app.py

# app.py

import os
import time
import traceback
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):
    try:
        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
        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)

    except Exception as e:
        tb = traceback.format_exc()
        print("[ERROR]", tb)
        return (f"❌ Unexpected error:\n{e}", pd.DataFrame())

# --- 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()