import time
import random
import ai_client

class UserSimAgent:
    def __init__(self, hook_client, model="gemini-2.0-flash"):
        self.hook_client = hook_client
        self.model = model
        self.system_prompt = (
            "You are a software engineer testing an AI coding assistant called 'Manual Slop'. "
            "You want to build a small Python project and verify the assistant's capabilities. "
            "Keep your responses concise and human-like. "
            "Do not use markdown blocks for your main message unless you are providing code."
        )

    def generate_response(self, conversation_history):
        """
        Generates a human-like response based on the conversation history.
        conversation_history: list of dicts with 'role' and 'content'
        """
        # Format history for ai_client
        # ai_client expects md_content and user_message.
        # It handles its own internal history.
        # We want the 'User AI' to have context of what the 'Assistant AI' said.
        
        # For now, let's just use the last message from Assistant as the prompt.
        last_ai_msg = ""
        for entry in reversed(conversation_history):
            if entry.get('role') == 'AI':
                last_ai_msg = entry.get('content', '')
                break
        
        # We need to set a custom system prompt for the User Simulator
        try:
            ai_client.set_custom_system_prompt(self.system_prompt)
            # We'll use a blank md_content for now as the 'User' doesn't need to read its own files 
            # via the same mechanism, but we could provide it if needed.
            response = ai_client.send(md_content="", user_message=last_ai_msg)
        finally:
            ai_client.set_custom_system_prompt("")
            
        return response

    def perform_action_with_delay(self, action_func, *args, **kwargs):
        """
        Executes an action with a human-like delay.
        """
        delay = random.uniform(0.5, 2.0)
        time.sleep(delay)
        return action_func(*args, **kwargs)