app.py

import os
import gradio as gr
import requests
import inspect # To get source code for __repr__
import pandas as pd # For displaying results in a table

# --- Constants ---
DEFAULT_API_URL = "https://jofthomas-unit4-scoring.hf.space/" # Default URL for your FastAPI app

# --- Basic Agent Definition ---

class BasicAgent:
    """
    A very simple agent placeholder.
    It just returns a fixed string for any question.
    """
    def __init__(self):
        print("BasicAgent initialized.")
        # Add any setup if needed

    def __call__(self, question: str) -> str:
        """
        The agent's logic to answer a question.
        This basic version ignores the question content.
        """
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        # Replace this with actual logic if you were building a real agent
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer

    def __repr__(self) -> str:
        """
        Return the source code required to reconstruct this agent.
        """
        imports = [
            "import inspect\n" # May not be strictly needed by the agent logic itself
        ]
        class_source = inspect.getsource(BasicAgent)
        full_source = "\n".join(imports) + "\n" + class_source
        return full_source

# --- Gradio UI and Logic ---

def get_current_script_content() -> str:
    """Attempts to read and return the content of the currently running script."""
    try:
        # __file__ holds the path to the current script
        script_path = os.path.abspath(__file__)
        print(f"Reading script content from: {script_path}")
        with open(script_path, 'r', encoding='utf-8') as f:
            return f.read()
    except NameError:
        # __file__ is not defined (e.g., running in an interactive interpreter)
        print("Warning: __file__ is not defined. Cannot read script content.")
        return "# Agent code unavailable: __file__ not defined"
    except FileNotFoundError:
        print(f"Warning: Script file '{script_path}' not found.")
        return f"# Agent code unavailable: Script file not found at {script_path}"
    except Exception as e:
        print(f"Error reading script file '{script_path}': {e}")
        return f"# Agent code unavailable: Error reading script file: {e}"


def run_and_submit_all( profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    
    if profile:
        username= f"{profile.username}"
        
    else:
        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 the Agent
    try:
        agent = BasicAgent()
        agent_code = agent.__repr__()
        # print(f"Agent Code (first 200): {agent_code[:200]}...") # Debug
    except Exception as e:
        print(f"Error instantiating agent or getting repr: {e}")
        return f"Error initializing agent: {e}", None
    agent_code=get_current_script_content()
    # 2. Fetch All Questions
    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:
             return "Fetched questions list is empty.", None
        print(f"Fetched {len(questions_data)} questions.")
        status_update = f"Fetched {len(questions_data)} questions. Running agent..."
        # Yield intermediate status if using gr.update
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run Agent on Each Question
    results_log = [] # To store data for the results table
    answers_payload = [] # To store data for the submission API
    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) # Call the agent's logic
            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}")
             # Decide how to handle agent errors - skip? submit default?
             # Here, we'll just log and potentially skip submission for this task if needed
             results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"AGENT ERROR: {e}"
            })


    if not answers_payload:
        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..."
    print(status_update)

    # 5. Submit to Leaderboard
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=45) # Increased timeout
        response.raise_for_status()
        result_data = response.json()

        # Prepare final status message and results table
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score')}% "
            f"({result_data.get('correct_count')}/{result_data.get('total_attempted')} correct)\n"
            f"Message: {result_data.get('message')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df

    except requests.exceptions.HTTPError as e:
        error_detail = e.response.text
        try:
            error_json = e.response.json()
            error_detail = error_json.get('detail', error_detail)
        except requests.exceptions.JSONDecodeError:
            pass
        status_message = f"Submission Failed (HTTP {e.response.status_code}): {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log) # Show attempts even if submission failed
        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(
        "Please cloen this space, then modify the code to what you deem relevant."
        "Connect to your Hugging Face account using the log in button in the space to use your username, then click Run. "
        "This will fetch all questions, run the *very basic* agent on them, "
        "submit all answers at once, and display the results."
    )

    gr.LoginButton()

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

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=4, interactive=False)
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    # --- Component Interaction ---
    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True)