app.py

import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool
# --- CORRECCIÓN FINAL: Esta es la clase estándar del curso ---
from smolagents import InferenceClientModel

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

def get_agent():
    # Usamos InferenceClientModel, que es el estándar actual
    model = InferenceClientModel(
        model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
        token=os.getenv("HF_TOKEN")
    )

    tools = [DuckDuckGoSearchTool(), VisitWebpageTool()]

    agent = CodeAgent(
        tools=tools,
        model=model,
        max_steps=20,
        verbosity_level=1,
        additional_authorized_imports=["pandas", "openpyxl", "json", "csv", "datetime", "requests", "bs4"]
    )
    return agent

def run_and_submit_all(profile: gr.OAuthProfile | None):
    if profile:
        username = f"{profile.username}"
    else:
        return "⚠️ Por favor, loguéate arriba.", None

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

    try:
        agent = get_agent()
        print("✅ Agente (InferenceClientModel) iniciado.")
    except Exception as e:
        return f"Error iniciando agente: {e}", None

    try:
        response = requests.get(questions_url, timeout=15)
        questions_data = response.json()
    except Exception as e:
        return f"Error descargando preguntas: {e}", None

    results_log = []
    answers_payload = []
    
    print(f"🚀 Procesando {len(questions_data)} preguntas...")

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        file_name = item.get("file_name")
        
        local_msg = ""
        if file_name:
            try:
                # Descarga del archivo (Vital para GAIA)
                f_url = f"{api_url}/files/{task_id}"
                content = requests.get(f_url).content
                with open(file_name, "wb") as f:
                    f.write(content)
                local_msg = f"\nARCHIVO LOCAL: '{file_name}'."
            except:
                pass

        # Prompt optimizado para GAIA
        prompt = f"{question_text}{local_msg}\nRESPONDER MUY CORTO. SOLO EL DATO EXACTO."

        try:
            ans = str(agent.run(prompt)).strip()
            answers_payload.append({"task_id": task_id, "submitted_answer": ans})
            results_log.append({"Task ID": task_id, "Answer": ans})
            print(f"Task {task_id}: {ans}")
        except Exception as e:
            print(f"Error Task {task_id}: {e}")
            answers_payload.append({"task_id": task_id, "submitted_answer": "Error"})

    submission = {"username": username, "agent_code": agent_code, "answers": answers_payload}
    
    try:
        res = requests.post(submit_url, json=submission, timeout=60).json()
        return f"🎉 Score: {res.get('score')}%", pd.DataFrame(results_log)
    except Exception as e:
        return f"Error envío: {e}", pd.DataFrame(results_log)

with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent - Final Version")
    gr.LoginButton()
    btn = gr.Button("EJECUTAR")
    out = gr.Textbox()
    tbl = gr.DataFrame()
    btn.click(run_and_submit_all, outputs=[out, tbl])

if __name__ == "__main__":
    demo.launch()