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

from smolagents import CodeAgent
from smolagents import DuckDuckGoSearchTool
from smolagents import PythonInterpreterTool
from smolagents import InferenceClientModel


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


# ---------------- AGENT ---------------- #

class SmartAgent:

    def __init__(self):

        print("Initializing SmartAgent")

        self.model = InferenceClientModel(
            model_id="meta-llama/Meta-Llama-3-8B-Instruct"
        )

        self.agent = CodeAgent(
            tools=[
                DuckDuckGoSearchTool(),
                PythonInterpreterTool()
            ],
            model=self.model,
            max_steps=8
        )

    def __call__(self, question: str) -> str:

        print("Question received:", question)

        try:
            answer = self.agent.run(question)

            if answer is None:
                return ""

            return str(answer).strip()

        except Exception as e:
            print("Agent error:", e)
            return ""


# ---------------- RUN EVALUATION ---------------- #

def run_and_submit_all(profile: gr.OAuthProfile | None):

    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
    else:
        return "Please login first.", None

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

    try:
        agent = SmartAgent()
    except Exception as e:
        return f"Agent initialization error: {e}", None

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

    # ---------------- GET QUESTIONS ---------------- #

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions = response.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    answers_payload = []
    results_log = []

    # ---------------- RUN AGENT ---------------- #

    for item in questions:

        task_id = item.get("task_id")
        question = item.get("question")

        if not task_id or not question:
            continue

        try:

            answer = agent(question)

            answers_payload.append(
                {
                    "task_id": task_id,
                    "submitted_answer": answer
                }
            )

            results_log.append(
                {
                    "Task ID": task_id,
                    "Question": question,
                    "Submitted Answer": answer
                }
            )

        except Exception as e:

            results_log.append(
                {
                    "Task ID": task_id,
                    "Question": question,
                    "Submitted Answer": f"ERROR: {e}"
                }
            )

    # ---------------- SUBMIT ---------------- #

    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 = response.json()

        final_status = (
            f"Submission Successful!\n"
            f"User: {result.get('username')}\n"
            f"Score: {result.get('score')}%\n"
            f"Correct: {result.get('correct_count')}/{result.get('total_attempted')}"
        )

        return final_status, pd.DataFrame(results_log)

    except Exception as e:

        return f"Submission failed: {e}", pd.DataFrame(results_log)


# ---------------- UI ---------------- #

with gr.Blocks() as demo:

    gr.Markdown("# Basic Agent Evaluation Runner")

    gr.Markdown(
        """
Instructions:

1. Login to Hugging Face
2. Click **Run Evaluation & Submit All Answers**
3. The agent will answer 20 GAIA questions
4. Your score will appear when finished
"""
    )

    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__":

    print("Starting Agent Evaluation App")

    demo.launch(debug=True)