Update app.py

app.py

@@ -3,210 +3,123 @@ import gradio as gr
 import requests
 import pandas as pd
 from typing import Optional
-from smolagents import CodeAgent, DuckDuckGoSearchTool, OpenAIServerModel
+from smolagents import CodeAgent, OpenAIServerModel, tool
+
+# --- 關鍵修改：手動定義搜尋工具，繞過 smolagents 的檢查錯誤 ---
+try:
+    from duckduckgo_search import DDGS
+except ImportError:
+    # 萬一真的沒裝到，這邊做最後一道防線
+    os.system('pip install duckduckgo-search==6.4.2')
+    from duckduckgo_search import DDGS
+
+@tool
+def web_search(query: str) -> str:
+    """
+    Performs a web search to find information about specific facts, events, or data.
+    
+    Args:
+        query: The search query string.
+    """
+    try:
+        results = DDGS().text(query, max_results=5)
+        return str(results)
+    except Exception as e:
+        return f"Search error: {str(e)}"
+# -----------------------------------------------------------
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 class GroqAgent:
-    """使用 smolagents + Groq API + Search Tool 的 Agent"""
     def __init__(self):
         self.api_key = os.getenv("GROQ_API_KEY")
         if not self.api_key:
-            print("✗ GROQ_API_KEY not found")
             self.agent = None
             return
             
-        # 1. 設定模型：使用 OpenAI 相容模式連接 Groq
         model = OpenAIServerModel(
             model_id="llama-3.3-70b-versatile",
             api_base="https://api.groq.com/openai/v1",
             api_key=self.api_key
         )
         
-        # 2. 建立 Agent：加入搜尋工具 (DuckDuckGoSearchTool)
-        # 這是拿到 > 30% 分數的關鍵，讓它能上網查資料
+        # 使用我們手動定義的 web_search 工具
         self.agent = CodeAgent(
-            tools=[DuckDuckGoSearchTool()], 
+            tools=[web_search], 
             model=model,
-            max_steps=4, # 限制思考步數避免超時
+            max_steps=4,
             verbosity_level=1
         )
-        print("✓ Groq agent (smolagents) initialized successfully")
     
     def __call__(self, question: str) -> str:
-        """回答問題"""
         if self.agent is None:
             return "Error: GROQ_API_KEY not configured."
         
         try:
-            # 增加提示詞，告訴 Agent 如果遇到圖片題該怎麼辦
             prompt = f"""
-            Answer the following question as strictly and concisely as possible.
-            If the question asks about a specific fact, use the search tool to find it.
-            If the question refers to an image, video, or audio file that you cannot see:
-               1. Try to infer the answer from the text context if possible.
-               2. Or search for the specific text descriptions in the question.
-               3. If absolutely impossible, make an educated guess.
+            Answer the question concisely.
+            If it's a factual question (dates, names, events), use the 'web_search' tool.
+            If it refers to an image/video you can't see, try to infer from the text or search for the description.
             
             Question: {question}
             """
-            
-            # 執行 Agent
-            answer = self.agent.run(prompt)
-            return str(answer)
-            
+            return str(self.agent.run(prompt))
         except Exception as e:
-            return f"Error running agent: {str(e)[:150]}"
-
+            return f"Error: {str(e)[:150]}"
 
 def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
-    """主要評估和提交函數"""
-    
-    # 檢查登入
     if profile is None:
-        return "⚠️ Please click 'Login with Hugging Face' button first!", None
+        return "⚠️ Please login first!", None
     
     username = profile.username
     space_id = os.getenv("SPACE_ID", "s1144662")
     api_url = DEFAULT_API_URL
     
-    # 初始化 Agent
     try:
         agent_wrapper = GroqAgent()
         if agent_wrapper.agent is None:
-            return "❌ Error: GROQ_API_KEY not found!\n\nPlease add your Groq API key to Secrets.", None
+            return "❌ Error: GROQ_API_KEY not found!", None
     except Exception as e:
-        return f"❌ Agent initialization failed: {str(e)}", None
+        return f"❌ Init failed: {str(e)}", None
     
-    # 獲取問題
     try:
-        print("Fetching questions...")
         response = requests.get(f"{api_url}/questions", timeout=30)
-        response.raise_for_status()
-        questions_data = response.json()
-        print(f"✓ Got {len(questions_data)} questions")
+        questions = response.json()
     except Exception as e:
-        return f"❌ Failed to fetch questions: {str(e)}", None
+        return f"❌ Fetch failed: {str(e)}", None
 
-    answers_payload = []
-    results_log = []
+    answers = []
+    logs = []
     
-    total = len(questions_data)
-    
-    # 回答每個問題
-    for idx, item in enumerate(questions_data, 1):
-        task_id = item.get("task_id")
-        question_text = item.get("question")
-        
-        print(f"[{idx}/{total}] Processing: {task_id[:8]}...")
+    for item in questions:
+        q = item.get("question")
+        tid = item.get("task_id")
+        print(f"Processing: {tid}...")
         
-        try:
-            # 呼叫我們的 Agent
-            answer = agent_wrapper(question_text)
-            
-            answers_payload.append({
-                "task_id": task_id,
-                "submitted_answer": answer
-            })
-            
-            results_log.append({
-                "Task ID": task_id[:12] + "...",
-                "Question": question_text[:70] + "...",
-                "Answer": str(answer)[:150]
-            })
-            
-        except Exception as e:
-            error_msg = str(e)[:100]
-            answers_payload.append({
-                "task_id": task_id,
-                "submitted_answer": f"Error: {error_msg}"
-            })
-            results_log.append({
-                "Task ID": task_id,
-                "Question": question_text[:50],
-                "Answer": f"Error: {error_msg}"
-            })
+        ans = agent_wrapper(q)
+        answers.append({"task_id": tid, "submitted_answer": ans})
+        logs.append({"Task": tid, "Q": q[:50], "A": ans[:100]})
 
-    # 提交答案
     try:
-        print("Submitting answers...")
-        submission_data = {
+        res = requests.post(f"{api_url}/submit", json={
             "username": username,
             "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
-            "answers": answers_payload
-        }
-        
-        response = requests.post(
-            f"{api_url}/submit",
-            json=submission_data,
-            timeout=120
-        )
-        response.raise_for_status()
-        data = response.json()
-        
+            "answers": answers
+        })
+        data = res.json()
         score = data.get('score', 0)
-        correct = data.get('correct_count', 0)
-        total_q = data.get('total_attempted', 0)
-        
-        print(f"✓ Score: {score}% ({correct}/{total_q})")
-        
-        # 生成結果訊息
-        if score >= 30:
-            status_msg = f"""🎉 CONGRATULATIONS! YOU PASSED! 🎉
-
-📊 Final Score: {score}% ({correct}/{total_q} correct)
-✅ Required: 30% (You exceeded it!)
-
-🎓 Next Step: Get Your Certificate
-👉 Visit: https://huggingface.co/spaces/agents-course/Unit4-Final-Certificate
-
-Great job! 🚀"""
-        else:
-            status_msg = f"""📊 Score: {score}% ({correct}/{total_q} correct)
-❌ Required: 30% to pass
-📈 You need {int((30 * total_q / 100) - correct)} more correct answers
-
-💡 Tip: Check if the search tool is working correctly."""
-            
-    except requests.exceptions.RequestException as e:
-        status_msg = f"❌ Submission failed (network error): {str(e)[:200]}"
+        return f"Score: {score}%", pd.DataFrame(logs)
     except Exception as e:
-        status_msg = f"❌ Submission failed: {str(e)[:200]}"
+        return f"Submit error: {str(e)}", pd.DataFrame(logs)
 
-    return status_msg, pd.DataFrame(results_log)
-
-
-# Gradio 介面
-with gr.Blocks(title="Unit 4 Final Assignment", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("""
-# 🎓 Unit 4 Final Project: AI Agent (Fixed with smolagents)
-
-### Using Groq API (Llama 3.3 70B) + Search Tool
-**Goal**: Score ≥ 30% to get certificate
-    """)
-    
+with gr.Blocks(title="Final Agent") as demo:
+    gr.Markdown("# 🚀 Final Agent (Custom Tool Version)")
     with gr.Row():
-        gr.LoginButton(scale=1)
-        run_btn = gr.Button("🚀 Run Evaluation & Submit All Answers", scale=3, variant="primary")
-    
-    gr.Markdown("---")
-    
-    status = gr.Textbox(label="📊 Submission Status", lines=8, interactive=False)
-    details = gr.DataFrame(label="📝 Detailed Results", interactive=False)
-    
-    run_btn.click(
-        fn=run_and_submit_all,
-        inputs=[],
-        outputs=[status, details]
-    )
-    
-    gr.Markdown("""
----
-### 💡 Tips
-- Make sure `GROQ_API_KEY` is in Secrets.
-- This agent uses DuckDuckGo Search to answer factual questions.
-- Image questions might still fail, but text questions should pass!
-    """)
+        gr.LoginButton()
+        btn = gr.Button("Run Evaluation", variant="primary")
+    out = gr.Textbox(label="Status")
+    tab = gr.DataFrame(label="Results")
+    btn.click(run_and_submit_all, outputs=[out, tab])
 
 if __name__ == "__main__":
     demo.launch()