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| '''HuggingFace Agents course final project GAIA agent benchmark.''' | |
| # Standard library | |
| import glob | |
| import logging | |
| import os | |
| import requests | |
| # Third-party | |
| import gradio as gr | |
| import pandas as pd | |
| # Local/Project | |
| from functions.agent import create_agent | |
| # --- Constants --- | |
| from configuration import QUESTIONS, DEFAULT_API_URL, INSTRUCTIONS | |
| # --- Logging Configuration --- | |
| # Create logs directory if it doesn't exist | |
| os.makedirs('logs', exist_ok=True) | |
| # Clean up old log files | |
| def cleanup_old_logs(): | |
| """Remove old log files from the logs directory.""" | |
| log_files = glob.glob('logs/*.log') | |
| for log_file in log_files: | |
| try: | |
| os.remove(log_file) | |
| print(f"Removed old log file: {log_file}") | |
| except OSError as e: | |
| print(f"Error removing log file {log_file}: {e}") | |
| # Clean up old logs before starting | |
| cleanup_old_logs() | |
| # Configure root logger | |
| logging.basicConfig( | |
| level=logging.DEBUG, | |
| format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', | |
| handlers=[ | |
| logging.FileHandler('logs/agent.log', encoding='utf-8'), | |
| logging.StreamHandler() # Also log to console | |
| ] | |
| ) | |
| # Get logger for this module | |
| logger = logging.getLogger(__name__) | |
| 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') | |
| if profile: | |
| username = f'{profile.username}' | |
| logger.info('User logged in: %s', username) | |
| else: | |
| logger.warning('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 (imported from agent.py) | |
| try: | |
| agent = create_agent() | |
| except Exception as e: # pylint: disable=W0703 | |
| logger.error("Error instantiating agent: %s", 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 (useful for others so please keep it public) | |
| agent_code = f'https://huggingface.co/spaces/{space_id}/tree/main' | |
| logger.info('Agent code URL: %s', agent_code) | |
| # 2. Fetch Questions | |
| logger.info('Fetching questions from: %s', questions_url) | |
| try: | |
| response = requests.get(questions_url, timeout=15) | |
| response.raise_for_status() | |
| questions_data = response.json() | |
| if not questions_data: | |
| logger.warning('Fetched questions list is empty.') | |
| return 'Fetched questions list is empty or invalid format.', None | |
| logger.info('Fetched %d questions.', len(questions_data)) | |
| except requests.exceptions.JSONDecodeError as e: | |
| logger.error('Error decoding JSON response from questions endpoint: %s', e) | |
| logger.debug('Response text: %s', response.text[:500]) | |
| return f'Error decoding server response for questions: {e}', None | |
| except requests.exceptions.RequestException as e: | |
| logger.error('Error fetching questions: %s', e) | |
| return f'Error fetching questions: {e}', None | |
| except Exception as e: # pylint: disable=W0703 | |
| logger.error('An unexpected error occurred fetching questions: %s', e) | |
| return f'An unexpected error occurred fetching questions: {e}', None | |
| with open('questions.json', 'w', encoding='utf-8') as f: | |
| # Save the fetched questions to a file for debugging purposes | |
| pd.DataFrame(questions_data).to_json(f, orient='records', lines=True, force_ascii=False) | |
| # 3. Run your Agent | |
| results_log = [] | |
| answers_payload = [] | |
| logger.info('Running agent on %d questions...', len(questions_data)) | |
| for question_number in QUESTIONS: | |
| item = questions_data[question_number - 1] # Adjust for zero-based index | |
| task_id = item.get("task_id") | |
| question_text = item.get("question") | |
| if not task_id or question_text is None: | |
| logger.warning('Skipping item with missing task_id or question: %s', item) | |
| continue | |
| try: | |
| submitted_answer = agent.run( | |
| INSTRUCTIONS + '\n' + 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: # pylint: disable=W0703 | |
| logger.error('Error running agent on task %s: %s', task_id, e) | |
| results_log.append({ | |
| "Task ID": task_id, | |
| "Question": question_text, | |
| "Submitted Answer": f"AGENT ERROR: {e}" | |
| }) | |
| if not answers_payload: | |
| logger.warning('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}"...' | |
| ) | |
| logger.info(status_update) | |
| # 5. Submit | |
| logger.info('Submitting %d answers to: %s', len(answers_payload), 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', '?')}/" | |
| f"{result_data.get('total_attempted', '?')} correct)\n" | |
| f"Message: {result_data.get('message', 'No message received.')}" | |
| ) | |
| logger.info('Submission successful.') | |
| results_df = pd.DataFrame(results_log) | |
| results_df.to_csv('results.csv', index=False) | |
| 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}" | |
| logger.error(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| results_df.to_csv('results.csv', index=False) | |
| return status_message, results_df | |
| except requests.exceptions.Timeout: | |
| status_message = "Submission Failed: The request timed out." | |
| logger.error(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| results_df.to_csv('results.csv', index=False) | |
| return status_message, results_df | |
| except requests.exceptions.RequestException as e: | |
| status_message = f"Submission Failed: Network error - {e}" | |
| logger.error(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| results_df.to_csv('results.csv', index=False) | |
| return status_message, results_df | |
| except Exception as e: # pylint: disable=W0703 | |
| status_message = f"An unexpected error occurred during submission: {e}" | |
| logger.error(status_message) | |
| results_df = pd.DataFrame(results_log) | |
| results_df.to_csv('results.csv', index=False) | |
| 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 | |
| separate 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) | |
| results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) | |
| run_button.click( # pylint: disable=E1101 | |
| fn=run_and_submit_all, | |
| outputs=[status_output, results_table] | |
| ) | |
| if __name__ == "__main__": | |
| # 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: | |
| logger.info("✅ SPACE_HOST found: %s", space_host_startup) | |
| logger.info(" Runtime URL should be: https://%s.hf.space", space_host_startup) | |
| else: | |
| logger.info("ℹ️ SPACE_HOST environment variable not found (running locally?).") | |
| if space_id_startup: # Print repo URLs if SPACE_ID is found | |
| logger.info("✅ SPACE_ID found: %s", space_id_startup) | |
| logger.info(" Repo URL: https://huggingface.co/spaces/%s", space_id_startup) | |
| logger.info( | |
| " Repo Tree URL: https://huggingface.co/spaces/%s/tree/main", | |
| space_id_startup | |
| ) | |
| else: | |
| logger.info( | |
| "ℹ️ SPACE_ID environment variable not found (running locally?). " \ | |
| "Repo URL cannot be determined." | |
| ) | |
| logger.info("Launching Gradio Interface for Basic Agent Evaluation...") | |
| demo.launch(debug=True, share=False) | |