import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent
from smolagents.models import InferenceClientModel
from smolagents.tools import DuckDuckGoSearchTool

# --- Agente Inteligente usando flan-t5-large + DuckDuckGo ---
class BasicAgent:
    def __init__(self):
        model = InferenceClientModel(model_id="google/flan-t5-large")
        tools = [DuckDuckGoSearchTool()]
        self.agent = CodeAgent(model=model, tools=tools, add_base_tools=False, max_steps=5)
        print("🤖 Agente inteligente inicializado.")

    def __call__(self, question: str) -> str:
        print(f"❓ Pregunta recibida: {question[:50]}...")
        try:
            answer = self.agent.run(question).strip()
            print(f"✅ Respuesta generada: {answer}")
            return answer
        except Exception as e:
            print(f"❌ Error del agente: {e}")
            return f"AGENT ERROR: {e}"

# --- Evaluación y envío GAIA ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"👤 Usuario: {username}")
    else:
        return "⚠️ Por favor, inicia sesión en Hugging Face antes de enviar.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    try:
        agent = BasicAgent()
    except Exception as e:
        return f"❌ Error al crear el agente: {e}", None

    # Descargar preguntas
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
    except Exception as e:
        return f"❌ Error al descargar preguntas: {e}", None

    # Responder preguntas
    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            continue
        try:
            submitted_answer = agent(question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": submitted_answer
            })
        except Exception as e:
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"AGENT ERROR: {e}"
            })

    # Enviar respuestas
    submission_data = {
        "username": username,
        "agent_code": agent_code,
        "answers": answers_payload
    }

    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"✅ ¡Envío realizado con éxito!\n"
            f"👤 Usuario: {result_data.get('username')}\n"
            f"📊 Puntuación: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correctas)\n"
            f"📬 Mensaje: {result_data.get('message', 'Sin mensaje.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"❌ Error durante el envío: {e}", pd.DataFrame(results_log)

# --- Interfaz Gradio ---
with gr.Blocks() as demo:
    gr.Markdown("# 🧠 Evaluador Agente GAIA - Curso Hugging Face")
    gr.Markdown("""
    1. Inicia sesión en Hugging Face.
    2. Pulsa el botón para ejecutar tu agente y enviar las respuestas.
    3. Espera unos minutos y revisa la puntuación.
    """)

    gr.LoginButton()
    run_button = gr.Button("▶️ Ejecutar y Enviar Respuestas")
    status_output = gr.Textbox(label="Resultado", lines=6, interactive=False)
    results_table = gr.DataFrame(label="Respuestas Generadas")

    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])

if __name__ == "__main__":
    print("🚀 Lanzando interfaz...")
    demo.launch(debug=True)