# app.py  –  vollständige, lauffähige Fassung
# -------------------------------------------
import os
import gradio as gr
import requests
import pandas as pd

from agent import agent_executor                  # dein LangGraph-Agent
from langchain_core.messages import HumanMessage  # NEU: benötigt für llm_input


# (Keep Constants as is)
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"


# ---------------------------------------------------------------------------
#  BasicAgent-Wrapper: ruft den LangGraph-Executor auf
# ---------------------------------------------------------------------------
class BasicAgent:
    def __init__(self):
        print("LLM Tool-Enhanced Agent initialized.")

    # nimmt jetzt ein Dict (messages + task_id) entgegen
    def __call__(self, llm_input: dict) -> str:
        try:
            result = agent_executor.invoke(llm_input)   # LangGraph ausführen
            answer = result["messages"][-1].content
            return answer.strip()
        except Exception as e:
            print(f"Agent error: {e}")
            return "I don't know."


# ---------------------------------------------------------------------------
#  GAIA-Runner: Fragen holen → Agent laufen lassen → Ergebnis submitten
# ---------------------------------------------------------------------------
def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetch GAIA questions, run agent, submit answers."""
    space_id = os.getenv("SPACE_ID")

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

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

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

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

    # Fragen holen
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Agent auf jede Frage anwenden
    results_log, answers_payload = [], []
    for item in questions_data:
        task_id        = item.get("task_id")
        question_text  = item.get("question")
        if not task_id or question_text is None:
            continue

        try:
            llm_input = {
                "messages": [HumanMessage(content=question_text)],
                "task_id": task_id,              # ←  WICHTIG!
            }
            submitted_answer = agent(llm_input)

            answers_payload.append(
                {"task_id": task_id, "submitted_answer": submitted_answer}
            )
            results_log.append(
                {"Task ID": task_id, "Question": question_text,
                 "Submitted Answer": submitted_answer}
            )
        except Exception as e:
            results_log.append(
                {"Task ID": task_id, "Question": question_text,
                 "Submitted Answer": f"AGENT ERROR: {e}"}
            )

    if not answers_payload:
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # Submission
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload,
    }
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/"
            f"{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        return final_status, pd.DataFrame(results_log)

    except Exception as e:
        status_message = f"Submission Failed: {e}"
        return status_message, pd.DataFrame(results_log)


# ---------------------------------------------------------------------------
#  Gradio-UI (unverändert)
# ---------------------------------------------------------------------------
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")

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

    status_output = gr.Textbox(label="Run Status / Submission Result",
                               lines=5, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(fn=run_and_submit_all,
                     outputs=[status_output, results_table])

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