app.py

import os

import gradio as gr
import pandas as pd
import requests

from agent import GaiaAgent
from config import (
    DEFAULT_API_URL,
    HF_PLANNER_USE_RESPONSE_FORMAT,
    HF_TEXT_MODEL,
    HF_VISION_MODEL,
)


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    拉取全部问题，使用 GaiaAgent 逐题作答，提交全部答案，并展示评测结果。
    """
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"用户已登录：{username}")
    else:
        print("用户未登录。")
        return "请先点击按钮登录 Hugging Face。", None

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

    try:
        agent = GaiaAgent()
    except Exception as e:
        print(f"实例化 Agent 时出错：{e}")
        return f"Agent 初始化失败：{e}", None

    agent_code = (
        f"https://huggingface.co/spaces/{space_id}/tree/main"
        if space_id
        else "本地运行，未提供 SPACE_ID。"
    )
    print(agent_code)

    print(f"正在从以下地址拉取问题：{questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("拉取到的问题列表为空。")
            return "拉取到的问题列表为空，或返回格式无效。", None
        print(f"已拉取 {len(questions_data)} 个问题。")
    except requests.exceptions.RequestException as e:
        print(f"拉取问题时发生网络错误：{e}")
        return f"拉取问题失败：{e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"解析问题接口的 JSON 响应时出错：{e}")
        print(f"响应文本：{response.text[:500]}")
        return f"解析问题接口响应失败：{e}", None
    except Exception as e:
        print(f"拉取问题时发生未预期错误：{e}")
        return f"拉取问题时发生未预期错误：{e}", None

    results_log = []
    answers_payload = []
    print(f"正在让 Agent 处理 {len(questions_data)} 个问题...")
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        file_name = item.get("file_name") or ""
        if not task_id or question_text is None:
            print(f"跳过缺少 task_id 或 question 的条目：{item}")
            continue
        try:
            result = agent.answer_task(question_text, task_id=task_id, file_name=file_name)
            submitted_answer = result.answer or "无法确定"
            answers_payload.append(
                {"task_id": task_id, "submitted_answer": submitted_answer}
            )
            results_log.append(
                {
                    "任务 ID": task_id,
                    "附件": file_name,
                    "问题": question_text,
                    "提交答案": submitted_answer,
                    "来源": result.source,
                    "置信度": result.confidence,
                    "错误": result.error,
                }
            )
        except Exception as e:
            print(f"Agent 处理任务 {task_id} 时出错：{e}")
            results_log.append(
                {
                    "任务 ID": task_id,
                    "附件": file_name,
                    "问题": question_text,
                    "提交答案": f"AGENT ERROR: {e}",
                    "来源": "agent.exception",
                    "置信度": "low",
                    "错误": str(e),
                }
            )

    if not answers_payload:
        print("Agent 没有生成任何可提交的答案。")
        return "Agent 没有生成任何可提交的答案。", pd.DataFrame(results_log)

    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload,
    }
    status_update = f"Agent 已完成作答。正在为用户 '{username}' 提交 {len(answers_payload)} 个答案..."
    print(status_update)

    print(f"正在向以下地址提交 {len(answers_payload)} 个答案：{submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"提交成功！\n"
            f"用户：{result_data.get('username')}\n"
            f"总分：{result_data.get('score', 'N/A')}% "
            f"（{result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} 正确）\n"
            f"消息：{result_data.get('message', '未收到消息。')}"
        )
        print("提交成功。")
        return final_status, pd.DataFrame(results_log)
    except requests.exceptions.HTTPError as e:
        error_detail = f"服务器返回状态码 {e.response.status_code}。"
        try:
            error_json = e.response.json()
            error_detail += f" 详情：{error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" 响应：{e.response.text[:500]}"
        status_message = f"提交失败：{error_detail}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        status_message = "提交失败：请求超时。"
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        status_message = f"提交失败：网络错误 - {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)
    except Exception as e:
        status_message = f"提交过程中发生未预期错误：{e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)


with gr.Blocks() as demo:
    gr.Markdown("# LangGraph GAIA Agent 评测运行器")
    gr.Markdown(
        f"""
        **当前策略：**

        1.  LangGraph 先调用 LLM 做题型分类，明确应该使用哪类工具。
        2.  工作流按题型进入规则、Python、Excel、Wikipedia、体育统计、网页读取/搜索等处理节点。
        3.  工具结果统一交给 LLM 做最终答案格式化；LLM 失败时使用工具候选答案兜底。
        4.  未识别类型或工具无结果时进入兜底节点，按低成本工具顺序尝试。
        5.  音频、视频和棋局图工具当前禁用；这类题会返回“无法确定”。
        6.  当前文本模型：`{HF_TEXT_MODEL}`；当前视觉模型：`{HF_VISION_MODEL or "未启用"}`。
        7.  强制 JSON response_format：`{HF_PLANNER_USE_RESPONSE_FORMAT}`。

        **提交前检查：**
        Space Secrets 至少需要 `HF_TOKEN`，且该 token 必须允许调用 Inference Providers。
        """
    )

    gr.LoginButton()
    run_button = gr.Button("运行评测并提交全部答案")
    status_output = gr.Textbox(label="运行状态 / 提交结果", lines=5, interactive=False)
    results_table = gr.DataFrame(label="问题与 Agent 答案", wrap=True)
    run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])


if __name__ == "__main__":
    print("\n" + "-" * 30 + " 应用启动中 " + "-" * 30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"找到 SPACE_HOST：{space_host_startup}")
        print(f"   运行地址应为：https://{space_host_startup}.hf.space")
    else:
        print("未找到 SPACE_HOST 环境变量（可能是在本地运行）。")

    if space_id_startup:
        print(f"找到 SPACE_ID：{space_id_startup}")
        print(f"   仓库地址：https://huggingface.co/spaces/{space_id_startup}")
        print(f"   代码树地址：https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("未找到 SPACE_ID 环境变量（可能是在本地运行）。无法确定仓库地址。")

    print("-" * (60 + len(" 应用启动中 ")) + "\n")
    print("正在启动 LangGraph GAIA Agent 评测 Gradio 界面...")
    demo.launch(debug=True, share=False)