"""Enhanced Agent Evaluation Runner with simplified Agent integration"""
import os
import time
import gradio as gr
import requests
import pandas as pd
from dotenv import load_dotenv
from agent import Agent  
agent = Agent() 

load_dotenv()

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

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the Agent on them, submits all answers,
    and displays the results.
    """
    # 登录检查
    if not profile:
        return "Please Login to Hugging Face with the button.", None
    username = profile.username

    # 初始化你的简易 Agent

    # 组装提交相关 URL
    space_id    = os.getenv("SPACE_ID")
    agent_code  = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "Unknown"
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url    = f"{DEFAULT_API_URL}/submit"

    # 1. 拉取题目
    try:
        resp = requests.get(questions_url, timeout=20)
        resp.raise_for_status()
        questions_data = resp.json()
        if not questions_data:
            return "No questions received from server.", None
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # 2. 遍历题目并调用 Agent 获取答案
    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id  = item.get("task_id")
        question = item.get("question")
        if not task_id or question is None:
            continue
        try:
        # 只调用一次，带 task_id
            answer = agent(question, task_id=task_id)

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

            time.sleep(0.3)          # 小延迟防止 QPS 超限
        except Exception as e:
            err = f"ERROR: {e}"
            answers_payload.append({"task_id": task_id, "submitted_answer": err})
            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": err})

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

    # 3. 提交答案
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    try:
        post = requests.post(submit_url, json=submission_data, timeout=60)
        post.raise_for_status()
        data = post.json()
        status = (
            f"✅ Submission Successful!\n"
            f"User: {data.get('username')}\n"
            f"Score: {data.get('score','N/A')}% "
            f"({data.get('correct_count','?')}/{data.get('total_attempted','?')})\n"
            f"Message: {data.get('message','No additional message.')}"
        )
        return status, pd.DataFrame(results_log)
    except Exception as e:
        return f"❌ Submission Failed: {e}", pd.DataFrame(results_log)

# --- Gradio 界面 ---
with gr.Blocks(title="Simplified GAIA Agent Evaluation") as demo:
    gr.Markdown("# Simplified GAIA Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Set your `GOOGLE_API_KEY` in the environment variables.
    2. Log in to your Hugging Face account using the button below.
    3. Click **Run Evaluation & Submit All Answers** to start.
    
    This runner uses:
    - A custom `agent.py` for answering GAIA questions.
    - Gradio for UI.
    - HTTP requests to fetch & submit answers.
    """)
    gr.LoginButton()

    run_btn     = gr.Button("Run Evaluation & Submit All Answers", variant="primary")
    status_out  = gr.Textbox(label="Status / Results", lines=6, interactive=False)
    table_out   = gr.DataFrame(label="Questions and Answers", wrap=True)

    run_btn.click(fn=run_and_submit_all, outputs=[status_out, table_out])

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