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| import os | |
| import gradio as gr | |
| import requests | |
| import pandas as pd | |
| # 這裡只導入最基本的組件，避開 HfApiModel 導入錯誤 | |
| from smolagents import CodeAgent, DuckDuckGoSearchTool | |
| # --- Constants --- | |
| DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" | |
| class BasicAgent: | |
| def __init__(self): | |
| # 使用字串定義模型，讓 smolagents 內部去處理載入，避開導入報錯 | |
| self.model_id = "Qwen/Qwen2.5-Coder-32B-Instruct" | |
| search_tool = DuckDuckGoSearchTool() | |
| self.agent = CodeAgent( | |
| tools=[search_tool], | |
| model=self.model_id, # 直接傳入模型 ID 字串 | |
| add_base_tools=True, | |
| ) | |
| print("Agent initialized successfully.") | |
| def __call__(self, question: str) -> str: | |
| try: | |
| # 執行 Agent 並確保回傳結果是字串 | |
| result = self.agent.run(question) | |
| return str(result) | |
| except Exception as e: | |
| print(f"Error: {e}") | |
| return "I am sorry, I cannot answer this right now." | |
| def run_and_submit_all(profile: gr.OAuthProfile | None): | |
| space_id = os.getenv("SPACE_ID") | |
| if profile: | |
| username = f"{profile.username}" | |
| else: | |
| 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" | |
| try: | |
| agent = BasicAgent() | |
| except Exception as e: | |
| return f"Error initializing agent: {e}", None | |
| agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" | |
| try: | |
| response = requests.get(questions_url, timeout=15) | |
| questions_data = response.json() | |
| except Exception as e: | |
| return f"Error fetching questions: {e}", None | |
| answers_payload = [] | |
| results_log = [] | |
| for item in questions_data: | |
| task_id = item.get("task_id") | |
| question_text = item.get("question") | |
| try: | |
| submitted_answer = 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"Error: {e}"}) | |
| submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload} | |
| try: | |
| response = requests.post(submit_url, json=submission_data, timeout=60) | |
| result_data = response.json() | |
| status_msg = f"Score: {result_data.get('score')}% ({result_data.get('correct_count')}/{result_data.get('total_attempted')} correct)" | |
| except Exception as e: | |
| status_msg = f"Submission Failed: {e}" | |
| return status_msg, pd.DataFrame(results_log) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Unit 4 Final Project: AI Agent") | |
| gr.LoginButton() | |
| run_button = gr.Button("Run Evaluation & Submit All Answers") | |
| status_output = gr.Textbox(label="Result") | |
| results_table = gr.DataFrame(label="Details") | |
| run_button.click( | |
| fn=run_and_submit_all, | |
| outputs=[status_output, results_table] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |