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( "Enter the API URL and 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)