import os
import gradio as gr
import requests
import pandas as pd
from duckduckgo_search import DDGS
from transformers import pipeline
from smolagents import tool

@tool
def web_search(query: str) -> str:
    """
    Searches for up-to-date facts, biased toward Wikipedia for accuracy.

    Args:
        query (str): The user's factual question.

    Returns:
        str: Best matching fact and URL.
    """
    refined = f"{query} site:en.wikipedia.org"
    with DDGS() as ddgs:
        results = ddgs.text(refined)
        for r in results[:5]:
            if "wikipedia.org" in r["href"].lower():
                snippet = r.get("body") or r.get("content") or r.get("snippet", "")
                if snippet:
                    return f"{snippet}\n\nSource: [{r['href']}]({r['href']})"
        return "Could not find a direct answer from Wikipedia."

@tool
def cite(input: str) -> str:
    """
    Formats a response and URL into a markdown citation.

    Args:
        input (str): A string like 'answer ||| source-url'.

    Returns:
        str: Answer followed by markdown citation.
    """
    try:
        answer, url = input.split("|||")
        return f"{answer.strip()}\n\nSource: [{url.strip()}]({url.strip()})"
    except:
        return "Could not format citation."

@tool
def python(code: str) -> str:
    """
    Evaluates math expressions using Python sandboxed eval.

    Args:
        code (str): A math expression or calculation.

    Returns:
        str: The result or error.
    """
    try:
        result = str(eval(code, {"__builtins__": {}}))
        return f"Answer: {result}"
    except Exception as e:
        return f"Error: {str(e)}"

@tool
def fallback(_: str) -> str:
    """
    Handles unclear or unanswerable queries politely.

    Args:
        _ (str): Unused.

    Returns:
        str: A polite fallback message.
    """
    return "Sorry, I couldn't confidently answer that. Could you rephrase?"

class BasicAgent:
    def __call__(self, question: str) -> str:
        q = question.lower()

        try:
            if "|||" in question:
                return cite(question)
            if any(op in q for op in ["+", "-", "*", "/"]) and any(c.isdigit() for c in q):
                return python(question)
            if len(q.split()) < 3:
                return fallback(question)
            return web_search(question)
        except Exception as e:
            return f"Agent error: {str(e)}"

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

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if profile:
        username = profile.username
    else:
        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"

    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"

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

    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:
            submitted_answer = agent(question_text)
            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_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', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Smart Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Login to your HF account using the button.
    2. Click 'Run Evaluation & Submit All Answers' to test your agent.
    """)

    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()