import os import gradio as gr import requests import inspect # To get source code for __repr__ import pandas as pd # --- Constants --- DEFAULT_API_URL = "https://jofthomas-unit4-scoring.hf.space/" # Default URL for your FastAPI app # --- Basic Agent Definition --- ## This is where you should implement your own agent and tools 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 # __repr__ seems intended to get the *source* code, not just representation # Let's keep it but note that get_current_script_content might be more robust # if the class definition changes significantly or relies on external state. def __repr__(self) -> str: """ Return the source code required to reconstruct this agent. NOTE: This might be brittle. Using get_current_script_content is likely safer. """ imports = [ "import inspect\n" ] try: class_source = inspect.getsource(BasicAgent) full_source = "\n".join(imports) + "\n" + class_source return full_source except Exception as e: print(f"Error getting source code via inspect: {e}") return f"# Could not get source via inspect: {e}" # --- 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 or frozen app) print("Warning: __file__ is not defined. Cannot read script content this way.") # Fallback or alternative method could be added here if needed 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. """ # --- Determine HF Space URL and Print Environment Info --- space_host = os.getenv("SPACE_HOST") hf_space_url = "Runtime: Locally or unknown environment (SPACE_HOST env var not found)" if space_host: # Construct the standard URL format for HF Spaces hf_space_url = f"Runtime: Hugging Face Space (https://{space_host}.hf.space)" # Print runtime info at the start of the function execution print("\n" + "="*60) print("Executing run_and_submit_all function...") print(hf_space_url) # Print the determined runtime URL # --- End Environment Info --- if profile: username= f"{profile.username}" print(f"User logged in: {username}") else: print("User not logged in.") print("="*60 + "\n") # Close the separator block return "Please Login to Hugging Face with the button.", None # Return early print("="*60 + "\n") # Separator after initial checks if logged in api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" submit_url = f"{api_url}/submit" # 1. Instantiate the Agent try: agent = BasicAgent() # Using get_current_script_content() is likely more reliable for submission # agent_code = agent.__repr__() # Keep if needed, but prefer file content # print(f"Agent Code via __repr__ (first 200): {agent_code[:200]}...") # Debug except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None # Get agent code by reading the current script file - generally more robust agent_code = get_current_script_content() if agent_code.startswith("# Agent code unavailable"): print("Warning: Using potentially incomplete agent code due to reading error.") # Optional: Fall back to agent.__repr__() if needed # agent_code = agent.__repr__() # 2. Fetch All Questions print(f"Fetching questions from: {questions_url}") try: response = requests.get(questions_url, timeout=15) response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx) questions_data = response.json() if not questions_data: print("Fetched questions list is empty.") return "Fetched questions list is empty or invalid format.", None print(f"Fetched {len(questions_data)} questions.") # status_update = f"Fetched {len(questions_data)} questions. Running agent..." # For yield/streaming except requests.exceptions.RequestException as e: print(f"Error fetching questions: {e}") return f"Error fetching questions: {e}", None except requests.exceptions.JSONDecodeError as e: print(f"Error decoding JSON response from questions endpoint: {e}") print(f"Response text: {response.text[:500]}") # Log response text for debugging return f"Error decoding server response for questions: {e}", None except Exception as e: # Catch other potential errors 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 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) # 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}") results_log.append({ "Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}" }) # Decide if you want to submit agent errors or skip: # answers_payload.append({"task_id": task_id, "submitted_answer": f"AGENT ERROR: {e}"}) if not answers_payload: print("Agent did not produce any answers to submit.") # Still show results log even if nothing submitted 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, # Using the code read from file "answers": answers_payload } status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..." print(status_update) # 5. Submit to Leaderboard print(f"Submitting {len(answers_payload)} answers to: {submit_url}") try: # Ensure submission_data is serializable, agent_code should be string response = requests.post(submit_url, json=submission_data, timeout=60) # Increased timeout further 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', '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: # Try to get more specific error detail from JSON response body error_json = e.response.json() error_detail += f" Detail: {error_json.get('detail', e.response.text)}" except requests.exceptions.JSONDecodeError: # If response is not JSON, use the raw text error_detail += f" Response: {e.response.text[:500]}" # Limit length status_message = f"Submission Failed: {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.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: # Catch unexpected errors during submission phase 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 clone this space, then modify the code to define your agent's logic within the `BasicAgent` class. " # Clarified instructions "Log in to your Hugging Face account using the button below. This uses your HF username for submission. " "Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score." ) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) # Increased lines results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True, max_rows=10) # Added max_rows # --- Component Interaction --- # Use the profile information directly from the LoginButton state (implicitly passed) run_button.click( fn=run_and_submit_all, # Input is implicitly the profile data from LoginButton state outputs=[status_output, results_table] ) if __name__ == "__main__": print("\n" + "-"*30 + " App Starting " + "-"*30) # Check for SPACE_HOST at startup for information space_host_startup = os.getenv("SPACE_HOST") if space_host_startup: print(f"✅ SPACE_HOST found: {space_host_startup}") print(f" App should be available at: https://{space_host_startup}.hf.space") else: print("ℹ️ SPACE_HOST environment variable not found (running locally or not on standard HF Space runtime).") print(" App will likely be available at local URLs printed by Gradio below.") print("-"*(60 + len(" App Starting ")) + "\n") print("Launching Gradio Interface for Basic Agent Evaluation...") # Set share=False as the primary access point is the HF Space URL demo.launch(debug=True, share=False)