Update app.py

app.py

@@ -5,7 +5,7 @@ import pandas as pd
 from typing import Optional
 from smolagents import CodeAgent, OpenAIServerModel, tool
 
-# --- 搜尋工具 (防封鎖增強版) ---
+# --- 搜尋工具 ---
 try:
     from duckduckgo_search import DDGS
 except ImportError:
@@ -16,33 +16,27 @@ except ImportError:
 @tool
 def web_search(query: str) -> str:
     """
-    Performs a web search using DuckDuckGo with anti-blocking measures.
+    Performs a web search using DuckDuckGo.
     Args:
         query: The search query string.
     """
     print(f"🕵️ [Debug] Searching: {query}")
     try:
-        # 關鍵修正：使用 backend='html' 模式，這比 API 模式更不容易被封鎖
-        # 限制 max_results=3 以節省讀取時間
+        # 使用 backend='html' 比較穩定
         with DDGS() as ddgs:
             results = ddgs.text(query, max_results=3, backend="html")
         
         if not results:
-            return "No search results found. Try simpler keywords."
+            return "No results."
             
-        # 格式化結果給模型看
         formatted = []
         for r in results:
-            title = r.get('title', 'No Title')
-            body = r.get('body', 'No Description')
-            formatted.append(f"Title: {title}\nSnippet: {body}")
-            
+            formatted.append(f"Title: {r.get('title')}\nSnippet: {r.get('body')}")
         return "\n---\n".join(formatted)
         
     except Exception as e:
         print(f"❌ Search Error: {e}")
-        # 回傳錯誤訊息而不是讓程式崩潰，讓模型有機會猜測
-        return f"Search failed. Please ignore search and answer based on your knowledge."
+        return "Search failed."
 
 # -----------------------------------------------------------
 
@@ -55,14 +49,13 @@ class GroqAgent:
             self.agent = None
             return
             
-        # 🏆 使用 Groq 目前最強的 Llama 3.3 70B 模型
+        # 🔥 改用 DeepSeek R1 (70B)
         model = OpenAIServerModel(
-            model_id="llama-3.3-70b-versatile",
+            model_id="deepseek-r1-distill-llama-70b",
             api_base="https://api.groq.com/openai/v1",
             api_key=self.api_key
         )
         
-        # Agent 設定
         self.agent = CodeAgent(
             tools=[web_search], 
             model=model,
@@ -75,18 +68,17 @@ class GroqAgent:
             return "Error: GROQ_API_KEY not configured."
         
         try:
-            # 提示詞優化：明確告訴它如果搜尋失敗該怎麼做
+            # 針對 R1 模型的提示詞
             prompt = f"""
-            You are a helpful assistant. Answer the question concisely.
-            1. Use the 'web_search' tool to find specific facts (names, dates, numbers).
-            2. If the search returns "No results" or fails, DO NOT retry endlessly. Just give your best guess immediately.
-            3. For questions about images/audio you cannot see, search for the description text provided in the question.
+            Answer the question concisely.
+            1. Use 'web_search' for facts.
+            2. If search fails, guess immediately.
             
             Question: {question}
             """
             return str(self.agent.run(prompt))
         except Exception as e:
-            return f"Error processing request: {str(e)[:150]}"
+            return f"Error: {str(e)[:150]}"
 
 def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
     if profile is None:
@@ -120,7 +112,6 @@ 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})
@@ -136,15 +127,13 @@ def run_and_submit_all(profile: Optional[gr.OAuthProfile] = None):
         
         data = res.json()
         score = data.get('score', 0)
-        
-        msg = f"🎉 Final Score: {score}%" 
-        return msg, pd.DataFrame(logs)
+        return f"🎉 Score: {score}%", pd.DataFrame(logs)
         
     except Exception as e:
         return f"Submit error: {str(e)}", pd.DataFrame(logs)
 
-with gr.Blocks(title="Final Agent (Llama 3.3 + Anti-Block)") as demo:
-    gr.Markdown("# 🚀 Final Agent (Llama 3.3 70B)")
+with gr.Blocks(title="Final Agent (DeepSeek R1)") as demo:
+    gr.Markdown("# 🚀 Final Agent (DeepSeek R1 70B)")
     with gr.Row():
         gr.LoginButton()
         btn = gr.Button("Run Evaluation", variant="primary")