Update app.py

app.py

@@ -2,92 +2,167 @@ 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
+from typing import Optional
+
+# 導入 smolagents 組件
+try:
+    from smolagents import CodeAgent, DuckDuckGoSearchTool
+except ImportError as e:
+    print(f"Warning: smolagents import failed: {e}")
+    DuckDuckGoSearchTool = None
+    CodeAgent = None
 
 # --- 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
+        """初始化 Agent，帶有完整的錯誤處理"""
         self.model_id = "Qwen/Qwen2.5-Coder-32B-Instruct"
-        search_tool = DuckDuckGoSearchTool()
+        self.agent = None
         
-        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.")
+        try:
+            if CodeAgent is None or DuckDuckGoSearchTool is None:
+                raise ImportError("smolagents not properly installed")
+            
+            # 初始化搜尋工具
+            search_tool = DuckDuckGoSearchTool()
+            
+            # 初始化 Agent
+            self.agent = CodeAgent(
+                tools=[search_tool],
+                model=self.model_id,
+                add_base_tools=True,
+                verbose=False,
+            )
+            print("✓ Agent initialized successfully.")
+        except Exception as e:
+            print(f"✗ Agent initialization error: {e}")
+            self.agent = None
 
     def __call__(self, question: str) -> str:
+        """執行 Agent 查詢"""
+        if self.agent is None:
+            return "Agent not initialized. Please check model loading."
+        
         try:
-            # 執行 Agent 並確保回傳結果是字串
             result = self.agent.run(question)
-            return str(result)
+            return str(result).strip()
         except Exception as e:
-            print(f"Error: {e}")
+            error_msg = f"Agent error: {str(e)[:200]}"
+            print(error_msg)
             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:
+
+def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
+    """主要評估和提交函數"""
+    
+    # 檢查登入狀態
+    if profile is None:
         return "Please Login to Hugging Face with the button.", None
+    
+    username = profile.username
+    space_id = os.getenv("SPACE_ID")
+    
+    if not space_id:
+        return "Error: SPACE_ID environment variable not set.", None
 
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
+    # 初始化 Agent
     try:
         agent = BasicAgent()
     except Exception as e:
-        return f"Error initializing agent: {e}", None
+        return f"Error initializing agent: {str(e)[:200]}", None
 
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
 
+    # 獲取問題
     try:
-        response = requests.get(questions_url, timeout=15)
+        response = requests.get(questions_url, timeout=30)
+        response.raise_for_status()
         questions_data = response.json()
-    except Exception as e:
-        return f"Error fetching questions: {e}", None
+    except requests.exceptions.RequestException as e:
+        return f"Error fetching questions: {str(e)[:200]}", None
+    except ValueError as e:
+        return f"Error parsing questions response: {str(e)[:200]}", None
 
     answers_payload = []
     results_log = []
 
-    for item in questions_data:
+    # 逐個回答問題
+    for idx, item in enumerate(questions_data):
         task_id = item.get("task_id")
         question_text = item.get("question")
+        
+        print(f"Processing question {idx+1}/{len(questions_data)}: {task_id}")
+        
         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})
+            answers_payload.append({
+                "task_id": task_id, 
+                "submitted_answer": submitted_answer
+            })
+            results_log.append({
+                "Task ID": task_id, 
+                "Question": question_text[:100],
+                "Submitted Answer": submitted_answer[:200]
+            })
         except Exception as e:
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"Error: {e}"})
+            error_answer = f"Error: {str(e)[:100]}"
+            answers_payload.append({
+                "task_id": task_id, 
+                "submitted_answer": error_answer
+            })
+            results_log.append({
+                "Task ID": task_id, 
+                "Question": question_text[:100],
+                "Submitted Answer": error_answer
+            })
 
-    submission_data = {"username": username, "agent_code": agent_code, "answers": answers_payload}
+    # 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)
+        response = requests.post(submit_url, json=submission_data, timeout=120)
+        response.raise_for_status()
         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}"
+        
+        score = result_data.get('score', 'N/A')
+        correct = result_data.get('correct_count', 0)
+        total = result_data.get('total_attempted', 0)
+        status_msg = f"Score: {score}% ({correct}/{total} correct)"
+    except requests.exceptions.RequestException as e:
+        status_msg = f"Submission Failed: {str(e)[:200]}"
+    except ValueError as e:
+        status_msg = f"Submission response parsing error: {str(e)[:200]}"
 
     return status_msg, pd.DataFrame(results_log)
 
-with gr.Blocks() as demo:
+
+# Gradio UI
+with gr.Blocks(title="Unit 4 Final Assignment") 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")
+    gr.Markdown("_Click 'Login with Hugging Face' first, then click 'Run Evaluation & Submit All Answers'_")
+    
+    with gr.Row():
+        gr.LoginButton(scale=1)
+        run_button = gr.Button("Run Evaluation & Submit All Answers", scale=2, variant="primary")
+    
+    status_output = gr.Textbox(label="Result", lines=3, interactive=False)
+    results_table = gr.DataFrame(label="Details", interactive=False)
 
     run_button.click(
         fn=run_and_submit_all,
+        inputs=[],  # 自動取得 profile
         outputs=[status_output, results_table]
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(debug=True)