# Hey, stranger! this code is for use of free rate of gemini llm # which is limited by RPM (15/30). Testing has shown that if I put # request delay 10 then search drops out timed out. # Nevertheless, it scrored 35% which is good for me while two questions # were dropped due to exceeding RPM. So, it is still possible to improve, # e.g. deploying gemini 2.0 flash lite which has double RPM limit. # Try it out! import os import gradio as gr import requests import inspect import pandas as pd import aiohttp import asyncio import json from agent import MagAgent from token_bucket import Limiter, MemoryStorage # (Keep Constants as is) # --- Constants --- DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" # Rate limiting configuration RATE_LIMIT = 10 # Requests per minute TOKEN_BUCKET_CAPACITY = RATE_LIMIT TOKEN_BUCKET_REFILL_RATE = RATE_LIMIT / 60.0 # Tokens per second # Initialize global token bucket with MemoryStorage storage = MemoryStorage() token_bucket = Limiter(rate=TOKEN_BUCKET_REFILL_RATE, capacity=TOKEN_BUCKET_CAPACITY, storage=storage) async def fetch_questions(session: aiohttp.ClientSession, questions_url: str) -> list: """Fetch questions asynchronously.""" try: async with session.get(questions_url, timeout=15) as response: response.raise_for_status() questions_data = await response.json() if not questions_data: print("Fetched questions list is empty.") return [] print(f"Fetched {len(questions_data)} questions.") return questions_data except aiohttp.ClientError as e: print(f"Error fetching questions: {e}") return None except Exception as e: print(f"An unexpected error occurred fetching questions: {e}") return None async def submit_answers(session: aiohttp.ClientSession, submit_url: str, submission_data: dict) -> dict: """Submit answers asynchronously.""" try: async with session.post(submit_url, json=submission_data, timeout=60) as response: response.raise_for_status() return await response.json() except aiohttp.ClientResponseError as e: print(f"Submission Failed: Server responded with status {e.status}. Detail: {e.message}") return None except aiohttp.ClientError as e: print(f"Submission Failed: Network error - {e}") return None except Exception as e: print(f"An unexpected error occurred during submission: {e}") return None async def process_question(agent, question_text: str, task_id: str, results_log: list): """Process a single question with global rate limiting.""" submitted_answer = None try: # Retry until a token is available while not token_bucket.consume(1): print(f"Rate limit reached for task {task_id}. Waiting to retry...") await asyncio.sleep(60 / RATE_LIMIT) submitted_answer = await agent(question_text) results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) return {"task_id": task_id, "submitted_answer": submitted_answer} except aiohttp.ClientResponseError as e: if e.status == 429: print(f"Rate limit hit for task {task_id}. Retrying after delay...") await asyncio.sleep(60 / RATE_LIMIT) while not token_bucket.consume(1): await asyncio.sleep(60 / RATE_LIMIT) try: submitted_answer = await agent(question_text) results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) return {"task_id": task_id, "submitted_answer": submitted_answer} except Exception as retry_e: submitted_answer = f"AGENT ERROR: {retry_e}" results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) return None else: submitted_answer = f"AGENT ERROR: {e}" results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) return None except Exception as e: submitted_answer = f"AGENT ERROR: {e}" results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) return None async def run_and_submit_all(profile: gr.OAuthProfile | None): """ Fetches all questions asynchronously, runs the MagAgent 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}" 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 try: agent =MagAgent() except Exception as e: print(f"Error instantiating agent: {e}") return f"Error initializing agent: {e}", None agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" print(agent_code) # 2. Fetch Questions Asynchronously async with aiohttp.ClientSession() as session: questions_data = await fetch_questions(session, questions_url) if questions_data is None: return "Error fetching questions.", None if not questions_data: return "Fetched questions list is empty or invalid format.", None # 3. Run Agent on Questions # Process questions sequentially with rate limiting results_log = [] answers_payload = [] print(f"Running agent on {len(questions_data)} questions...") for item in questions_data: if item.get("task_id") and item.get("question"): result = await process_question(agent, item["question"], item["task_id"], results_log) if result: answers_payload.append(result) 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 Answers Asynchronously result_data = await submit_answers(session, submit_url, submission_data) if result_data is None: status_message = "Submission Failed." print(status_message) results_df = pd.DataFrame(results_log) return status_message, results_df 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 # --- Build Gradio Interface using Blocks --- with gr.Blocks() as demo: gr.Markdown("# Magus Agent Evaluation Runner") gr.Markdown( """ **Instructions:** 1. Log in to your Hugging Face account using the button below. 2. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, and submit answers. --- **Notes:** The agent uses asynchronous operations for efficiency. Answers are processed and submitted asynchronously. """ ) 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 Mag Agent Evaluation...") demo.launch(debug=True, share=False)