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agentes-unit4

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from fastapi import FastAPI
from pydantic import BaseModel
from typing import List, Dict, Union
from datasets import load_dataset
import random
import os
from huggingface_hub import login

app = FastAPI()

# Logueo para acceder al dataset gated
token = os.getenv("token_curso")
if token:
    login(token)
else:
    print("⚠️ WARNING: No se encontró el token de autenticación en la variable token_curso")

# Carga y filtra nivel 1 GAIA (validation split)
#ds = load_dataset("gaia-benchmark/GAIA", "2023_level1", split="validation",trust_remote_code=True)
ds = load_dataset(
    "gaia-benchmark/GAIA",
    data_dir="2023_level1",
    split="validation",
    revision="refs/convert/parquet"
)

QUESTIONS = []
GROUND_TRUTH: Dict[str, str] = {}

for item in ds:
    task_id = str(item["task_id"])
    QUESTIONS.append({
        "task_id": task_id,
        "question": item["Question"]
    })
    GROUND_TRUTH[task_id] = str(item["Final answer"])

class AnswerItem(BaseModel):
    task_id: str
    submitted_answer: Union[str, int, float]

class Submission(BaseModel):
    username: str
    agent_code: str
    answers: List[AnswerItem]

class ScoreResponse(BaseModel):
    username: str
    score: float
    correct_count: int
    total_attempted: int
    message: str

@app.get("/questions")
def get_questions():
    # Devuelve las 20 preguntas aleatorias de nivel 1 cada vez
    chosen = random.sample(QUESTIONS, k=min(20, len(QUESTIONS)))
    return chosen

@app.post("/submit")
def submit(sub: Submission):
    correct = sum(
        1 for ans in sub.answers
        if GROUND_TRUTH.get(ans.task_id, "") == str(ans.submitted_answer).strip()
    )
    total = len(sub.answers)
    score = correct / total * 100 if total > 0 else 0.0
    return ScoreResponse(
        username=sub.username,
        score=score,
        correct_count=correct,
        total_attempted=total,
        message=f"Puntuación: {correct}/{total} = {score:.1f}%"
    )
 # GET /random-question: Fetch a single random question from the list.
@app.get("/random-question")
def get_random_question():
    if not QUESTIONS:
        return {"error": "No questions available"}
    question = random.choice(QUESTIONS)
    return question

# GET /files/{task_id}: Download a specific file associated with a given task ID.
@app.get("/files/{task_id}")
def get_file(task_id: str):
    file_path = f"files/{task_id}.txt"
    if not os.path.exists(file_path):
        return {"error": "File not found"}
    with open(file_path, "r") as file:
        content = file.read()
    return {"task_id": task_id, "content": content}   



from fastapi.responses import HTMLResponse

@app.get("/", response_class=HTMLResponse)
def read_root():
    return """
    <!DOCTYPE html>
    <html>
    <head>
      <title>GAIA Agents - Test Interface</title>
      <style>
        body { font-family: Arial, sans-serif; margin: 40px; background: #f7f7fa; }
        h1 { color: #6d28d9; }
        button {
          background: #ec4899;
          color: white;
          border: none;
          padding: 10px 20px;
          margin: 10px 0;
          border-radius: 5px;
          font-size: 16px;
          cursor: pointer;
        }
        button:hover { background: #be185d; }
        #questions div {
          background: #fff;
          margin: 10px 0;
          padding: 10px;
          border-radius: 5px;
          box-shadow: 0 1px 4px rgba(0,0,0,0.05);
        }
        input[type="text"] {
          padding: 5px;
          border-radius: 3px;
          border: 1px solid #ddd;
          width: 60%;
        }
        #result { margin-top: 20px; }
      </style>
    </head>
    <body>
      <h1>GAIA Agents - Preguntas y Respuestas</h1>
      <button onclick="loadQuestions()">Cargar preguntas</button>
      <div id="questions"></div>
      <button onclick="submitAnswers()">Enviar respuestas</button>
      <div id="result"></div>
      <script>
        let answers = {};

        async function loadQuestions() {
          const resp = await fetch('/questions');
          const data = await resp.json();
          const container = document.getElementById('questions');
          container.innerHTML = '';
          data.forEach(q => {
            const div = document.createElement('div');
            div.innerHTML = `
              <p><b>${q.task_id}</b>: ${q.question}</p>
              <input type="text" id="answer_${q.task_id}" placeholder="Tu respuesta">
            `;
            container.appendChild(div);
          });
        }

        async function submitAnswers() {
          const container = document.getElementById('questions');
          answers = {};
          Array.from(container.querySelectorAll('input')).forEach(input => {
            const tid = input.id.replace('answer_', '');
            answers[tid] = input.value;
          });

          const payload = {
            username: "tu_usuario",
            agent_code: "https://huggingface.co/spaces/jbaselga/agentes-unit4/tree/main",
            answers: Object.entries(answers).map(([tid, ans]) => ({task_id: tid, submitted_answer: ans}))
          };

          const resp = await fetch('/submit', {
            method: 'POST',
            headers: {'Content-Type': 'application/json'},
            body: JSON.stringify(payload)
          });

          const result = await resp.json();
          document.getElementById('result').innerHTML = `<h3>Resultado</h3><p>${result.message}</p>`;
        }
      </script>
    </body>
    </html>
    """

import threading

def run_agent():
    import app
    app.main()

# Ejecuta el agente en un hilo aparte al arrancar el servidor
threading.Thread(target=run_agent, daemon=True).start()