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| 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: | |
| print(profile) | |
| username= f"{profile.name}" | |
| 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) |