app.py

# app.py

import os
import requests
import pandas as pd
import gradio as gr
from agent import answer_question
import asyncio

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

class GAIALlamaAgent:
    def __init__(self):
        print("✅ LangChain/LlamaIndex Agent initialized.")

    def __call__(self, question: str) -> str:
        print(f"📨 Agent received: {question[:50]}...")
        try:
            return asyncio.run(answer_question(question))
        except Exception as e:
            return f"[ERROR] {str(e)}"

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

    print(f"👤 User: {username}")
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" if space_id else ""

    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        print(f"📥 Fetched {len(questions_data)} questions")
    except Exception as e:
        return f"❌ Error fetching questions: {e}", None

    agent = GAIALlamaAgent()
    answers_payload = []
    results_log = []

    for item in questions_data:
        qid = item.get("task_id")
        question = item.get("question")
        if not qid or not question:
            continue
        try:
            answer = agent(question)
        except Exception as e:
            answer = f"[AGENT ERROR] {e}"
        answers_payload.append({"task_id": qid, "submitted_answer": answer})
        results_log.append({"Task ID": qid, "Question": question, "Submitted Answer": answer})

    if not answers_payload:
        return "No answers to submit.", pd.DataFrame(results_log)

    submission_data = {
        "username": username,
        "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()
        status = (
            f"✅ Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Score: {result_data.get('score')}%\n"
            f"Correct: {result_data.get('correct_count')}/{result_data.get('total_attempted')}\n"
            f"Message: {result_data.get('message')}"
        )
        return status, pd.DataFrame(results_log)
    except Exception as e:
        return f"❌ Submission failed: {e}", pd.DataFrame(results_log)

# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("""
        # 🧠 GAIA Agent Evaluation

        This app runs a LlamaIndex + LangChain powered agent through the GAIA benchmark.

        1. Login to Hugging Face below
        2. Click **Run Evaluation** to test all questions
        3. Answers will be submitted and scored
        """)
    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Status", 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🔁 App Starting Up...")
    if os.getenv("SPACE_ID"):
        print(f"🔗 Space: https://huggingface.co/spaces/{os.getenv('SPACE_ID')}")
    demo.launch(debug=True)