import os import gradio as gr import requests import inspect import pandas as pd import json import yaml from smolagents import ToolCallingAgent, Tool, InferenceClientModel, DuckDuckGoSearchTool from smolagents import WikipediaSearchTool, PythonInterpreterTool from smolagents import OpenAIServerModel from tools import fetch_file from phoenix.otel import register from openinference.instrumentation.smolagents import SmolagentsInstrumentor register() SmolagentsInstrumentor().instrument() # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" import os token = os.getenv("HF_TOKEN") if token is None: raise ValueError('You must set the HF_TOKEN environment variable') else: print('Token was found') #model = InferenceClientModel(model_id="Qwen/Qwen3-32B", provider="nscale") #model = InferenceClientModel(model_id="deepseek-ai/DeepSeek-R1", provider="nebius") # Set your Gemini API key in the environment variable GEMINI_API_KEY_1 model = OpenAIServerModel( model_id="gemini-2.5-flash", api_base="https://generativelanguage.googleapis.com/v1beta", api_key=os.getenv("GEMINI_API_KEY_1") ) print('GEMINI_API_KEY_1 was found:', os.getenv("GEMINI_API_KEY_1") is not None) # --- Basic Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class BasicAgent: def __init__(self): print("BasicAgent initialized.") def __call__(self, taskid: str, 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}") # Define the prompt for the agent # Report your thoughts, and finish your answer with the following template: prompt = f""" You are a general AI assistant. I will ask you a question and you can use 10 steps to answer the question. You can use the tools I provide you to answer my question. Every tool call reduces the number of remaining steps available to answer the question. YOUR FINAL ANSWER has be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, do not use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, do not use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. Finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER]. The taskid is {taskid}. If you need to download a file that comes with the question then use the taskid to fetch the file The question is '{question}'. """ agent = ToolCallingAgent( tools=[ DuckDuckGoSearchTool(), WikipediaSearchTool(), fetch_file, ], model=model, max_steps=10, #tool_choice="auto", ) # Run the agent with the prompt fixed_answer = agent.run(prompt) # Clean the answer cleaned_answer = self.clean_answer(fixed_answer) print(f"Agent returned cleaned answer: {cleaned_answer}") return cleaned_answer def clean_answer(self, answer: str) -> str: # Remove "FINAL ANSWER:" (with or without trailing space) answer = answer.replace("FINAL ANSWER:", "").strip() # Remove last character if it is a period if answer.endswith("."): answer = answer[:-1] return answer 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...") item = questions_data[0] if questions_data else None #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(task_id, question_text) answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) print(f"Task ID: {task_id}, Question: {question_text[:50]}..., 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) # delete this the next line final_status = "Dummy status" 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)