import os import gradio as gr import requests import inspect import pandas as pd from my_agent import SmolAgent # Import the new agent # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # --- Basic Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class BasicAgent: def __init__(self): print("BasicAgent initialized.") def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") fixed_answer = "This is a default answer." print(f"Agent returning fixed answer: {fixed_answer}") return fixed_answer def instantiate_agent(): """Instantiates the agent.""" try: # agent = BasicAgent() agent = SmolAgent() # Use the new agent return agent, None # Return agent and no error except Exception as e: print(f"Error instantiating agent: {e}") return None, f"Error initializing agent: {e}" # Return None and error message def fetch_questions(questions_url: str): """Fetches questions from the specified URL.""" 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 None, "Fetched questions list is empty or invalid format." print(f"Fetched {len(questions_data)} questions.") return questions_data, None # Return data and no error except requests.exceptions.RequestException as e: print(f"Error fetching questions: {e}") return None, f"Error fetching questions: {e}" except requests.exceptions.JSONDecodeError as e: print(f"Error decoding JSON response from questions endpoint: {e}") print(f"Response text: {response.text[:500]}") return None, f"Error decoding server response for questions: {e}" except Exception as e: print(f"An unexpected error occurred fetching questions: {e}") return None, f"An unexpected error occurred fetching questions: {e}" def run_agent_on_questions(agent, questions_data): """Runs the agent on each question and collects results.""" 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}", } ) return answers_payload, results_log def dev_run(): """ Fetches all questions, runs the BasicAgent on them, and displays the results. """ api_url = DEFAULT_API_URL questions_url = f"{api_url}/questions" agent, error_message = instantiate_agent() if error_message: return error_message, None # Return error message and None for results_df # 2. Fetch Questions questions_data, error_message = fetch_questions(questions_url) if error_message: # Return the error message from fetch_questions and None for the results DataFrame return error_message, None # 3. Run your Agent answers_payload, results_log = run_agent_on_questions(agent, questions_data) 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) return answers_payload, pd.DataFrame(results_log) 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) agent, error_message = instantiate_agent() if error_message: return error_message, None # Return error message and None for results_df # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public) agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(agent_code) # 2. Fetch Questions questions_data, error_message = fetch_questions(questions_url) if error_message: # Return the error message from fetch_questions and None for the results DataFrame return error_message, None # 3. Run your Agent answers_payload, results_log = run_agent_on_questions(agent, questions_data) 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 '{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. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async. # """ # ) # gr.LoginButton() # run_button = gr.Button("Run Evaluation & Submit All Answers") # status_output = gr.Textbox( # label="Run Status / Submission Result", lines=5, interactive=False # ) # # Removed max_rows=10 from DataFrame constructor # 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) # # Check for SPACE_HOST and SPACE_ID at startup for information # space_host_startup = os.getenv("SPACE_HOST") # space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup # 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 repo URLs if SPACE_ID is found # print(f"✅ SPACE_ID found: {space_id_startup}") # print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}") # print( # f" Repo Tree URL: 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) if __name__ == "__main__": answers_payload, results_log = dev_run() print(answers_payload) print(results_log)