app.py

import json
import os
import gradio as gr
import requests
import pandas as pd
from agent import gaia_hf_agent

from langchain_core.messages import HumanMessage
from logging_config import setup_logging, get_logger

# Set up logging
setup_logging(log_level="INFO", log_file="logs/app.log")
logger = get_logger(__name__)

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
FETCH_QUESTIONS_FROM_FILE = True

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
def run_and_submit_all( profile: gr.OAuthProfile | None):
    """Fetches all questions, runs the BasicAgent on them, submits all answers, and displays the results.
    
    Args:
        profile: The profile of the user who is logged in.
    """
    logger.info("=== Starting evaluation process ===")
    
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
    logger.debug(f"Space ID retrieved: {space_id}")

    # Step 1: User Authentication
    logger.info("Step 1: Checking user authentication")
    if profile:
        username= f"{profile.username}"
        logger.info(f"User successfully authenticated: {username}")
    else:
        logger.warning("User authentication failed - no profile found")
        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"
    logger.info(f"API URLs configured - Questions: {questions_url}, Submit: {submit_url}")

    # Step 2: Agent Instantiation
    logger.info("Step 2: Instantiating agent")
    try:
        agent = gaia_hf_agent
        logger.info("Agent successfully instantiated")
    except Exception as e:
        logger.error(f"Error instantiating agent: {e}", exc_info=True)
        return f"Error initializing agent: {e}", None
    
    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    logger.info(f"Agent code URL: {agent_code}")

    # Step 3: Question Fetching
    logger.info("Step 3: Fetching questions")
    if FETCH_QUESTIONS_FROM_FILE:
        logger.info("Fetching questions from local file")
        try:
            with open("gaia_test_files/filtered_question_list.json", "r", encoding='utf-8') as file:
                questions_data = json.load(file)
            logger.info(f"Successfully loaded {len(questions_data)} questions from file")
        except FileNotFoundError as e:
            logger.error(f"Question file not found: {e}")
            return f"Question file not found: {e}", None
        except json.JSONDecodeError as e:
            logger.error(f"Error parsing question file JSON: {e}")
            return f"Error parsing question file: {e}", None
        except Exception as e:
            logger.error(f"Unexpected error loading question file: {e}", exc_info=True)
            return f"Error loading questions: {e}", None
    else:
        logger.info(f"Fetching questions from API: {questions_url}")
        try:
            response = requests.get(questions_url, timeout=15)
            response.raise_for_status()
            questions_data = response.json()
            if not questions_data:
                logger.warning("Fetched questions list is empty")
                return "Fetched questions list is empty or invalid format.", None
            logger.info(f"Successfully fetched {len(questions_data)} questions from API")
        except requests.exceptions.RequestException as e:
            logger.error(f"Network error fetching questions: {e}")
            return f"Error fetching questions: {e}", None
        except requests.exceptions.JSONDecodeError as e:
            logger.error(f"Error decoding JSON response from questions endpoint: {e}")
            logger.debug(f"Response text: {response.text[:500]}")
            return f"Error decoding server response for questions: {e}", None
        except Exception as e:
            logger.error(f"Unexpected error occurred fetching questions: {e}", exc_info=True)
            return f"An unexpected error occurred fetching questions: {e}", None

    # Step 4: Agent Execution
    logger.info(f"Step 4: Running agent on {len(questions_data)} questions")
    results_log = []
    answers_payload = []
    successful_answers = 0
    failed_answers = 0
    
    for i, item in enumerate(questions_data, 1):
        task_id = item.get("task_id")
        question_text = item.get("question")
        file_name = item.get("file_name")
        
        logger.info(f"Processing question {i}/{len(questions_data)} - Task ID: {task_id}")

        current_question = {
            "question": question_text
        }

        if file_name:
            current_question["question_file_url"] = f"{DEFAULT_API_URL}/files/{task_id}"
            logger.debug(f"Question includes file: {file_name}")

        agent_input = [HumanMessage(json.dumps(current_question))]

        if not task_id or question_text is None:
            logger.warning(f"Skipping item with missing task_id or question: {item}")
            failed_answers += 1
            continue
        
        try:
            logger.debug(f"Invoking agent for task {task_id}")
            response = agent.invoke({"messages": agent_input})
            submitted_answer = response['messages'][-1].content
            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})
            successful_answers += 1
            logger.info(f"Successfully processed task {task_id} ({i}/{len(questions_data)})")
            logger.debug(f"Answer for task {task_id}: {submitted_answer}")
        except Exception as e:
            logger.error(f"Error running agent on task {task_id}: {e}", exc_info=True)
            error_msg = f"AGENT ERROR: {e}"
            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": error_msg})
            failed_answers += 1

    logger.info(f"Agent execution completed - Success: {successful_answers}, Failed: {failed_answers}")

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

    # Step 5: Submission Preparation
    logger.info("Step 5: Preparing submission")
    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}'..."
    logger.info(status_update)
    logger.debug(f"Submission payload prepared with {len(answers_payload)} answers")

    # Step 6: Answer Submission
    logger.info(f"Step 6: Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        logger.debug("Sending POST request to submission endpoint")
        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.')}"
        )
        
        logger.info("=== Submission successful ===")
        logger.info(f"Final score: {result_data.get('score', 'N/A')}% "
                   f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)")
        logger.debug(f"Full submission result: {result_data}")
        
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
        
    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]}"
        status_message = f"Submission Failed: {error_detail}"
        logger.error(f"HTTP error during submission: {status_message}")
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        logger.error("Submission request timed out after 60 seconds")
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        logger.error(f"Network error during submission: {e}", exc_info=True)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
        
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        logger.error(f"Unexpected error during submission: {e}", exc_info=True)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


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

    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(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    logger.info("=" * 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:
        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: # Print repo URLs if SPACE_ID is found
        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 (running locally?). Repo URL cannot be determined.")

    logger.info("=" * (60 + len(" App Starting ")))
    logger.info("Launching Gradio Interface for Basic Agent Evaluation...")
    
    demo.launch(debug=True, share=False)