app.py

import os
import traceback

import gradio as gr
import pandas as pd
import requests

from agent import GaiaAgent

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


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """Fetch GAIA questions, run the agent, submit answers, render results."""
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        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"

    # 1. Instantiate agent
    try:
        agent = GaiaAgent()
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

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

    # 2. Fetch questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=30)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run agent over all questions
    results_log = []
    answers_payload = []
    print(f"Running agent on {len(questions_data)} questions...")
    for i, item in enumerate(questions_data, 1):
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        print(f"\n=== [{i}/{len(questions_data)}] task {task_id} ===")
        try:
            submitted_answer = agent(question_text, task_id=task_id)
        except Exception as e:
            traceback.print_exc()
            submitted_answer = f"AGENT ERROR: {e}"
        print(f"  -> {submitted_answer!r}")
        answers_payload.append(
            {"task_id": task_id, "submitted_answer": submitted_answer}
        )
        results_log.append(
            {
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": submitted_answer,
            }
        )

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

    # 4. Prepare submission
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload,
    }
    print(
        f"Agent finished. Submitting {len(answers_payload)} answers for "
        f"user '{username}'..."
    )

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    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"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/"
            f"{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        return final_status, pd.DataFrame(results_log)
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        return f"Submission Failed: {error_detail}", pd.DataFrame(results_log)
    except requests.exceptions.Timeout:
        return "Submission Failed: The request timed out.", pd.DataFrame(results_log)
    except requests.exceptions.RequestException as e:
        return f"Submission Failed: Network error - {e}", pd.DataFrame(results_log)
    except Exception as e:
        return (
            f"An unexpected error occurred during submission: {e}",
            pd.DataFrame(results_log),
        )


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent — Final Assignment Runner")
    gr.Markdown(
        """
        **Instructions:**
        1. Add `HF_TOKEN` and `SERPER_API_KEY` as Space secrets.
        2. Log in to Hugging Face with the button below.
        3. Click **Run Evaluation & Submit All Answers**. Running 20 questions
           can take 10–20 minutes; stay on the tab.
        """
    )

    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("\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: {space_host_startup}")
        print(f"  Runtime URL: https://{space_host_startup}.hf.space")
    else:
        print("SPACE_HOST not set (running locally?).")

    if space_id_startup:
        print(f"SPACE_ID: {space_id_startup}")
        print(f"  Repo URL: https://huggingface.co/spaces/{space_id_startup}")

    print("-" * (60 + len(" App Starting ")) + "\n")
    print("Launching Gradio Interface for GAIA Agent Runner...")
    demo.launch(debug=True, share=False)