app.py

import pickle
import os
import gradio as gr
import requests
import inspect
import pandas as pd
import logging
from universal_agent import AnswerFormat
# Setup logger
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

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

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
# class BasicAgent:
#     def __init__(self):
#         logger.info("BasicAgent initialized.")
#     def __call__(self, question: str) -> str:
#         logger.info(f"Agent received question (first 50 chars): {question[:50]}...")
#         fixed_answer = "This is a default answer."
#         logger.info(f"Agent returning fixed answer: {fixed_answer}")
#         return fixed_answer

def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code

    if profile:
        username= f"{profile.username}"
        logger.info(f"User logged in: {username}")
    else:
        logger.info("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 ( modify this part to create your agent)
    # try:
    #     agent = BasicAgent()
    # except Exception as e:
    #     logger.info(f"Error instantiating agent: {e}")
    #     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(agent_code)

    # # 2. Fetch Questions
    # logger.info(f"Fetching questions from: {questions_url}")
    # try:
    #     response = requests.get(questions_url, timeout=15)
    #     response.raise_for_status()
    #     questions_data = response.json()
    #     if not questions_data:
    #          logger.info("Fetched questions list is empty.")
    #          return "Fetched questions list is empty or invalid format.", None
    #     logger.info(f"Fetched {len(questions_data)} questions.")
    # except requests.exceptions.RequestException as e:
    #     logger.info(f"Error fetching questions: {e}")
    #     return f"Error fetching questions: {e}", None
    # except requests.exceptions.JSONDecodeError as e:
    #      logger.info(f"Error decoding JSON response from questions endpoint: {e}")
    #      logger.info(f"Response text: {response.text[:500]}")
    #      return f"Error decoding server response for questions: {e}", None
    # except Exception as e:
    #     logger.info(f"An unexpected error occurred fetching questions: {e}")
    #     return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    # results_log = []
    # answers_payload = []
    # logger.info(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:
    #         logger.info(f"Skipping item with missing task_id or question: {item}")
    #         continue
    #     try:
    #         submitted_answer = agent(question_text)
    #         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:
    #          logger.info(f"Error running agent 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:
    #     logger.info("Agent did not produce any answers to submit.")
    #     return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
    with open("all_questions.pkl", "rb") as f:
        all_questions = pickle.load(f)

    with open("results_gpt_mini.pkl", "rb") as f:
        results = pickle.load(f)
    answers = [{"task_id":j['task_id'],
            "submitted_answer": results[i]["structured_response"].answer
            if isinstance(results[i], dict) else "No answer"}
 for i,j in enumerate(all_questions)]

    # 4. Prepare Submission 
    submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers}
    # submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
    status_update = f"Agent finished. Submitting {len(answers)} answers for user '{username}'..."
    logger.info(status_update)
    results_log = [1,2]

    # 5. Submit
    logger.info(f"Submitting {len(answers)} 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.')}"
        )
        logger.info("Submission successful.")
        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.info(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.info(status_message)
        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.info(status_message)
        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.info(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


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

    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]
        # outputs=[status_output, results_table]
    )

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") # 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: # logger.info 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 ")) + "\n")

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