app.py

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

from agent import create_agent, fetch_random_question

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


def run_and_submit_all(profile):
    """
    Fetch all questions, run the agent on them, submit all answers,
    and return the status and results table.
    """
    if profile is None:
        return "Please login to Hugging Face with the login button.", None
    username = profile.username

    # Instantiate agent
    try:
        agent = create_agent()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main"

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

    if not questions:
        return "No questions fetched.", None

    # Run agent on each question
    results = []
    payload = []
    for item in questions:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            continue
        try:
            ans = agent.run(question=question_text)
        except Exception as e:
            ans = f"ERROR: {e}"
        results.append({"Task ID": task_id, "Question": question_text, "Answer": ans})
        payload.append({"task_id": task_id, "submitted_answer": ans})

    # Submit answers
    submission = {"username": username, "agent_code": agent_code, "answers": payload}
    try:
        r = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
        r.raise_for_status()
        data = r.json()
        status = (
            f"Submission Successful!\n"
            f"User: {data.get('username')}\n"
            f"Score: {data.get('score')}% ({data.get('correct_count')}/{data.get('total_attempted')})\n"
            f"Message: {data.get('message')}"
        )
    except Exception as e:
        status = f"Submission Failed: {e}"

    return status, pd.DataFrame(results)


def test_random_question(profile):
    """
    Fetch a random question and return the question and agent's answer.
    """
    if profile is None:
        return "Please login to Hugging Face with the login button.", ""
    try:
        q = fetch_random_question()
        agent = create_agent()
        ans = agent.run(question=q.get("question", ""))
        return q.get("question", ""), ans
    except Exception as e:
        return f"Error: {e}", ""

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown("# SmolAgent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone this space and implement your agent logic in agent.py.
        2. Log in with your Hugging Face account using the login button below.
        3. Use the buttons to run evaluation or test a random question.
        """
    )

    login = gr.LoginButton()
    state = gr.State(value=None)
    # Store OAuthProfile in state when user logs in
    login.click(fn=lambda profile: profile, inputs=[], outputs=[state])

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

    status_output = gr.Textbox(label="Status / Result", lines=5, interactive=False)
    results_table = gr.DataFrame(label="Results Table", wrap=True)
    question_box = gr.Textbox(label="Random Question", lines=3, interactive=False)
    answer_box = gr.Textbox(label="Agent Answer", lines=3, interactive=False)

    run_all_btn.click(fn=run_and_submit_all, inputs=[state], outputs=[status_output, results_table])
    test_btn.click(fn=test_random_question, inputs=[state], outputs=[question_box, answer_box])

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