app_dev.py

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

from my_agent import SmolAgent  # Import the new agent

# (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):
        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer


def instantiate_agent():
    """Instantiates the agent."""
    try:
        # agent = BasicAgent()
        agent = SmolAgent()  # Use the new agent
        return agent, None  # Return agent and no error
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return None, f"Error initializing agent: {e}"  # Return None and error message


def fetch_questions(questions_url: str):
    """Fetches questions from the specified URL."""
    print(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:
            print("Fetched questions list is empty.")
            return None, "Fetched questions list is empty or invalid format."
        print(f"Fetched {len(questions_data)} questions.")
        return questions_data, None  # Return data and no error
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return None, f"Error fetching questions: {e}"
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return None, f"Error decoding server response for questions: {e}"
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return None, f"An unexpected error occurred fetching questions: {e}"


def run_agent_on_questions(agent, questions_data):
    """Runs the agent on each question and collects results."""
    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 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:
            print(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}",
                }
            )
    return answers_payload, results_log


def dev_run():
    """
    Fetches all questions, runs the BasicAgent on them,
    and displays the results.
    """
    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"

    agent, error_message = instantiate_agent()
    if error_message:
        return error_message, None  # Return error message and None for results_df

    # 2. Fetch Questions
    questions_data, error_message = fetch_questions(questions_url)
    if error_message:
        # Return the error message from fetch_questions and None for the results DataFrame
        return error_message, None

    # 3. Run your Agent
    answers_payload, results_log = run_agent_on_questions(agent, questions_data)

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

    return answers_payload, pd.DataFrame(results_log)


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}"
        print(f"User logged in: {username}")
    else:
        print("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)
    agent, error_message = instantiate_agent()
    if error_message:
        return error_message, None  # Return error message and None for results_df

    # 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"
    print(agent_code)

    # 2. Fetch Questions
    questions_data, error_message = fetch_questions(questions_url)
    if error_message:
        # Return the error message from fetch_questions and None for the results DataFrame
        return error_message, None

    # 3. Run your Agent
    answers_payload, results_log = run_agent_on_questions(agent, questions_data)

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

    # 4. Prepare 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}'..."
    print(status_update)

    # 5. Submit
    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.")
        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}"
        print(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."
        print(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}"
        print(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}"
        print(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("# 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__":
#     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."
#         )

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

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

if __name__ == "__main__":
    answers_payload, results_log = dev_run()
    print(answers_payload)
    print(results_log)