app.py

# app.py

import os
import time
import requests
import pandas as pd
import gradio as gr

# --- Config from Env ---
API_URL      = os.getenv("API_URL", "https://agents-course-unit4-scoring.hf.space")
MODEL_ID     = os.getenv("MODEL_ID", "meta-llama/Llama-2-7b-instruct")
HF_TOKEN_ENV = os.getenv("HUGGINGFACEHUB_API_TOKEN")

WELCOME = """
## GAIA Benchmark Runner 🎉

Build your agent, score **≥30%** to earn your Certificate,  
and see where you land on the Student Leaderboard!
"""

# --- Simple HF-Inference Agent ---
class GAIAAgent:
    def __init__(self, model_id: str, token: str):
        self.model_id = model_id
        self.headers = {"Authorization": f"Bearer {token}"}

    def answer(self, prompt: str) -> str:
        payload = {
            "inputs": prompt,
            "parameters": {
                "max_new_tokens": 512,
                "temperature": 0.2
            }
        }
        url = f"https://api-inference.huggingface.co/models/{self.model_id}"
        resp = requests.post(url, headers=self.headers, json=payload, timeout=60)
        resp.raise_for_status()
        data = resp.json()
        if isinstance(data, list) and data and "generated_text" in data[0]:
            return data[0]["generated_text"].strip()
        return str(data)

# --- Gradio callback ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    if profile is None:
        return "⚠️ Please log in with your Hugging Face account.", pd.DataFrame()
    username = profile.username
    hf_token = HF_TOKEN_ENV or getattr(profile, "access_token", None)
    if not hf_token:
        return (
            "❌ No Hugging Face token found.\n"
            "Set HUGGINGFACEHUB_API_TOKEN in Secrets or log in via the button.",
            pd.DataFrame()
        )

    # 1) Fetch GAIA questions
    q_resp = requests.get(f"{API_URL}/questions", timeout=15)
    q_resp.raise_for_status()
    questions = q_resp.json() or []
    if not questions:
        return "❌ No questions found. Check your API_URL.", pd.DataFrame()

    # 2) Init agent
    agent = GAIAAgent(MODEL_ID, hf_token)

    # 3) Answer each
    results = []
    payload = []
    for item in questions:
        tid  = item.get("task_id")
        qtxt = item.get("question", "")
        try:
            ans = agent.answer(qtxt)
        except Exception as e:
            ans = f"ERROR: {e}"
        results.append({"Task ID": tid, "Question": qtxt, "Answer": ans})
        payload.append({"task_id": tid, "submitted_answer": ans})
        time.sleep(0.5)

    # 4) Submit (no agent_code)
    submission = {
        "username": username,
        "answers":  payload
    }
    s_resp = requests.post(f"{API_URL}/submit", json=submission, timeout=60)
    s_resp.raise_for_status()
    data = s_resp.json()

    # 5) Build status text
    status = (
        f"✅ **Submission Successful!**\n\n"
        f"**User:** {data.get('username')}\n"
        f"**Score:** {data.get('score')}% "
        f"({data.get('correct_count')}/{data.get('total_attempted')} correct)\n"
        f"**Message:** {data.get('message')}"
    )
    return status, pd.DataFrame(results)

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown(WELCOME)
    login    = gr.LoginButton()
    run_btn  = gr.Button("▶️ Run GAIA Benchmark")
    status   = gr.Markdown()
    table_df = gr.Dataframe(headers=["Task ID", "Question", "Answer"], wrap=True)

    run_btn.click(
        fn=run_and_submit_all,
        inputs=[login],
        outputs=[status, table_df]
    )

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