app.py

import os
import gradio as gr
import pandas as pd
from langchain_core.messages import HumanMessage
from graph_builder import build_graph
from api_client import fetch_questions, submit_answers

class GaiaAgent:
    def __init__(self):
        self.graph = build_graph()

    def __call__(self, question):
        state = {"question": question}
        result_state = self.graph.invoke(state)
        return result_state["final_answer"]

def run_and_submit_all(profile):
    space_id = os.getenv("SPACE_ID")
    username = profile.username if profile else None
    if not username:
        return "Please log in to Hugging Face.", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    agent = GaiaAgent()

    questions_data = fetch_questions()
    answers_payload = []
    results_log = []

    for item in questions_data:
        task_id = item["task_id"]
        question = item["question"]
        answer = agent(question)
        answers_payload.append({"task_id": task_id, "submitted_answer": answer})
        results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})

    result = submit_answers(username, agent_code, answers_payload)
    final_status = f"Submission Successful!\nUser: {result.get('username')}\nScore: {result.get('score')}%\nCorrect: {result.get('correct_count')}/{result.get('total_attempted')}\nMessage: {result.get('message', '')}"
    return final_status, pd.DataFrame(results_log)

with gr.Blocks() as demo:
    gr.Markdown("# GAIA LangGraph Agent")
    gr.Markdown("Log in and run your agent to evaluate on GAIA benchmark.")

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Run Status", lines=5)
    results_table = gr.DataFrame(label="Results")

    run_button.click(run_and_submit_all, outputs=[status_output, results_table])

if __name__ == "__main__":
    demo.launch()