import os import gradio as gr import requests import inspect import pandas as pd import google.generativeai as genai import spaces import asyncio # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" ANSWER_CACHE = {} # --- Basic Agent Definition --- # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------ class GeminiAgent: def __init__(self): print("Initializing Gemini Agent...") api_key = os.getenv("GOOGLE_API_KEY") if not api_key: raise ValueError("GOOGLE_API_KEY environment variable not set. Please set it in your Hugging Face Space secrets.") genai.configure(api_key=api_key) # Define a system prompt to guide the model's behavior self.system_prompt = ( "You are an expert AI assistant. Your goal is to answer the following question as accurately and concisely as possible. " "Analyze the question carefully. If the question involves a file, you will receive the question text only. " "Acknowledge that you cannot access files. If you cannot answer the question, state that clearly. " "Do not guess." "Only reply with the answer." "Do not restate a part of the question." "Do not say Of course." "Do not say based on" ) self.model = genai.GenerativeModel('gemini-2.5-pro') print("Gemini Agent initialized successfully.") def __call__(self, question: str) -> str: print(f"Agent received question (first 50 chars): {question[:50]}...") try: response = self.model.generate_content(question) answer = response.text print(f"Agent returning Gemini answer (first 50 chars): {answer[:50]}...") return answer except ValueError: # This can happen if the response was blocked. print("Response was blocked or content is not available.") return "The response was blocked by the content filter." except Exception as e: print(f"Error calling Gemini API: {e}") return f"Error during Gemini API call: {e}" @spaces.GPU def run_and_submit_all( profile: gr.OAuthProfile | None): """ Fetches all questions, runs the GeminiAgent on them asynchronously, caches the answers, 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 = GeminiAgent() 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 Asynchronously with Caching async def process_question(item, agent_instance): 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}") return None, None # Check cache first if task_id in ANSWER_CACHE: print(f"Cache hit for task {task_id}.") submitted_answer = ANSWER_CACHE[task_id] log_entry = {"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer} answer_payload = {"task_id": task_id, "submitted_answer": submitted_answer} return log_entry, answer_payload # If not in cache, run agent try: print(f"Cache miss for task {task_id}. Running agent...") # Run the synchronous agent call in a separate thread to avoid blocking the event loop submitted_answer = await asyncio.to_thread(agent_instance, question_text) # Cache the new answer ANSWER_CACHE[task_id] = submitted_answer log_entry = {"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer} answer_payload = {"task_id": task_id, "submitted_answer": submitted_answer} return log_entry, answer_payload except Exception as e: print(f"Error running agent on task {task_id}: {e}") error_message = f"AGENT ERROR: {e}" log_entry = {"Task ID": task_id, "Question": question_text, "Submitted Answer": error_message} # Do not create a payload for submission in case of an error return log_entry, None async def run_all_questions_async(): print(f"Running agent on {len(questions_data)} questions asynchronously...") tasks = [process_question(item, agent) for item in questions_data] results = await asyncio.gather(*tasks) results_log = [res[0] for res in results if res and res[0] is not None] answers_payload = [res[1] for res in results if res and res[1] is not None] return results_log, answers_payload results_log, answers_payload = asyncio.run(run_all_questions_async()) 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=True)