app.py

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

from agent import create_agent, fetch_random_question

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

def run_and_submit_all(profile):
    if not profile:
        return "Please login to Hugging Face with the login button.", None
    username = getattr(profile, "username", None) or getattr(profile, "name", None)
    if not username:
        return "Login error: username not found.", None

    # Fetch questions
    try:
        qs = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
        qs.raise_for_status()
        questions = qs.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Instantiate agent
    agent = create_agent()

    # Run and collect answers
    results, payload = [], []
    for q in questions:
        tid, text = q.get("task_id"), q.get("question")
        if not (tid and text):
            continue
        try:
            ans = agent.run(text)   # <-- positional!
        except Exception as e:
            ans = f"ERROR: {e}"
        results.append({"Task ID": tid, "Question": text, "Answer": ans})
        payload.append({"task_id": tid, "submitted_answer": ans})

    if not payload:
        return "Agent returned no answers.", pd.DataFrame(results)

    # Submit
    submission = {
        "username": username,
        "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
        "answers": payload
    }
    try:
        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
        resp.raise_for_status()
        data = resp.json()
        status = (
            f"Submission Successful!\n"
            f"User: {data['username']}\n"
            f"Score: {data['score']}% ({data['correct_count']}/{data['total_attempted']})\n"
            f"Message: {data['message']}"
        )
    except Exception as e:
        status = f"Submission Failed: {e}"

    return status, pd.DataFrame(results)


def test_random_question(profile):
    if not profile:
        return "Please login to Hugging Face with the login button.", ""
    q = fetch_random_question()
    agent = create_agent()
    # Positional call again
    answer = agent.run(q["question"])
    return q["question"], answer


with gr.Blocks() as demo:
    gr.Markdown("# SmolAgent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone this space and define your agent logic in `agent.py`.
        2. Log in with your Hugging Face account using the login button below.
        3. Use **Run Evaluation & Submit All Answers** or **Test Random Question**.
        """
    )

    login = gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers")
    test_btn = gr.Button("Test Random Question")

    status_out = gr.Textbox(label="Status / Result", lines=5, interactive=False)
    table_out = gr.DataFrame(label="Full Results Table", wrap=True)
    question_out = gr.Textbox(label="Random Question", lines=3, interactive=False)
    answer_out = gr.Textbox(label="Agent Answer", lines=3, interactive=False)

    run_btn.click(fn=run_and_submit_all, inputs=[login], outputs=[status_out, table_out])
    test_btn.click(fn=test_random_question, inputs=[login], outputs=[question_out, answer_out])

if __name__ == "__main__":
    demo.launch(debug=True, share=False)