import os # import gradio as gr import requests import inspect import pandas as pd from langchain_core.messages import HumanMessage from agent import build_graph from huggingface_hub import HfApi, hf_hub_download import logging logger = logging.getLogger(__name__) # (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.") self.graph = build_graph() def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") messages = [HumanMessage(content=question)] result = self.graph.invoke({"messages": messages}) answer = result['messages'][-1].content return answer def file_extract(local_file_path, task_id): if not local_file_path: return None token = os.getenv("HUGGINGFACEHUB_API_TOKEN") or os.getenv("HF_TOKEN") # GAIA files are usually placed in date-based subdirectories prefixes = ["2023/validation/", "2023/test/", "2023/train/", ""] for prefix in prefixes: try: resolved_path = hf_hub_download( repo_id="gaia-benchmark/GAIA", filename=f"{prefix}{local_file_path}", repo_type="dataset", token=token ) return resolved_path except Exception: continue logger.warning(f"Could not download file '{local_file_path}' for task_id {task_id}. Make sure you accepted GAIA terms on HF and set HF_TOKEN.") return None agent = BasicAgent() questions_url = f"{DEFAULT_API_URL}/questions" response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() import time print(f"Running agent on {len(questions_data)} questions sequentially to avoid 429 errors...") for item in questions_data[:2]: question_text = item.get("question") if question_text is None: continue files_text = item.get("files") task_id = item.get("task_id") file_name = item.get("file_name") if file_name: # Actually download the file to local cache and get absolute path resolved_path = file_extract(file_name, task_id) if resolved_path: question_text += f"\n\n[Attached File Local Path: {resolved_path}]" else: question_text += f"\n\n[Attached File: {file_name} (Download Failed)]" print(f"Processing Task ID: {task_id}") output = agent(question_text) print("Q:", question_text) print("A:", output) print("-" * 40) # Stagger requests to refill Token bucket and provide space for other concurrent tasks if any time.sleep(5) # 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 ( 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 # 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...") # print(f"Running agent on {len(questions_data[:5])} questions temporarily...") # for item in questions_data[:5]: # 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 '{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)