Update app.py

app.py

@@ -2,42 +2,42 @@ import os
 import gradio as gr
 import requests
 import pandas as pd
+import time
 from typing import Optional
 from smolagents import CodeAgent, OpenAIServerModel, tool
 
-# --- 搜尋工具 (極速減肥版) ---
+# --- 搜尋工具 (修正安裝指令版) ---
 try:
     from duckduckgo_search import DDGS
 except ImportError:
     import os
-    os.system('pip install ddgs')
+    # 修正：這裡必須用完整的套件名稱
+    os.system('pip install duckduckgo-search')
     from duckduckgo_search import DDGS
 
 @tool
 def web_search(query: str) -> str:
     """
-    Search with strict length limits to prevent model hanging.
+    Lite search with strict token limits for Groq free tier.
     Args:
         query: The search query string.
     """
     print(f"🕵️ [Debug] Searching: {query}")
     try:
-        # 1. 使用 Lite 模式 (最快、不被擋)
-        # 2. 只抓 1 筆結果 (減少資料量)
+        # 強制冷靜 1 秒
+        time.sleep(1.0)
+        
         with DDGS() as ddgs:
+            # 使用 lite 模式繞過封鎖
             results = list(ddgs.text(query, max_results=1, backend="lite"))
         
         if not results:
-            return "No results. Guess the answer."
+            return "No results. Guess answer."
             
-        # 3. 關鍵修改：強制截斷文字！
-        # 只回傳前 400 個字，避免 70B 模型「噎住」
-        first_result = results[0]
-        title = first_result.get('title', '')
-        body = first_result.get('body', '')
-        summary = f"Title: {title}\nInfo: {body}"
-        
-        return summary[:400] # <--- 這裡就是防卡死的關鍵
+        # 截斷內容，節省 Token
+        first = results[0]
+        summary = f"Title: {first.get('title','')}\nInfo: {first.get('body','')}"
+        return summary[:400] 
         
     except Exception as e:
         print(f"❌ Error: {e}")
@@ -54,7 +54,6 @@ class GroqAgent:
             self.agent = None
             return
             
-        # 堅持使用 Llama 3.3 70B (它是唯一夠聰明能過關的)
         model = OpenAIServerModel(
             model_id="llama-3.3-70b-versatile",
             api_base="https://api.groq.com/openai/v1",
@@ -64,7 +63,7 @@ class GroqAgent:
         self.agent = CodeAgent(
             tools=[web_search], 
             model=model,
-            max_steps=2, # 減少步數，逼它快點回答
+            max_steps=2, 
             verbosity_level=1
         )
     
@@ -73,12 +72,14 @@ class GroqAgent:
             return "Error: GROQ_API_KEY not configured."
         
         try:
-            # 提示詞：極度簡化，逼它直接回答
+            # 強制冷靜 2 秒
+            time.sleep(2.0)
+            
             prompt = f"""
             Task: Answer concisely.
             1. Search ONCE.
-            2. If search fails, GUESS immediately.
-            3. Do not overthink.
+            2. If search fails, GUESS.
+            3. Keep answer under 20 words.
             
             Question: {question}
             """
@@ -118,7 +119,6 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
         
         print(f"🚀 [{idx}/{total}] Task: {tid}")
         
-        # 執行 Agent
         ans = agent_wrapper(q)
         
         answers.append({"task_id": tid, "submitted_answer": ans})
@@ -139,8 +139,8 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
     except Exception as e:
         return f"Submit error: {str(e)}", pd.DataFrame(logs)
 
-with gr.Blocks(title="Final Agent (Truncated)") as demo:
-    gr.Markdown("# 🚀 Final Agent (Anti-Hang Version)")
+with gr.Blocks(title="Final Agent (Fixed Import)") as demo:
+    gr.Markdown("# 🚀 Final Agent (Fixed Import)")
     with gr.Row():
         gr.LoginButton()
         btn = gr.Button("Run Evaluation", variant="primary")