app.py

"""
GAIA Agent - HuggingFace Spaces Evaluation Runner
基于 LangGraph 的 GAIA benchmark 评估智能体
"""

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

from config import (
    SCORING_API_URL,
    DEBUG,
    BATCH_QUESTION_DELAY,
)
from agent import GaiaAgent

# --- Constants ---
DEFAULT_API_URL = SCORING_API_URL


# --- Agent Wrapper ---
class GAIAAgentWrapper:
    """
    包装 GaiaAgent，适配 HuggingFace Spaces 评估接口
    """
    def __init__(self):
        print("Initializing GAIA Agent...")
        self._agent = None

    @property
    def agent(self) -> GaiaAgent:
        """延迟初始化 Agent"""
        if self._agent is None:
            self._agent = GaiaAgent()
            print("GAIA Agent initialized.")
        return self._agent

    def __call__(self, question: str, task_id: str = "") -> str:
        """
        处理问题并返回答案

        Args:
            question: 问题文本
            task_id: 任务 ID（用于下载附件）

        Returns:
            答案字符串
        """
        if DEBUG:
            print(f"Agent received question (first 100 chars): {question[:100]}...")

        try:
            if task_id:
                answer = self.agent(question, task_id=task_id)
            else:
                answer = self.agent(question)

            if DEBUG:
                print(f"Agent returning answer: {answer[:100] if len(answer) > 100 else answer}")

            return answer
        except Exception as e:
            error_msg = f"Agent error: {type(e).__name__}: {str(e)}"
            print(error_msg)
            return error_msg


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the GAIA Agent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        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"

    # 1. Instantiate Agent
    try:
        agent = GAIAAgentWrapper()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    # Agent code link for HuggingFace Spaces
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else "local"
    print(f"Agent code: {agent_code}")

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=30)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run Agent on all questions
    results_log = []
    answers_payload = []
    total_questions = len(questions_data)
    print(f"Running agent on {total_questions} questions...")

    for idx, item in enumerate(questions_data):
        task_id = item.get("task_id")
        question_text = item.get("question")

        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        # Rate limit delay (skip first question)
        if idx > 0 and BATCH_QUESTION_DELAY > 0:
            print(f"Waiting {BATCH_QUESTION_DELAY}s before next question (rate limit)...")
            time.sleep(BATCH_QUESTION_DELAY)

        print(f"\n[{idx + 1}/{total_questions}] Processing task: {task_id}")

        try:
            submitted_answer = agent(question_text, task_id=task_id)
            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": submitted_answer
            })
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": submitted_answer
            })
        except Exception as e:
            error_msg = f"AGENT ERROR: {type(e).__name__}: {e}"
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100] + "..." if len(question_text) > 100 else question_text,
                "Submitted Answer": error_msg
            })

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

    # 4. Prepare Submission
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    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.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks(title="GAIA Agent Evaluation") as demo:
    gr.Markdown("# GAIA Agent Evaluation Runner")
    gr.Markdown(
        """
        **GAIA Agent** - 基于 LangGraph 的智能体，支持：
        - RAG 知识库检索（高相似度直接返回答案）
        - 网络搜索（DuckDuckGo）
        - 文件处理（文本、ZIP、PDF、Excel）
        - 代码执行（沙箱环境）

        ---
        **Instructions:**
        1. Log in to your Hugging Face account using the button below.
        2. Click 'Run Evaluation & Submit All Answers' to start evaluation.
        3. Wait for the agent to process all questions (this may take a while).
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers", variant="primary")

    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("\n" + "-" * 30 + " GAIA Agent Starting " + "-" * 30)

    # Clear proxy settings for localhost
    os.environ['NO_PROXY'] = 'localhost,127.0.0.1'
    os.environ.pop('HTTP_PROXY', None)
    os.environ.pop('HTTPS_PROXY', None)
    os.environ.pop('http_proxy', None)
    os.environ.pop('https_proxy', None)

    # Check for SPACE_HOST and SPACE_ID at startup
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"SPACE_HOST found: {space_host_startup}")
        print(f"Runtime URL: https://{space_host_startup}.hf.space")
    else:
        print("SPACE_HOST not found (running locally)")

    if space_id_startup:
        print(f"SPACE_ID found: {space_id_startup}")
        print(f"Repo URL: https://huggingface.co/spaces/{space_id_startup}")
    else:
        print("SPACE_ID not found (running locally)")

    print("-" * (60 + len(" GAIA Agent Starting ")) + "\n")

    print("Launching GAIA Agent Evaluation Interface...")
    demo.launch(debug=True, share=False)