple lines). It can be bug report or new feature requirement.\n"
|
|
f"You are currently in the #{self.phase_env['cycle_index']} human feedback with a total of {self.phase_env['cycle_num']} feedbacks\n"
|
|
f"Type 'end' on a separate line to submit.\n"
|
|
f"You can type \"Exit\" to quit this mode at any time.\n"
|
|
)
|
|
provided_comments = []
|
|
while True:
|
|
user_input = input(">>>>>>")
|
|
if user_input.strip().lower() == "end":
|
|
break
|
|
if user_input.strip().lower() == "exit":
|
|
provided_comments = ["exit"]
|
|
break
|
|
provided_comments.append(user_input)
|
|
self.phase_env["comments"] = '\n'.join(provided_comments)
|
|
log_visualize(
|
|
f"**[User Provided Comments]**\n\n In the #{self.phase_env['cycle_index']} of total {self.phase_env['cycle_num']} comments: \n\n" +
|
|
self.phase_env["comments"])
|
|
if self.phase_env["comments"].strip().lower() == "exit":
|
|
return chat_env
|
|
|
|
self.seminar_conclusion = \
|
|
self.chatting(chat_env=chat_env,
|
|
task_prompt=chat_env.env_dict['task_prompt'],
|
|
need_reflect=need_reflect,
|
|
assistant_role_name=self.assistant_role_name,
|
|
user_role_name=self.user_role_name,
|
|
phase_prompt=self.phase_prompt,
|
|
phase_name=self.phase_name,
|
|
assistant_role_prompt=self.assistant_role_prompt,
|
|
user_role_prompt=self.user_role_prompt,
|
|
chat_turn_limit=chat_turn_limit,
|
|
placeholders=self.phase_env,
|
|
memory=chat_env.memory,
|
|
model_type=self.model_type)
|
|
chat_env = self.update_chat_env(chat_env)
|
|
return chat_env
|
|
|
|
|
|
class TestErrorSummary(Phase):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
chat_env.generate_images_from_codes()
|
|
(exist_bugs_flag, test_reports) = chat_env.exist_bugs()
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"codes": chat_env.get_codes(),
|
|
"test_reports": test_reports,
|
|
"exist_bugs_flag": exist_bugs_flag})
|
|
log_visualize("**[Test Reports]**:\n\n{}".format(test_reports))
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
chat_env.env_dict['error_summary'] = self.seminar_conclusion
|
|
chat_env.env_dict['test_reports'] = self.phase_env['test_reports']
|
|
|
|
return chat_env
|
|
|
|
def execute(self, chat_env, chat_turn_limit, need_reflect) -> ChatEnv:
|
|
self.update_phase_env(chat_env)
|
|
if "ModuleNotFoundError" in self.phase_env['test_reports']:
|
|
chat_env.fix_module_not_found_error(self.phase_env['test_reports'])
|
|
log_visualize(
|
|
f"Software Test Engineer found ModuleNotFoundError:\n{self.phase_env['test_reports']}\n")
|
|
pip_install_content = ""
|
|
for match in re.finditer(r"No module named '(\S+)'", self.phase_env['test_reports'], re.DOTALL):
|
|
module = match.group(1)
|
|
pip_install_content += "{}\n```{}\n{}\n```\n".format("cmd", "bash", f"pip install {module}")
|
|
log_visualize(f"Programmer resolve ModuleNotFoundError by:\n{pip_install_content}\n")
|
|
self.seminar_conclusion = "nothing need to do"
|
|
else:
|
|
self.seminar_conclusion = \
|
|
self.chatting(chat_env=chat_env,
|
|
task_prompt=chat_env.env_dict['task_prompt'],
|
|
need_reflect=need_reflect,
|
|
assistant_role_name=self.assistant_role_name,
|
|
user_role_name=self.user_role_name,
|
|
phase_prompt=self.phase_prompt,
|
|
phase_name=self.phase_name,
|
|
assistant_role_prompt=self.assistant_role_prompt,
|
|
user_role_prompt=self.user_role_prompt,
|
|
memory=chat_env.memory,
|
|
chat_turn_limit=chat_turn_limit,
|
|
placeholders=self.phase_env)
|
|
chat_env = self.update_chat_env(chat_env)
|
|
return chat_env
|
|
|
|
|
|
class TestModification(Phase):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"test_reports": chat_env.env_dict['test_reports'],
|
|
"error_summary": chat_env.env_dict['error_summary'],
|
|
"codes": chat_env.get_codes()
|
|
})
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
if "```".lower() in self.seminar_conclusion.lower():
|
|
chat_env.update_codes(self.seminar_conclusion)
|
|
chat_env.rewrite_codes("Test #" + str(self.phase_env["cycle_index"]) + " Finished")
|
|
log_visualize(
|
|
"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
|
|
return chat_env
|
|
|
|
|
|
class EnvironmentDoc(Phase):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"codes": chat_env.get_codes()})
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
chat_env._update_requirements(self.seminar_conclusion)
|
|
chat_env.rewrite_requirements()
|
|
log_visualize(
|
|
"**[Software Info]**:\n\n {}".format(get_info(chat_env.env_dict['directory'], self.log_filepath)))
|
|
return chat_env
|
|
|
|
|
|
class Manual(Phase):
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
def update_phase_env(self, chat_env):
|
|
self.phase_env.update({"task": chat_env.env_dict['task_prompt'],
|
|
"modality": chat_env.env_dict['modality'],
|
|
"ideas": chat_env.env_dict['ideas'],
|
|
"language": chat_env.env_dict['language'],
|
|
"codes": chat_env.get_codes(),
|
|
"requirements": chat_env.get_requirements()})
|
|
|
|
def update_chat_env(self, chat_env) -> ChatEnv:
|
|
chat_env._update_manuals(self.seminar_conclusion)
|
|
chat_env.rewrite_manuals()
|
|
return chat_env
|