agent.py

import re
from langgraph.prebuilt import create_react_agent
from agent_util import Agent_Util
from prompts import *
from tools import *
from langgraph_supervisor import create_supervisor
from langchain.chat_models import init_chat_model

import glob

class Agent:
    def __init__(self):
        print("Initializing Agent....")
        
        print("--> Audio Agent")
        self.audio_agent = create_react_agent(
            model=init_chat_model("openai:gpt-4o-mini", temperature=0),
            tools=[extract_text_from_url_tool, extract_text_from_file_tool],
            prompt= AUDIO_AGENT_PROMPT,
            name="audio_agent",
        )
        
        print("--> Web Search Agent")
        self.web_search_agent = create_react_agent(
            model=init_chat_model("openai:gpt-4o-mini", temperature=0),
            tools=[search_web_tool],
            prompt= WEB_SEARCH_AGENT_PROMPT,
            name="web_research_agent",
        )
        
        print("--> Supervisor")
        self.supervisor = create_supervisor(
            model=init_chat_model("openai:gpt-4o-mini", temperature=0),
            agents=[self.web_search_agent, self.audio_agent],
            tools=[bird_video_count_tool,chess_image_to_fen_tool,chess_fen_get_best_next_move_tool,
                get_excel_columns_tool, calculate_excel_sum_by_columns_tool,execute_python_code_tool,
                text_inverter_tool, check_table_commutativity_tool],
            prompt= SUPERVISOR_PROMPT,
            add_handoff_back_messages=True,
            output_mode="final_response",
        ).compile()
        
        print("Agent initialized.")     
        
    
    def __call__(self, question: str, task_id: str, task_file_name: str) -> str:
        print(f"Agent (nova forma de invocar) received question({task_id}) (first 50 chars): {question[:50]}...")
    
        file_prefix = ""
        if task_file_name:
            print(f"Task com arquivo {task_file_name}")
            File_Util.baixa_arquivo_task(task_file_name)
            file_prefix = f"File: {task_file_name}  . "

        # Chamando sem stream
        response = self.supervisor.invoke(
            {
                "messages": [
                    {
                        "role": "user",
                        "content": f"{file_prefix}{question}",
                    }
                ]
            }
        )

        print(f"Resposta LLM: {response}")
        answer_llm = response.get("output", "").strip()
        print(f"Resposta pré-processada: {answer_llm}")
        final_answer = re.sub(r"^FINAL ANSWER:\s*", "", answer_llm, flags=re.IGNORECASE)
        print(f"Agent returning answer for task {task_id}: {final_answer}")
        
        return final_answer