import os import gradio as gr import requests import inspect import pandas as pd import asyncio from smolagents import ToolCallingAgent, InferenceClientModel, HfApiModel from smolagents import DuckDuckGoSearchTool, Tool, CodeAgent from huggingface_hub import login DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" login(token=os.environ["HUGGINGFACEHUB_API_TOKEN"]) search_tool = DuckDuckGoSearchTool() async def run_and_submit_all(profile: gr.OAuthProfile | None): try: agent = CodeAgent( tools=[search_tool], model=InferenceClientModel(model="mistralai/Magistral-Small-2506"), max_steps=5, verbosity_level=2 ) except Exception as e: return f"Error initializing agent: {e}", None space_id = os.getenv("SPACE_ID") agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" questions_url = f"{DEFAULT_API_URL}/questions" try: response = requests.get(questions_url, timeout=15) response.raise_for_status() questions_data = response.json() if not questions_data: return "Fetched questions list is empty or invalid format.", None except Exception as e: return f"Error fetching questions: {e}", None results_log = [] answers_payload = [] 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: continue try: loop = asyncio.get_event_loop() submitted_answer = await loop.run_in_executor(None, 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: results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"}) if not answers_payload: return "Agent did not produce any answers to submit.", pd.DataFrame(results_log) username = profile.username if profile else "unknown" submit_url = f"{DEFAULT_API_URL}/submit" submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload} 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.')}" ) results_df = pd.DataFrame(results_log) return final_status, results_df except Exception as e: status_message = f"Submission Failed: {e}" results_df = pd.DataFrame(results_log) return status_message, results_df with gr.Blocks() as demo: gr.Markdown("# Basic Agent Evaluation Runner") gr.Markdown(""" **Instructions:** 1. Clone this space and define your agent logic. 2. Log in to your Hugging Face account. 3. Click 'Run Evaluation & Submit All Answers'. --- **Note:** The run may take time. Async is now used to improve responsiveness. """) 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(fn=run_and_submit_all, outputs=[status_output, results_table]) if __name__ == "__main__": print("\n" + "-"*30 + " App Starting " + "-"*30) space_host_startup = os.getenv("SPACE_HOST") space_id_startup = os.getenv("SPACE_ID") if space_host_startup: print(f"✅ SPACE_HOST: https://{space_host_startup}.hf.space") if space_id_startup: print(f"✅ SPACE_ID: https://huggingface.co/spaces/{space_id_startup}") print("Launching Gradio Interface...") demo.launch(debug=True, share=False)