app.py

import gradio as gr
import os
from langchain_community.agent_toolkits.load_tools import load_tools
from langchain.agents import initialize_agent, AgentType
from langchain_community.llms import OpenAI

ICON_URL = "https://diegoromero.es/wp-content/uploads/2021/10/cropped-network-icon-3.png"

def get_response(input_text, google_cse_id, google_api_key, openai_api_key):
    try:
        os.environ["GOOGLE_CSE_ID"] = google_cse_id
        os.environ["GOOGLE_API_KEY"] = google_api_key
        os.environ["OPENAI_API_KEY"] = openai_api_key

        llm = OpenAI(model_name="gpt-4.1-mini", temperature=0)
        tools = load_tools(["google-search", "llm-math"], llm=llm)
        agent = initialize_agent(
            tools, llm,
            agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
            verbose=True,
            return_intermediate_steps=True
        )

        response = agent({"input": input_text})
        return response["output"], response["intermediate_steps"]
    except Exception as e:
        error_msg = f"Error interno: {e}"
        return "", [{"error": error_msg}]

with gr.Blocks() as demo:
    # Título con icono
    gr.Markdown(f"<img src='{ICON_URL}' alt='icon' style='height:24px;vertical-align:middle;margin-right:8px;'>"
                f"**GPT Agents Demo**")

    with gr.Row():
        # Columna de configuración (colapsable)
        with gr.Column(scale=1):
            with gr.Accordion("API Keys e IDs", open=False):
                google_cse_id = gr.Textbox(label="Google CSE - ID:", type="text")
                google_api_key = gr.Textbox(label="Google API KEY:", type="password")
                openai_api_key = gr.Textbox(label="OpenAI API KEY:", placeholder="sk-...", type="password")

        # Columna de Inputs/Outputs
        with gr.Column(scale=2):
            goal_input = gr.Textbox(label="Goal definition:")
            goal_output = gr.Textbox(label="Goal output:")
            intermediate_steps = gr.JSON(label="Intermediate Steps")
            run_btn = gr.Button("Run")

    # Evento de ejecución
    run_btn.click(
        fn=get_response,
        inputs=[goal_input, google_cse_id, google_api_key, openai_api_key],
        outputs=[goal_output, intermediate_steps]
    )

# 1) Configura la cola antes de lanzar
demo.queue(max_size=20, default_concurrency_limit=1)

# 2) Lanza solo con parámetros permitidos
demo.launch(
    server_port=7860        # puerto de escucha
)