app.py

import os
import pandas as pd
import gradio as gr
import logging
import time

from settings import Settings
from evaluator import Evaluator
from runner import Runner

# Configure logging
logging.basicConfig(level=logging.INFO, force=True)
logger = logging.getLogger(__name__)

settings = Settings(
    username=os.getenv("USERNAME"),
    space_id=os.getenv("SPACE_ID")
)
evaluator = Evaluator(settings)
runner = Runner(settings)


LOGIN_MESSAGE = "Please Login to Hugging Face with the button."
EMPTY_RESULTS_TABLE = pd.DataFrame(columns=['task_id', 'question', 'answer'])
        
def _format_elapsed_time(elapsed_time):
    """Formats elapsed time into minutes and seconds."""
    minutes = int(elapsed_time // 60)
    seconds = elapsed_time % 60
    if minutes > 0:
        return f"Elapsed time: {minutes} minutes {seconds:.2f} seconds"
    else:
        return f"Elapsed time: {seconds:.2f} seconds"
    
def _run_agent_on_questions(questions_list: list, username: str) -> tuple[str, pd.DataFrame]:
    """
    Helper function to run the agent on a list of questions and return status and results.
    """
    start_time = time.time()
    logger.info(f"Starting agent run for user: {username} on {len(questions_list)} questions.")
    
    # The runner handles the agent execution and saving of answers
    question_answer_pairs_df = runner.run_agent(questions_list, username)
    
    end_time = time.time()
    elapsed_time_str = _format_elapsed_time(end_time - start_time)
    message = f"Agent run complete. {elapsed_time_str}"
    logger.info(message)
    return message, question_answer_pairs_df
    
def run_one(profile: gr.OAuthProfile | None) -> tuple[str, pd.DataFrame]:
    """Runs the agent on one random question."""
    if profile: 
        try:
            question = evaluator.get_one_question()
            return _run_agent_on_questions([question], profile.username)
        except Exception as e:
            logger.error(f"Error getting one question: {e}")
            return f"Error getting question: {e}", EMPTY_RESULTS_TABLE
    else:
        return LOGIN_MESSAGE, EMPTY_RESULTS_TABLE

def run_all(profile: gr.OAuthProfile | None) -> tuple[str, pd.DataFrame]:
    """Runs the agent on all questions."""
    if profile: 
        try:
            questions = evaluator.get_questions()
            return _run_agent_on_questions(questions, profile.username)
        except Exception as e:
            logger.error(f"Error getting all questions: {e}")
            return f"Error getting questions: {e}", EMPTY_RESULTS_TABLE
    else:
        return LOGIN_MESSAGE, EMPTY_RESULTS_TABLE

def submit(profile: gr.OAuthProfile | None) -> str:
    """Submits cached answers for evaluation."""
    if profile: 
        return evaluator.submit_answers(profile.username)
    else:
        return LOGIN_MESSAGE


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# GAIA Agent Evaluation Runner")
    gr.Markdown(
        """
        **Instructions:**

        1.  Log in to your Hugging Face account using the button below. 
        2.  Click 'Get One Answer' to run the agent on a random question or 'Get All Answers' to run all. 
        3.  Click 'Submit Answers' to submit answers for evaluation. **Your HF username will be submitted for leaderboard tracking.**

        ---
        **Disclaimers:**
        * Running 'Get All Answers' can take significant time as the agent processes all 20 questions.
        * Agent logs are detailed (DEBUG level) and may appear interleaved due to parallel execution.
        * The 'Submit Answers' button uses the most recent agent answers cached locally for your username.
        * **API Keys Required:** Ensure `GEMINI_API_KEY`, `USERNAME`, and `SPACE_ID` are set as Space Secrets (or environment variables if running locally).
        """
    )

    gr.LoginButton()

    run_one_button = gr.Button("Get One Answer")
    run_all_button = gr.Button("Get All Answers")
    submit_button = gr.Button("Submit 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_one_button.click(
        fn=run_one, outputs=[status_output, results_table]
    )
    run_all_button.click(
        fn=run_all, outputs=[status_output, results_table]
    )
    submit_button.click(
        fn=submit, outputs=[status_output]
    )

if __name__ == "__main__":
    logger.info("\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")

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

    if space_id_startup:
        logger.info(f"✅ SPACE_ID found: {space_id_startup}")
        logger.info(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        logger.info(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        logger.info("ℹ️  SPACE_ID environment variable not found. Repo URL cannot be determined.")

    logger.info("-"*(60 + len(" App Starting ")) + "\n")

    logger.info("Launching Gradio Interface for GAIA Agent Evaluation...")
    demo.launch(debug=True, share=False)