Update app.py

app.py

@@ -2,41 +2,34 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
-from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel
+# 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"
 
-# --- Basic Agent Definition ---
 class BasicAgent:
     def __init__(self):
-        # 1. 初始化模型 (使用 Qwen 2.5 Coder，它是目前最強的開源 Coding 模型之一)
-        model = HfApiModel(model_id="Qwen/Qwen2.5-Coder-32B-Instruct")
-        
-        # 2. 初始化搜尋工具
+        # 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()
         
-        # 3. 建立 Agent (CodeAgent 會寫 Python 程式碼來解決問題)
         self.agent = CodeAgent(
             tools=[search_tool],
-            model=model,
-            add_base_tools=True, # 允許 Agent 使用 Python 執行運算
+            model=self.model_id, # 直接傳入模型 ID 字串
+            add_base_tools=True,
         )
-        print("BasicAgent initialized with smolagents.")
+        print("Agent initialized successfully.")
 
     def __call__(self, question: str) -> str:
-        print(f"Agent received question: {question}")
         try:
-            # 讓 Agent 思考並回答問題
-            # 這裡我們直接回傳 Agent 的執行結果
-            answer = self.agent.run(question)
-            print(f"Agent answer: {answer}")
-            return str(answer)
+            # 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 during agent execution: {e}")
-            return "I couldn't find the answer."
+            print(f"Error: {e}")
+            return "I am sorry, I cannot answer this right now."
 
-# --- 下面是評分系統的標準邏輯 (不用改) ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     space_id = os.getenv("SPACE_ID")
     if profile:
@@ -67,8 +60,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
-        
-        # 執行 Agent
         try:
             submitted_answer = agent(question_text)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
@@ -76,7 +67,6 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         except Exception as e:
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"Error: {e}"})
 
-    # 提交答案
     submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
@@ -87,9 +77,8 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
 
     return status_msg, pd.DataFrame(results_log)
 
-# --- Gradio Interface ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Unit 4 Final Project: My AI Agent")
+    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")