# app.py  –  Gradio UI + Orchestrierung
# =====================================
import os
import gradio as gr
import pandas as pd

from agent import GaiaAgent              # deine LangChain/LangGraph-Implementierung
import logic                             # Netzwerk- / Loader- / Submit-Utilities


# ---------------------------------------------------------------------
# Callback für den Gradio-Button
# ---------------------------------------------------------------------
def run_and_submit_all(profile: gr.OAuthProfile | None):
    # 0) Login prüfen
    if profile is None:
        return "⚠️  Please log in with the Hugging Face button.", None

    username = profile.username
    space_id = os.getenv("SPACE_ID", "your-space-id")       # Fallback für lokales Testen
    agent_code_url = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"👤  User: {username} | Repo: {agent_code_url}")

    # 1) Agent instanziieren
    try:
        gaia_agent = GaiaAgent()
    except Exception as e:
        return f"❌ Error initialising GaiaAgent: {e}", None

    # 2) Fragen (und evtl. Dateien) laden
    try:
        questions = logic.fetch_all_questions()
    except Exception as e:
        return f"❌ Could not fetch questions: {e}", None

    # 3) Agent auf alle Fragen loslassen
    results_log, answers_payload = logic.run_agent(gaia_agent, questions)
    if not answers_payload:
        return "⚠️  Agent produced no answers.", pd.DataFrame(results_log)

    # 4) Einsenden & Score abrufen
    submission = {
        "username": username.strip(),
        "agent_code": agent_code_url,
        "answers": answers_payload,
    }
    status_msg, results_df = logic.submit_answers(submission, results_log)
    return status_msg, results_df


# ---------------------------------------------------------------------
# Gradio-Interface
# ---------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Level-1 Agent – Evaluation Runner")
    gr.Markdown(
        "1. **Clone** this Space and implement your logic in `agent.py`.\n"
        "2. **Log in** with your HF account.\n"
        "3. Click **Run** to fetch questions, run the agent, and submit answers."
    )

    gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers")

    status_box = gr.Textbox(label="Status / Score", lines=4, interactive=False)
    results_df = gr.DataFrame(label="Questions & Answers", wrap=True)

    run_btn.click(run_and_submit_all, outputs=[status_box, results_df])

# Standard-Start – auch lokal lauffähig
if __name__ == "__main__":
    demo.launch()