Update app.py

app.py

@@ -5,27 +5,33 @@ import pandas as pd
 from typing import Optional
 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 ---
+# --- 自動安裝與防卡死搜尋工具 ---
 try:
     from duckduckgo_search import DDGS
 except ImportError:
-    # 萬一真的沒裝到，這邊做最後一道防線
+    import os
     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.
-    
+    Performs a web search.
     Args:
         query: The search query string.
     """
+    print(f"🕵️ [Debug] Searching for: {query}")  # 讓你知道它正在工作
     try:
-        results = DDGS().text(query, max_results=5)
+        # 關鍵修改：加上 timeout=15 (秒)，並限制結果數量以加速
+        # backend='html' 通常比 api 模式更不容易被擋
+        results = DDGS(timeout=15).text(query, max_results=3)
+        if not results:
+            return "No search results found."
         return str(results)
     except Exception as e:
-        return f"Search error: {str(e)}"
+        print(f"❌ [Error] Search failed: {e}")
+        return f"Search connection failed: {e}"
+
 # -----------------------------------------------------------
 
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -43,7 +49,6 @@ class GroqAgent:
             api_key=self.api_key
         )
         
-        # 使用我們手動定義的 web_search 工具
         self.agent = CodeAgent(
             tools=[web_search], 
             model=model,
@@ -56,10 +61,12 @@ class GroqAgent:
             return "Error: GROQ_API_KEY not configured."
         
         try:
+            # 簡化 Prompt 讓模型反應更快
             prompt = f"""
-            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.
+            You are an expert agent. Answer the question concisely.
+            1. Use 'web_search' for facts (dates, names, events).
+            2. If search fails, make your best guess.
+            3. Answer directly.
             
             Question: {question}
             """
@@ -83,6 +90,7 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
         return f"❌ Init failed: {str(e)}", None
     
     try:
+        print("Fetching questions...")
         response = requests.get(f"{api_url}/questions", timeout=30)
         questions = response.json()
     except Exception as e:
@@ -91,34 +99,43 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
     answers = []
     logs = []
     
-    for item in questions:
+    total = len(questions)
+    for idx, item in enumerate(questions, 1):
         q = item.get("question")
         tid = item.get("task_id")
-        print(f"Processing: {tid}...")
         
+        print(f"🚀 [{idx}/{total}] Processing task: {tid}...")
+        
+        # 這裡會觸發 agent 思考
         ans = agent_wrapper(q)
+        
         answers.append({"task_id": tid, "submitted_answer": ans})
-        logs.append({"Task": tid, "Q": q[:50], "A": ans[:100]})
+        logs.append({"Task": tid, "Status": "Done", "Answer Preview": ans[:50]})
 
     try:
+        print("Submitting answers...")
         res = requests.post(f"{api_url}/submit", json={
             "username": username,
             "agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main",
             "answers": answers
-        })
+        }, timeout=60)
+        
         data = res.json()
         score = data.get('score', 0)
-        return f"Score: {score}%", pd.DataFrame(logs)
+        msg = f"🎉 Final Score: {score}%" if score >= 30 else f"Score: {score}% (Try again!)"
+        return msg, pd.DataFrame(logs)
+        
     except Exception as e:
         return f"Submit error: {str(e)}", pd.DataFrame(logs)
 
-with gr.Blocks(title="Final Agent") as demo:
-    gr.Markdown("# 🚀 Final Agent (Custom Tool Version)")
+with gr.Blocks(title="Final Agent (Timeout Optimized)") as demo:
+    gr.Markdown("# 🚀 Final Agent (Fast Version)")
+    gr.Markdown("Has timeout protection to prevent hanging.")
     with gr.Row():
         gr.LoginButton()
         btn = gr.Button("Run Evaluation", variant="primary")
     out = gr.Textbox(label="Status")
-    tab = gr.DataFrame(label="Results")
+    tab = gr.DataFrame(label="Progress Log")
     btn.click(run_and_submit_all, outputs=[out, tab])
 
 if __name__ == "__main__":