app.py

import os
import gradio as gr
import requests
import pandas as pd
from smolagents import CodeAgent, DuckDuckGoSearchTool
from smolagents.models import OpenAIServerModel

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

# === Define the Smol Agent ===
class MyAgent:
    def __init__(self):
        self.model = OpenAIServerModel(model_id="gpt-4")  # or "gpt-3.5-turbo"
        self.agent = CodeAgent(
            tools=[DuckDuckGoSearchTool()],
            model=self.model,
            system_message="""You are a general AI assistant. I will ask you a question.
Report your thoughts, and finish your answer with the following template:
FINAL ANSWER: [YOUR FINAL ANSWER].
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list 
of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string."""
        )

    def __call__(self, question: str) -> str:
        return self.agent.run(question)

# === Submission Logic ===
def run_and_submit_all(profile: gr.OAuthProfile | None):
    space_id = os.getenv("SPACE_ID")
    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        return "Please Login to Hugging Face with the button.", None

    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    questions_url = f"{DEFAULT_API_URL}/questions"
    submit_url = f"{DEFAULT_API_URL}/submit"

    try:
        agent = MyAgent()
    except Exception as e:
        return f"Error initializing agent: {e}", None

    # Fetch Questions
    try:
        res = requests.get(questions_url, timeout=15)
        res.raise_for_status()
        questions_data = res.json()
    except Exception as e:
        return f"Failed to fetch questions: {e}", None

    results_log = []
    answers_payload = []

    for item in questions_data:
        task_id = item.get("task_id")
        question = item.get("question")
        if not task_id or question is None:
            continue
        try:
            answer = agent(question)
            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": answer})
            answers_payload.append({"task_id": task_id, "submitted_answer": answer})
        except Exception as e:
            results_log.append({"Task ID": task_id, "Question": question, "Submitted Answer": f"ERROR: {e}"})

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

    submission_data = {
        "username": username,
        "agent_code": agent_code,
        "answers": answers_payload
    }

    try:
        res = requests.post(submit_url, json=submission_data, timeout=60)
        res.raise_for_status()
        result_data = res.json()
        summary = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Score: {result_data.get('score', '?')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')})\n"
            f"Message: {result_data.get('message', '')}"
        )
        return summary, pd.DataFrame(results_log)
    except Exception as e:
        return f"Submission failed: {e}", pd.DataFrame(results_log)

# === Gradio UI ===
with gr.Blocks() as demo:
    gr.Markdown("# Agent Evaluation Runner (SmolAgents)")
    gr.Markdown("""
**Instructions:**
1. Clone this space and customize your agent.
2. Log in with Hugging Face.
3. Click 'Run Evaluation' to answer and submit.
""")

    gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")
    status_output = gr.Textbox(label="Status", lines=4, interactive=False)
    results_table = gr.DataFrame(label="Results", wrap=True)

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

if __name__ == "__main__":
    print("Launching...")
    demo.launch(debug=True)