app.py

import os
import requests
import gradio as gr
from dotenv import load_dotenv

# Load API key dari file .env
load_dotenv()
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")

# GAIA API Endpoint
GAIA_API = "https://agents-course-unit4-scoring.hf.space"

# Ambil semua pertanyaan
def get_questions():
    response = requests.get(f"{GAIA_API}/questions")
    if response.status_code == 200:
        return response.json()
    else:
        raise Exception("Gagal mengambil pertanyaan dari GAIA API")

# Ambil file multimodal (jika ada)
def get_file(task_id):
    response = requests.get(f"{GAIA_API}/files/{task_id}")
    if response.status_code == 200:
        file_path = f"temp_{task_id}"
        with open(file_path, "wb") as f:
            f.write(response.content)
        return file_path
    else:
        return None

# Gunakan OpenAI atau model Hugging Face untuk menjawab
def generate_answer(question_text, context=None):
    prompt = f"""
Jawablah pertanyaan berikut dengan jelas dan akurat:

Pertanyaan:
{question_text}

Tambahan konteks:
{context if context else "Tidak ada"}

Jawaban:
    """
    # Jika pakai OpenAI
    import openai
    openai.api_key = OPENAI_API_KEY
    response = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[
            {"role": "user", "content": prompt}
        ],
        temperature=0.7,
        max_tokens=300
    )
    return response.choices[0].message.content.strip()

# Fungsi utama: ambil pertanyaan dan jawab
def answer_question(index):
    try:
        questions = get_questions()
        if index < 0 or index >= len(questions):
            return "Indeks pertanyaan tidak valid", "", ""

        question = questions[index]
        task_id = question["task_id"]
        q_text = question["question"]

        # Cek dan ambil file multimodal jika ada
        file_path = None
        if question.get("has_file"):
            file_path = get_file(task_id)
        
        # Bisa dikembangkan: gunakan image/audio processing untuk context
        context = f"File tersedia: {file_path}" if file_path else ""

        # Generate jawaban
        answer = generate_answer(q_text, context)

        return q_text, context, answer

    except Exception as e:
        return f"Terjadi kesalahan: {str(e)}", "", ""

# Untuk mengirim jawaban ke leaderboard (opsional)
def submit_to_leaderboard(username, agent_code_url, task_id, answer):
    data = {
        "username": username,
        "agent_code": agent_code_url,
        "answers": [
            {
                "task_id": task_id,
                "submitted_answer": answer
            }
        ]
    }
    response = requests.post(f"{GAIA_API}/submit", json=data)
    if response.status_code == 200:
        return f"Berhasil submit! Hasil: {response.json()}"
    else:
        return f"Gagal submit: {response.text}"

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("# 🤖 GAIA Question Answering Agent")
    gr.Markdown("Masukkan indeks pertanyaan (0-19) untuk dijawab oleh agen AI.")
    
    index_input = gr.Number(label="Index Pertanyaan (0-19)", value=0, precision=0)
    get_answer_btn = gr.Button("Dapatkan Jawaban")

    q_output = gr.Textbox(label="Pertanyaan")
    file_info = gr.Textbox(label="Info File (jika ada)")
    answer_output = gr.Textbox(label="Jawaban Agen")

    get_answer_btn.click(
        fn=answer_question,
        inputs=[index_input],
        outputs=[q_output, file_info, answer_output]
    )

demo.launch()