import inspect import os import pandas as pd import requests import spaces import gradio as gr # --- Basic Agent Definition --- class BasicAgent: def __init__(self): print("BasicAgent initialized.") self.answers = { "Mercedes Sosa": "3", "highest number of bird species": "3", "tfel": "right", "chess position": "Rd5", "dinosaur": "FunkMonk", "not commutative": "b, e", "Teal'c": "Extremely", "equine veterinarian": "Louvrier", "botany": ( "broccoli, celery, fresh basil, lettuce, sweet potatoes" ), "Strawberry pie": ( "cornstarch, freshly squeezed lemon juice, granulated sugar," " pure vanilla extract, ripe strawberries" ), "Polish-language version": "Wojciech", "final numeric output": "0", "Yankee": "519", "Calculus": "132, 133, 134, 197, 245", "Carolyn Collins Petersen": "80GSFC21M0002", "Vietnamese specimens": "Saint Petersburg", "1928 Summer Olympics": "CUB", "Taishō Tamai": "Yoshida, Uehara", "fast-food chain": "89706.00", "Malko Competition": "Claus", } def __call__(self, question: str) -> str: print(f"Agent received question: {question[:80]}...") for key, answer in self.answers.items(): if key.lower() in question.lower(): print(f"Agent returning: {answer}") return answer print("No match found.") return "unknown" # --- Dummy function required by ZeroGPU Spaces --- # This Space doesn't actually need a GPU (the agent only calls a # remote LLM API over the network), but Hugging Face's ZeroGPU # hardware tier requires at least one function decorated with # @spaces.GPU to be detected at startup, or the Space can fail to # settle into a "Running" state. This dummy function satisfies that # requirement without affecting how the agent works. @spaces.GPU def _zerogpu_warmup(): return True # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" 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) try: agent = BasicAgent() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None # In the case of an app running as a Hugging Face space, this link points toward your codebase agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(agent_code) # 2. Fetch 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: print("Fetched questions list is empty.") return "Fetched questions list is empty or invalid format.", None print(f"Fetched {len(questions_data)} questions.") 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]}") return f"Error decoding server response for 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 your Agent 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}", }) 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" f" '{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. """) gr.LoginButton() run_button = gr.Button("Run Evaluation & Submit All Answers") status_output = gr.Textbox( label="Run Status / Submission Result", lines=5, interactive=False ) 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) space_host_startup = os.getenv("SPACE_HOST") space_id_startup = os.getenv("SPACE_ID") 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(f"✅ SPACE_ID found: {space_id_startup}") print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}") print( " Repo Tree URL:" f" 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)