import os
import autogen
from IPython.display import display, Markdown

with open("together_ai_api.txt") as file:
 LLAMA_API_KEY = file.read().strip()
os.environ["LLAMA_API_KEY"] = LLAMA_API_KEY

# Agent 1: Handles Human Input for Client Requirements
client_agent = autogen.UserProxyAgent(
 name="Client_Input_Agent",
 human_input_mode="ALWAYS", # asks the human for input
 max_consecutive_auto_reply=1, # Only reply once
 is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
 system_message="""You are the primary point of contact for the user.
 Your first task is to provide the initial project details received from the human user (client requirements, product details, timeline, budget) to the group chat.
 After the Scope Architect asks questions, relay the human user's answers about their skills, experience, tools, and time estimate back to the chat.
 Reply TERMINATE when the final proposal is generated and satisfactory, or if the user wishes to stop. Otherwise, relay the user's input.
 """,
)

# Agent 2: Gathers User's Profile and Estimates
scope_architect_agent = autogen.AssistantAgent(
 name="Scope_Architect",
 llm_cnotallow=llm_config,
 human_input_mode="ALWAYS",
 max_consecutive_auto_reply=1, # Only reply once 
 is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
 system_message="""You are a Scope Architect. Your role is to understand the project requirements provided initially and then gather necessary details *from the Client_Input_Agent (representing the user/freelancer)*.
 1. Wait for the initial project details from Client_Input_Agent.
 2. Once you have the project details, formulate clear questions for the Client_Input_Agent to ask the human user about their:
 - Relevant past work/projects and collaborations.
 - Key skills and tools applicable to this project.
 - Their estimated time to complete the defined work.
 3. Do NOT proceed to proposal generation. Wait for the Client_Input_Agent to provide the user's answers.
 4. Once you have both the client requirements AND the user's details (skills, experience, time estimate), summarize this information clearly for the Rate Recommender. Signal that you have all necessary info.
 """,
)

rate_recommender_agent = autogen.AssistantAgent(
 name="Rate_Recommender",
 llm_cnotallow=llm_config,
 max_consecutive_auto_reply=1, # Only reply once
 system_message=f"""
You are a Proposal Generator and Rate Recommender. Your task is to create a structured project proposal.
Wait until the Scope_Architect shares a summary containing BOTH the client's project requirements AND the user's profile (skills, experience, time estimate, past work if available).
Analyze all received data: client needs, user expertise, estimated time, and any prior rate insights.
Generate a well-structured proposal addressed to the client, including the following sections:
Custom Introduction: Professionally introduce the user's services and reference the client's company and project.
Project Scope & Timeline: Clearly outline the deliverables with estimated timelines based on user input.
Suggested Pricing Tiers: Provide 1–3 pricing options (hourly, fixed fee, retainer) with justifications based on scope, user experience, or complexity.
Next Steps (CTA): Recommend scheduling a brief kickoff call to finalize and clarify details.
Present ONLY the final formatted proposal. Do not include additional commentary unless clarification is requested.""",)

user_proxy = autogen.UserProxyAgent(
 name="user_proxy",
 max_consecutive_auto_reply=1,
 # is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
 llm_cnotallow=llm_config,
 system_message="""you are an helpful assistant and initate the conversation"""
)

# --- Group Chat Setup ---
groupchat = autogen.GroupChat(
 agents=[client_agent, scope_architect_agent, rate_recommender_agent],
 messages=[],
 max_round=4,
 speaker_selection_method="round_robin",
)

manager = autogen.GroupChatManager(
 groupchat=groupchat,
 llm_cnotallow=llm_config,
 # System message for the manager guiding the overall flow
 system_message="""Manage the conversation flow between the agents.
 1. Start with the Client_Input_Agent providing project details.
 2. Ensure the Scope_Architect asks the necessary questions about the user's background.
 3. Ensure the Client_Input_Agent relays the user's answers.
 4. Ensure the Rate_Recommender waits for all info before generating the final proposal in the specified format.
 The conversation finishes when the final proposal is generated or the Client_Input_Agent says TERMINATE."""
)

# --- Initiate Chat ---


print("Starting the proposal generation process...")
print("Please provide the initial client and project details when prompted.")


initial_prompt_message = """
Start the process. First, I need the client/project details from the user (via Client_Input_Agent).
Then, Scope_Architect should ask the user (via Client_Input_Agent) about their background.
Finally, Rate_Recommender should generate the proposal.
"""


user_proxy.initiate_chat(
 manager,
 message=initial_prompt_message
)

chat_history = manager.chat_messages[client_agent] # Or potentially just manager.chat_messages if structure differs slightly


# Find the last message from the Rate_Recommender agent
final_proposal_message = None
for msg in reversed(chat_history):
 if msg.get("role") == "assistant" and msg.get("name") == rate_recommender_agent.name:
 if "Custom Introduction:" in msg.get("content", ""):
 final_proposal_message = msg
 break
if final_proposal_message:
 final_proposal_string = final_proposal_message.get("content", "Proposal content not found.")
 try:
 display(Markdown(final_proposal_string))
 except NameError:
 print("\n(Displaying raw Markdown text as rich output is unavailable)\n")
 print(final_proposal_string)


else:
 print("\nCould not automatically extract the final proposal from the chat history.")
 print("You may need to review the full chat history above.")