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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| from agent import create_agent, fetch_random_question | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| def run_and_submit_all(profile): | |
| if not profile: | |
| return "Please login to Hugging Face with the login button.", None | |
| username = getattr(profile, "username", None) or getattr(profile, "name", None) | |
| if not username: | |
| return "Login error: username not found.", None | |
| # Fetch questions | |
| try: | |
| qs = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15) | |
| qs.raise_for_status() | |
| questions = qs.json() | |
| except Exception as e: | |
| return f"Error fetching questions: {e}", None | |
| # Instantiate agent | |
| agent = create_agent() | |
| # Run and collect answers | |
| results, payload = [], [] | |
| for q in questions: | |
| tid, text = q.get("task_id"), q.get("question") | |
| if not (tid and text): | |
| continue | |
| try: | |
| ans = agent.run(text) # <-- positional! | |
| except Exception as e: | |
| ans = f"ERROR: {e}" | |
| results.append({"Task ID": tid, "Question": text, "Answer": ans}) | |
| payload.append({"task_id": tid, "submitted_answer": ans}) | |
| if not payload: | |
| return "Agent returned no answers.", pd.DataFrame(results) | |
| # Submit | |
| submission = { | |
| "username": username, | |
| "agent_code": f"https://huggingface.co/spaces/{os.getenv('SPACE_ID')}/tree/main", | |
| "answers": payload | |
| } | |
| try: | |
| resp = requests.post(f"{DEFAULT_API_URL}/submit", json=submission, timeout=60) | |
| resp.raise_for_status() | |
| data = resp.json() | |
| status = ( | |
| f"Submission Successful!\n" | |
| f"User: {data['username']}\n" | |
| f"Score: {data['score']}% ({data['correct_count']}/{data['total_attempted']})\n" | |
| f"Message: {data['message']}" | |
| ) | |
| except Exception as e: | |
| status = f"Submission Failed: {e}" | |
| return status, pd.DataFrame(results) | |
| def test_random_question(profile): | |
| if not profile: | |
| return "Please login to Hugging Face with the login button.", "" | |
| q = fetch_random_question() | |
| agent = create_agent() | |
| # Positional call again | |
| answer = agent.run(q["question"]) | |
| return q["question"], answer | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# SmolAgent Evaluation Runner") | |
| gr.Markdown( | |
| """ | |
| **Instructions:** | |
| 1. Clone this space and define your agent logic in `agent.py`. | |
| 2. Log in with your Hugging Face account using the login button below. | |
| 3. Use **Run Evaluation & Submit All Answers** or **Test Random Question**. | |
| """ | |
| ) | |
| login = gr.LoginButton() | |
| run_btn = gr.Button("Run Evaluation & Submit All Answers") | |
| test_btn = gr.Button("Test Random Question") | |
| status_out = gr.Textbox(label="Status / Result", lines=5, interactive=False) | |
| table_out = gr.DataFrame(label="Full Results Table", wrap=True) | |
| question_out = gr.Textbox(label="Random Question", lines=3, interactive=False) | |
| answer_out = gr.Textbox(label="Agent Answer", lines=3, interactive=False) | |
| run_btn.click(fn=run_and_submit_all, inputs=[login], outputs=[status_out, table_out]) | |
| test_btn.click(fn=test_random_question, inputs=[login], outputs=[question_out, answer_out]) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True, share=False) | |