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

from tools import FinalAnswerTool
from smolagents import CodeAgent, OpenAIServerModel

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

class BasicAgent:
    def __init__(self):
        # Use GPT-3.5-turbo for quota-friendly operation
        model = OpenAIServerModel(model_id="gpt-3.5-turbo")
        final_tool = FinalAnswerTool()
        self.agent = CodeAgent(
            model=model,
            tools=[final_tool],
            max_steps=3,
            verbosity_level=1
        )

    def __call__(self, question: str) -> str:
        return self.agent.run(question)


def extract_username(profile):
    """
    Extract username from the Hugging Face OAuthProfile or dict.
    """
    if not profile:
        return None
    # If profile is a dict (Gradio may return dict), extract keys
    if isinstance(profile, dict):
        return profile.get('username') or profile.get('name') or profile.get('login') or profile.get('id')
    # Otherwise, assume object with attributes
    return getattr(profile, 'username', None) or getattr(profile, 'name', None) or getattr(profile, 'login', None)


def run_and_submit_all(profile):
    """
    Fetch all questions, run the BasicAgent, submit answers, and return status and DataFrame.
    """
    username = extract_username(profile)
    if not username:
        return "Please login to Hugging Face with the login button.", None

    # Fetch questions
    try:
        resp = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15)
        resp.raise_for_status()
        questions = resp.json()
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Run agent on each question
    agent = BasicAgent()
    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(text)
        except Exception as e:
            ans = f"ERROR: {e}"
        results.append({'Task ID': tid, 'Question': text, 'Answer': ans})
        payload.append({'task_id': tid, 'submitted_answer': ans})

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

    # Prepare and submit
    submission = {
        'username': username,
        'agent_code': f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main",
        'answers': payload
    }
    try:
        sub_resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60)
        sub_resp.raise_for_status()
        data = sub_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 question and return question and agent answer.
    """
    username = extract_username(profile)
    if not username:
        return "Please login to Hugging Face with the login button.", ""
    try:
        q = requests.get(f"{DEFAULT_API_URL}/random-question", timeout=15).json()
        ans = BasicAgent()(q.get('question', ''))
        return q.get('question', ''), ans
    except Exception as e:
        return f"Error during test: {e}", ""

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Clone this space and define your agent in `tools.py`.
        2. Log in with your Hugging Face account.
        3. Use **Run Evaluation & Submit All Answers** or **Test Random Question**.
        """
    )
    login = gr.LoginButton()
    run_btn = gr.Button("Run Evaluation & Submit All Answers")
    test_btn = gr.Button("Test Random Question")
    status_out = gr.Textbox(label="Status / Result", lines=5, interactive=False)
    table_out = gr.DataFrame(label="Full Results Table", wrap=True)
    question_out = gr.Textbox(label="Random Question", lines=3, interactive=False)
    answer_out = gr.Textbox(label="Agent Answer", lines=3, interactive=False)

    run_btn.click(fn=run_and_submit_all, inputs=[login], outputs=[status_out, table_out])
    test_btn.click(fn=test_random_question, inputs=[login], outputs=[question_out, answer_out])

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