app.py

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()