app.py

"""Application"""

import os
import gradio as gr

from questions import get_questions_data
from submit import submit_answers
from payload import get_answer_payload_results_log

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

API_URL = DEFAULT_API_URL
QUESTIONS_URL = f"{API_URL}/questions"
SUBMIT_URL = f"{API_URL}/submit"
FILES_URL = f"{API_URL}/files"

SPACE_ID = os.getenv("SPACE_ID")
if SPACE_ID:
    AGENT_CODE = f"https://huggingface.co/spaces/{SPACE_ID}/tree/main"
    print(f"Agent Code URL: {AGENT_CODE}")


def _check_agent_dependent_environment_variables() -> str:
    openai_api_key = os.getenv("OPENAI_API_KEY")
    if not openai_api_key:
        return "OPENAI_API_KEY environment variable must be set"
    manger_model_id = os.getenv("MANAGER_MODEL_ID")
    if not manger_model_id:
        return "MANAGER_MODEL_ID environment variable must be set"
    manager_base_url = os.getenv("MANGER_BASE_URL")
    if not manager_base_url:
        return "MANGER_BASE_URL environment variable must be set"

    return None


def run_and_submit_all(
    profile: gr.OAuthProfile | None,
):  # pylint: disable=too-many-return-statements
    """Run and Submit All"""
    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
    if not SPACE_ID:
        print("SPACE_ID must be set")
        return "SPACE_ID environment variable must be set", None

    error_message = _check_agent_dependent_environment_variables()
    if error_message:
        return error_message, None

    questions_data = get_questions_data(
        questions_url=QUESTIONS_URL, files_url=FILES_URL
    )
    if not questions_data:
        print("Questions list is empty.")
        return "Questions list is empty or invalid format.", None
    print(f"Retrieved {len(questions_data)} questions.")

    answers_payload, results_df, error_message = get_answer_payload_results_log(
        questions_data=questions_data
    )
    if error_message:
        return error_message, results_df

    submission_data = {
        "username": username.strip(),
        "agent_code": AGENT_CODE,
        "answers": answers_payload,
    }
    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."  # pylint: disable=line-too-long
    print(status_update)
    print(f"Submitting {len(answers_payload)} answers to: {SUBMIT_URL}")

    final_status, error_message = submit_answers(
        submit_url=SUBMIT_URL, submission_data=submission_data
    )

    if final_status:
        return final_status, results_df
    return error_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    # pylint: disable=line-too-long
    gr.Markdown(
        """
        **Instructions:**

        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
        2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
        3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.

        ---
        **Disclaimers:**
        Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )
    # pylint: enable=line-too-long

    gr.LoginButton()

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

    status_output = gr.Textbox(
        label="Run Status / Submission Result", lines=5, interactive=False
    )
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(  # pylint: disable=no-member
        fn=run_and_submit_all, outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-" * 30 + " App Starting " + "-" * 30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID")  # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup:  # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {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(
            "ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."  # pylint: disable=line-too-long
        )  # pylint: disable=line-too-long

    print("-" * (60 + len(" App Starting ")) + "\n")

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