app.py

import os
import gradio as gr
import requests
import pandas as pd

from agent import SubmissionAgent

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


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetch all questions, run the agent on them, submit answers,
    and display the final score plus a results table.
    """
    space_id = os.getenv("SPACE_ID")

    if profile:
        username = profile.username
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please login to Hugging Face first.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

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

    # Required code URL for benchmark
    if space_id:
        agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    else:
        agent_code = "SPACE_ID_NOT_AVAILABLE"

    print(f"Agent code URL: {agent_code}")

    # Fetch questions
    print(f"Fetching questions from: {questions_url}")

    try:
        response = requests.get(questions_url, timeout=20)
        response.raise_for_status()
        questions_data = response.json()

        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty.", None

        print("First question keys:", questions_data[0].keys())
        print("First question sample:", questions_data[0])
        print(f"Fetched {len(questions_data)} questions.")

    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except ValueError as e:
        print(f"Error decoding questions JSON: {e}")
        return f"Error decoding questions JSON: {e}", None
    except Exception as e:
        print(f"Unexpected error fetching questions: {e}")
        return f"Unexpected error fetching questions: {e}", None

    # Run agent
    results_log = []
    answers_payload = []

    print(f"Running agent on {len(questions_data)} questions...")

    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:
            print(f"Skipping malformed item: {item}")
            continue

        try:
            submitted_answer = agent(
                question_text,
                task_id=task_id,
                task_item=item,
            )

            print("=" * 100)
            print(f"TASK ID: {task_id}")
            print(f"QUESTION: {question_text}")
            print(f"SUBMITTED ANSWER: {submitted_answer}")
            print("=" * 100)

            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,
                }
            )

        except Exception as e:
            print(f"Error on task {task_id}: {e}")
            results_log.append(
                {
                    "Task ID": task_id,
                    "Question": question_text,
                    "Submitted Answer": f"AGENT ERROR: {e}",
                }
            )

    if not answers_payload:
        print("No answers generated.")
        return "Agent did not generate any answers.", pd.DataFrame(results_log)

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

    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")

    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.')}"
        )

        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 ValueError:
            error_detail += f" Response: {e.response.text[:500]}"

        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)

    except requests.exceptions.Timeout:
        status_message = "Submission Failed: Request timed out."
        print(status_message)
        return status_message, pd.DataFrame(results_log)

    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)

    except Exception as e:
        status_message = f"Unexpected submission error: {e}"
        print(status_message)
        return status_message, pd.DataFrame(results_log)


# Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# Hugging Face Unit 4 Agent Evaluation Runner")
    gr.Markdown(
        """
        Log in with your Hugging Face account, run your agent on all benchmark questions,
        submit the answers, and view the score plus answer log.
        """
    )

    login_button = gr.LoginButton()
    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(
        label="Run Status / Submission Result",
        lines=6,
        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}")
    else:
        print("SPACE_HOST not found. Probably 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(f"Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("SPACE_ID not found. Probably running locally.")

    print("-" * 75 + "\n")
    print("Launching Gradio app...")

    demo.launch(debug=True)