app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from smolagents import ToolCallingAgent, InferenceClientModel, HfApiModel
from smolagents import DuckDuckGoSearchTool, Tool, CodeAgent
from huggingface_hub import login
#h
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])

search_tool = DuckDuckGoSearchTool()

async def run_and_submit_all(profile: gr.OAuthProfile | None):
    log_output = ""

    try:
        agent = CodeAgent(
            tools=[search_tool],
            model=HfApiModel(model="MiniMaxAI/MiniMax-M1-80k"),
            max_steps=5,
            verbosity_level=2        
        )
    except Exception as e:
        yield f"Error initializing agent: {e}", None, log_output
        return

    space_id = os.getenv("SPACE_ID")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    questions_url = f"{DEFAULT_API_URL}/questions"
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()[:5]
        if not questions_data:
            yield "Fetched questions list is empty or invalid format.", None, log_output
            return
    except Exception as e:
        yield f"Error fetching questions: {e}", None, log_output
        return

    results_log = []
    answers_payload = []
    loop = asyncio.get_event_loop()

    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

        log_output += f"🔍 Solving Task ID: {task_id}...\n"
        yield None, None, log_output

        try:
            system_prompt = (
                "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.\n\n"
            )
            full_prompt = system_prompt + f"Question: {question_text.strip()}"

            agent_result = await loop.run_in_executor(None, agent, full_prompt)

            # Extract final answer cleanly
            if isinstance(agent_result, dict) and "final_answer" in agent_result:
                final_answer = str(agent_result["final_answer"]).strip()
            elif isinstance(agent_result, str):
                response_text = agent_result.strip()

                # Remove known boilerplate
                if "Here is the final answer from your managed agent" in response_text:
                    response_text = response_text.split(":", 1)[-1].strip()

                if "FINAL ANSWER:" in response_text:
                    _, final_answer = response_text.rsplit("FINAL ANSWER:", 1)
                    final_answer = final_answer.strip()
                else:
                    final_answer = response_text
            else:
                final_answer = str(agent_result).strip()

            answers_payload.append({
                "task_id": task_id,
                "submitted_answer": final_answer
            })

            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": final_answer
            })

            log_output += f"✅ Done: {task_id} — Answer: {final_answer[:60]}\n"
            yield None, None, log_output

        except Exception as e:
            print(f"Error running agent on task {task_id}: {e}")
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"AGENT ERROR: {e}"
            })
            log_output += f"⛔️ Error: {task_id} — {e}\n"
            yield None, None, log_output

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

    username = profile.username if profile else "unknown"
    submit_url = f"{DEFAULT_API_URL}/submit"
    submission_data = {"username": username.strip(), "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()
        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.')}"
        )
        results_df = pd.DataFrame(results_log)
        yield final_status, results_df, log_output
    except Exception as e:
        status_message = f"Submission Failed: {e}"
        results_df = pd.DataFrame(results_log)
        yield status_message, results_df, log_output

with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    gr.Markdown("""
    **Instructions:**
    1. Clone this space and define your agent logic.
    2. Log in to your Hugging Face account.
    3. Click 'Run Evaluation & Submit All Answers'.
    ---
    **Note:**
    The run may take time. Async is now used to improve responsiveness.
    """)

    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)
    progress_log = gr.Textbox(label="Progress Log", lines=10, interactive=False)

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

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")

    if space_host_startup:
        print(f"✅ SPACE_HOST: https://{space_host_startup}.hf.space")
    if space_id_startup:
        print(f"✅ SPACE_ID: https://huggingface.co/spaces/{space_id_startup}")

    print("Launching Gradio Interface...")
    demo.launch(debug=True, share=False)