app.py

import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool, VisitWebpageTool, InferenceClientModel
import spaces

# Dummy function per soddisfare il requisito ZeroGPU
@spaces.GPU(duration=1)
def dummy_gpu():
    pass

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

def build_agent():
    model = InferenceClientModel(
        model_id="Qwen/Qwen2.5-Coder-32B-Instruct",
        token=os.getenv("HF_TOKEN"),
    )
    agent = CodeAgent(
        tools=[
            DuckDuckGoSearchTool(),
            VisitWebpageTool(),
        ],
        model=model,
        max_steps=10,
        additional_authorized_imports=["math", "datetime", "re", "json"],
    )
    return agent

def run_agent(agent, question: str) -> str:
    prompt = f"""Answer the following question.
Your answer must be as concise as possible — just the final answer, no explanation.
If the answer is a number, return just the number.
If the answer is a name, return just the name.
If the answer is a list, return comma-separated values.

Question: {question}"""
    try:
        response = agent.run(prompt)
        return str(response).strip()
    except Exception as e:
        print(f"Error: {e}")
        return "I don't know"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
    else:
        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"

    try:
        agent = build_agent()
        print("Agent initialized!")
    except Exception as e:
        return f"Error initializing agent: {e}", None

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

    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

    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            continue
        try:
            submitted_answer = run_agent(agent, question_text)
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100],
                "Submitted Answer": submitted_answer
            })
            print(f"✅ {task_id}: {submitted_answer[:50]}")
        except Exception as e:
            print(f"❌ {task_id}: {e}")
            results_log.append({
                "Task ID": task_id,
                "Question": question_text[:100],
                "Submitted Answer": "I don't know"
            })
            answers_payload.append({"task_id": task_id, "submitted_answer": "I don't know"})

    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }

    try:
        response = requests.post(submit_url, json=submission_data, timeout=120)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"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.')}"
        )
        return final_status, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission Failed: {e}", pd.DataFrame(results_log)


with gr.Blocks() as demo:
    gr.Markdown("# 🎩 GAIA Agent — sofials")
    gr.Markdown("""
    **Instructions:**
    1. Log in with your Hugging Face account
    2. Click 'Run Evaluation & Submit All Answers'
    """)

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    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("-"*30 + " App Starting " + "-"*30)
    demo.launch(debug=True, share=False)