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: gr.OAuthProfile | None):
    """
    Fetch all questions, run the SmolAgent on them, submit all answers,
    and display the results.
    """
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please login to Hugging Face with the button.", None

    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"

    try:
        agent = create_agent()
        print("SmolAgent initialized.")
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code URL: {agent_code}")

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions = response.json()
        if not questions:
            return "No questions fetched.", None
        print(f"Fetched {len(questions)} questions.")
    except Exception as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None

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

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

    submission = {"username": username, "agent_code": agent_code, "answers": payload}
    try:
        resp = requests.post(submit_url, json=submission, 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:
        print(f"Submission error: {e}")
        status = f"Submission Failed: {e}"

    return status, pd.DataFrame(results)


def test_random_question(profile: gr.OAuthProfile | None):
    """
    Fetch a random GAIA question and return the agent's answer.
    """
    if not profile:
        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:
        print(f"Test error: {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'.
        """
    )
    login = gr.LoginButton()
    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)

    run_all.click(fn=run_and_submit_all, inputs=[login], outputs=[status, table])
    test.click(fn=test_random_question, inputs=[login], outputs=[qbox, abox])

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