app.py

import os
import gradio as gr
import requests
import pandas as pd
import asyncio
import json
import concurrent.futures
from huggingface_hub import login
from smolagents import CodeAgent, InferenceClientModel, DuckDuckGoSearchTool

# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
SUBMIT_URL = f"{DEFAULT_API_URL}/submit"

# --- Hugging Face Login ---
login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"])

# --- Define Tools ---
search_tool = DuckDuckGoSearchTool()


# --- Main Function ---
async def run_and_submit_all(profile: gr.OAuthProfile | None):
    # Initialize Agent
    try:
        agent = CodeAgent(
            tools=[search_tool],
            model=InferenceClientModel(model="mistralai/Magistral-Small-2506"),
            max_steps=5,
            verbosity_level=2
        )
    except Exception as e:
        return f"Error initializing agent: {e}", None

    # Get Space ID for agent_code link
    space_id = os.getenv("SPACE_ID", "unknown")
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"

    # Fetch questions
    try:
        response = requests.get(QUESTIONS_URL, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "No questions received.", None
    except Exception as e:
        return f"Error fetching questions: {e}", None

    # Prepare results
    answers_payload = []
    results_log = []
    loop = asyncio.get_event_loop()

    for item in questions_data:
        task_id = item.get("task_id")
        question = item.get("question")
        if not task_id or not question:
            continue

        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"
        )
        prompt = system_prompt + f"Question: {question.strip()}"

        # Run agent with timeout
        try:
            with concurrent.futures.ThreadPoolExecutor() as executor:
                future = executor.submit(agent, prompt)
                agent_result = await loop.run_in_executor(None, future.result, 60)  # timeout=60s

            # Clean model output
            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()

                # Extract final answer
                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()

        except Exception as e:
            print(f"[ERROR] Task {task_id} failed: {e}")
            final_answer = f"AGENT ERROR: {e}"

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

    # Clean invalid entries
    valid_answers = [a for a in answers_payload if isinstance(a["task_id"], str) and isinstance(a["model_answer"], str)]

    if not valid_answers:
        return "Agent produced no valid answers.", pd.DataFrame(results_log)

    # Prepare submission
    username = profile.username if profile else "unknown"
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": valid_answers
    }

    print("[DEBUG] Submission Payload:\n", json.dumps(submission_data, indent=2))

    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"Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')})\n"
            f"Message: {result_data.get('message', 'No message.')}"
        )
        return final_status, 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 Interface")
    gr.Markdown("""
    **Instructions:**
    1. Clone and customize the agent logic.
    2. Log in to Hugging Face.
    3. Click "Run Evaluation" to test and submit your answers.
    """)

    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="Agent Answers", wrap=True)

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


# --- App Launch ---
if __name__ == "__main__":
    print("\n--- Launching Gradio Space ---")
    print(f"✅ SPACE_HOST: {os.getenv('SPACE_HOST')}")
    print(f"✅ SPACE_ID: {os.getenv('SPACE_ID')}")
    demo.launch(debug=True, share=False)