import os
import gradio as gr
import requests
from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
from pathlib import Path
import tempfile
import pandas as pd
import re

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

# --- File Download Helper ---
def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
    url = f"{base_api_url}/files/{task_id}"
    try:
        resp = requests.get(url, timeout=30)
        if resp.status_code == 404:
            return None
        resp.raise_for_status()
    except requests.exceptions.HTTPError as e:
        raise e

    cdisp = resp.headers.get("content-disposition", "")
    filename = task_id
    if "filename=" in cdisp:
        m = re.search(r'filename="([^\"]+)"', cdisp)
        if m:
            filename = m.group(1)

    tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
    tmp_dir.mkdir(exist_ok=True)
    file_path = tmp_dir / filename
    with open(file_path, "wb") as f:
        f.write(resp.content)
    return str(file_path)

# --- Basic Agent ---
class BasicAgent:
    def __init__(self):
        self.agent = CodeAgent(
            model=OpenAIServerModel(model_id="gpt-4o"),
            tools=[DuckDuckGoSearchTool(), WikipediaSearchTool()],
            add_base_tools=True,
            additional_authorized_imports=[]
        )
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = self.agent.run(question)
        print(f"Agent returning answer: {fixed_answer}")
        return fixed_answer

# --- Evaluation Logic ---
def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = "YajieXu/Final_Assignment_Template"
    if profile:
        username = f"{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")

        try:
            file_path = download_file_if_any(api_url, task_id)
        except Exception as e:
            file_path = None
            print(f"[file fetch error] {task_id}: {e}")

        q_for_agent = (
            f"{question_text}\n\n---\nA file was downloaded for this task and saved locally at:\n{file_path}\n---\n\n"
            if file_path else question_text
        )

        if not task_id or question_text is None:
            continue
        try:
            submitted_answer = agent(q_for_agent)
            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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
        **Instructions:**
        1. Log in to your Hugging Face account.
        2. Click the button to run the agent and submit answers.
        3. Your score will be printed below.
    """)
    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("Launching GAIA agent app...")
    demo.launch(debug=True, share=False)