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 SmolAgent on them, submit all answers,
    and display the results.
    """
    space_id = os.getenv("SPACE_ID")

    if profile is None:
        return "Please login to Hugging Face with the button.", None
    username = profile.username

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

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch questions
    try:
        response = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
        response.raise_for_status()
        questions = response.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 q in questions:
        task_id = q.get("task_id")
        question_text = q.get("question")
        if not task_id or not question_text:
            continue
        try:
            answer = agent.run(question=question_text)
        except Exception as e:
            answer = f"ERROR: {e}"
        results.append({"Task ID": task_id, "Question": question_text, "Answer": answer})
        payload.append({"task_id": task_id, "submitted_answer": answer})

    # Submit answers
    submit_payload = {"username": username, "agent_code": agent_code, "answers": payload}
    try:
        resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submit_payload, timeout=60)
        resp.raise_for_status()
        data = resp.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 GAIA question and return its answer by the agent.
    """
    if profile is None:
        return "Please login to test.", ""
    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(
        """
        **Istruzioni:**
        1. Clone questo space e definisci la logica in agent.py.
        2. Effettua il login con il tuo account Hugging Face.
        3. Usa 'Run Evaluation & Submit All Answers' o 'Test Random Question'.
        """
    )

    # OAuth login and user state
    login = gr.LoginButton()
    user = gr.State()
    # On login, store profile in state
    login.click(fn=lambda profile: profile, inputs=[login], outputs=[user])

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

    status = gr.Textbox(label="Status / Risultato", lines=5, interactive=False)
    table = gr.DataFrame(label="Risultati Completi", wrap=True)
    qbox = gr.Textbox(label="Domanda Casuale", lines=3, interactive=False)
    abox = gr.Textbox(label="Risposta Agente", lines=3, interactive=False)

    # Use stored user state as input
    run_all.click(fn=run_and_submit_all, inputs=[user], outputs=[status, table])
    test.click(fn=test_random_question, inputs=[user], outputs=[qbox, abox])

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